Determination of Risk Factors for Neonatal Calf Diarrhea Using Survival Analysis

Determination of Risk Factors for Neonatal Calf Diarrhea Using Survival Analysis | 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 Research Article Determination of Risk Factors for Neonatal Calf Diarrhea Using Survival Analysis Güven Güngör, Savas Sariozkan, Aytac Akcay This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8590511/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract This study aimed to investigate the distribution of neonatal calf diarrhea according to various risk factors, estimate calf survival times and probabilities, and establish a multivariate model to identify significant risk factors. During the study period, 689 calves born on a farm were monitored throughout the neonatal period (1–28 days). Potential risk factors included calf-related variables (birth season, sex, breed, colostrum intake, colostrum intake method, twinning, birth weight, colostrum quality) and dam-related variables (dry period length, gestation length, calving type, age at first service, parity, number of artificial inseminations per conception). Cumulative survival probabilities and survival times were estimated using the Kaplan–Meier method. The Cox proportional hazards regression model was used to identify independent risk factors significantly associated with the hazard of neonatal diarrhea. Diarrhea was observed in 377 (54.7%) of the total 689 calves. The cumulative survival probability was 42.9% by day 28. The mean and median times to diarrhea onset were 17.9 and 14.0 days, respectively. The final multivariate model identified significant interactions: higher colostrum quality (HR: 0.848), higher birth weight in the absence of dystocia (HR: 0.991), and higher birth weight over time (HR: 0.997) were associated with reduced hazard of diarrhea. In conclusion, the risk factors and their ratios constitute a predictive model for the time to diarrhea diagnosis and provide managerial decision support for farmers. Also, the results highlight the positive impact that strategies aimed at improving colostrum quality, achieving optimal birth weights, and reducing dystocia will have on calf health. Calf Cox regression diarrhea Kaplan-Meier risk Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction In dairy farming, profitability is significantly influenced not only by the primary income from milk sales but also by the management strategies of secondary revenue sources, such as calves. Ensuring healthy calf management from birth through rearing is vital for the sustainability of the farm, encompassing both economic and animal welfare dimensions (Godden 2008 ; Gulliksen et al. 2009 ). The neonatal period, including the first 28 days of the calves, is the phase during which they are most susceptible to the external environment. Although variable depending on farm conditions, disease-induced mortality rates during this period are notably high (Astiz et al. 2017 ; Rocha et al. 2017 ). Furthermore, infections in the neonatal period negatively impact the calf's future production potential, hindering genetic progress and impeding the raising of quality breeding stock (Torsein et al. 2011 ; Zucali et al. 2013 ). The primary health challenges encountered in dairy calves arise from inadequate care and nutrition, metabolic disorders, and various diseases associated with low immunity (Svensson et al. 2003 ). While respiratory diseases, congenital anomalies, and calving complications are frequently encountered, neonatal calf diarrhea is of particular concern due to its high morbidity and mortality rates (Hadimli 2020 ; Zhang et al. 2019 ). Calf diarrhea affects not only the digestive system but also leads to serious complications such as dehydration, metabolic acidosis, electrolyte imbalances, hypothermia, and sepsis (Alfieri et al. 2018 ; Gitau et al. 2010 ; Radostits et al. 2006). Given the multifactorial aetiology of neonatal calf diarrhea, curative interventions have significant drawbacks, including high costs, production losses, and compromised welfare. Consequently, strategic plans based on proactive prevention present a more rational approach for disease management (Wiggans 1994 ; Rai et al. 2023 ). Systematic prophylactic health plans specifically designed for the neonatal period can reduce disease prevalence, minimize treatment costs, and enhance animal welfare and future productivity (Bartlett et al. 1986 ; Gulliksen et al. 2009 ). The success of such strategic plans and preventive measures hinges on the detailed identification of influential factors and the quantification of their associated risks (Frank and Kaneene 1993 ). In this context, the effective application of statistical methods like survival analysis, which enables the modeling of risk factors, will significantly contribute to the successful management of neonatal calf diarrhea (Al Mawly et al. 2015 ). This study aims to investigate the distribution of neonatal calf diarrhea according to various risk factors, to estimate calf survival times and probabilities using survival analysis, and to establish a multivariate model to identify significant risk factors and explain their hazard ratios. Materials and Methods Study Area This study was conducted on a private dairy farm in the city of Kayseri, located in the Central Anatolia region of Türkiye, between June 1, 2022, and July 1, 2023. Kayseri is situated at 37° 45'-38° 18' North latitude and 34° 56'-36° 59' East longitude. The city's altitude is 1054 meters above sea level, with an average annual rainfall of 390.5 mm and an average annual temperature of 10.7°C (Mgm, 2025). Study Design and Data Collection This longitudinal prospective study was designed to determine the risk factors and disease morbidity associated with neonatal calf diarrhea. Conducted with the approval of the Animal Experiments Local Ethics Committee of Erciyes University (Approval No: 22/157; Date: 07.07.2022), the study data were collected through periodic research visits conducted twice a week at a commercial dairy cattle operation. During the study period, 689 calves born on the farm were monitored throughout the neonatal period (1–28 days), and potential risk factors were recorded. The sample size for the study was determined based on the literature on survival analysis. To obtain reliable results from prediction models developed using Cox regression, it is recommended to have at least 20 events per independent variable (Ogundimu et al., 2016 ). Accordingly, a total of 377 events were observed in the present study conducted on 689 calves. The mean number of events per variable for the 14 risk factors (independent variables) included in the analysis was calculated as 377/14 = 26.9, which is above the recommended minimum threshold. Standard management practices were routinely applied to calves included in the study. Immediately after birth, the umbilical cord was disinfected with a 10% iodine solution. The first colostrum feeding was administered within the first 30 minutes postpartum, and calves were transferred to individual rearing hutches within one hour of birth. Starting from the second day, calves were fed twice daily with farm-produced milk at a rate of 2.5 liters per feeding throughout the neonatal period. Disbudding via chemical cauterization using potassium hydroxide was performed between the 5th and 15th days of life. In the current operation, a standardized protocol is in place to ensure adequate colostrum intake. For any calf that does not consume colostrum equal to 5% of its live body weight within the first six hours postpartum, tube feeding is performed. The colostrum used for this supplementation was sourced from a pooled colostrum reserve, collected from other parturitions occurring on the same date. Furthermore, throughout the neonatal period, calves housed in individual hutches are provided with pelleted starter feed, water, and dry alfalfa hay on an ad libitum basis. The schedule of vaccines was as follows: At birth, calves received a combined antiserum against S. typhimurium , S. dublin , E. coli K99, Cl. perfringens type C, T. pyogenes , and M. haemolytica types 1–2. On the 5th day of life, the first dose of a viral vaccine against Parainfluenza-3 virus and BRSV was administered. The first dose of a combined toxoid vaccine, targeting Cl. novyi , Cl. septicum , Cl. sordellii , Cl. perfringens types B, C, and D, Cl. chauvoei , Cl. haemolyticum , and E. coli K99, was given on the 15th day. Finally, the first dose of a pasteurellosis vaccine against P. multocida and M. haemolytica types 1–2 was administered on the 28th day. For dams of the neonatal calves, standardized protocols were followed. For the dams, these practices included the administration of two doses of a vaccine against calf septicemia ( E. coli , Rotavirus , Coronavirus ) during the 7th and 8th months of gestation. At drying-off, an intermittent milking method and teat sealant application were implemented. An anionic feeding program was introduced for the last 20 days of gestation, and cows were moved to a dedicated maternity unit approximately one week before the expected calving date. Veterinary intervention was performed within 2 hours in heifers, or within 4 hours in multiparous cows when dystocia occurred. Data Description and Statistical Analysis The risk factors for the survival analysis were categorized into two main groups: calf-related and dam-related. The calf-related factors included birth season (Spring-Summer, Autumn-Winter), sex (Female, Male), breed (Holstein, Simmental), colostrum intake method (Bottle, Tube), colostrum intake adequacy (Adequate, Inadequate), twinning (Yes, No), birth weight (kg), and colostrum quality (Brix %). The birth season variable was consolidated into two broad periods (Spring-Summer: Mar-Aug; Autumn-Winter: Sep-Feb) due to intermittent data collection. This was necessitated by the farm's strict biosecurity protocols during periods of heightened regional disease risk. For colostrum intake, the method "Bottle" indicates feeding with a nipple bottle, whereas "Tube" indicates feeding via an oesophageal tube. Adequacy was defined as an intake of ≥ 10% of birth weight within the first 24 hours of life (Adequate) versus < 10% (Inadequate). The dam-related risk factors comprised dry period length (Heifer, 60 days), gestation length ( 290 days), calving type (Dystocia, Normal), age at first service ( 16 months), parity (1, 2, 3, ≥ 4), and number of artificial inseminations (1, 2, ≥ 3). All maternal data were sourced from the farm's herd management software. Survival analysis was conducted to evaluate the time to diarrhea occurrence in calves. The event of interest was defined as the onset of diarrhea symptoms. Calves were enrolled at birth and monitored throughout the neonatal period (days 1–28), which was designated as the follow-up period. The age (in days) at which diarrhea was first observed was recorded as the survival time. Calves that were lost to follow-up (due to death, sale, or other reasons) or did not develop diarrhea by the end of the 28-day observation period were right-censored in the analysis (Fig. 1 ). Figure 1 will be inserted here The survival probabilities and survival times for the risk factors examined in the study were estimated using the Kaplan–Meier method. Survival times across variable groups were compared with the Log-Rank (Mantel-Cox) test, and mean and median values were reported with their 95% confidence intervals. A Cox proportional hazards regression model was used to identify significant risk factors for neonatal calf diarrhea. Model building began with univariate analysis to select candidate variables for the multivariate model. All candidate variables were initially included in a full multivariate model, which was then refined using backward stepwise elimination to obtain a significant model. Subsequently, significant interactions between the variables retained in the model and any time-dependent variables were tested and incorporated, resulting in the final extended Cox regression model. The validity of the proportional hazards assumption in the Cox regression model was evaluated with multiple approaches. Quantitatively, the correlation between Schoenfeld residuals and the ranked survival times was tested. For graphical validation, the scatterplot of martingale residuals against time and the parallelism of log(-log) survival curves were examined. Statistical significance levels were defined as follows: a p-value < 0.05 was used for the log-rank test and the correlation test between Schoenfeld residuals and the rank of survival time. For Cox regression analysis, separate thresholds were applied at different stages: p < 0.20 for univariate analysis, p < 0.05 for entry into the multivariate model, p < 0.10 for removal from the model, and p < 0.01 for evaluating interaction terms. All statistical analyses were performed using the IBM SPSS (version 27.0) and Stata (version 13.0) software packages. Results During the study, diarrhea developed in 377 (54.7%) of the 689 monitored calves. The distribution of neonatal diarrhea risk factors is presented in Table 1 and Table 2 . Table 1 Descriptive characteristics and baseline data of the calf-related variables Variable Class N (%) n (%) Birth season Spring-Summer 450 (65.31) 247 (54.89) Autumn-Winter 239 (34.69) 130 (54.39) Sex Female 384 (55.73) 214 (55.73) Male 305 (44.27) 163 (53.44) Breed Holstein 484 (70.25) 261 (53.93) Simmental 205 (29.75) 116 (56.59) Colostrum intake method Bottle 370 (53.70) 198 (53.51) Tube 319 (46.30) 179 (56.11) Colostrum intake Adequate 629 (91.29) 341 (54.21) Inadequate 60 (8.71) 36 (60.00) Twinning Yes 50 (7.26) 33 (66.00) No 639 (92.74) 344 (53.83) \(\:\stackrel{\text{-}}{\text{X}}\text{±}\text{S}\) Med (Q1-Q2) Range Birth Weight 39.25 ± 5.69 39 (36–42) 20-60.5 Colostrum Quality 26.20 ± 1.21 26 (25–27) 23–30 N: Total calves; n: Calves with diarrhea (event); X̄: Mean; S: Standard deviation; Med: Median; Q1-Q2: 1st-2nd Quartile; Range: Min-max value. Table 2 Descriptive characteristics and baseline data of the dam-related variables Variable Class N (%) n (%) Dry period length Heifer 284 (41.22) 163 (57.39) 60 149 (21.63) 79 (53.02) Gestation length 290 27 (3.92) 15 (55.56) Calving type Dystocia 61 (8.85) 41 (67.21) Normal 628 (91.15) 336 (53.50) Age at first service 16 229 (33.24) 130 (56.77) Parity 1 285 (41.36) 164 (57.54) 2 155 (22.50) 83 (53.55) 3 136 (19.74) 80 (58.82) ≥ 4 113 (16.40) 50 (44.25) Number of AI per conception 1 376 (54.57) 207 (55.05) 2 172 (24.96) 97 (56.40) ≥ 3 141 (20.47) 73 (51.77) \(\:\stackrel{\text{-}}{\text{X}}\text{±}\text{S}\) Med (Q1-Q3) Range Parity 2.24 ± 1.38 2 (1–3) 1–10 Number of AI 1.75 ± 1.02 1 (1–2) 1–6 N: Total dams; n: Calves with diarrhea (event), AI: Artificial insemination; *: Parity and number of AI are also presented as continuous variables with descriptive statistics X̄: Mean; S: Standard deviation; Med: Median; Q1-Q2: 1st-2nd Quartile; Range: Min-max value. The majority of calves were born in the spring-summer season (65.31%), were female (55.73%), and were of the Holstein breed (70.25%). Regarding colostrum management, most calves were fed via a bottle (53.70%), and received an adequate volume of colostrum (91.29%). Single births were predominant (92.74%). The mean (± SD) birth weight was 39.25 ± 5.69 kg, and the mean colostrum quality was 26.20 ± 1.21 Brix%. Diarrhea was notably higher in certain subgroups. A higher event rate was observed among calves born in spring-summer (54.89%), female calves (55.73%), those of the Simmental breed (56.59%), those fed via an oesophageal tube (56.11%), and those with inadequate colostrum intake (60.00%). The highest incidence was recorded among twin calves (66.00%) (Table 1 ). Table 1 will be inserted here The largest proportion of calves (41.22%) was born from heifers. An important majority (80.84%) were born from dams with a gestation length of 270–290 days. Normal calvings were predominant (91.15%). Regarding age at first service, the most common category was 14–16 months (52.25%). In terms of parity, first-lactation dams were the most frequent group (41.36%), and a single artificial insemination per conception was the most common outcome (54.57%). The highest diarrhea rates were observed in the following categories: calves born from heifers (57.39%), calves born from dams with a gestation period of less than 270 days (60.0%), cases of dystocia (difficult calving) (67.21%), dams with an age at first service higher than 16 months (56.77%), dams in their third lactation (58.82%), and dams that required two artificial inseminations per conception (56.40%) (Table 2 ). Table 2 will be inserted here Data on diarrhea development and survival analysis are summarized below. Survival analysis indicated a cumulative survival probability of 42.9% by the end of the neonatal period (Fig. 2 ). The mean time to diarrhea onset was 17.9 days (95% CI: 17.19–18.61), with a median survival time of 14.0 days (95% CI: 11.62–16.38) (Table 3 ). Table 3 Overall survival times of neonatal calves with diarrhea (days) Mean Median Estimate \(\:{\text{S}}_{\stackrel{\text{-}}{\text{X}}}\) 95% CI Estimate \(\:{\text{S}}_{\stackrel{\text{-}}{\text{X}}}\) 95% CI 17.90 0.36 17.19 18.61 14.00 1.21 11.62 16.38 \(\:{\text{S}}_{\stackrel{\text{-}}{\text{X}}}\) : Standard Error; CI: Confidence Interval Table 3 will be inserted here Figure 2 will be inserted here Colostrum intake had a statistically significant effect on calf survival time (p < 0.05). The cumulative survival probability for the adequate intake group was 44.1%, while it was 27.8% for the inadequate intake group (Fig. 3 ). The mean survival time estimated by Kaplan-Meier analysis was 18.07 days (95% CI: 17.33–18.81) for calves with adequate colostrum intake, and 15.71 days (95% CI: 13.34–18.08) for those with inadequate intake (Table 4 ). Table 4 Comparison of survival times according to calf-related variables (days) Variable Class Mean Median P value Log-Rank Estimate \(\:{\text{S}}_{\stackrel{\text{-}}{\text{X}}}\) 95% CI Estimate \(\:{\text{S}}_{\stackrel{\text{-}}{\text{X}}}\) 95% CI Birth season Spring-Summer 17.65 0.45 16.77 18.53 14.00 0.99 12.05 15.95 0.393 Autumn-Winter 18.33 0.61 17.14 19.52 16.00 3.81 8.54 23.47 Sex Female 17.84 0.48 16.90 18.78 14.00 1.35 11.36 16.64 0.744 Male 17.96 0.55 16.88 19.04 15.00 2.56 9.98 20.02 Breed Holstein 18.09 0.43 17.25 18.94 15.00 1.94 11.20 18.80 0.303 Simmenthal 17.41 0.67 16.10 18.73 14.00 1.23 11.60 16.41 Colostrum intake method Bottle 17.58 0.53 16.55 18.62 14.00 1.08 11.88 16.12 0.452 Tube 18.16 0.50 17.19 19.14 15.00 3.16 8.81 21.19 Colostrum intake Adequate 18.07 0.38 17.33 18.81 15.00 1.67 11.72 18.28 0.040 Inadequate 15.71 1.21 13.34 18.08 12.00 0.99 10.07 13.93 Twinning Yes 16.15 1.29 13.64 18.67 12.00 1.43 9.19 14.81 0.111 No 18.03 0.38 17.29 18.77 15.00 1.53 12.01 17.99 \(\:{\text{S}}_{\stackrel{\text{-}}{\text{X}}}\) : Standard Error; CI: Confidence Interval Calving type was found to have a statistically significant effect on survival time (p < 0.05). The cumulative survival probability was 28.4% for the dystocia group and 44.3% for the normal birth group (Fig. 4 ). The Kaplan-Meier estimated mean survival time was 15.07 days (95% CI: 12.76–17.38) for calves born with dystocia and 18.16 days (95% CI: 17.42–18.91) for those born normally (Table 5 ). Table 5 Comparison of survival times according to dam-related variables (days) Variable Class Mean Median P value Log-Rank Estimate \(\:{\text{S}}_{\stackrel{\text{-}}{\text{X}}}\) 95% CI Estimate \(\:{\text{S}}_{\stackrel{\text{-}}{\text{X}}}\) 95% CI Dry period length Heifer 17.39 0.56 16.30 18.48 13.00 1.05 10.95 15.05 0.743 60 18.50 0.78 16.98 20.02 15.00 4.91 5.38 24.62 Gestation length 290 17.42 1.78 13.92 20.91 13.00 1.66 9.74 16.26 Calving type Dystocia 15.07 1.18 12.76 17.38 11.00 1.24 8.57 13.43 0.007 Normal 18.16 0.38 17.42 18.91 15.00 1.68 11.71 18.30 Age at first service 16 17.63 0.62 16.41 18.84 14.00 1.28 11.49 16.51 Parity 1 17.39 0.56 16.30 18.47 13.00 1.02 11.00 15.00 0.225 2 17.88 0.80 16.32 19.44 14.00 5.10 4.00 24.00 3 17.63 0.78 16.09 19.17 14.00 1.14 11.77 16.23 ≥ 4 19.58 0.91 17.79 21.36 - - - - Number of AI per conception 1 18.01 0.49 17.05 18.96 15.00 1.98 11.12 18.88 0.936 2 17.64 0.71 16.25 19.04 14.00 1.33 11.39 16.61 ≥ 3 17.92 0.84 16.28 19.56 14.00 5.23 3.75 24.25 \(\:{\text{S}}_{\stackrel{\text{-}}{\text{X}}}\) : Standard Error; CI: Confidence Interval Table 4 will be inserted here Table 5 will be inserted here Figure 3 will be inserted here Figure 4 will be inserted here Candidate variables for the multivariate model were selected using univariate Cox regression analysis. Variables meeting the statistical threshold of p < 0.20 were identified for inclusion in the subsequent multivariate model. Among the calf-related variables, colostrum intake, twin status, birth weight, and colostrum quality were selected as candidates. For the dam-related variables, gestation length and calving type were chosen as candidate variables. The multivariate Cox regression model was constructed using a backward elimination method (removal threshold: p > 0.10) with the candidate variables identified in the univariate analysis. Following the elimination process, the variables birth weight, colostrum quality, and dystocia remained in the final significant model (p < 0.10). The model fit statistic, the − 2 Log Likelihood (-2LL) value for the model, was calculated as 4613.38 (χ² = 33.91; df = 3; p < 0.001). According to the results, each one-unit increase in birth weight and colostrum quality reduced the risk of diarrhea in calves by 3.3% (HR: 0.967, 95% CI: 0.950–0.985) and 15.3% (HR: 0.847, 95% CI: 0.773–0.928), respectively. Furthermore, the absence of dystocia (i.e., normal calving) was identified as a factor that reduced this risk by 30.7% (HR: 0.693, 95% CI:0.500–0.960) (Table 6 ). Table 6 Multivariate Cox regression model Variables* β \(\:{\text{S}}_{\stackrel{\text{-}}{\text{X}}}\) z df P (z) HR 95% CI Birth weight -0.033 0.009 13.200 1 < 0.001 0.967 0.950–0.985 Colostrum quality -0.166 0.046 12.773 1 < 0.001 0.847 0.773–0.928 Calving type -0.366 0.166 4.859 1 0.028 0.693 0.500–0.960 -2 LL = 4613.38, χ 2 = 33.91, df = 3, p < 0.001 β: Estimated regression coefficient (beta); \(\:{\text{S}}_{\stackrel{\text{-}}{\text{X}}}\) : Standard error of the coefficient; z: Wald's z-statistic; df: Degrees of freedom; HR (Exp(β)): Hazard ratio; *The first categories for categorical variables were set as the reference. Table 6 will be inserted here The validity of the model was assessed by testing the proportional hazards assumption. The evaluation revealed that birth weight, among the significant variables, violated this assumption (p < 0.05). Additionally, interactions between the variables were examined. The interaction terms birth weight × colostrum quality, birth weight × dystocia, and colostrum quality × dystocia were found to be statistically significant (p < 0.01). A new extended model was created, incorporating the birth weight variable as a time-dependent covariate, which violates the proportional hazards assumption, along with significant interaction terms. The model was completed through a four-step backward elimination process. In the first step of the model, the goodness-of-fit statistic, the − 2 Log Likelihood (-2LL) value, was found to be 4608.04 (χ² = 39.24; df = 6; p < 0.001). At the end of the backward elimination process, the variables colostrum quality, the birth weight × calving type interaction, and the birth weight × time interaction retained their statistical significance and remained in the final model (p < 0.05). The − 2 Log Likelihood (-2LL) value of the final model was calculated as 4609.09, and the model is statistically significant (χ² = 37.60; df = 3; p < 0.001). According to the model, each unit increase in colostrum quality was found to reduce the risk of diarrhea in calves by 15.2% (HR = 0.848, 95% CI: 0.774–0.929). The interaction between birth weight and dystocia was associated with a 0.9% decrease in risk compared to the reference category (normal calving) (HR = 0.991, 95% CI: 0.982–0.999). Additionally, the time-dependent effect of birth weight was found to reduce the risk by 0.3% (HR = 0.997, 95% CI: 0.995–0.999) (Table 7 ). Table 7 Extended Cox regression model (final model) Variables* β \(\:{\text{S}}_{\stackrel{\text{-}}{\text{X}}}\) z df P (z) HR %95 GA Colostrum quality -0.165 0.047 12.452 1 < 0.001 0.848 0.774–0.929 Birth weight x Calving type -0.011 0.004 4.691 1 0.030 0.991 0.982–0.999 Birth weight x g(t) -0.003 0.001 10.459 1 0.001 0.997 0.995–0.999 -2 LL = 4609.09, χ 2 = 37.63, df = 3, p < 0.001 β: Estimated regression coefficient (beta); \(\:{\text{S}}_{\stackrel{\text{-}}{\text{X}}}\) : Standard error of the coefficient; z: Wald's z-statistic; df: Degrees of freedom; HR (Exp(β)): Hazard ratio; g(t): Time-dependent covariate; *The first categories for categorical variables were set as the reference. Table 7 will be inserted here The final model showing the risk factors effective on calf diarrhea is; HR = exp[ -0.165 × Colostrum quality − 0.011 × Birth weight × Calving type − 0.003 × Birth weight × g(t) ] Discussion In this research, survival analysis methods were used to determine the risk factors associated with neonatal calf diarrhea. These methods prevent the loss of information by including disease-free individuals in the analysis, thereby enabling more reliable and unbiased results to be obtained (Ozen et al. 2021 ). Similar methodological approaches have been adopted in many studies on calf diseases (Alemu et al. 2022 ; Barry et al. 2022 ; Chung et al. 2019 ; Gulliksen et al. 2009 ). The occurrence of neonatal calf diarrhea was found to be 54.74% in this study. While the reported rates in the literature vary by country, they cover a wide range: 14.6% in France, 18.42% in Nigeria, 24–50% in Germany, 46.2–52.9% in Spain, and 68.40% in Ethiopia (Araujo et al. 2015 ; Asmare and Kiros 2016 ; Bartels et al. 2010 ; Bendali et al. 1999 ; Olaogun et al. 2016 ; Perez et al. 1990 ). In Türkiye, the reported rates range from 22.90% to 57.98% (Keles et al. 2022 ; Tokgoz et al. 2013 ). This global prevalence, ranging from 14.60% to 68.40%, indicates that the incidence of the disease is strongly influenced by regional conditions, husbandry practices, and management strategies. The high incidence values observed in this study and the literature highlight that the neonatal period is a critical window for calf health and underscore the vital importance of management practices during this time. According to the Kaplan-Meier analysis results, the mean time to diarrhea occurrence (survival time) in calves was calculated as 17.90 days (95% CI: 17.19–18.61), with a median of 14.00 days (95% CI: 11.62–16.38). This median value aligns with the 14–15 day values reported by Wolking et al. ( 2016 ) and Barkley et al. (2021) on some farms but differs from studies reporting lower median values (7–12 days) (Feldman et al. 2019; Lucey et al. 2021 ). Univariate analyses revealed that colostrum intake and the presence of dystocia significantly affected calves' survival time against diarrhea (p < 0.05). The mean survival time was shorter in calves with inadequate colostrum intake (15.71 days) compared to those with adequate intake (18.07 days). This finding is consistent with the importance of colostrum, which forms the basis of passive immunity in calves due to the placental structure. Inadequate intake reduces immunoglobulin (IgG) transfer, leaving calves vulnerable to infections (Kucukoflaz and Sariozkan 2023 ; Walsh et al. 2007 ). Indeed, diarrhea has been reported to occur earlier in calves fed milk replacer instead of colostrum (Bristol et al. 2021). Similarly, the survival time of calves born from dystocia (mean 15.07 days) was significantly shorter than that of calves from normal births (mean 18.16 days). Dystocia can lead to immunosuppression due to birth stress and increased cortisol release, delayed first suckling, and reduced colostrum intake, making the calf more susceptible to pathogens (Awol et al. 2016 ; Vasseur et al. 2009 ). The literature also reports that dystocia increases the risk of calf disease by 2.4–2.96 times (Abebe et al. 2023 ; Hordofa et al. 2021 ; Toombs et al. 1994 ). To identify independent risk factors influencing diarrhea, a Cox proportional hazards model was constructed. The model included colostrum quality, the interaction between birth weight and dystocia, and birth weight as a time-dependent variable as statistically significant factors (p < 0.05). In the model, the intake of high-quality colostrum was identified as a protective factor that reduced the risk of diarrhea (HR: 0.848). This finding supports the central role of passive immune transfer in calf health (Godden et al. 2019 ). It is consistent with studies in the literature demonstrating that calves fed low-quality colostrum have a significantly increased risk of diarrhea, pneumonia, and mortality (Crannel and Abuelo 2023; Raboisson et al. 2016 ). Both birth weight alone and its interaction with dystocia emerged as factors modulating risk in the model. While low birth weight is typically associated with poor viability and prematurity (Olson et al. 2019 ), high birth weight can also be linked to dystocia and fetal stress (McMillen et al. 2001 ). Our findings indicate that birth weight plays a complex role in diarrhea risk and should be evaluated in conjunction with other factors, such as dystocia. This may partly explain the differing study results that identify birth weight as a risk factor (Schinwald et al. 2022 ; Windeyer et al. 2014 ) or find it insignificant (Glover et al. 2019 ). The results of our multivariate model align in principle with other Cox regression studies that report factors such as time to first colostrum intake, housing conditions, calf age, and season as risk factors (Alemu et al. 2022 ; Hordofa et al. 2021 ; Tamrat et al. 2020 ). However, our study highlights different variable combinations specific to the population and conditions under investigation. This study aimed to identify significant risk factors for neonatal calf diarrhea using survival analysis methods. The findings demonstrate that adequate intake of high-quality colostrum and the prevention of dystocia are critical for prolonging calves' survival time against diarrhea and reducing morbidity risk. Furthermore, our study has underlined birth weight as an important parameter that should be evaluated in consideration of its interaction with other factors. Neonatal calf diarrhea causes significant economic losses, including treatment costs, production losses, increased labor, and higher herd replacement expenses, in addition to compromising animal welfare. Therefore, implementing improved management strategies targeting the risk factors emphasized in our study—such as optimizing colostrum management protocols and enhancing calving observation and intervention—will significantly contribute to reducing calf losses and improving economic sustainability on dairy farms. Declarations Acknowledgments We express our gratitude to the Erciyes University Scientific Research Projects Coordination Office for providing the essential facilities to conduct this study. We would also like to thank the Proofreading & Editing Office of the Dean for Research at Erciyes University for the copyediting and proofreading service for this manuscript. Data Availability Data will be made available on request. Statement of Animal Rights Conducted with the approval of the Animal Experiments Local Ethics Committee of Erciyes University (Approval No: 22/157; Date: 07.07.2022). Funding This work was supported by Erciyes University Scientific Research Projects Coordination Office (Project No: TDK-2022-12273). Author Contributions All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Guven Gungor. The first draft of the manuscript was created by Savas Sariozkan and Aytac Akcay. All authors commented and approved on previous versions of the manuscript. Conflict of Interest Statement The authors declare no competing interests. References Abebe R, Dema T, Libiyos Y, Teherku W, Regassa A, Fekadu A, Sheferaw D (2023) Longitudinal study of calf morbidity and mortality and the associated risk factors on urban and peri-urban dairy farms in southern Ethiopia. BMC Vet Res 19(15): 1-10. https://doi.org/10.1186/s12917-023-03574-8 Al Mawly J, Grinberg A, Prattley D, Moffat J, Marshall J, French N (2015) Risk factors for neonatal calf diarrhoea and enteropathogen shedding in New Zealand dairy farms. Vet J 203(2): 155-160. https://doi.org/10.1016/j.tvjl.2015.01.010 Alemu YF, Jemberu WT, Mekuriaw Z, Abdi RD (2022) Incidence and predictors of calf morbidity and mortality from birth to 6-months of age in dairy farms of northwestern Ethiopia. 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Animals 9(730): 1-11. https://doi.org/10.3390/ani9100730 Zucali M, Bava TA, Guerci M, Sandrucci A (2013) Management risk factors for calf mortality in intensive Italian dairy farms. Ital J Anim Sci 12(2): 162-166. https://doi.org/10.4081/ijas.2013.e26 Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 02 Feb, 2026 Reviewers invited by journal 02 Feb, 2026 Editor assigned by journal 22 Jan, 2026 First submitted to journal 18 Jan, 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. We do this by developing innovative software and high quality services for the global research community. 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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-8590511","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":584383151,"identity":"81157404-c8ea-4e6f-bd45-51b476564320","order_by":0,"name":"Güven Güngör","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYBADOQNmEFUAxAeI1GIM0WIA1sLYQIyWxA0MxGqR919j9vBLxb307ey8h198MGCQ47uRwP64Ao8WwxtvzI1lzhTn7mzmS7OcYcBgLHkjgbHxDD4tM86YSUu2JeRuOMxjZsxjAHQhSAs+l8G0pBtAtdQT1CLP32Mm+bEtIQGoxfgxUEuCASEtBhJsZdIMZxIMdzbzmDHOMJAwnHnmYeNMvLb0H94m+aMiQd6c/4zxhw8VNvJ8x5MPfMRry40EBmYeCJtNgoEBiAjFpHw/MOZ+QNjMH/AqHQWjYBSMghELAGWtTfyy/l6iAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-3695-9443","institution":"Bingol University: Bingol Universitesi","correspondingAuthor":true,"prefix":"","firstName":"Güven","middleName":"","lastName":"Güngör","suffix":""},{"id":584383152,"identity":"c48ec768-b7fe-40df-9cbe-f6f8a992ce20","order_by":1,"name":"Savas Sariozkan","email":"","orcid":"","institution":"Erciyes University: Erciyes Universitesi","correspondingAuthor":false,"prefix":"","firstName":"Savas","middleName":"","lastName":"Sariozkan","suffix":""},{"id":584383153,"identity":"a9527200-e0f0-4adb-8ddd-41bcb03dc310","order_by":2,"name":"Aytac Akcay","email":"","orcid":"","institution":"Ankara University: Ankara Universitesi","correspondingAuthor":false,"prefix":"","firstName":"Aytac","middleName":"","lastName":"Akcay","suffix":""}],"badges":[],"createdAt":"2026-01-13 10:20:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8590511/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8590511/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101841002,"identity":"ead1be9f-b735-45db-bf56-d30cb29e2489","added_by":"auto","created_at":"2026-02-04 08:33:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":20969,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of survival analysis\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8590511/v1/8593a0686c0007780f75a91f.png"},{"id":101841000,"identity":"2daff532-75b2-4bb1-ac02-f53f6f65c7d1","added_by":"auto","created_at":"2026-02-04 08:33:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":20747,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival function chart for neonatal calf diarrhea\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8590511/v1/952181fd6f94fca2c3348cb3.png"},{"id":101841001,"identity":"94f3100f-ca43-4b85-ad3a-86a9f65022b1","added_by":"auto","created_at":"2026-02-04 08:33:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28150,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of survival functions by colostrum intake\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8590511/v1/df9b4d77e22159a071a34868.png"},{"id":101841003,"identity":"588c31f6-c9cf-496c-a47d-7e66ab32f977","added_by":"auto","created_at":"2026-02-04 08:33:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":28139,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of survival functions by calving type\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8590511/v1/85293b601cad52e0d4469cb1.png"},{"id":101881736,"identity":"45e00a29-91dd-409d-8c12-74d3423c92e5","added_by":"auto","created_at":"2026-02-04 15:15:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":993505,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8590511/v1/1f5eb5c4-db16-45b2-9c45-1f4f4225974f.pdf"}],"financialInterests":"","formattedTitle":"Determination of Risk Factors for Neonatal Calf Diarrhea Using Survival Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn dairy farming, profitability is significantly influenced not only by the primary income from milk sales but also by the management strategies of secondary revenue sources, such as calves. Ensuring healthy calf management from birth through rearing is vital for the sustainability of the farm, encompassing both economic and animal welfare dimensions (Godden \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Gulliksen et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe neonatal period, including the first 28 days of the calves, is the phase during which they are most susceptible to the external environment. Although variable depending on farm conditions, disease-induced mortality rates during this period are notably high (Astiz et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rocha et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Furthermore, infections in the neonatal period negatively impact the calf's future production potential, hindering genetic progress and impeding the raising of quality breeding stock (Torsein et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Zucali et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe primary health challenges encountered in dairy calves arise from inadequate care and nutrition, metabolic disorders, and various diseases associated with low immunity (Svensson et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). While respiratory diseases, congenital anomalies, and calving complications are frequently encountered, neonatal calf diarrhea is of particular concern due to its high morbidity and mortality rates (Hadimli \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Calf diarrhea affects not only the digestive system but also leads to serious complications such as dehydration, metabolic acidosis, electrolyte imbalances, hypothermia, and sepsis (Alfieri et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gitau et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Radostits et al. 2006).\u003c/p\u003e \u003cp\u003eGiven the multifactorial aetiology of neonatal calf diarrhea, curative interventions have significant drawbacks, including high costs, production losses, and compromised welfare. Consequently, strategic plans based on proactive prevention present a more rational approach for disease management (Wiggans \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Rai et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Systematic prophylactic health plans specifically designed for the neonatal period can reduce disease prevalence, minimize treatment costs, and enhance animal welfare and future productivity (Bartlett et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Gulliksen et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe success of such strategic plans and preventive measures hinges on the detailed identification of influential factors and the quantification of their associated risks (Frank and Kaneene \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). In this context, the effective application of statistical methods like survival analysis, which enables the modeling of risk factors, will significantly contribute to the successful management of neonatal calf diarrhea (Al Mawly et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study aims to investigate the distribution of neonatal calf diarrhea according to various risk factors, to estimate calf survival times and probabilities using survival analysis, and to establish a multivariate model to identify significant risk factors and explain their hazard ratios.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area\u003c/h2\u003e \u003cp\u003eThis study was conducted on a private dairy farm in the city of Kayseri, located in the Central Anatolia region of T\u0026uuml;rkiye, between June 1, 2022, and July 1, 2023. Kayseri is situated at 37\u0026deg; 45'-38\u0026deg; 18' North latitude and 34\u0026deg; 56'-36\u0026deg; 59' East longitude. The city's altitude is 1054 meters above sea level, with an average annual rainfall of 390.5 mm and an average annual temperature of 10.7\u0026deg;C (Mgm, 2025).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Design and Data Collection\u003c/h3\u003e\n\u003cp\u003eThis longitudinal prospective study was designed to determine the risk factors and disease morbidity associated with neonatal calf diarrhea. Conducted with the approval of the Animal Experiments Local Ethics Committee of Erciyes University (Approval No: 22/157; Date: 07.07.2022), the study data were collected through periodic research visits conducted twice a week at a commercial dairy cattle operation. During the study period, 689 calves born on the farm were monitored throughout the neonatal period (1\u0026ndash;28 days), and potential risk factors were recorded.\u003c/p\u003e \u003cp\u003eThe sample size for the study was determined based on the literature on survival analysis. To obtain reliable results from prediction models developed using Cox regression, it is recommended to have at least 20 events per independent variable (Ogundimu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Accordingly, a total of 377 events were observed in the present study conducted on 689 calves. The mean number of events per variable for the 14 risk factors (independent variables) included in the analysis was calculated as 377/14\u0026thinsp;=\u0026thinsp;26.9, which is above the recommended minimum threshold.\u003c/p\u003e \u003cp\u003eStandard management practices were routinely applied to calves included in the study. Immediately after birth, the umbilical cord was disinfected with a 10% iodine solution. The first colostrum feeding was administered within the first 30 minutes postpartum, and calves were transferred to individual rearing hutches within one hour of birth. Starting from the second day, calves were fed twice daily with farm-produced milk at a rate of 2.5 liters per feeding throughout the neonatal period. Disbudding via chemical cauterization using potassium hydroxide was performed between the 5th and 15th days of life. In the current operation, a standardized protocol is in place to ensure adequate colostrum intake. For any calf that does not consume colostrum equal to 5% of its live body weight within the first six hours postpartum, tube feeding is performed. The colostrum used for this supplementation was sourced from a pooled colostrum reserve, collected from other parturitions occurring on the same date. Furthermore, throughout the neonatal period, calves housed in individual hutches are provided with pelleted starter feed, water, and dry alfalfa hay on an \u003cem\u003ead libitum\u003c/em\u003e basis. The schedule of vaccines was as follows: At birth, calves received a combined antiserum against \u003cem\u003eS. typhimurium\u003c/em\u003e, \u003cem\u003eS. dublin\u003c/em\u003e, \u003cem\u003eE. coli\u003c/em\u003e K99, \u003cem\u003eCl. perfringens\u003c/em\u003e type C, \u003cem\u003eT. pyogenes\u003c/em\u003e, and \u003cem\u003eM. haemolytica\u003c/em\u003e types 1\u0026ndash;2. On the 5th day of life, the first dose of a viral vaccine against Parainfluenza-3 virus and BRSV was administered. The first dose of a combined toxoid vaccine, targeting \u003cem\u003eCl. novyi\u003c/em\u003e, \u003cem\u003eCl. septicum\u003c/em\u003e, \u003cem\u003eCl. sordellii\u003c/em\u003e, \u003cem\u003eCl. perfringens types\u003c/em\u003e B, C, and D, \u003cem\u003eCl. chauvoei\u003c/em\u003e, \u003cem\u003eCl. haemolyticum\u003c/em\u003e, and \u003cem\u003eE. coli\u003c/em\u003e K99, was given on the 15th day. Finally, the first dose of a pasteurellosis vaccine against \u003cem\u003eP. multocida\u003c/em\u003e and \u003cem\u003eM. haemolytica\u003c/em\u003e types 1\u0026ndash;2 was administered on the 28th day.\u003c/p\u003e \u003cp\u003eFor dams of the neonatal calves, standardized protocols were followed. For the dams, these practices included the administration of two doses of a vaccine against calf septicemia (\u003cem\u003eE. coli\u003c/em\u003e, \u003cem\u003eRotavirus\u003c/em\u003e, \u003cem\u003eCoronavirus\u003c/em\u003e) during the 7th and 8th months of gestation. At drying-off, an intermittent milking method and teat sealant application were implemented. An anionic feeding program was introduced for the last 20 days of gestation, and cows were moved to a dedicated maternity unit approximately one week before the expected calving date. Veterinary intervention was performed within 2 hours in heifers, or within 4 hours in multiparous cows when dystocia occurred.\u003c/p\u003e\n\u003ch3\u003eData Description and Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eThe risk factors for the survival analysis were categorized into two main groups: calf-related and dam-related. The calf-related factors included birth season (Spring-Summer, Autumn-Winter), sex (Female, Male), breed (Holstein, Simmental), colostrum intake method (Bottle, Tube), colostrum intake adequacy (Adequate, Inadequate), twinning (Yes, No), birth weight (kg), and colostrum quality (Brix %).\u003c/p\u003e \u003cp\u003eThe birth season variable was consolidated into two broad periods (Spring-Summer: Mar-Aug; Autumn-Winter: Sep-Feb) due to intermittent data collection. This was necessitated by the farm's strict biosecurity protocols during periods of heightened regional disease risk. For colostrum intake, the method \"Bottle\" indicates feeding with a nipple bottle, whereas \"Tube\" indicates feeding via an oesophageal tube. Adequacy was defined as an intake of \u0026ge;\u0026thinsp;10% of birth weight within the first 24 hours of life (Adequate) versus \u0026lt;\u0026thinsp;10% (Inadequate).\u003c/p\u003e \u003cp\u003eThe dam-related risk factors comprised dry period length (Heifer, \u0026lt;\u0026thinsp;45, 45\u0026ndash;60, \u0026gt;\u0026thinsp;60 days), gestation length (\u0026lt;\u0026thinsp;270, 270\u0026ndash;290, \u0026gt;\u0026thinsp;290 days), calving type (Dystocia, Normal), age at first service (\u0026lt;\u0026thinsp;14, 14\u0026ndash;16, \u0026gt;\u0026thinsp;16 months), parity (1, 2, 3, \u0026ge;\u0026thinsp;4), and number of artificial inseminations (1, 2, \u0026ge;\u0026thinsp;3). All maternal data were sourced from the farm's herd management software.\u003c/p\u003e \u003cp\u003eSurvival analysis was conducted to evaluate the time to diarrhea occurrence in calves. The event of interest was defined as the onset of diarrhea symptoms. Calves were enrolled at birth and monitored throughout the neonatal period (days 1\u0026ndash;28), which was designated as the follow-up period. The age (in days) at which diarrhea was first observed was recorded as the survival time. Calves that were lost to follow-up (due to death, sale, or other reasons) or did not develop diarrhea by the end of the 28-day observation period were right-censored in the analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003ewill be inserted here\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe survival probabilities and survival times for the risk factors examined in the study were estimated using the Kaplan\u0026ndash;Meier method. Survival times across variable groups were compared with the Log-Rank (Mantel-Cox) test, and mean and median values were reported with their 95% confidence intervals.\u003c/p\u003e \u003cp\u003eA Cox proportional hazards regression model was used to identify significant risk factors for neonatal calf diarrhea. Model building began with univariate analysis to select candidate variables for the multivariate model. All candidate variables were initially included in a full multivariate model, which was then refined using backward stepwise elimination to obtain a significant model. Subsequently, significant interactions between the variables retained in the model and any time-dependent variables were tested and incorporated, resulting in the final extended Cox regression model. The validity of the proportional hazards assumption in the Cox regression model was evaluated with multiple approaches. Quantitatively, the correlation between Schoenfeld residuals and the ranked survival times was tested. For graphical validation, the scatterplot of martingale residuals against time and the parallelism of log(-log) survival curves were examined.\u003c/p\u003e \u003cp\u003eStatistical significance levels were defined as follows: a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was used for the log-rank test and the correlation test between Schoenfeld residuals and the rank of survival time. For Cox regression analysis, separate thresholds were applied at different stages: p\u0026thinsp;\u0026lt;\u0026thinsp;0.20 for univariate analysis, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for entry into the multivariate model, p\u0026thinsp;\u0026lt;\u0026thinsp;0.10 for removal from the model, and p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for evaluating interaction terms. All statistical analyses were performed using the IBM SPSS (version 27.0) and Stata (version 13.0) software packages.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDuring the study, diarrhea developed in 377 (54.7%) of the 689 monitored calves. The distribution of neonatal diarrhea risk factors is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\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 characteristics and baseline data of the calf-related variables\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\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBirth season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpring-Summer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e450 (65.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e247 (54.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAutumn-Winter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e239 (34.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130 (54.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e384 (55.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e214 (55.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e305 (44.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e163 (53.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBreed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHolstein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e484 (70.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e261 (53.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSimmental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e205 (29.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116 (56.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eColostrum intake method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBottle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e370 (53.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e198 (53.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTube\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e319 (46.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179 (56.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eColostrum intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdequate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e629 (91.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e341 (54.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInadequate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (8.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (60.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTwinning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (7.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (66.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e639 (92.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e344 (53.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{\\text{-}}{\\text{X}}\\text{\u0026plusmn;}\\text{S}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMed (Q1-Q2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eRange\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth Weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.25\u0026thinsp;\u0026plusmn;\u0026thinsp;5.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (36\u0026ndash;42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20-60.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColostrum Quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (25\u0026ndash;27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eN: Total calves; n: Calves with diarrhea (event); X̄: Mean; S: Standard deviation; Med: Median; Q1-Q2: 1st-2nd Quartile; Range: Min-max value.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive characteristics and baseline data of the dam-related variables\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\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eDry period length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeifer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e284 (41.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e163 (57.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (4.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (55.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e222 (32.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116 (52.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149 (21.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79 (53.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGestation length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105 (15.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (60.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e270\u0026ndash;290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e557 (80.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e299 (53.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (3.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (55.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCalving type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDystocia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (8.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (67.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e628 (91.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e336 (53.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge at first service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (14.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 (55.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u0026ndash;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e360 (52.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e192 (53.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e229 (33.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130 (56.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e285 (41.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e164 (57.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155 (22.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83 (53.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136 (19.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 (58.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113 (16.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 (44.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNumber of AI per conception\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e376 (54.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e207 (55.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e172 (24.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97 (56.40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141 (20.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73 (51.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{\\text{-}}{\\text{X}}\\text{\u0026plusmn;}\\text{S}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMed (Q1-Q3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eRange\u003c/b\u003e\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\u003e2.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of AI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.75\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eN: Total dams; n: Calves with diarrhea (event), AI: Artificial insemination; *: Parity and number of AI are also presented as continuous variables with descriptive statistics X̄: Mean; S: Standard deviation; Med: Median; Q1-Q2: 1st-2nd Quartile; Range: Min-max value.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe majority of calves were born in the spring-summer season (65.31%), were female (55.73%), and were of the Holstein breed (70.25%). Regarding colostrum management, most calves were fed via a bottle (53.70%), and received an adequate volume of colostrum (91.29%). Single births were predominant (92.74%). The mean (\u0026plusmn;\u0026thinsp;SD) birth weight was 39.25\u0026thinsp;\u0026plusmn;\u0026thinsp;5.69 kg, and the mean colostrum quality was 26.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21 Brix%. Diarrhea was notably higher in certain subgroups. A higher event rate was observed among calves born in spring-summer (54.89%), female calves (55.73%), those of the Simmental breed (56.59%), those fed via an oesophageal tube (56.11%), and those with inadequate colostrum intake (60.00%). The highest incidence was recorded among twin calves (66.00%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003ewill be inserted here\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe largest proportion of calves (41.22%) was born from heifers. An important majority (80.84%) were born from dams with a gestation length of 270\u0026ndash;290 days. Normal calvings were predominant (91.15%). Regarding age at first service, the most common category was 14\u0026ndash;16 months (52.25%). In terms of parity, first-lactation dams were the most frequent group (41.36%), and a single artificial insemination per conception was the most common outcome (54.57%). The highest diarrhea rates were observed in the following categories: calves born from heifers (57.39%), calves born from dams with a gestation period of less than 270 days (60.0%), cases of dystocia (difficult calving) (67.21%), dams with an age at first service higher than 16 months (56.77%), dams in their third lactation (58.82%), and dams that required two artificial inseminations per conception (56.40%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003ewill be inserted here\u003c/b\u003e\u003c/p\u003e \u003cp\u003eData on diarrhea development and survival analysis are summarized below. Survival analysis indicated a cumulative survival probability of 42.9% by the end of the neonatal period (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The mean time to diarrhea onset was 17.9 days (95% CI: 17.19\u0026ndash;18.61), with a median survival time of 14.0 days (95% CI: 11.62\u0026ndash;16.38) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverall survival times of neonatal calves with diarrhea (days)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEstimate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{S}}_{\\stackrel{\\text{-}}{\\text{X}}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eEstimate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{S}}_{\\stackrel{\\text{-}}{\\text{X}}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{S}}_{\\stackrel{\\text{-}}{\\text{X}}}\\)\u003c/span\u003e\u003c/span\u003e: Standard Error; CI: Confidence Interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003ewill be inserted here\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003ewill be inserted here\u003c/b\u003e\u003c/p\u003e \u003cp\u003eColostrum intake had a statistically significant effect on calf survival time (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The cumulative survival probability for the adequate intake group was 44.1%, while it was 27.8% for the inadequate intake group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The mean survival time estimated by Kaplan-Meier analysis was 18.07 days (95% CI: 17.33\u0026ndash;18.81) for calves with adequate colostrum intake, and 15.71 days (95% CI: 13.34\u0026ndash;18.08) for those with inadequate intake (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of survival times according to calf-related variables (days)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003cp\u003eLog-Rank\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eEstimate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{S}}_{\\stackrel{\\text{-}}{\\text{X}}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eEstimate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{S}}_{\\stackrel{\\text{-}}{\\text{X}}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBirth season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpring-Summer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAutumn-Winter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e20.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBreed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHolstein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSimmenthal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eColostrum intake method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBottle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTube\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e21.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eColostrum intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdequate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInadequate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTwinning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e17.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{S}}_{\\stackrel{\\text{-}}{\\text{X}}}\\)\u003c/span\u003e\u003c/span\u003e: Standard Error; CI: Confidence Interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCalving type was found to have a statistically significant effect on survival time (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The cumulative survival probability was 28.4% for the dystocia group and 44.3% for the normal birth group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The Kaplan-Meier estimated mean survival time was 15.07 days (95% CI: 12.76\u0026ndash;17.38) for calves born with dystocia and 18.16 days (95% CI: 17.42\u0026ndash;18.91) for those born normally (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of survival times according to dam-related variables (days)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003cp\u003eLog-Rank\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eEstimate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{S}}_{\\stackrel{\\text{-}}{\\text{X}}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eEstimate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{S}}_{\\stackrel{\\text{-}}{\\text{X}}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eDry period length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeifer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e17.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e26.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGestation length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e270\u0026ndash;290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e21.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCalving type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDystocia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge at first service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e20.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.618\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u0026ndash;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNumber of AI per conception\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{S}}_{\\stackrel{\\text{-}}{\\text{X}}}\\)\u003c/span\u003e\u003c/span\u003e: Standard Error; CI: Confidence Interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cb\u003ewill be inserted here\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cb\u003ewill be inserted here\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003ewill be inserted here\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cb\u003ewill be inserted here\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCandidate variables for the multivariate model were selected using univariate Cox regression analysis. Variables meeting the statistical threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.20 were identified for inclusion in the subsequent multivariate model. Among the calf-related variables, colostrum intake, twin status, birth weight, and colostrum quality were selected as candidates. For the dam-related variables, gestation length and calving type were chosen as candidate variables.\u003c/p\u003e \u003cp\u003eThe multivariate Cox regression model was constructed using a backward elimination method (removal threshold: p\u0026thinsp;\u0026gt;\u0026thinsp;0.10) with the candidate variables identified in the univariate analysis. Following the elimination process, the variables birth weight, colostrum quality, and dystocia remained in the final significant model (p\u0026thinsp;\u0026lt;\u0026thinsp;0.10). The model fit statistic, the \u0026minus;\u0026thinsp;2 Log Likelihood (-2LL) value for the model, was calculated as 4613.38 (χ\u0026sup2; = 33.91; df\u0026thinsp;=\u0026thinsp;3; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eAccording to the results, each one-unit increase in birth weight and colostrum quality reduced the risk of diarrhea in calves by 3.3% (HR: 0.967, 95% CI: 0.950\u0026ndash;0.985) and 15.3% (HR: 0.847, 95% CI: 0.773\u0026ndash;0.928), respectively. Furthermore, the absence of dystocia (i.e., normal calving) was identified as a factor that reduced this risk by 30.7% (HR: 0.693, 95% CI:0.500\u0026ndash;0.960) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate Cox regression model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{S}}_{\\stackrel{\\text{-}}{\\text{X}}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ez\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP (z)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.950\u0026ndash;0.985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColostrum quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.773\u0026ndash;0.928\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalving type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.500\u0026ndash;0.960\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e-2 LL\u0026thinsp;=\u0026thinsp;4613.38, χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;33.91, df\u0026thinsp;=\u0026thinsp;3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eβ: Estimated regression coefficient (beta); \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{S}}_{\\stackrel{\\text{-}}{\\text{X}}}\\)\u003c/span\u003e\u003c/span\u003e: Standard error of the coefficient; z: Wald's z-statistic; df: Degrees of freedom; HR (Exp(β)): Hazard ratio; *The first categories for categorical variables were set as the reference.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u003cb\u003ewill be inserted here\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe validity of the model was assessed by testing the proportional hazards assumption. The evaluation revealed that birth weight, among the significant variables, violated this assumption (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, interactions between the variables were examined. The interaction terms birth weight \u0026times; colostrum quality, birth weight \u0026times; dystocia, and colostrum quality \u0026times; dystocia were found to be statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eA new extended model was created, incorporating the birth weight variable as a time-dependent covariate, which violates the proportional hazards assumption, along with significant interaction terms. The model was completed through a four-step backward elimination process. In the first step of the model, the goodness-of-fit statistic, the \u0026minus;\u0026thinsp;2 Log Likelihood (-2LL) value, was found to be 4608.04 (χ\u0026sup2; = 39.24; df\u0026thinsp;=\u0026thinsp;6; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eAt the end of the backward elimination process, the variables colostrum quality, the birth weight \u0026times; calving type interaction, and the birth weight \u0026times; time interaction retained their statistical significance and remained in the final model (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The \u0026minus;\u0026thinsp;2 Log Likelihood (-2LL) value of the final model was calculated as 4609.09, and the model is statistically significant (χ\u0026sup2; = 37.60; df\u0026thinsp;=\u0026thinsp;3; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). According to the model, each unit increase in colostrum quality was found to reduce the risk of diarrhea in calves by 15.2% (HR\u0026thinsp;=\u0026thinsp;0.848, 95% CI: 0.774\u0026ndash;0.929). The interaction between birth weight and dystocia was associated with a 0.9% decrease in risk compared to the reference category (normal calving) (HR\u0026thinsp;=\u0026thinsp;0.991, 95% CI: 0.982\u0026ndash;0.999). Additionally, the time-dependent effect of birth weight was found to reduce the risk by 0.3% (HR\u0026thinsp;=\u0026thinsp;0.997, 95% CI: 0.995\u0026ndash;0.999) (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExtended Cox regression model (final model)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{S}}_{\\stackrel{\\text{-}}{\\text{X}}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ez\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP (z)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e%95 GA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColostrum quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.774\u0026ndash;0.929\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth weight x Calving type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.982\u0026ndash;0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth weight x g(t)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.995\u0026ndash;0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e-2 LL\u0026thinsp;=\u0026thinsp;4609.09, χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;37.63, df\u0026thinsp;=\u0026thinsp;3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eβ: Estimated regression coefficient (beta); \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{S}}_{\\stackrel{\\text{-}}{\\text{X}}}\\)\u003c/span\u003e\u003c/span\u003e: Standard error of the coefficient; z: Wald's z-statistic; df: Degrees of freedom; HR (Exp(β)): Hazard ratio; g(t): Time-dependent covariate; *The first categories for categorical variables were set as the reference.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e \u003cb\u003ewill be inserted here\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe final model showing the risk factors effective on calf diarrhea is;\u003c/p\u003e \u003cp\u003eHR\u0026thinsp;=\u0026thinsp;exp[ -0.165 \u0026times; Colostrum quality\u0026thinsp;\u0026minus;\u0026thinsp;0.011 \u0026times; Birth weight \u0026times; Calving type\u0026thinsp;\u0026minus;\u0026thinsp;0.003 \u0026times; Birth weight \u0026times; g(t) ]\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this research, survival analysis methods were used to determine the risk factors associated with neonatal calf diarrhea. These methods prevent the loss of information by including disease-free individuals in the analysis, thereby enabling more reliable and unbiased results to be obtained (Ozen et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Similar methodological approaches have been adopted in many studies on calf diseases (Alemu et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Barry et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chung et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gulliksen et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe occurrence of neonatal calf diarrhea was found to be 54.74% in this study. While the reported rates in the literature vary by country, they cover a wide range: 14.6% in France, 18.42% in Nigeria, 24\u0026ndash;50% in Germany, 46.2\u0026ndash;52.9% in Spain, and 68.40% in Ethiopia (Araujo et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Asmare and Kiros \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Bartels et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Bendali et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Olaogun et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Perez et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). In T\u0026uuml;rkiye, the reported rates range from 22.90% to 57.98% (Keles et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tokgoz et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This global prevalence, ranging from 14.60% to 68.40%, indicates that the incidence of the disease is strongly influenced by regional conditions, husbandry practices, and management strategies. The high incidence values observed in this study and the literature highlight that the neonatal period is a critical window for calf health and underscore the vital importance of management practices during this time.\u003c/p\u003e \u003cp\u003eAccording to the Kaplan-Meier analysis results, the mean time to diarrhea occurrence (survival time) in calves was calculated as 17.90 days (95% CI: 17.19\u0026ndash;18.61), with a median of 14.00 days (95% CI: 11.62\u0026ndash;16.38). This median value aligns with the 14\u0026ndash;15 day values reported by Wolking et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Barkley et al. (2021) on some farms but differs from studies reporting lower median values (7\u0026ndash;12 days) (Feldman et al. 2019; Lucey et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnivariate analyses revealed that colostrum intake and the presence of dystocia significantly affected calves' survival time against diarrhea (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The mean survival time was shorter in calves with inadequate colostrum intake (15.71 days) compared to those with adequate intake (18.07 days). This finding is consistent with the importance of colostrum, which forms the basis of passive immunity in calves due to the placental structure. Inadequate intake reduces immunoglobulin (IgG) transfer, leaving calves vulnerable to infections (Kucukoflaz and Sariozkan \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Walsh et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Indeed, diarrhea has been reported to occur earlier in calves fed milk replacer instead of colostrum (Bristol et al. 2021). Similarly, the survival time of calves born from dystocia (mean 15.07 days) was significantly shorter than that of calves from normal births (mean 18.16 days). Dystocia can lead to immunosuppression due to birth stress and increased cortisol release, delayed first suckling, and reduced colostrum intake, making the calf more susceptible to pathogens (Awol et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Vasseur et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The literature also reports that dystocia increases the risk of calf disease by 2.4\u0026ndash;2.96 times (Abebe et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hordofa et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Toombs et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo identify independent risk factors influencing diarrhea, a Cox proportional hazards model was constructed. The model included colostrum quality, the interaction between birth weight and dystocia, and birth weight as a time-dependent variable as statistically significant factors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eIn the model, the intake of high-quality colostrum was identified as a protective factor that reduced the risk of diarrhea (HR: 0.848). This finding supports the central role of passive immune transfer in calf health (Godden et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It is consistent with studies in the literature demonstrating that calves fed low-quality colostrum have a significantly increased risk of diarrhea, pneumonia, and mortality (Crannel and Abuelo 2023; Raboisson et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBoth birth weight alone and its interaction with dystocia emerged as factors modulating risk in the model. While low birth weight is typically associated with poor viability and prematurity (Olson et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), high birth weight can also be linked to dystocia and fetal stress (McMillen et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Our findings indicate that birth weight plays a complex role in diarrhea risk and should be evaluated in conjunction with other factors, such as dystocia. This may partly explain the differing study results that identify birth weight as a risk factor (Schinwald et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Windeyer et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) or find it insignificant (Glover et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results of our multivariate model align in principle with other Cox regression studies that report factors such as time to first colostrum intake, housing conditions, calf age, and season as risk factors (Alemu et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hordofa et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tamrat et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, our study highlights different variable combinations specific to the population and conditions under investigation.\u003c/p\u003e \u003cp\u003eThis study aimed to identify significant risk factors for neonatal calf diarrhea using survival analysis methods. The findings demonstrate that adequate intake of high-quality colostrum and the prevention of dystocia are critical for prolonging calves' survival time against diarrhea and reducing morbidity risk. Furthermore, our study has underlined birth weight as an important parameter that should be evaluated in consideration of its interaction with other factors. Neonatal calf diarrhea causes significant economic losses, including treatment costs, production losses, increased labor, and higher herd replacement expenses, in addition to compromising animal welfare. Therefore, implementing improved management strategies targeting the risk factors emphasized in our study\u0026mdash;such as optimizing colostrum management protocols and enhancing calving observation and intervention\u0026mdash;will significantly contribute to reducing calf losses and improving economic sustainability on dairy farms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe express our gratitude to the Erciyes University Scientific Research Projects Coordination Office for providing the essential facilities to conduct this study. We would also like to thank the Proofreading \u0026amp; Editing Office of the Dean for Research at Erciyes University for the copyediting and proofreading service for this manuscript.\u003c/p\u003e\n\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e\n\u003cp\u003eStatement of Animal Rights\u003c/p\u003e\n\u003cp\u003eConducted with the approval of the Animal Experiments Local Ethics Committee of Erciyes University (Approval No: 22/157; Date: 07.07.2022).\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by Erciyes University Scientific Research Projects Coordination Office (Project No: TDK-2022-12273).\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Guven Gungor. The first draft of the manuscript was created by Savas Sariozkan and Aytac Akcay. All authors commented and approved on previous versions of the manuscript.\u003c/p\u003e\n\u003cp\u003eConflict of Interest Statement\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbebe R, Dema T, Libiyos Y, Teherku W, Regassa A, Fekadu A, Sheferaw D (2023) Longitudinal study of calf morbidity and mortality and the associated risk factors on urban and peri-urban dairy farms in southern Ethiopia. BMC Vet Res 19(15): 1-10. https://doi.org/10.1186/s12917-023-03574-8\u003c/li\u003e\n\u003cli\u003eAl Mawly J, Grinberg A, Prattley D, Moffat J, Marshall J, French N (2015) Risk factors for neonatal calf diarrhoea and enteropathogen shedding in New Zealand dairy farms. 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Ital J Anim Sci 12(2): 162-166. https://doi.org/10.4081/ijas.2013.e26\u003c/li\u003e\n\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":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"tropical-animal-health-and-production","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trop","sideBox":"Learn more about [Tropical Animal Health and Production](https://www.springer.com/journal/11250)","snPcode":"11250","submissionUrl":"https://submission.nature.com/new-submission/11250/3","title":"Tropical Animal Health and Production","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Calf, Cox regression, diarrhea, Kaplan-Meier, risk","lastPublishedDoi":"10.21203/rs.3.rs-8590511/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8590511/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aimed to investigate the distribution of neonatal calf diarrhea according to various risk factors, estimate calf survival times and probabilities, and establish a multivariate model to identify significant risk factors. During the study period, 689 calves born on a farm were monitored throughout the neonatal period (1\u0026ndash;28 days). Potential risk factors included calf-related variables (birth season, sex, breed, colostrum intake, colostrum intake method, twinning, birth weight, colostrum quality) and dam-related variables (dry period length, gestation length, calving type, age at first service, parity, number of artificial inseminations per conception). Cumulative survival probabilities and survival times were estimated using the Kaplan\u0026ndash;Meier method. The Cox proportional hazards regression model was used to identify independent risk factors significantly associated with the hazard of neonatal diarrhea. Diarrhea was observed in 377 (54.7%) of the total 689 calves. The cumulative survival probability was 42.9% by day 28. The mean and median times to diarrhea onset were 17.9 and 14.0 days, respectively. The final multivariate model identified significant interactions: higher colostrum quality (HR: 0.848), higher birth weight in the absence of dystocia (HR: 0.991), and higher birth weight over time (HR: 0.997) were associated with reduced hazard of diarrhea. In conclusion, the risk factors and their ratios constitute a predictive model for the time to diarrhea diagnosis and provide managerial decision support for farmers. Also, the results highlight the positive impact that strategies aimed at improving colostrum quality, achieving optimal birth weights, and reducing dystocia will have on calf health.\u003c/p\u003e","manuscriptTitle":"Determination of Risk Factors for Neonatal Calf Diarrhea Using Survival Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-04 08:33:01","doi":"10.21203/rs.3.rs-8590511/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-02-03T03:52:48+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-02T13:42:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-22T05:39:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Tropical Animal Health and Production","date":"2026-01-19T04:59:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"tropical-animal-health-and-production","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trop","sideBox":"Learn more about [Tropical Animal Health and Production](https://www.springer.com/journal/11250)","snPcode":"11250","submissionUrl":"https://submission.nature.com/new-submission/11250/3","title":"Tropical Animal Health and Production","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2b925898-a732-40b4-8f20-5da5c4f6ef02","owner":[],"postedDate":"February 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-15T18:03:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-04 08:33:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8590511","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8590511","identity":"rs-8590511","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}