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Determining relevant risk factors associated with mid- and late-feeding-stage bovine respiratory disease morbidity in cohorts of beef feedlot cattle

      ABSTRACT

      Objective

      Previous bovine respiratory disease (BRD) research has focused on events early in the feeding phase, but the objective of this study was to determine characteristics and risk factors associated with cohort-level BRD morbidity in the middle and late portions of the feeding phase.

      Materials and Methods

      The analysis was performed on records from 13 commercial feedlots in the United States from 2017 through 2020. Cohorts were analyzed over their first 100 d on feed. Two methods of classification were used. In the veterinarian classification method, hierarchical clustering created 20 temporal patterns, which were categorized by veterinary consultants as early-feeding-stage, mid-feeding-stage, or late-feeding-stage morbidity curves. In the days classification method, cohorts were categorized based on which portion of the feeding period (0 to 42 d, 43 to 71 d, or 72 to 100 d) had the greatest percentage of treatments for BRD. An events/trials model was used to determine morbidity and mortality across different stages of the feeding phase. Ordinal regression was used to determine associations between cohort characteristics and BRD timing. Year and feedlot were included as random effects to account for the hierarchical structure of the data.

      Results and Discussion

      Combined classification yielded 2,429 early-feeding-stage cohorts, 108 mid-feeding-stage cohorts, and 61 late-feeding-stage cohorts. The only factor significantly (P < 0.05) associated with cohort-level BRD morbidity timing was quarter of arrival. Cattle arriving in the second quarter (Q) were more likely to be mid-feeding stage or late-feeding stage (5.5%, 10.2%, respectively) compared with cattle arriving in the other quarters of the calendar year (Q1: 2.7%, 4.5%; Q3: 1.4%, 2.2%, or Q4: 1.4%, 2.3%).

      Implications and Applications

      This study evaluated risk factor relationships with cohort-level BRD timing. No cattle characteristics were significantly associated with BRD timing; however, cattle arriving in Q2 were at higher risk for cohort-level mid- or late-feeding-stage BRD.

      Key words

      INTRODUCTION

      Bovine respiratory disease (BRD) has persisted as the leading cause of morbidity and mortality in North American feedlots, which also makes it known as the most economically impactful cause. Estimates have shown that 67 to 82% of total feedlot morbidity is due to BRD (
      • Smith R.A.
      Impact of disease on feedlot performance: A review..
      ). Previous research has focused on BRD soon after arrival to the feedlot (
      • Babcock A.H.
      • Renter D.G.
      • White B.J.
      • Dubnicka S.R.
      • Scott H.M.
      Temporal distributions of respiratory disease events within cohorts of feedlot cattle and associations with cattle health and performance indices..
      ;
      • Vogel G.J.
      • Bokenkroger C.D.
      • Rutten-Ramos S.C.
      • Bargen J.L.
      A retrospective evaluation of animal mortality in US Feedlots: Rate, timing and cause of death..
      ). Research between 1986 and 1994 indicates that approximately 65 to 80% of morbidity occurred before 45 d on feed (DOF), 13 to 22% from 45 to 90 DOF, and 6 to 15% after 90 DOF (
      • Edwards A.
      Respiratory diseases of feedlot cattle in central USA..
      ). There is a gap in the literature about the timing of BRD at the cohort or individual level, and the existing literature is over a decade old. Recent literature suggests a possible shift from the previously reported epidemiological pattern (
      • Theurer M.E.
      • Johnson M.D.
      • Fox T.
      • McCarty T.M.
      • McCollum R.M.
      • Jones T.M.
      • Alkire D.O.
      Bovine respiratory disease during the mid-portion of the feeding period: Observations of frequency, timing, and population from the field..
      ). An attempt was made recently to determine differences in timing of BRD morbidity between high-risk and high-performing cattle cohorts throughout the feeding phase; however, no potential causes of BRD later in the feeding period were evaluated (
      • Theurer M.E.
      • Johnson M.D.
      • Fox T.
      • McCarty T.M.
      • McCollum R.M.
      • Jones T.M.
      • Alkire D.O.
      Bovine respiratory disease during the mid-portion of the feeding period: Observations of frequency, timing, and population from the field..
      ). Due to a possible increase in incidence later in the feeding period, more research focused throughout the entirety of the feeding period is necessary. From an economic perspective, loses later in the feeding period are more detrimental as the cumulative resources expended are greater than a loss at the beginning of feeding.
      A major deficiency in the literature to date has been no definition for “late” feeding mortality (
      • Engler M.
      • Defoor P.
      • King C.
      • Gleghorn J.
      The impact of bovine respiratory disease: The current feedlot experience..
      ), and late-feeding-stage morbidity suffers from similar issues. Without a clear case definition, determining potential influence of risk factors is challenging. The objective of this work was to determine cohort-level associations with the timing of BRD morbidity and to define a case definition for the timing of cohort-level BRD. A secondary object was to determine differences in amount of morbidity and mortality across different stages of the feeding phase.

      MATERIALS AND METHODS

      Data Source

      Lot-level data from 13 feedlots were collected under confidentiality agreement with the individual feedlots. Institutional Animal Care and Use Committee (IACUC) approval was not required as historical operational data were used for the analysis. The Institutional Review Board at Kansas State University, application #10348, was deemed exempt from in-depth review and granted permission for use of a survey. All veterinarians that participated in the survey did so anonymously.
      A “cohort” was defined to be a group of animals that arrived at the feedlot together; they did not have to finish out the entire time on feed in the same pen. Data were imported into RStudio (9) for analysis; there were 8,764 cohorts, each cohort containing at least one record for an animal treated for BRD. Records were from May 25, 2017, through June 17, 2020. Our case definition for “BRD” was any animal identified by feedlot personnel with clinical signs consistent with BRD for the first time in the feeding period and subsequently treated with an antimicrobial by feedlot personnel. Animals were not excluded due to multiple treatments; however, only the first pull was considered for this analysis. Cohorts were limited to 30 and 500 animals at arrival, greater than 7% total BRD morbidity, single-sex cohorts, and an average arrival weight between 181 kg (400 lb) and 454 kg (1,000 lb). There were 3,550 cohorts that met these initial criteria (Figure 1).
      Figure 1
      Figure 1Flowchart describing the inclusion criteria for 13 feedlots into the working data set. All numbers are representative of number of cohorts. S = steer; H = heifer; 400–1,000 lbs = 181–454 kg.
      Data were transformed into a format in which every cohort had an observation for each individual day from 0 to 100, creating a data set for number of animals treated for BRD each day. This was done to create equal weight in the temporal patterns for each cohort as animals were fed to many different final days. Variables were calculated for the cumulative treatments out of the total treatments per cohort and of the treatments in a lot out of the total animals in that lot. Continuous variables were categorized based on biological cutoffs or quarters to avoid violating the linearity assumption (Table 1).
      Table 1Variables used in the working data set
      VariableDescription
      HeadinNumber of animals in the cohort at arrival
      SexSteer or heifer
      QuarterArrivalQuarter of arrival at the feedlot (1: January–March, 2: April–June, 3: July–September, 4: October–December)
      TimeBovine respiratory disease incidence curve category (early-feeding stage, mid-feeding stage, late-feeding stage)
      NumberTrtTotal number treated from d 0–100 per cohort
      DOFCount of days on feed from d 0–100 per cohort
      NumberTrtPerDayCount of number treated from d 0–100 per cohort
      Total100dTrtTotal treatments in the cohort from d 0–100
      CumsumPercentCumulative number treated by day/total treated
      CumsumCohortCumulative number treated by day/total animals in the cohort at arrival
      YardlotUnique identifier for each cohort
      There is no previously published case definition of early-feeding-stage, mid-feeding-stage, and late-feeding-stage timing for cohort BRD morbidity; therefore, we decided to combine 2 categorization methods to classify the timing of BRD, the first of which used hierarchical clustering based on cumulative BRD morbidity to create daily incidence curves and asking consultant veterinarians to classify each curve as having a pattern consistent with early-feeding-stage, mid-feeding-stage, or late-feeding-stage BRD. The second approach was to categorize cohorts based on which feeding period (early-feeding stage, mid-feeding stage, or late-feeding stage) had the greatest percentage of BRD morbidity. Combining 2 classification methods was done to increase specificity of our case definition so that any cohort classified as having late-feeding-stage BRD would likely receive that classification by a strong majority of feedlot veterinarians.

      Veterinary Consultant Survey Classification

      In the first classification, clusters of BRD incidence curves were created using Ward’s method, which is an agglomerative clustering technique that minimizes sum-of-squares (
      • Ward Jr., J.H.
      Hierarchical grouping to optimize an objective function..
      ). Agglomerative clustering is a type of clustering that starts with each individual observation and works forward grouping them to a specified cut point. Twenty clusters were formed using a process where a cut point was chosen when an adequate number of cohorts remained in each cluster and merging groups one more time showed an evident loss of information. Each cluster represented at least 46 cohorts, and clusters were graphed as percentage of treatments per day for visualization. The percentage of treatments per day curves for each of the 20 clusters were built into a survey on Qualtrics (2005; www.qualtrics.com), which was sent out as a convenience sample of consulting veterinarians. The survey asked each veterinarian to classify each plot as depicting early-feeding-stage, mid-feeding-stage, or late-feeding-stage cohort-level BRD morbidity timing (Figures 2, 3, and 4).
      Figure 2
      Figure 2Example of 1 of the 20 clusters of incidence for the percentage of a pen pulled from 0 to 100 d on feed. This cluster represents 46 different cohorts and was determined to be late-feeding-stage morbidity by all veterinarians consulted.
      Figure 3
      Figure 3Example of 1 of the 20 clusters of incidence for the percentage of a pen pulled from 0 to 100 d on feed. This cluster represents 209 different cohorts and was determined to be early-feeding-stage morbidity by all veterinarians consulted.
      Figure 4
      Figure 4Example of 1 of the 20 clusters of incidence for the percentage of a pen pulled from 0 to 100 d on feed. This cluster represents 158 different cohorts and was determined to be mid-feeding-stage morbidity by all veterinarians consulted.

      Days Classification

      In the second classification, method cohorts were categorized based on which feeding period had the greatest percentage of BRD cases. Feeding intervals were set as d 0 to 42, 43 to 71, and 72 to 100. Previous literature has stated that 75% of BRD morbidity occurs by d 42 on feed (
      • Babcock A.H.
      • Renter D.G.
      • White B.J.
      • Dubnicka S.R.
      • Scott H.M.
      Temporal distributions of respiratory disease events within cohorts of feedlot cattle and associations with cattle health and performance indices..
      ), and for our purposes, this was defined as early-feeding stage. The mid-feeding-stage and late-feeding-stage categories were decided on by allotting half the remaining DOF to each category. Percentage of overall BRD morbidity within cohort was calculated for each interval, and whichever interval had the greatest number of treatments was assigned the classification. Agreement of classification methods was used to create a data set of cohorts that met our final case definition for the timing of cohort-level BRD morbidity.

      Statistical Analysis

      An events-over-trials regression mixed-effects model was fit using the ‘glmer’ function from the ‘lme4’ package in RStudio (

      RStudio Team. n.d. RStudio: Integrated Development Environment for R (1.5.185) [Computer software]. RStudio, PBC. http://www.rstudio.com/.

      ). The outcome variable was the number of treatments or population at risk within cohort. “Population at risk” was defined by number of cattle in the cohort at arrival less number treated per day as defined previously (
      • Cernicchiaro N.
      • Renter D.G.
      • White B.J.
      • Babcock A.H.
      • Fox J.T.
      Associations between weather conditions during the first 45 days after feedlot arrival and daily respiratory disease risks in autumn-placed feeder cattle in the United States..
      ). The independent variable for this analysis was the ordinal variable “time,” which had categories for early-feeding stage, mid-feeding stage, or late-feeding stage. Feedlot and year were included as random effects to account for the hierarchical nature of the data. A similar model was fit to identify differences in mortality for the independent variable “time” with random effects for feedlot and year. This model used outcome variable deaths(events)/population at risk (trials), with “population at risk” defined as number of cattle in the cohort at arrival less the number dead by day. The logistic regression model was fit to evaluate differences in morbidity and mortality across cohorts that were classified as early-feeding stage, mid-feeding stage, or late-feeding stage.
      An ordinal regression mixed-effects model was fit using the ‘clmm’ function from the ‘ordinal’ package in RStudio (

      RStudio Team. n.d. RStudio: Integrated Development Environment for R (1.5.185) [Computer software]. RStudio, PBC. http://www.rstudio.com/.

      ) to analyze which cohort-level risk factors were associated with the outcome of interest (early-feeding-stage, mid-feeding-stage, or late-feeding-stage cohort BRD timing). Covariates evaluated for the model were sex, arrival quarter, in-weight category, lot size category at arrival, and all possible interactions. Random effects of feedlot and year were included to account for lack of independence of the feedlots and years. All potential interactions were evaluated using forward manual selection. Main effects variables were screened by backward elimination until only statistically significant (P < 0.05) variables remained for the final model. Probabilities were used to evaluate the predictor variable.

      RESULTS AND DISCUSSION

      Previous literature evaluated factors related to BRD at arrival and within the early weeks in the feedlot. Bovine respiratory disease morbidity is most predominant within the first 30 d for feedlot cattle in Brazil (
      • Baptista A.L.
      • Rezende A.L.
      • Fonseca P. De A.
      • Massi R.P.
      • Nogueira G.M.
      • Magalhães L.Q.
      • Headley S.A.
      • Menezes G.L.
      • Alfieri A.A.
      • Saut J.P.E.
      Bovine respiratory disease complex associated mortality and morbidity rates in feedlot cattle from southeastern Brazil..
      ), and a US study found that 74% of BRD cases occurred within the first 42 DOF (
      • Babcock A.H.
      • White B.J.
      • Dritz S.S.
      • Thomson D.U.
      • Renter D.G.
      Feedlot health and performance effects associated with the timing of respiratory disease treatment..
      ). Although a study recently investigated BRD at ≥45 DOF (
      • Theurer M.E.
      • Johnson M.D.
      • Fox T.
      • McCarty T.M.
      • McCollum R.M.
      • Jones T.M.
      • Alkire D.O.
      Bovine respiratory disease during the mid-portion of the feeding period: Observations of frequency, timing, and population from the field..
      ), the current study helps fill in the gap of research specifically addressing factors associated with timing of disease through the first 100 DOF. Previously, there was no case definition of late-day cohort-level morbidity and no previous methodology to assign cohorts to a timing of disease category. Our case definition for cohort-level BRD timing was created based on agreement between a classification method that surveyed consultants to classify clusters of BRD incidence curves and classification based on which of 3 feeding periods had the greatest percentage of BRD cases for each cohort.

      Classifying Cohorts Based on the Timing of BRD Morbidity

      Thirteen surveys were returned and evaluated. Using the survey answers, the BRD timing classifications for individual cohorts were defined based on the classification of each cluster by most the consultants. Using this method, there were 2,581 early-feeding-stage, 892 mid-feeding-stage, and 120 late-feeding-stage cohorts. Agreement among the consultants’ cohort-level classification was at minimum 61.5% for each of the 20 clusters. There was at least one cluster from early-feeding stage, mid-feeding stage, and late-feeding stage that had 100% agreement among consultants, but more often than not, at least one consultant disagreed with the rest. Some possible explanations for disagreement among consultants would be that different consultants work with different populations of cattle and are accustomed to different disease patterns. Another explanation could be that some cluster graphs were not a clean-cut distribution of disease, in that some curves had 2 peaks and consultants were not provided a bimodal option. Based on the method of percentage BRD morbidity by interval, 3,280 cohorts were defined as early-feeding stage, 239 as mid-feeding stage, 74 were defined as late-feeding stage.

      Combination of Both BRD Timing Classification Systems

      Creating a data set of cohorts with perfect agreement when both classification methods were combined (n = 2,598) increased specificity of our case definition and resulted in 1,121 cohorts being removed due to disagreement among classification methods. The final, combined method data set contained 2,429 early-feeding-stage, 108 mid-feeding-stage, and 61 late-feeding-stage cohorts (Table 2). Of the cohorts removed, the greatest loss was where the days classification method classified the cohort as being early-feeding stage, but the consultant survey method classified them as mid-feeding stage.
      Table 2Descriptive demographics of cohorts by timing category
      VariableTime
      EarlyMidLate
      Quarter of arrival
       16203419
       22923319
       3567176
       49502417
      Sex
       Steer17946738
       Heifer6354123
      Cohort size
       30–1005294218
       101–1505832120
       151–200596209
       201–3005262012
       301–50019452
      Average weight at arrival (kg)
       181–2265332
       227–272196117
       273–3186383016
       319–3637533018
       364–4095602413
       410–455229105
      Results from the logistic regression events-over-trials morbidity model showed a greater incidence of morbidity in the early-feeding-stage category (16.08%) compared with mid-feeding-stage (14.31%) or late-feeding-stage (12.23%) categories. The logistic regression events-over-trials mortality model identified the greatest mortality in early-feeding-stage cohorts (2.97%) compared with mid-feeding-stage (2.48%) or late-feeding-stage (2.55%) cohorts.
      No interactions were found significant at the P < 0.05 level, and therefore, all were excluded from the model. The only variable significantly (P < 0.01) associated with timing of BRD at the pen level in this study was quarter of arrival into the feedlot. The percentage of cohorts that arrived in the first, second, third, or fourth quarter that were classified as having early-feeding-stage BRD were 92.9, 84.3, 96.4, and 96.2%, respectively. Cohorts arriving in the second quarter had a 5.5% probability of being classified as having mid-feeding-stage BRD, which was significantly different compared with 2.7, 1.4, and 1.4% for cohorts arriving in the first, third, or fourth quarters (respectively). Cohorts arriving in the second quarter had a 10.2% probability of being classified as having late-feeding-stage BRD, which was significantly different compared with 4.5, 2.2, and 2.3% for the cohorts arriving in the first, third, and fourth quarters (respectively). Cattle cohorts were most likely to be either mid-feeding stage or late-feeding stage when BRD morbidity occurred if they arrived in the second quarter (April to June; Table 3). Out of all the quarters, the second quarter represented the lowest percentage (14%) of animals arriving at the feedlot. Possible explanations for quarter of arrival being associated with BRD morbidity is that the type of cattle arriving in the second quarter may have some differences compared with those arriving the rest of the year. Some of these differences could be that the cattle market and production cycle dictate a large influx of predominantly high-risk animals arrive in the feedlot in the fall (
      • Ribble C.S.
      • Meek A.H.
      • Jim G.K.
      • Guichon P.T.
      The pattern of fatal fibrinous pneumonia (shipping fever) affecting calves in a large feedlot in Alberta (1985–1988)..
      ;
      • Taylor J.D.
      • Fulton R.W.
      • Lehenbauer T.W.
      • Step D.L.
      • Confer A.W.
      The epidemiology of bovine respiratory disease: What is the evidence for predisposing factors?.
      ;
      • Babcock A.H.
      • Cernicchiaro N.
      • White B.J.
      • Dubnicka S.R.
      • Thomson D.U.
      • Ives S.E.
      • Scott H.M.
      • Milliken G.A.
      • Renter D.G.
      A multivariable assessment quantifying effects of cohort-level factors associated with combined mortality and culling risk in cohorts of U.S. commercial feedlot cattle..
      ). Commonly, cattle are spring born and either shipped to the feedlot in the fall immediately after weaning or retained and backgrounded for a short 30- to 60-d program or longer 90- to 120-d program (
      • Taylor J.D.
      • Fulton R.W.
      • Lehenbauer T.W.
      • Step D.L.
      • Confer A.W.
      The epidemiology of bovine respiratory disease: What is the evidence for predisposing factors?.
      ).
      Table 3Model estimated probabilities and standard error of BRD morbidity by quarter of arrival
      Time and quarter of arrivalProbability (%)SE
      Early-feeding stage
       192.5a0.019
       284.2b0.039
       396.4a0.011
       496.3a0.011
      Mid-feeding stage
       12.8a0.007
       25.5b0.014
       31.4a0.004
       41.4a0.004
      Late-feeding stage
       14.7a0.013
       210.3b0.028
       32.2a0.007
       42.3a0.007
      a,bDifferent superscripts within a category indicate statistically significant differences (P < 0.05).
      This analysis was performed on cohort-level data to identify factors associated with timing. It is not surprising to the authors that factors known to be associated with the incidence of BRD in the feedlot were not significantly associated with the timing of BRD. Further research is necessary to identify risk factors associated with mid-feeding-stage and late-feeding-stage BRD morbidity and mortality at the individual animal level. We recognize that the BRD morbidity reported in this study is greater than reality due to the inclusion criteria of >7% BRD morbidity per cohort. Across the United States in 2011, NAHMS stated that 13.4% of cattle placed in a feedlot were treated with an antimicrobial for BRD, so the cut point used here is still well below the average morbidity across feedlots (

      NAHMS (National Animal Health Monitoring System). 2011. Health and Health Management on U.S. Feedlots with a Capacity of 1,000 or More Head. Accessed May 12, 2021. https://www.aphis.usda.gov/animal_health/nahms/feedlot/downloads/feedlot2011/Feed11_dr_PartIV_1.pdf.

      ). Additionally, there was an imbalance of cohorts in the timing categories, with the fewest cohorts being considered late. Due to the total number of cohorts involved in this study and statistical tests used, this is not of major concern but should be noted as potential associations could have been missed because of the smaller number of cohorts. In addition, interpretation of observational studies of feedlot data must recognize that different personnel are identifying sick calves with different diagnostic protocols in place at each feedlot. Potential confounding factors need to be considered when interpreting the results of this study; some examples of this would be differences in management between feedlots, biological differences between animals, background, and previous management of animals. Our analysis was done to account for sources of variability that we know have an effect on our outcome by including feedlot and year as random effects in the model.

      APPLICATIONS

      Evaluating the timing of BRD morbidity in the feedlot illustrated a significance of the quarter of arrival, with the greatest amount of late-feeding-stage BRD occurring in cattle that arrived between April and June. Further investigation into why animals arriving in the second quarter of the year have greater risk for late-feeding-stage BRD morbidity needs to be done. Our data represented 13 feedlots from the central United States, so caution needs to be taken when interpreting these results to feedlots in other geographic locations and with other management practices.

      ACKNOWLEDGMENTS

      The authors thank the International Consortium for Antimicrobial Stewardship in Agriculture Program through the Foundation for Food and Agriculture Research Organization (ICASA; Washington, DC) for funding this work.

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