Edited by: Javiera Cornejo Kelly, Universidad de Chile, Chile
Reviewed by: Nour Eissa, University of Manitoba, Canada; Jean-Claude Desfontis, INRA UMR703 Ecole Nationale Vétérinaire, Agroalimentaire et de l’alimentation de Nantes-Atlantique, France; Gabriel Gutkind, Universidad de Buenos Aires, Argentina; Shankar Thangamani, Midwestern University, United States
Specialty section: This article was submitted to Veterinary Pharmacology and Toxicology, a section of the journal Frontiers in Veterinary Science
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Bovine respiratory disease (BRD) is the most important illness of feedlot cattle. Disease management targets the associated bacterial pathogens,
Bovine respiratory disease (BRD) in newly received calves continues to be the most predominant health issue for North American beef production, with incidences that range from 5 to 44% and estimated costs to producers at $13.90 per animal (
There is evidence of declining efficacy of the antimicrobials commonly used to manage these pathogens (
Knowledge of regional antimicrobial sensitivity patterns can help veterinarians design effective treatment protocols, inform management strategies to support responsible antimicrobial stewardship, reduce costs of production, and improve animal health and welfare. The objectives of this study were to (i) investigate the feasibility of a collaborative network of private practice veterinarians, industry representatives, government agencies, and a diagnostic laboratory for monitoring antimicrobial resistance (AMR) in beef cattle and (ii) conduct a cross-sectional study to describe the pathogens isolated in clinical BRD cases and the frequency of AMR in the isolated pathogens.
Sixty commercial feedlots located in southern Alberta, managed by seven private veterinary practices, and ranging in capacity from 2,000 to 25,000 head, participated in the study. An estimation of the sample size needed to assess AMR to BRD pathogens was calculated. The assumptions were: (i) significance level, 0.05, (ii)
Veterinarians and feedlot managers who participated in this study did so voluntarily with the assurance that we would respect the anonymity and confidentiality of their data. Most of the cattle sampled in this study had died as a result of disease. Live animal sampling was within the veterinary scope of practice for commercial beef production in Canada.
Samples were collected from both morbid cattle and those that had succumbed to BRD. Morbid cattle were sampled if pulled for treatment and diagnosed chute-side with BRD based on elevated rectal temperature (>40°C) and clinical signs consistent with the disease. Guarded, deep nasopharyngeal swabs (Jorgensen Laboratories, Inc., Loveland, CO, USA) were employed for live animal sampling. Swabs were stored in Amies® bacterial transport medium (Starplex Scientific, Inc., Etobicoke, ON, USA) at 4°C until delivered to the diagnostic laboratory within 3 days of sampling. Samples were frozen and stored at −20°C until processing if delivery time was projected to exceed 72 h. Mortalities were sampled based on gross pathological evidence of infectious pneumonia at postmortem. Samples collected at postmortem included: lung tissue; nasal, tracheal, and laryngeal swabs; pleural fluid; heart or pericardium; joint fluid; peritoneal fluid and tissue; and abscesses. These were collected aseptically avoiding contamination by environmental bacteria and stored in sterile containers without media at 4°C until delivered to the diagnostic laboratory within 3 days of sampling from participating practices. Descriptive and clinical information regarding the sampled animal were also collected if possible (Table
Variables collected for animals entered in the study.
Descriptor |
---|
Animal identification number |
Ear tag number |
Veterinary practice code |
Farm |
Region |
Alive? (True or false) |
Animal type (fall calves, winter calves, yearlings, adults) |
Number of days on feed |
Field diagnosis/diagnoses |
Treatment on arrival |
Additional treatment(s) |
Swab samples were inoculated directly onto Tryptic Soy Agar containing 5% blood (TSA-B) (VWR, Mississauga, ON, Canada) and incubated 16–72 h at 37°C for isolation of
Tissue samples were manually homogenized in 10 mL of brain heart infusion (BHI) broth for approximately 1 min or until even consistency was achieved. Both the homogenized tissue suspensions and raw fluid samples were serially diluted 1:10 in BHI broth. For isolation of
Isolates displaying appropriate morphologies for
Antimicrobial susceptibility testing was performed on all isolates using broth microdilution and a commercially available bovine/porcine panel (Sensititre; Trek Diagnostic Systems, Cleveland, OH, USA) and standardized breakpoints. Briefly, all isolates were suspended in 0.9% saline to a McFarland standard of 0.5 with 10 µL of the resulting suspension used to inoculate 11 mL of Mueller-Hinton Broth with TES containing lysed horse blood (Trek Diagnostic Systems, Cleveland, OH, USA). This final suspension was used to inoculate the Sensititre plates per the manufacturer’s instructions. For
The proportion of samples positive for
Two feedlots (Feedlot A and Feedlot B) serviced by different veterinary practices provided a substantial number of samples (15.2 and 33.2% of total samples, respectively) with which we could compare MDR profiles. For each of the pathogens, two sample Wilcoxon rank-sum test was used to compare the MDR at Feedlot A to Feedlot B.
The seven participating practices submitted samples from 60 feedlots located in 10 municipal counties in southern Alberta (Figure
Map of the study area.
One or more of the targeted pathogens (
Samples collected, isolates recovered, and isolates used in susceptibility analysis (
Sample | Isolate | No. isolates | % recovery | No. isolates with antimicrobial resistance results |
---|---|---|---|---|
Lung ( |
213 | 44.4 | 208 | |
198 | 41.3 | 194 | ||
86 | 17.9 | 85 | ||
64 | 13.3 | 63 | ||
69 | 14.4 | 69 | ||
Pleural fluid ( |
7 | 35 | 7 | |
1 | 5 | 1 | ||
0 | 0 | 0 | ||
1 | 5 | 1 | ||
0 | 0 | 0 | ||
Nasal swab ( |
14 | 18.7 | 6 | |
8 | 10.7 | 7 | ||
15 | 20 | 15 | ||
2 | 2.7 | 2 | ||
0 | 0 | 0 | ||
Deep nasal swab ( |
15 | 21.1 | 10 | |
16 | 22.5 | 16 | ||
15 | 21.1 | 15 | ||
5 | 7 | 3 | ||
15 | 21.1 | 15 | ||
Laryngeal/tracheal swab ( |
0 | 0 | 0 | |
1 | 25 | 1 | ||
1 | 25 | 1 | ||
0 | 0 | 0 | ||
2 | 50 | 2 | ||
Heart/pericardium ( |
1 | 5.5 | 1 | |
4 | 22.2 | 2 | ||
0 | 0 | 0 | ||
6 | 33.3 | 5 | ||
1 | 5.4 | 1 | ||
Peritoneum ( |
0 | 0 | 0 | |
1 | 100 | 1 | ||
0 | 0 | 0 | ||
0 | 0 | 0 | ||
0 | 0 | 0 | ||
Joint fluid ( |
1 | 1.4 | 1 | |
4 | 5.7 | 3 | ||
0 | 0 | 1 | ||
1 | 1.4 | 0 | ||
7 | 10 | 7 | ||
Abscess ( |
0 | 0 | 0 | |
1 | 100 | 1 | ||
1 | 100 | 0 | ||
1 | 100 | 1 | ||
0 | 0 | 0 |
Treatment data on entry to the feedlot was provided for 46.6% of the submissions and of these, to 95.5% of the animals were administered a macrolide antimicrobial. Information about in-feed antimicrobials was not collected but feedlots in Western Canada routinely add tetracyclines to the feed during the early (approximately 10–20 days) feeding period for BRD prevention, and either tylosin or tetracyclines throughout the feeding period for liver abscess control (C. Dorin, personal communication, November 2015).
There was AMU information provided for 444 treatments from 235 (38%) animals. The number of antimicrobial treatments per animal, including entry, ranged from 1 to 5. The average number of antimicrobial classes represented in the treatments was 1.86 (SD, 0.99). In the cattle selected for treatment, florfenicol was used in 35.4% of cases, enrofloxacin in 27.5%, and ceftiofur in 14%. Macrolides (8.8%), tetracyclines (7.4%), and trimethoprim sulfas (7.0%) were used in the remaining treated cattle.
The distributions of the MIC’s for the five target organisms are provided in Tables
Distribution of minimum inhibitory concentrations among
Distribution (%) of MICs (μg/mL) | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Class | Category |
Antimicrobial | MIC 50 | %R |
≤0.12 | 0.25 | 0.5 | 1 | 2 | 4 | 8 | 16 | 32 | 64 |
Aminoglycoside | II | GEN | 4 | 3.4 |
4.3 | 38.6 | 50.6 | 3.0 | 3.4 | |||||
II | NEO | 8 | 49.4 |
13.7 | 36.9 | 1.3 | 48.1 | |||||||
III | SPE | 32 | 4.3 | 1.7 | 20.2 | 73.0 | 64 = 0.9 |
|||||||
Fluorquinolone | I | ENRO | 0.12 | 3.0 | 94 | 1.3 | 1.3 | 0.4 | 3.0 | |||||
I | DANO | 0.12 | 3.9 |
91 | 4.3 | 0.9 | 3.9 | |||||||
Macrolide | II | TYLT | 32 | 99.1 |
0.4 | 0.4 | 4.3 | 94.8 | ||||||
II | TUL | 16 | 37.8 | 4.3 | 6.9 | 14.6 | 17.6 | 14.2 | 4.7 | 37.8 | ||||
II | TIL | 16 | 44.2 | 34.8 | 8.6 | 12.4 | 5.6 | 38.6 | ||||||
B lactams | I | XNL | 0.25 | 0.9 | 96.6 | 0.4 | 1.7 | 0.4 | 0.9 | |||||
II | PEN | 0.12 | 7.2 | 51.9 | 36.5 | 4.3 | 1.7 | 0.4 | 0.4 | 4.7 | ||||
II | AMP | 0.25 | 5.1 |
92.3 | 1.3 | 1.3 | 0.9 | 0.4 | 0.4 | 3.4 | ||||
Lincosamides | II | CLIN | 8 | 77.7 |
0.9 | 0.9 | 2.1 | 18.5 | 57.5 | 20.2 | ||||
Phenicol | III | FFN | 1 | 4.3 | 1.7 | 39.9 | 51.1 | 2.1 | 0.9 | 4.3 | ||||
Tetracycline | III | OXY | 8 | 53.6 | 20.6 | 20.6 | 4.3 | 0.9 | 53.6 | |||||
III | CTET | 2 | 11.2 | 8.2 | 30.0 | 25.8 | 24.9 | 11.2 | ||||||
Pleuromutilin | III | TIA | 16 | 19.7 |
0.4 | 1.7 | 3.9 | 9.4 | 64.8 | 19.7 |
Distribution of minimum inhibitory concentrations among
Distribution (%) of MICs (µg/mL) | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Class | Category |
Antimicrobial | MIC 50 | %R |
≤0.12 | 0.25 | 0.5 | 1 | 2 | 4 | 8 | 16 | 32 | 64 |
Aminoglycoside | II | GEN | 16 | 58 | 1.3 | 1.3 | 8 | 31.4 | 58 | |||||
II | NEO | 32 | 97.7 | 1.3 | 0.9 | 0.4 | 97.3 | |||||||
III | SPE | 8 | 0.9 | 88.9 | 8.8 | 0.9 | 64 = 0.4 |
|||||||
Fluorquinolone | I | ENRO | 0.25 | 8 | 44.2 | 41.6 | 40 | 2.2 | 8 | |||||
I | DANO | 0.25 | 17.7 | 15.5 | 42.5 | 24.3 | 17.7 | |||||||
Macrolide | II | TYLT | 32 | 97.7 | 0.9 | 0.4 | 0.9 | 0.4 | 97.3 | |||||
II | TUL | 64 | 92 | 0.4 | 0.9 | 0.4 | 0.9 | 3.5 | 1.8 | 92 | ||||
II | TIL | 64 | 98.2 | 1.3 | 0.4 | 98.2 | ||||||||
B lactams | I | XNL | 8 | 98.2 | 1.8 | 98.2 | ||||||||
II | PEN | 8 | 98.6 | 0.13 | 0.4 | 0.4 | 98.2 | |||||||
II | AMP | 16 | 98.6 | 0.9 | 0.4 | 0.4 | 98.2 | |||||||
Lincosamides | II | CLIN | 16 | 73 |
8.4 | 7.1 | 6.2 | 4.0 | 1.3 | 3.1 | 69.9 | |||
Phenicol | III | FFN | 4 | 25.7 | 1.3 | 1.3 | 5.8 | 19.9 | 46 | 25.7 | ||||
Tetracycline | III | OXY | 8 | 80.1 | 1.3 | 0.9 | 8 | 9.7 | 80.1 | |||||
III | CTET | 8 | 69.5 | 2.2 | 2.2 | 7.1 | 19 | 69.5 | ||||||
Pleuromutilin | TIA | 1 | 2.2 |
42 | 27.9 | 11.5 | 5.3 | 8.4 | 2.7 | 2.2 |
Distribution of minimum inhibitory concentrations among
Distribution (%) of MICs (µg/mL) | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Class | Category |
Antimicrobial | MIC 50 | %R |
≤0.12 | 0.25 | 0.5 | 1 | 2 | 4 | 8 | 16 | 32 | 64 |
Aminoglycoside | II | GEN | 4 | 8.5 |
8.5 | 53.0 | 29.9 | 8.5 | ||||||
II | NEO | 16 | 65.8 |
5.1 | 29.1 | 28.2 | 37.6 | |||||||
III | SPE | 32 | 27.4 | 1.7 | 36.8 | 32.5 | 64 >64 |
|||||||
Fluorquinolone | I | ENRO | 0.12 | 0 | 91.5 | 6.0 | 1.7 | 0.9 | ||||||
I | DANO | 0.12 | 1.7 |
88.9 | 6.8 | 2.6 | 1.7 | |||||||
Macrolide | II | TYLT | 32 | 99.1 |
0.9 | 6.8 | 92.3 | |||||||
II | TUL | 4 | 29.9 | 13.7 | 30.8 | 11.1 | 5.1 | 7.7 | 1.7 | 29.9 | ||||
II | TIL | 16 | 41.9 | 12.9 | 33.3 | 12.0 | 0.9 | 41.0 | ||||||
B lactams | I | XNL | 0.25 | 0.9 | 94.9 | 0.9 | 2.6 | 0.9 | 0.9 | |||||
II | PEN | 0.25 | 1.7 | 30.8 | 56.4 | 11.1 | 1.7 | |||||||
II | AMP | 0.25 | 1.8 |
72.6 | 24.8 | 0.9 | 0.9 | 0.9 | ||||||
Lincosamides | II | CLIN | 16 | 100 |
1.7 | 98.3 | ||||||||
Phenicol | III | FFN | 0.5 | 1.7 | 10.3 | 58.1 | 25.6 | 4.3 | 1.7 | |||||
Tetracycline | III | OXY | 8 | 55.6 | 29.1 | 7.7 | 3.4 | 4.3 | 55.6 | |||||
III | CTET | 4 | 43.6 | 16.2 | 21.4 | 11.1 | 7.7 | 43.6 | ||||||
Pleuromutilin | TIA | 32 | 86.3 |
0.9 | 0.9 | 12.0 | 86.3 |
Distribution of minimum inhibitory concentrations among
Distribution (%) of MICs (µg/mL) | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Class | Category |
Antimicrobial | MIC 50 | %R |
≤0.12 | 0.25 | 0.5 | 1 | 2 | 4 | 8 | 16 | 32 | ≥64 |
Aminoglycoside | II | GEN | 8 | 32.0 |
8.0 | 9.3 | 20.0 | 30.7 | 32 | |||||
II | NEO | 32 | 85.3 |
5.3 | 9.3 | 13.3 | 72.0 | |||||||
III | SPE | 16 | 10.7 | 17.3 | 48.0 | 24.0 | 10.7 | |||||||
Fluorquinolone | I | ENRO | 0.12 | 4.0 | 88.0 | 1.3 | 1.3 | 5.3 | 4.0 | |||||
I | DANO | 0.12 | 10.7 |
84.0 | 5.3 | 10.7 | ||||||||
Macrolide | II | TYLT | 8 | 34.6 |
2.7 | 5.3 | 13.3 | 24.0 | 20.0 | 21.3 | 13.3 | |||
II | TUL | 8 | 21.3 | 2.7 | 10.7 | 33.3 | 25.3 | 6.7 | 21.3 | |||||
II | TIL | 4 | 18.7 | 54.7 | 24.0 | 2.7 | 18.7 | |||||||
B lactams | I | XNL | 0.25 | 0 | 97.3 | 2.7 | ||||||||
II | PEN | 0.12 | 13.3 | 84.0 | 2.7 | 4.0 | 4.0 | 5.3 | ||||||
II | AMP | 0.25 | 11.9 |
85.3 | 2.7 | 5.3 | 1.3 | 1.3 | 4.0 | |||||
Lincosamides | II | CLIN | 1 | 12.0 |
13.3 | 37.3 | 32.0 | 4.0 | 1.3 | 12.0 | ||||
Phenicol | III | FFN | 0.25 | 1.3 | 78.7 | 9.3 | 10.7 | 1.3 | ||||||
Tetracycline | III | OXY | 8 | 54.7 | 20.0 | 4.0 | 10.7 | 10.7 | 54.7 | |||||
III | CTET | 2 | 16.0 | 26.7 | 10.7 | 22.7 | 24.0 | 16.0 | ||||||
Pleuromutilin | TIA | 1 | 0 |
16.0 | 25.3 | 48.0 | 8.0 | 1.3 | 1.3 |
Distribution of minimum inhibitory concentrations among
Distribution (%) of MICs (µg/mL) | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Class | Category |
Antimicrobial | MIC 50 | %R |
≤0.12 | 0.25 | 0.5 | 1 | 2 | 4 | 8 | 16 | 32 | ≥64 |
Aminoglycoside | II | GEN | 1 | 9.6 | 83.0 | 3.2 | 2.1 | 2.1 | 9.6 | |||||
II | NEO | 4 | 9.6 | 84.0 | 6.4 | 1.1 | 8.5 | |||||||
III | SPE | 8 | 1.1 | 95.7 | 2.1 | 1.1 | 1.1 | |||||||
Fluorquinolone | I | ENRO | 1 | 0 | 43.6 | 54.3 | ||||||||
I | DANO | 1 | 91.5 | 3.2 | 5.3 | 91.5 | ||||||||
Macrolide | II | TYLT | 32 | 79.7 | 16.0 | 1.1 | 3.2 | 10.6 | 69.1 | |||||
II | TUL | 64 | 57.4 | 23.4 | 4.3 | 3.2 | 5.3 | 5.3 | 1.1 | 57.4 | ||||
II | TIL | 64 | 75.6 | 22.3 | 2.1 | 1.1 | 74.5 | |||||||
B lactams | I | XNL | 0.5 | 1.1 | 12.8 | 40.4 | 42.6 | 2.1 | 1.1 | 1.1 | ||||
II | PEN | 0.12 | 1.1 | 94.7 | 2.1 | 2.1 | 1.1 | |||||||
II | AMP | 0.25 | 1.1 | 95.8 | 2.1 | 1.1 | 1.1 | |||||||
Lincosamides | II | CLIN | 16 | 83.0 | 12.8 | 2.1 | 1.1 | 1.1 | 83.0 | |||||
Phenicol | III | FFN | 1 | 30.9 | 2.1 | 25.5 | 27.7 | 7.4 | 6.4 | 30.9 | ||||
Tetracycline | III | OXY | 8 | 94.7 | 1.1 | 2.1 | 2.1 | 94.7 | ||||||
III | CTET | 8 | 92.6 | 2.1 | 3.2 | 2.1 | 92.6 | |||||||
Pleuromutilin | III | TIA | 0.5 | 1.1 | 93.6 | 1.1 | 1.1 | 3.2 | 1.1 |
The frequencies of multiclass resistance in the isolates are shown in Table
Number of antimicrobial classes in resistance patterns for important Bovine respiratory disease pathogens.
Isolate | Number of isolates | Number of isolates by number of antimicrobial classes in the resistance pattern |
|||||
---|---|---|---|---|---|---|---|
0 | 1 | 2−3 | 4−5 | 6−7 | 8−9 | ||
233 | 1 | 14 | 88 | 107 | 17 | 6 | |
226 | 0 | 1 | 18 | 138 | 69 | 0 | |
117 | 0 | 0 | 24 | 45 | 48 | 0 | |
75 | 3 | 14 | 33 | 20 | 5 | 0 | |
94 | 0 | 0 | 18 | 42 | 34 | 0 |
Number of isolates resistant to tulathromycin as well as other antimicrobials.
Number of isolates resistant to TUL | 88 (37.8) | 208 (92) | 35 (29.9) | 16 (21.3) | 54 (57.4) | |
Number of isolates also resistant to | SPE | 9 | 1 | 29 | 2 | 1 |
GEN | 8 | 125 | 4 | 11 | 4 | |
NEO | 78 | 204 | 34 | 16 | 9 | |
ENRO | 6 | 17 | 0 | 3 | 0 | |
DANO | 7 | 39 | 0 | 8 | 50 | |
TYLT | 88 | 204 | 35 | 15 | 53 | |
TIL | 85 | 207 | 35 | 14 | 52 | |
PEN | 9 | 207 | 35 | 1 | 0 | |
AMP | 8 | 207 | 0 | 1 | 1 | |
XNL | 1 | 206 | 0 | 0 | 1 | |
CLI | 58 | 120 | 35 | 8 | 54 | |
FFN | 7 | 57 | 2 | 1 | 24 | |
SXT | 76 | 128 | 33 | 14 | 46 | |
TMS | 1 | 203 | 21 | 0 | 1 | |
OXY | 80 | 151 | 32 | 11 | 54 | |
CTET | 19 | 172 | 18 | 8 | 53 | |
TIA | 15 | 5 | 34 | 0 | 1 |
We compared the distribution of the MDR for the isolates at Feedlot A and Feedlot B (Table
Comparing the number of antimicrobial classes to which the target organisms were resistant at Feedlot A and Feedlot B using the two sample Wilcoxon rank-sum test.
Number of isolates of each pathogen by the number antimicrobial classes to which they are resistant |
Two sample Wilcoxon rank-sum test | ||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Feedlot A | Feedlot B | ||||||||||||||||
Number of classes | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | |
0 | 4 | 8 | 3 | 1 | 6 | 0 | 1 | 0 | 0 | 4 | 4 | 24 | 33 | 8 | 0 | 0.0002 | |
0 | 1 | 2 | 2 | 6 | 21 | 2 | 5 | 0 | 0 | 0 | 8 | 26 | 25 | 22 | 2 | 0.810 | |
0 | 0 | 0 | 4 | 3 | 9 | 1 | 0 | 0 | 0 | 0 | 2 | 9 | 4 | 24 | 1 | 0.0015 | |
0 | 3 | 4 | 4 | 1 | 1 | 2 | 1 | 1 | 2 | 7 | 5 | 6 | 2 | 1 | 0 | 0.978 | |
0 | 0 | 0 | 1 | 2 | 7 | 6 | 2 | 0 | 0 | 1 | 4 | 3 | 7 | 8 | 4 | 0.675 |
This study was a successful collaboration of private practice veterinarians, industry, government, and a diagnostic laboratory for monitoring AMR in BRD pathogens from feedlot cattle in southern Alberta and quantified the phenotypic AMR in BRD-affected feedlot cattle.
Most animals in this study were calves that had arrived at the feedlots within the previous 60 days would be considered classical cases of shipping fever.
Difficulties can arise when comparing microbial prevalence data between BRD studies due to the complex etiology of this illness, inconsistencies in the specific agents targeted among studies, and the effects that differing sampling and lab processing strategies have on recovery rates. For example, comparisons against similar surveillance of BRD mortalities performed by Klima et al. (
Sixty-six percent of
High levels of tetracycline resistance (>90%) and macrolide resistance (>75%) were observed in
High levels of resistance (>80%) were also observed against tiamulin (TIA) in the
In North America, there is a trend toward increasing frequencies of MDR in pathogens involved in high mortality BRD cases (
Currently, macrolides are the industry’s standard for BRD preventative therapy, but the data here suggests that this may have to change in the future to effectively maintain animal health. However, the need to identify both MDR and understand the consequences for intrinsic resistance in some of the bacteria involved in BRD is important. For example, tulathromycin-resistant isolates were co-resistant to oxytetracycline, chlortetracycline, neomycin, and/or sulphadimethoxine, suggesting that the use of any of these antimicrobials as secondary therapies would likely have resulted in treatment failure. Additionally, the use of ceftiofur for treatment of
Extreme multidrug resistance was identified in 24% and resistance to eight or nine classes in 0.81% of the isolates. These isolates are significant for further genotypic research and may not have been recovered with less extensive sampling strategies. The capacity to detect important changes in AMR patterns within circulating bacterial strains is enhanced with molecular subtyping and identification of integrative conjugative elements within XDR strains. With further research, identifying relatively rare XDR strains circulating in the population may be possible with targeted (risk-based) sampling if the characteristics that influence the probability of an animal carrying XDR strains can be identified. The role of metagenomic sequencing in identifying AMR within the larger animal population is also one that will need to be examined. If used appropriately, some of the newer genomics based technologies might be able to help overcome some of the burden associated with microbiological surveillance methods.
It is important to note that, in this study, the cohorts were diseased animals and may not be representative of the broader cattle population that includes healthy and diseased animals. This study selected animals suspected of having BRD or at postmortem and many would have received multiple antimicrobials. Susceptible strains of the target organisms would have been removed from the sample resulting in a higher proportion of resistant isolates. Klima et al. (
Antimicrobial resistance patterns varied between Feedlot A and Feedlot B. Feedlot A’s veterinarian did not provide AMU data, so it was not possible to characterize the different proportions of AMR seen at each feedlot in terms of AMU. Additional information about how and where the cattle were sourced and different animal management practices could also be useful for understanding variable AMR patterns. This finding highlights the challenge of identifying a representative sample from a region and the complexity of AMR surveillance. Questions remain about the appropriate sample size for AMR studies. AMR has been shown to cluster, influenced by the ecology of the location (
Substantial effort and cost is required to collect samples and the associated epidemiological data as well as complete microbiological testing. To promote the optimum use of public resources, AMR surveillance systems must balance benefits with costs and consider alternative designs to generate the most meaningful data and meet the purpose of the system.
Further studies are required to investigate the epidemiological factors that contributed to AMR. Prospective cohort studies that can accurately measure AMU in the individual, its source herd, and other epidemiological factors commonly associated with BRD, could provide measures of associations between exposures and AMR. Notably, there have been relatively few observational studies or randomized trials comparing interventions to manage AMR in veterinary medicine (
As was seen here, it can be difficult to motivate veterinary practices to provide AMU data. This is an issue that may need to be resolved in the future before real progress can be made toward AMR intervention in the agricultural setting as collection of AMR data in isolation of AMU is of little value in terms of developing strategies that help control the spread of resistance (
Antimicrobial resistance surveillance in beef production can be challenging, specifically when trying to encompass both animal and regional variability. In addition, obtaining both animal metadata and treatment histories from private veterinary practices can be difficult with constraints on veterinary practitioners’ time and reluctance to share information. This study successfully provides an estimate of the current magnitude of AMR in BRD-affected feedlot cattle in Alberta, encompassing samples from a wide geographic range that are representative of different veterinary practices from this region. The results demonstrated the challenge of effective antimicrobial management in these animals.
This study was exempt from Animal Care Review. This was an observational, cross-sectional study. The animals that were sampled for the study were ill or had died of naturally occurring BRD. Most of the animals examined were mortalities. Sample collection from morbid animals was considered within the normal scope of practice.
RMA made significant contributions to the design of the project, was responsible for the analysis and interpretation of the work and drafting and revising the manuscript including the final version to be published. She agrees to be accountable for all aspects of the work and will ensure that questions related to accuracy are investigated and resolved. CK contributed to the interpretation of data, and drafting and revising the manuscript including the final version. She agrees to be accountable for all aspects of the work and will ensure that questions related to accuracy are investigated and resolved. NA, HM-B, CS, and PA contributed to the concept and design of the study, were responsible for data acquisition and participated in critical review of the manuscript including the final version. They will be accountable for all aspects of the work and will ensure that questions related to accuracy are investigated and resolved. SO, DP, KS, MO, TM, and BR contributed to the concept and design of the study, interpretation of the data, and critical review of the manuscript including the final version. They will be accountable for the work and will ensure that questions related to accuracy are investigated and resolved.
RMA is the owner and director of POV Inc., a professional corporation. She was hired as a consultant, funded by the Growing Forward2 Grant, to provide expertise in the study design, analysis, and interpretation of the data, writing the report and decision to submit the report for publication. POV and MR did not receive payment from a third party for any aspect of the submitted work. She had full access to the data throughout the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. NA and MO are owners and directors of Chinook Contract Research (CCR). CCR administered the Growing Forward2 grant that covered the cost of HM-B to work as a laboratory technician on the project. CCR contributed all overhead costs associated with maintenance of the grant and human resources. CCR and its directors did not receive payment from a third party for any aspect of the submitted work. The directors and HM-B had full access to the data throughout the study and take responsibility for the integrity of the data and accuracy of the data analysis. Institute of Applied Poultry Technologies (IAPT) is a not-for-profit organization of poultry industry stakeholders. At the time of the sample collection and analysis for this project, NA and MO were directors of IAPT. IAPT administered the Alberta Livestock and Meat Agency part of the grant to cover the materials costs of the project and the cost of PA to work as a laboratory technician on the project. Labor and overhead were covered as direct contributions by IAPT. IAPT and its directors did not receive payment from a third party for any aspect of the submitted work. The directors and PA had full access to the data throughout the study and take responsibility for the integrity of the data and accuracy of the data analysis. Bow Valley Research (BVR) is owned by MO. BVR was provided funds through the Growing Forward2 grant that covered the costs of Crystal Schatz to work as a laboratory technician on the project. BVR also donated technical expertise and equipment to the project. BVR and its owner did not receive payment from a third party for any aspect of the submitted work. The directors and CS had full access to the data throughout the study and take responsibility for the integrity of the data and accuracy of the data analysis. MO is owner and director of Alberta Veterinary Laboratories (AVL). AVL donated technical expertise and equipment to the project. AVL and its director did not receive payment from a third party for any aspect of the submitted work. MO had full access to the data throughout the study and take responsibility for the integrity of the data and accuracy of the data analysis. All other authors declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
This work has been supported by funds from Growing Forward 2, a federal-provincial, territorial initiative, and the Alberta Livestock and Meat Agency. Facilities were provided by Alberta Agriculture and Forestry. The MALDI-TOF analysis was supported by the Institute for Applied Poultry Technologies. The authors would also like to acknowledge and thank the veterinary practices and feedlots across Alberta that participated in this study. The views and opinions expressed in this manuscript are not necessarily those of Agriculture and Agri-food Canada or Alberta Agriculture and Forestry.