| Literature DB >> 33194938 |
Folorunso O Fasina1,2, Lerica LeRoux-Pullen3,4, Peter Smith5, Legesse K Debusho6, Aminu Shittu7, Saleh M Jajere8,9, Oluwawemimo Adebowale10, Ismail Odetokun11, Michael Agbaje12, Modupe M Fasina13, Olubunmi G Fasanmi14, Deborah van Dyk5, Mohammed S Abubakar15, Monday M Onakpa16, Masaad G Ali17, Hozaifa S Yousuf18, Waliedin E Elmgboul17, Mohammed M Sirdar5,19.
Abstract
In African countries, antimicrobial resistance (AMR) issue remains pertinent. Despite this, little efforts have been made to assess the future veterinary prescribers on their knowledge, attitudes and practices (KAP) related to antimicrobial usage. This multi-country survey attempts to explore the KAP of future veterinarians on stewardship of antimicrobial and identify knowledge gaps. Eight veterinary schools participated from Nigeria, Sudan and South Africa. Data regarding perceptions and knowledge were analyzed using Chi-square χ2 test, Spearman's (Rho) Rank order correlation and factor analysis using principal component factoring extraction method. Fifty-two percent of the study participants were final year veterinary students, respectively, and majority (77.2%) had no previous knowledge of biomedical sciences. Majority age were 22-27 years (24.7 ± 2.8) 79% and multiple career fields post-graduation were preferred. Overall, poor perceptions and knowledge of antimicrobial stewardship were observed with variations among countries and only 36.3% (n = 123) of the students were confident in their ability to choose the ideal antimicrobial agents for a specific patient/group of animals. The majority of the final year students were confident of their knowledge regarding AMR (68%), making of Gram staining (69.2%) and in choosing the most ideal route for administering a specific antimicrobial (74.7%). The final year students had significantly (p < 0.05) higher confidence level for knowledge compared with the pre-final year students. Tetracyclines, penicillins, and sulphonamides represent the three most abused veterinary antimicrobials with similar ranking across countries. South African (69.7 ± 20.5) and Sudanese (68.1 ± 15.4) had significantly (p < 0.0001) higher mean scores compared to the Nigerian students (44.3 ± 6.8) in the student's ability to correctly match some specific antimicrobials against their classes but Nigerian students performed better in ranking antimicrobials. This survey revealed poor to average knowledge of antimicrobial stewardship among veterinary students with significant knowledge gaps across the countries. It is recommended that the relevant regulatory and standardization authorities should make concerted efforts and interventions to regularly review curricula to ensure the delivery of targeted formative and normative training, and improved lectures on antimicrobial usage and stewardship in order to improve the awareness and behaviors of future prescribers. The identified knowledge gaps of veterinary medical students on antimicrobial stewardship must be bridge to safeguard the future.Entities:
Keywords: AMR; Africa; antimicrobial resistance; antimicrobial stewardship; antimicrobial use; prescribers; veterinary training
Year: 2020 PMID: 33194938 PMCID: PMC7609782 DOI: 10.3389/fpubh.2020.517964
Source DB: PubMed Journal: Front Public Health ISSN: 2296-2565
Baseline demography of the pre- and final-year Veterinary students (n = 353) sampled from eight Veterinary Faculties from Nigeria, South Africa and Sudan, 2014.
| Nigeria | 105 (29.7) |
| South Africa | 71 (20.1) |
| Sudan | 177 (50.1) |
| Pre-final year | 163 (46.2) |
| Final year | 184 (52.1) |
| Male | 204 (57.8) |
| Female | 145 (41.1) |
| 20–27 | 279 (79.0) |
| 28–35 | 30 (8.5) |
| 36–43 | 4 (1.1) |
| Small animal practice | 77 (21.8) |
| Equine practice | 42 (11.9) |
| Mixed practice | 74 (21.0) |
| Feedlot | 44 (12.5) |
| Dairy | 36 (10.2) |
| Wildlife | 31 (8.8) |
| Gross pathology | 32 (9.1) |
| Pharmaceutical industry | 68 (19.3) |
| State service | 60 (17.0) |
| Beef Cattle | 56 (15.9) |
| Sheep and goats | 45 (12.8) |
| Pig | 17 (4.8) |
| Poultry | 94 (26.6) |
| Laboratory medicine/Clinical pathology | 36 (10.2) |
| Exotic pet medicine | 36 (10.2) |
| Education | 32 (9.1) |
| Undecided | 27 (7.7) |
| Other choices | 32 (9.1) |
| No | 268 (77.2) |
| Yes | 79 (22.8) |
6 missing data;
4 missing data;
40 missing data; Mean age of the study participants = 24.7 ± 2.8 years;
the mean for number of likely career choice = 2 ± 2 (5 persons made no choice).
previous knowledge in the field meant that student has done previous studies at post-secondary school levels in pharmacology, biological research, microbiology or pharmacy, which may bias the opinion of respondents or influence the responses to antimicrobial-related questions.
Distribution of pre- and final-year Veterinary students sampled from eight Veterinary faculties of Nigeria, South Africa and Sudan, according to the measured variables, 2014.
| Nigeria | 59 (36.2) | 46 (25.0) |
| South Africa | 42 (25.8) | 29 (15.8) |
| Sudan | 62 (38.0) | 109 (59.2) |
| Male | 82 (50.3) | 119 (64.7) |
| Female | 80 (49.1) | 64 (34.8) |
| 20–27 | 129 (90.8) | 146 (87.4) |
| 28–35 | 11 (7.7) | 19 (11.4) |
| 36–43 | 2 (1.4) | 2 (1.2) |
6 missing data,
4 missing data,
40 missing data; Mean age of the study participants = 24.7 ± 2.8 years.
Perception on antimicrobials of students who agreed or strongly agreed to the questions across eight Faculty of Veterinary Medicines, Africa, 2014.
| 1. Antimicrobial resistance is an increasing global threat to human and animal health | 101 (98.1) | 3 (1.8) | 71 (100) | 97 (55.7) | 77 (44.3) | 174 (50.7) | 11.55 | 0.001 |
| 2. The misuse of antimicrobials by veterinary practitioners contributes significantly to antimicrobial resistance | 92 (89.3) | 22 (12.6) | 60 (84.5) | 91 (52.9) | 81 (47.1) | 172 (50.1) | 5.60 | 0.06 |
| 3. The misuse of antimicrobials by farmers contributes significantly to antimicrobial resistance | 94 (93.1) | 12 (7.0) | 70 (98.6) | 98 (56.0) | 77 (44.0) | 175 (51.5) | 13.99 | 0.001 |
| 4. The inappropriate use of antimicrobials in food-producing animals significantly contributes to antimicrobial resistance in human pathogens | 89 (87.3) | 17 (9.9) | 43 (60.6) | 84 (56.8) | 64 (43.2) | 148 (43.5) | 9.89 | 0.002 |
| 5. The inappropriate prescription of antimicrobials by human medical doctors is the main contributor to antimicrobial resistance in human pathogens | 76 (74.5) | 19 (11.1) | 67 (94.4) | 84 (51.9) | 78 (48.1) | 162 (47.5) | 3.01 | 0.083 |
| 6. I have received formal lectures on the rational use of antimicrobials during my under-graduate training | 100 (98.0) | 23 (13.2) | 70 (98.6) | 103 (53.6) | 89 (46.4) | 192 (56.1) | 8.28 | 0.004 |
| 7. My under-graduate training has prepared me well for making informed decisions when choosing an ideal antimicrobial for an individual patient | 92 (90.2) | 8 (4.6) | 54 (76.1) | 83 (53.9) | 71 (46.1) | 154 (44.9) | 5.43 | 0.02 |
| 8. As an individual in practice, I can significantly contribute to preventing an increase in antimicrobial resistance | 99 (97.1) | 13 (7.4) | 60 (84.5) | 98 (57.0) | 74 (43.0) | 172 (50.0) | 15.14 | <0.0001 |
| 9. The misuse of antimicrobials was evident in the facilities where I have trained | 41 (41.0) | 67 (39.4) | 17 (23.9) | 53 (42.4) | 72 (57.6) | 125 (37.1) | 1.40 | 0.237 |
| 10. Governing bodies in Africa are doing enough to help prevent a rise in antimicrobial resistance | 25 (24.5) | 74 (42.0) | 1 (1.4) | 42 (42.9) | 56 (57.1) | 100 (28.5) | 0.74 | 0.381 |
| 11. Educating lay people on the importance of antimicrobials as controlled scheduled compounds will have a positive effect on decreasing the rise in antimicrobial resistance | 85 (84.2) | 10 (5.8) | 62 (87.3) | 84 (53.8) | 72 (46.2) | 156 (45.9) | 5.81 | 0.016 |
| 12. The use of antimicrobials in the food-producing animal industry (farm animals) contributes more to antimicrobials resistance than their use in companion animals | 87 (85.3) | 34 (19.8) | 36 (50.7) | 76 (48.7) | 80 (51.3) | 156 (45.7) | 0.51 | 0.477 |
| 13. Banning the use of prophylactic antimicrobials in food-producing animals will have a negative effect on animal welfare | 53 (52.0) | 53 (31.9) | 32 (45.1) | 64 (46.7) | 73 (53.3) | 137 (40.9) | 0.002 | 0.963 |
| 14. Banning the use of prophylactic antimicrobials in food-producing animals will have a positive effect on decreasing the rise in antimicrobial resistance | 68 (68.7) | 45 (26.6) | 40 (56.3) | 77 (50.7) | 75 (49.3) | 152 (45.2) | 1.72 | 0.189 |
| 15. Banning the use of antimicrobials as growth promoters in food-producing animals will have a positive effect on decreasing the rise in antimicrobial resistance | 74 (74.0) | 27 (16.0) | 41 (57.7) | 71 (50.7) | 69 (49.3) | 140 (41.7) | 1.31 | 0.252 |
| 16. Improved use of vaccines, biosecurity measures, and hygiene will decrease the need for antimicrobials in the food-producing industry | 87 (86.1) | 7 (4.1) | 68 (95.8) | 87 (53.7) | 75 (46.3) | 162 (47.9) | 6.58 | 0.01 |
| 17. Adhering to meat and milk withdrawal periods will help decrease the rise in antimicrobial resistance in human pathogens | 94 (94.0) | 6 (3.5) | 48 (67.6) | 80 (54.1) | 68 (45.9) | 148 (43.8) | 5.65 | 0.017 |
| 18. Broad-spectrum antimicrobials are ideal to use as first-line antimicrobials | 62 (61.4) | 61 (36.3) | 27 (38.0) | 83 (56.1) | 65 (43.9) | 148 (44.0) | 9.91 | 0.002 |
| 19. Third and fourth generation antimicrobials should only be used as a last resort in treatment | 50 (51.0) | 23 (14.0) | 62 (87.3) | 76 (56.3) | 59 (43.7) | 135 (40.9) | 8.14 | 0.017 |
| 20. Long-acting antimicrobials are more ideal for use in food-producing animals than shorter-acting equivalents | 31 (30.7) | 81 (47.9) | 15 (21.1) | 58 (46.4) | 67 (53.6) | 125 (37.1) | 0.019 | 0.891 |
| 21. Cultures and antibiotic sensitivity testing (antibiograms should be done as frequently as possible to guide antimicrobial use) | 94 (93.1) | 8 (4.7) | 69 (97.2) | 91 (53.2) | 80 (46.8) | 171 (50.7) | 5.07 | 0.024 |
| 22. Financial constraints of animal owners in Africa disallow the use of cultures and antibiotic sensitivity testing e.g., antibiograms during an infection | 77 (77.0) | 21 (12.3) | 55 (77.5) | 82 (53.6) | 71 (46.4) | 153 (45.3) | 5.74 | 0.017 |
| 23. Drug legislation in Africa is on par with legislation in the rest of the world | 40 (39.2) | 82 (48.5) | 13 (18.3) | 66 (49.6) | 67 (50.4) | 133 (39.3) | 0.587 | 0.444 |
| 24. I am confident that new classes of antimicrobials will be available in the near future to solve current resistance problems | 57 (55.9) | 20 (11.8) | 5 (7.0) | 37 (45.1) | 45 (54.9) | 82 (24.2) | 0.187 | 0.665 |
| 25. The choice of an antimicrobial(s) by a veterinarian should largely be determined based on the cost implications to the farmers | 71 (69.6) | 51 (30.0) | 14 (19.7) | 63 (46.3) | 73 (53.7) | 136 (40.1) | 0.070 | 0.792 |
| 26. I am confident in my ability to choose the ideal antimicrobial agents for a specific patient/group of animals in order to ensure optimal efficacy and safety | 87 (86.1) | 8 (4.7) | 28 (39.4) | 59 (48.0) | 64 (52.0) | 123 (36.3) | 0.088 | 0.767 |
χ;
p < 0.05 refers to the significant statistical difference in the percentage/proportions between pre-final year and final-year veterinary students who strongly agreed/agreed to the questions regarding antimicrobial resistance.
Perceived knowledge of antimicrobials of all participating pre-final and final-year Veterinary students, Faculty of Veterinary Medicines, Africa.
| 1. Spectrum, effect, distribution, indications, side effects, and contra-indications of the most commonly used antimicrobial classes in veterinary medicine, as well as the implication thereof | 60 (61.2) | 113 (68.1) | 12 (16.9) | 76 (41.5) | 107 (58.5) | 183 (55.3) | 4.61 | 0.032 |
| 2. The difference between time-dependent and concentration-dependent antimicrobials | 30 (30.9) | 84 (50.3) | 47 (66.2) | 71 (44.7) | 88 (55.3) | 159 (48.1) | 0.58 | 0.446 |
| 3. Resistance mechanisms | 68 (71.6) | 135 (80.8) | 23 (32.9) | 104 (46.6) | 119 (53.4) | 223 (68.0) | 0.028 | 0.868 |
| 4. Making a Gram-stain | 85 (87.6) | 101 (62.7) | 40 (56.3) | 95 (42.2) | 130 (57.8) | 225 (69.2) | 6.92 | 0.009 |
| 5. Interpreting antibiograms | 54 (56.3) | 68 (41.7) | 45 (63.4) | 67 (40.9) | 97 (59.1) | 164 (50.3) | 4.90 | 0.027 |
| 6. Finding reliable sources of information to guide empirical use of antimicrobials | 50 (51.5) | 104 (64.6) | 35 (49.3) | 84 (45.2) | 102 (54.8) | 186 (57.1) | 0.75 | 0.386 |
| 7. Choosing the most ideal route for administering a specific antimicrobial | 73 (76.0) | 135 (79.9) | 42 (59.2) | 100 (40.3) | 148 (59.7) | 248 (74.7) | 15.95 | <0.0001 |
| 8. Choosing the desired time-frame for (duration of) therapy | 60 (62.5) | 106 (64.2) | 26 (36.6) | 77 (40.3) | 114 (59.7) | 191 (58.2) | 8.09 | 0.004 |
| 9. Choosing an alternative if my first choice of antimicrobial therapy failed | 55 (57.3) | 123 (73.7) | 21 (29.6) | 82 (41.4) | 116 (58.6) | 198 (60.0) | 6.13 | 0.013 |
| 10. Designing an integrated treatment protocol for a specific animal with an infection which includes supportive therapy | 55 (57.3) | 113 (68.1) | 18 (25.4) | 70 (38.0) | 114 (62.0) | 184 (55.9) | 13.78 | <0.0001 |
,
p < 0.05 refers to the significant statistical difference in the percentage/proportions of confidence regarding antimicrobial knowledge between pre-final year and final-year veterinary students.
Ranking on the degree of abuse of antimicrobials based on students' perceptions from eight Faculties of Veterinary Medicine, Africa.
| Tetracyclines | 1st | 1st | 1st | 1st |
| Penicillins | 2nd | 2nd | 2nd | 2nd |
| Sulphonamides | 3rd | 3rd | 3rd | 3rd |
| Macrolides | 4th | 5th | 8th | 6th |
| Aminoglycosides | 5th | 4th | 4th | 4th |
| Quinolones | 6th | 6th | 4th | 7th |
| Amphenicols | 7th | 7th | 10th | 5th |
| Polypeptides | 8th | 9th | 7th | 8th |
| Cephalosporins | 9th | 8th | 8th | 9th |
| Combination of antimicrobials | 10th | 10th | 6th | 10th |
| Others | 11th | 11th | 11th | 11th |
Spearman (Rho) rank-order correlation coefficient (r.
Figure 1Spider-web analysis of knowledge of characteristics of individual antimicrobial agents by veterinary students from selected African schools (n = 353).
Correct Matching of specific antimicrobials against their class (n = 353).
| 1. Beta-lactams | 43.8 | 68.0 | 81.4 | 9.9 | <0.01 |
| 2. Penicillins | 39.1 | 67.2 | 39.4 | 2.9 | 0.24 |
| 3. Cephalosporins | 43.8 | 55.5 | 62.0 | 2.1 | 0.35 |
| 4. Tetracyclines | 44.8 | 75.2 | 84.5 | 15.2 | <0.001 |
| 5. Aminoglycosides | 53.3 | 85.2 | 94.4 | 50.4 | <0.0001 |
| 6. Macrolides | 34.3 | 42.0 | 38.0 | 1.7 | 0.42 |
| 7. Amphenicols | 41.9 | 70.0 | 62.0 | 3.2 | 0.20 |
| 8. Fluoroquinolones | 42.9 | 65.5 | 77.5 | 8.8 | 0.01 |
| 9. Sulphonamides | 41.0 | 56.1 | 63.4 | 2.1 | 0.35 |
| 10. Peptide antibiotics | 58.1 | 96.1 | 95.8 | 128.9 | <0.0001 |