| Literature DB >> 34621809 |
Steven A Kemp1,2,3, Gina L Pinchbeck1, Eric M Fèvre1,4, Nicola J Williams1.
Abstract
Background: Antimicrobial resistance (AMR) is one of the most important global health crises in recent times and is driven primarily by antimicrobial consumption. In East Africa, there is a paucity of data regarding the knowledge, attitudes, and practices (KAP) related to antimicrobial use (AMU). We investigate the ways in which antimicrobial users in the veterinary sector accessed veterinary antimicrobials, and common behaviors of veterinary antimicrobial users and prescribers associated with AMU and AMR.Entities:
Keywords: AMR; AMU; KAP; access to antimicrobials; antimicrobial stewardship
Year: 2021 PMID: 34621809 PMCID: PMC8490823 DOI: 10.3389/fvets.2021.727365
Source DB: PubMed Journal: Front Vet Sci ISSN: 2297-1769
Figure 1Map of Busia county, western Kenya, indicating the locations of all interviewed farmers, agrovet staff (within their shops) and veterinary professionals.
Participant demographics and education.
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| Gender | Male | 25 | 51.0 | 27 | 96.4 | 48 | 68.6 |
| Female | 24 | 49.0 | 1 | 3.6 | 22 | 31.4 | |
| Age group | 18–24 | 8 | 16.3 | – | – | 2 | 2.9 |
| 25–44 | 35 | 71.4 | 20 | 71.4 | 40 | 57.1 | |
| 45–64 | 5 | 10.2 | 6 | 21.4 | 17 | 24.3 | |
| 65+ | 1 | 2.0 | 2 | 7.1 | 11 | 15.7 | |
| Job position | Animal healthcare worker | 1 | 2.0 | 14 | 50.0 | – | – |
| Artificial insemination technician | – | – | 1 | 3.6 | – | – | |
| Sub-country veterinary officer | – | – | 3 | 10.7 | – | – | |
| Agrovet assistant | 39 | 79.6 | 1 | 3.6 | – | – | |
| Laboratory staff/vet technician | 1 | 2.0 | 3 | 10.7 | – | – | |
| Livestock Officer | – | – | 5 | 17.6 | – | – | |
| Veterinarian | – | – | 1 | 3.6 | – | – | |
| Manager | 1 | 2.0 | – | – | – | – | |
| Owner | 9 | 18.4 | – | – | – | – | |
| Length of time at job | <1 Year | 14 | 28.6 | 1 | 3.6 | – | – |
| 1–2 Years | 4 | 8.2 | – | – | – | – | |
| >3 Years | 31 | 63.3 | 27 | 96.4 | – | – | |
| Highest education level | No formal education | – | – | – | – | 4 | 5.7 |
| Primary education | – | – | – | – | 24 | 34.3 | |
| Secondary education | 27 | 55.1 | 3 | 10.7 | 33 | 47.1 | |
| College (certificate/diploma) | 20 | 40.8 | 23 | 82.1 | 7 | 10 | |
| University | 2 | 4.1 | 2 | 7.1 | 2 | 2.9 | |
| Nature of training | Professional qualification | 8 | 16.3 | 26 | 92.9 | – | – |
| Pharmaceutical company | 15 | 30.6 | – | – | – | – | |
| None/Informal training | 22 | 44.9 | 2 | 7.1 | – | – | |
| Cannot remember | 3 | 6.1 | – | – | – | – | |
List of the most commonly used/purchased/prescribed antimicrobials according to farmers, agrovets, and veterinary professionals, to treat livestock.
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| Oxytetracycline | 26 | 92.9 | 46 | 93.9 | 55 | 78.6 |
| Penicillin-streptomycin | 27 | 96.4 | 39 | 79.6 | 33 | 47.1 |
| Sulfachloropyrazine | 9 | 32.1 | 27 | 55.1 | – | – |
| Sulfadimidine | 9 | 32.1 | 13 | 26.5 | 2 | 2.9 |
| Trimethoprim and Sulfadiazine | 9 | 32.1 | 8 | 16.3 | 4 | 5.7 |
| Tylosin and Doxycycline | – | – | 18 | 36.7 | 2 | 2.9 |
| Sulfamethoxazole | 3 | 10.7 | 8 | 16.3 | – | – |
| Gentamicin | 6 | 21.4 | – | – | 1 | 1.4 |
| Tylosin | 4 | 14.3 | – | – | – | – |
| Tetracycline | 1 | 3.6 | 3 | 6.1 | – | – |
| Fosfomycin and Tylosin | – | – | 4 | 8.2 | – | – |
| Sulfamethoxazole and Trimethoprim | – | – | 4 | 8.2 | – | – |
| Erythromycin | 2 | 7.1 | – | – | 1 | 1.4 |
| Gentamicin and Doxycycline | – | – | 3 | 6.1 | – | – |
| Neomycin | – | – | 3 | 6.1 | – | – |
| Cefalexin | 1 | 3.6 | – | – | 1 | 1.4 |
| Metronidazole | 1 | 3.6 | – | – | 1 | 1.4 |
| Ampicillin | 1 | 3.6 | – | – | – | – |
| Streptomycin | 1 | 3.6 | – | – | – | – |
| Amoxicillin | – | – | 1 | 2.0 | – | – |
| Dexamethasone | – | – | 1 | 2.0 | – | – |
| Erythromycin and Oxytetracycline | – | – | 1 | 2.0 | – | – |
| Colistin | – | – | – | – | 1 | 1.4 |
Up to five “most common” antimicrobials were volunteered; therefore, each antimicrobial was counted once each time it featured in the respondents’ answer.
Highest priority critically important antimicrobials.
Not an antimicrobial but described by the respondent as one.
Figure 2Average sale cost of antimicrobials from 49 agrovet shops across Busia county. Error bars represent standard deviation where more than one agrovet shop reported pricing data.
Figure 3Information given to farmers regarding AMU, AMR, and withdrawal periods at point-of-sale (agrovet shop) or when receiving a prescription (veterinary professional). Error bars represent 95% confidence interval.
Figure 4Most common responses given by participants indicating what they thought were the main causes of antimicrobial resistance. Error bars are 95% CI.