Knowledge and attitudes toward antimicrobial use, resistance, and stewardship among final-year veterinary students: a multinational cross-sectional survey

Abstract

This study assessed final-year veterinary students’ knowledge and attitudes regarding antimicrobial use (AMU), antimicrobial stewardship (AMS), and antimicrobial resistance (AMR). Students were invited to complete an online semi-structured questionnaire. A total of 907 participants from 7 countries and 43 veterinary educational establishments (VEEs) completed the questionnaire. Data were analysed using descriptive and inferential statistics. The total knowledge and attitude scores for each student were calculated, and a general average was obtained. AMR was mostly taught as an addendum under the core subjects of microbiology and pharmacology. Students exhibited low AMS knowledge scores (mean score 32.1 ± 22.7), but higher scores for positive attitudes towards reducing AMR (64.2 ± 25.2). Only 6.6% could name at least one national regulation guiding AMU in their countries, while 30-50% of the students knew that antibiotics were inappropriate for various viral infections. About 42% considered it acceptable to use antimicrobials for growth promotion and prophylaxis. Students exhibited positive attitudes towards using antimicrobial susceptibility testing, and 50.1% considered improved veterinary education very important for reducing AMR. Multivariate analyses determined that students’ countries of origin were significantly associated with their knowledge and attitudes; for example, Nigerian students were more likely to have good knowledge than Egyptian students, (aOR: 2.372, 95% CI:1.085 – 5.551), while Indian students were less likely (aOR: 0.144, 95% CI: 0.063 – 0.343). Greater curricula emphasis should be placed on national and international AMS guidelines. Concepts related to AMR and AMS may be better retained if taught as a standalone module. Further studies should be conducted across countries where students have greater knowledge to evaluate curricula and potentially highlight replicable models for the inclusion of AMR and AMS.