Opportunities and barriers to implementing antibiotic stewardship in low and middle-income countries: Lessons from a mixed-methods study in a tertiary care hospital in Ethiopia
Bibliographic record
Abstract
BACKGROUND: Global action plans to tackle antimicrobial resistance (AMR) include implementation of antimicrobial stewardship (AMS), but few studies have directly addressed the challenges faced by low and middle-income countries (LMICs). Our aim was to explore healthcare providers' knowledge and perceptions on AMR, and barriers/facilitators to successful implementation of a pharmacist-led AMS intervention in a referral hospital in Ethiopia. METHODS: Tikur Anbessa Specialized Hospital (TASH) is an 800-bed tertiary center in Addis Ababa, and the site of an ongoing 4-year study on AMR. Between May and July 2017, using a mixed approach of quantitative and qualitative methods, we performed a cross-sectional survey of pharmacists and physicians using a pre-tested questionnaire and semi-structured interviews of purposively selected respondents until thematic saturation. We analyzed differences in proportions of agreement between physicians and pharmacists using χ2 and fisher exact tests. Qualitative data was analyzed thematically. FINDINGS: A total of 406 survey respondents (358 physicians, 48 pharmacists), and 35 key informants (21 physicians and 14 pharmacists) were enrolled. The majority of survey respondents (>90%) strongly agreed with statements regarding the global scope of AMR, the need for stewardship, surveillance and education, but their perceptions on factors contributing to AMR and their knowledge of institutional resistance profiles for common bacteria were less uniform. Close to 60% stated that a significant proportion of S. aureus infections were caused by methicillin-resistant strains (an incorrect statement), while only 48% thought a large proportion of gram-negative infections were caused by cephalosporin-resistant strains (a true statement). Differences were noted between physicians and pharmacists: more pharmacists agreed with statements on links between use of broad-spectrum antibiotics and AMR (p<0.022), but physicians were more aware that lack of diagnostic tests led to antibiotic overuse (p<0.01). More than cost, fear of treatment failure and of retribution from senior physicians were major drivers of antibiotic prescription behavior particularly among junior physicians. All respondents identified high turnover of pharmacists, poor communication between the laboratory, pharmacists and clinicians as potential challenges; but the existing hierarchical culture and academic setting were touted as opportunities to implement AMS in Ethiopia. CONCLUSIONS: This knowledge and perceptions survey identified specific educational priorities and implementation strategies for AMS in our setting. This is likely also true in other LMICs, where expertise and infrastructure may be lacking.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".