Sustained Reduction in Third-generation Cephalosporin Usage in Adult Inpatients Following Introduction of an Antimicrobial Stewardship Program in a Large, Urban Hospital in Malawi
Bibliographic record
Abstract
BACKGROUND: Third-generation cephalosporins (3GC) remain the first-choice empiric antibiotic for severe infection in many sub-Saharan African hospitals. In Malawi, the limited availability of alternatives means that strategies to prevent the spread of 3GC resistance are imperative; however, suitable approaches to antimicrobial stewardship (AMS) in low-income settings are not well studied. METHODS: We introduced an AMS intervention to Queen Elizabeth Central Hospital in Blantyre. The intervention consisted of a prescribing application for smartphones and regular point-prevalence surveys with prescriber feedback. We evaluate the effects of the intervention on 3GC usage and on the cost of providing antibiotics. Using a thematic analysis of semi-structured interviews and participant observations, we additionally evaluate the acceptability of the stewardship program. RESULTS: The proportion of antibiotic prescriptions for a 3GC reduced from 193/241 (80.1%) to 177/330 (53.6%; percentage decrease, 26.5%; 95% confidence interval, 18.7-34.1) with no change in the case-fatality rate. The cost analysis estimated an annual savings of US$15 000. Qualitative research revealed trust in the guideline and found that its accessibility through smartphones helpful to guide clinical decisions. Operational health-system barriers and hierarchal clinical relationships lead to continued reliance on 3GC. CONCLUSIONS: We report the successful introduction of an antimicrobial stewardship approach in Malawi. By focusing on pragmatic interventions and simple aims, we demonstrate the feasibility, acceptability, and cost savings of a stewardship program where resources are limited. In doing so, we provide a suitable starting point for expansions of AMS interventions in this and other low-income settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".