Point prevalence of evidence-based antimicrobial use among hospitalized patients in sub-Saharan Africa: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Excessive and improper use of antibiotics causes antimicrobial resistance which is a major threat to global health security. Hospitals in sub-Saharan Africa (SSA) has the highest prevalence of antibiotic use. This systematic review and meta-analysis aimed to determine the pooled point prevalence (PPP) of evidence-based antimicrobial use among hospitalized patients in SSA. Literature was retrieved from CINAHL, EMBASE, Google Scholar, PubMed, Scopus, and Web of Science databases. Meta-analysis was conducted using STATA version 17. Forest plots using the random-effect model were used to present the findings. The heterogeneity and publication bias were assessed using the I 2 statistics and Egger’s test. The protocol was registered in PROSPERO with code CRD42023404075. The review was conducted according to PRISMA guidelines. A total of 26, 272 study participants reported by twenty-eight studies published from 10 countries in SSA were included. The pooled point prevalence of antimicrobial use in SSA were 64%. The pooled estimate of hospital wards with the highest antibiotic use were intensive care unit (89%). The pooled prevalence of the most common clinical indication for antibiotic use were community acquired infection (41%). The pooled point prevalence of antimicrobial use among hospitalized patients were higher in SSA. Higher use of antibiotics was recorded in intensive care units. Community acquired infection were most common clinical case among hospitalized patients. Health systems in SSA must design innovative digital health interventions to optimize clinicians adhere to evidence-based prescribing guidelines and improve antimicrobial stewardship.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".