Multicentre point-prevalence survey of antibiotic use and healthcare-associated infections in Ethiopian hospitals
Bibliographic record
Abstract
OBJECTIVE: Effective antimicrobial containment strategies such as Antimicrobial Stewardship Programs (ASPs) require comprehensive data on antibiotics use which are scarce in Ethiopia. This study sought to assess antibiotics use and healthcare-associated infections (HCAIs) in Ethiopian public hospitals. DESIGN: We conducted a cross-sectional study using the WHO point-prevalence survey protocol for systemic antibiotics use and HCAIs for low/middle-income countries. SETTING: The study was conducted among 10 public hospitals in 2021. PARTICIPANTS: All patients admitted to adult and paediatric inpatient and emergency wards before or at 08:00 on the survey date were enrolled. OUTCOME MEASURE: The primary outcome measures were the prevalence of antibiotic use, HCAIs and the hospitals' readiness to implement ASP. RESULTS: Data were collected from 1820 patient records. None of the surveyed hospitals had functional ASP. The common indication for antibiotics was for HCAIs (40.3%). Pneumonia was the most common bacterial infection (28.6%) followed by clinical sepsis (17.8%). Most treatments were empiric (96.7%) and the overall prevalence of antibiotic use was 63.8% with antibiotics prescription per patient ratio of 1.77. Ceftriaxone was the most commonly prescribed antibiotic (30.4%) followed by metronidazole (15.4%). Age, having HIV infection, ward type, type of hospital, catheterisation and intubation history had significant association with antibiotic use. Patients who were treated in paediatric surgical wards were about four times more likely to be on antibiotics compared with patients treated at an adult emergency ward. Patients on urinary catheter (adjusted OR (AOR)=2.74, 95% CI: 2.04 to 3.68) and intubation device (AOR=2.62, 95% CI: 1.02 to 6.76) were more likely to be on antibiotics than their non-intubated/non-catheterised counterparts. Patients treated at secondary-level hospitals had 0.34 times lower odds of being on antibiotics compared with those in tertiary hospitals. CONCLUSIONS: Antibiotic use across the surveyed hospitals was common and most were empiric which has both practical and policy implications for strengthening ASP and promoting rational antibiotics use.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".