Antibiotic prescribing patterns in the pediatric emergency department at Georgetown Public Hospital Corporation: a retrospective chart review
Bibliographic record
Abstract
BACKGROUND: The increase in antimicrobial-resistant infections has led to significant morbidity, mortality, and healthcare costs. The impact of antimicrobial resistance is greatest on low-income countries, which face the double burden of fewer antibiotic choices and higher rates of infectious diseases. Currently, Guyana has no national policy on rational prescribing. This study aims to characterize antibiotic prescribing patterns in children discharged from the emergency department at Georgetown Public Hospital Corporation (GPHC), as per the World Health Organization (WHO) prescribing indicators. METHODS: A retrospective chart review of pediatric patients (aged 1 month-13 years) seen in the GPHC emergency department between January and December 2012 was conducted. Outpatient prescriptions for eligible patients were reviewed. Patient demographics, diagnosis, and drugs prescribed were recorded. The following WHO Prescribing Indicators were calculated: i) average number of drugs prescribed per patient encounter, ii) percentage of encounters with an antibiotic prescribed, iii) percentage of antibiotics prescribed by generic name, and iv) percentage of antibiotics prescribed from essential drugs list or formulary. RESULTS: Eight hundred eleven patient encounters were included in the study. The mean patient age was 5.55 years (s = 3.98 years). 59.6 % (n = 483) patients were male. An average of 2.5 drugs were prescribed per encounter (WHO standard is 2.0). One or more antibiotic was prescribed during 36.9 % (n = 299) of all encounters (WHO standard is 30 %). 90.83 % of antibiotics were prescribed from the essential drugs formulary list and 30 % of the prescriptions included the drug's generic name. The average duration of antibiotic therapy was 5.73 days (s = 3.53 days). Of the 360 antibiotics prescribed, 74.7 % (n = 269) were broad-spectrum. B-lactam penicillins were prescribed most frequently (51.4 %), with amoxicillin being the most popular choice (33.9 %). The most common diagnoses were injuries (25.8 %), asthma (20 %), respiratory infections (19.5 %), and gastrointestinal infections (12.1 %). CONCLUSIONS: Per WHO prescribing indicators, the pediatric emergency department at GPHC has higher than standard rates of antibiotic use and polypharmacy. The department excels in adhering to the essential drug formulary. Our findings provide support for investigating drug utilization in other Guyanese settings, and to work towards developing a national rational prescribing strategy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 0.001 |
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".