Antibiotic Prescribing Patterns in Pediatric Patients using the WHO AWaRe Framework in a Quaternary Hospital in Nampula, Mozambique

Research Square (Research Square)(2023)

引用 0|浏览0
暂无评分
摘要
Background: Antibiotics are often prescribed inappropriately, either when they are not needed or with an unnecessarily broad spectrum of activity. This is a serious problem that can lead to the development of antimicrobial resistance (AMR). This study was conducted to assess the antibiotic prescribing pattern in pediatric patients hospitalized at a quaternary hospital in Nampula, Mozambique, using the WHO indicators and Framework as a reference. Methods: A cross-sectional study with a quantitative approach was conducted in 2020. The population consisted of children aged 0-10 years hospitalized in a ward of a quaternary-level hospital in Nampula, Mozambique. The prescription pattern was assessed using indicators and the WHO classification of antibiotics into AWaRe categories. Descriptive statistics were applied. Results: A total of 464 antibiotics were prescribed during the study. The age group of 1-3 years and 28 days-12 months were prescribed more antibiotics. The most common antibiotics were ceftriaxone and crystallized penicillin, which were frequently prescribed for patients suffering from bronchopneumonia, gastroenteritis, and malaria. 74.8% of the antibiotics prescribed belonged to the Access group, while 23.7% belonged to the Watch group. There were no prescriptions of antibiotics from the Reserve group. The average number of antibiotics per prescription was 1.51 (SD ± 0.725). The percentage of antibiotic prescribing was 97.5%, with 96.20% by injection. All antibiotics prescribed were on the essential medicines list and prescribed by generic name. Conclusion: These results are concerning and highlight the urgency of strengthening antimicrobial optimization measures, as well as implementing the AWaRe framework in antibiotic prescribing as an essential strategy to combat AMR.
更多
查看译文
关键词
antibiotic,pediatric patients,quaternary hospital
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要