社区药房的处方模式和相关因素:埃塞俄比亚Dire Dawa使用AWa重新分类和世界卫生组织抗生素处方指标的跨部门研究。

IF 1.9 Q3 PHARMACOLOGY & PHARMACY Drugs - Real World Outcomes Pub Date : 2023-09-01 Epub Date: 2023-06-10 DOI:10.1007/s40801-023-00367-1
Beyene Dereje, Alemseged Workneh, Alemayehu Megersa, Shegaye Yibabie
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引用次数: 0

摘要

背景:抗微生物药物是更可能自然引发耐药性发展的药物。因此,需要更加谨慎地开处方、配药和用药。为了强调正确使用抗生素的重要性,抗生素分为AWaRe:获取、观察和储备。根据AWaRe分类,及时提供药物使用、处方模式、影响抗生素处方的因素及其使用百分比的证据,将有助于决策者起草能够更合理使用药物的指南。方法:对Dire Dawa的七家社区药店进行前瞻性和横断面研究,以评估当前与世界卫生组织(世界卫生组织)相关的处方实践指标和AWaRe分类,包括抗生素使用和相关因素。使用分层随机抽样技术,在2022年10月1日至10月31日期间对1200次遭遇进行了回顾,并使用SPSS版本27进行分析。结果:每个处方的平均用药量为1.96。47.8%的患者使用抗生素,43.1%的患者使用观察组处方。13.5%的患者接受了注射。在多变量模型中,患者年龄、性别和开药次数与抗生素处方显著相关。18岁以下患者服用抗生素的可能性是65岁及以上受试者的2.5倍[调整比值比(AOR):2.51,95%置信区间(CI):1.88-5.42;P结论:根据本研究,社区药店的抗生素处方量远高于世界卫生组织标准(20-26.2%)Access组的抗生素处方为55.3%,略低于世界卫生组织推荐水平(60%)。抗生素的处方与患者的年龄、性别和药物数量显著相关。本研究的预印本可在研究广场上获得,链接如下:https://doi.org/10.21203/rs.3.rs-2547932/v1。
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Prescribing Pattern and Associated Factors in Community Pharmacies: A Cross-Sectional Study Using AWaRe Classification and WHO Antibiotic Prescribing Indicators in Dire Dawa, Ethiopia.

Background: Antimicrobials are drugs that are more likely to trigger the development of resistance naturally. Thus, they need to be prescribed, dispensed, and administered with greater caution. To underline the significance of their proper usage, antibiotics are divided as AWaRe: Access, Watch, and Reserve. Timely evidence on medicine use, prescribing patterns, and the factors affecting prescribing of antibiotic and their use percentage from AWaRe classification would help decision-makers to draft guidelines that can enable more rational use of medicines.

Methods: Prospective and cross-sectional study was conducted among seven community pharmacies in Dire Dawa to assess current prescribing practices related World Health Organization (WHO) indicators and AWaRe classification including antibiotic use and associated factors. Using stratified random sampling techniques, 1200 encounters were reviewed between 1 October and 31 October 2022, and SPSS version 27 was used for the analysis.

Results: The average of medications per prescription was 1.96. Antibiotics were included in 47.8% of encounters, while 43.1% were prescribed from the Watch groups. In 13.5% of the encounters, injections were administered. In multivariate models, patient age, gender, and the number of medications prescribed were significantly associated to prescription of antibiotics. Antibiotics were about 2.5 times more likely to be prescribed to patients under the age of 18 years than to subjects 65 years and older [adjusted odds ratio (AOR): 2.51, 95% confidence interval (CI): 1.88-5.42; P < 0.001]. Men were also more likely than women to receive an antibiotic prescription (AOR: 1.74, 95% CI: 1.18-2.33; P = 0.011). Subjects who received more than two drugs were 2.96 times more likely to receive an antibiotic drug (AOR: 2.96, 95% CI: 1.77-6.55; P < 0.003). The probability of prescribing antibiotics was increased by 2.57 for every one-unit increase in the number of medications [crude odds ratio (OR): 2.57; 95% CI: 2.16-3.47; P < 0.002].

Conclusion: According to the present study, the amount of prescriptions with antibiotics at community pharmacies is much higher than the WHO standard (20-26.2%). The antibiotics prescribed from Access group were 55.3%, which is slightly lower than WHO recommended level (60%). The prescribing of antibiotics was significantly correlated to the patient's age, gender, and number of medications. The preprint version of the present study is available on Research Square with the following link: https://doi.org/10.21203/rs.3.rs-2547932/v1 .

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来源期刊
Drugs - Real World Outcomes
Drugs - Real World Outcomes PHARMACOLOGY & PHARMACY-
CiteScore
3.60
自引率
5.00%
发文量
49
审稿时长
8 weeks
期刊介绍: Drugs - Real World Outcomes targets original research and definitive reviews regarding the use of real-world data to evaluate health outcomes and inform healthcare decision-making on drugs, devices and other interventions in clinical practice. The journal includes, but is not limited to, the following research areas: Using registries/databases/health records and other non-selected observational datasets to investigate: drug use and treatment outcomes prescription patterns drug safety signals adherence to treatment guidelines benefit : risk profiles comparative effectiveness economic analyses including cost-of-illness Data-driven research methodologies, including the capture, curation, search, sharing, analysis and interpretation of ‘big data’ Techniques and approaches to optimise real-world modelling.
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