Van Staa Tjeerd, Li Yan, Gold Natalie, Chadborn Tim, Welfare William, Palin Victoria, Ashcroft Darren M, Bircher Joanna
Division of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, UK
Division of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, UK.
BMJ Qual Saf. 2022 Oct 19;31(11):831-838. doi: 10.1136/bmjqs-2020-012108.
There is a need to reduce antimicrobial uses in humans. Previous studies have found variations in antibiotic (AB) prescribing between practices in primary care. This study assessed variability of AB prescribing between clinicians.
Clinical Practice Research Datalink, which collects electronic health records in primary care, was used to select anonymised clinicians providing 500+ consultations during 2012-2017. Eight measures of AB prescribing were assessed, such as overall and incidental AB prescribing, repeat AB courses and extent of risk-based prescribing. Poisson regression models with random effect for clinicians were fitted.
6111 clinicians from 466 general practices were included. Considerable variability between individual clinicians was found for most AB measures. For example, the rate of AB prescribing varied between 77.4 and 350.3 per 1000 consultations; percentage of repeat AB courses within 30 days ranged from 13.1% to 34.3%; predicted patient risk of hospital admission for infection-related complications in those prescribed AB ranged from 0.03% to 0.32% (5th and 95th percentiles). The adjusted relative rate between clinicians in rates of AB prescribing was 5.23. Weak correlation coefficients (<0.5) were found between most AB measures. There was considerable variability in case mix seen by clinicians. The largest potential impact to reduce AB prescribing could be around encouraging risk-based prescribing and addressing repeat issues of ABs. Reduction of repeat AB courses to prescribing habit of median clinician would save 21 813 AB prescriptions per 1000 clinicians per year.
The wide variation seen in all measures of AB prescribing and weak correlation between them suggests that a single AB measure, such as prescribing rate, is not sufficient to underpin the optimisation of AB prescribing.
有必要减少人类对抗菌药物的使用。先前的研究发现，初级保健机构之间在抗生素处方方面存在差异。本研究评估了临床医生之间抗生素处方的变异性。
利用收集初级保健电子健康记录的临床实践研究数据链，选取在2012年至2017年期间提供500多次会诊的匿名临床医生。评估了八项抗生素处方指标，如总体和偶然抗生素处方、重复抗生素疗程以及基于风险的处方范围。拟合了具有临床医生随机效应的泊松回归模型。
纳入了来自466家全科诊所的6111名临床医生。大多数抗生素指标在个体临床医生之间存在相当大的变异性。例如，每1000次会诊的抗生素处方率在77.4至350.3之间；30天内重复抗生素疗程的百分比在13.1%至34.3%之间；开具抗生素的患者因感染相关并发症入院的预测风险在0.03%至0.32%之间（第5和第95百分位数）。临床医生之间抗生素处方率的调整后相对率为5.23。大多数抗生素指标之间的相关系数较弱（<0.5）。临床医生所见的病例组合存在相当大的变异性。减少抗生素处方的最大潜在影响可能在于鼓励基于风险的处方并解决抗生素的重复使用问题。将重复抗生素疗程减少到临床医生中位数的处方习惯，每年每1000名临床医生可节省21813张抗生素处方。
抗生素处方的所有指标都存在广泛差异且它们之间相关性较弱，这表明单一的抗生素指标，如处方率，不足以支持抗生素处方的优化。
