The impact of COVID-19 on antibiotic prescribing in primary care in England: Evaluation and risk prediction of appropriateness of type and repeat prescribing

Abstract

Background: This study aimed to predict risks of potentially inappropriate antibiotic type and repeat prescribing and assess changes during COVID-19.

Methods: With the approval of NHS England, we used OpenSAFELY platform to access the TPP SystmOne electronic health record (EHR) system and selected patients prescribed antibiotics from 2019 to 2021. Multinomial logistic regression models predicted patient's probability of receiving inappropriate antibiotic type or repeat antibiotic course for each common infection.

Results: The population included 9.1 million patients with 29.2 million antibiotic prescriptions. 29.1% of prescriptions were identified as repeat prescribing. Those with same day incident infection coded in the EHR had considerably lower rates of repeat prescribing (18.0%) and 8.6% had potentially inappropriate type. No major changes in the rates of repeat antibiotic prescribing during COVID-19 were found. In the 10 risk prediction models, good levels of calibration and moderate levels of discrimination were found.

Conclusions: Our study found no evidence of changes in level of inappropriate or repeat antibiotic prescribing after the start of COVID-19. Repeat antibiotic prescribing was frequent and varied according to regional and patient characteristics. There is a need for treatment guidelines to be developed around antibiotic failure and clinicians provided with individualised patient information.

Keywords: Antibiotic stewardship; Antibiotics; COVID-19 pandemic; Infection; Primary care.

Fig. 1

A. Monthly percentages of repeat antibiotic prescribing over calendar time. 1B. Monthly percentages of repeat antibiotic prescribing for all same day infection coded records over calendar time. A. Numerator is the number of repeat antibiotic prescriptions, and the denominator is the number of all antibiotic prescriptions, stratified by same day coded or uncoded records for a specific infection record (antibiotic user cohort). Boxplots represent the historical average (median and IQR) percentage of the repeat antibiotic prescribing from January 2019 to December 2021. Vertical solid lines indicate the start of COVID-19-related national restrictions (1st March 2020–31st March 2020). B. Repeat prescribing stratified by incident infection (A record with no other infection recorded in the 90 days before, and no antibiotic prescription in the 30 days before the index date) or prevalent infection from the coded prescription cohort (infection coded cohort).

Fig. 2

Monthly percentages of repeat antibiotic prescribing over calendar time (stratified by age(A), sex(B) and region(C) in infection coded cohort). The numerator is the number of repeat antibiotic prescriptions, and the denominator is the number of all antibiotic prescriptions. Boxplots represent the historical average (median and IQR) percentage of repeat antibiotic prescribing. Vertical solid lines indicate the COVID-19-related national restriction started month (1st March 2020–31st March 2020).

Fig. 3

Parametric nominal calibration plot for Overall/LRTI/Sore throat/Cough models. Parametric nominal calibration plot showing observed probabilities (Y-axis) versus predicted probabilities (X-axis) for different outcome categories. The observed probabilities were calculated from the recalibration framework (see Eq. (S3)). P1: the appropriate antibiotic was prescribed and no repeat antibiotic (30 days after the index date). P2: a potentially inappropriate antibiotic was prescribed with no repeat antibiotic. P3: other antibiotics issues 30 days after the index date. The plot was generated from the validation cohort.