Antimicrobial resistance has become one of the greatest threats to global health. Over 80% of antibiotics are prescribed in primary care, with many prescriptions considered to be issued inappropriately. The aim of this study was to examine the association between prescribing rates and demographic, practice, geographic, and socioeconomic characteristics using a multilevel modelling approach. Antibiotic prescribing data by 320 GP surgeries in Northern Ireland were obtained from Business Services Organisation for the years 2014–2020. A linear mixed-effects model was used to identify factors influencing antibiotic prescribing rates. Overall, the number of antibacterial prescriptions decreased by 26.2%, from 1,564,707 items in 2014 to 1,155,323 items in 2020. Lower levels of antibiotic prescribing were associated with urban practices (p < 0.001) and practices in less deprived areas (p = 0.005). The overall decrease in antibacterial drug prescriptions over time was larger in less deprived areas (p = 0.03). Higher prescribing rates were linked to GP practices located in areas with a higher percentage of the population aged ≥65 (p < 0.001) and <15 years (p < 0.001). There were also significant regional differences in antibiotic prescribing. We advocate that any future antibiotic prescribing targets should account for local factors.
Bibliographical noteFunding Information:
To address the threat posed by AMR in the UK, the first fully integrated five-year strategy for tackling AMR was published in 2013 to address the actions needed in key AMR areas including infection prevention and control, prescribing practice, education and public engagement, development of treatment and technologies, surveillance, and research . One of its main objectives was to reduce the levels of inappropriate antimicrobial prescribing by 50% by 2020 . The delivery of key components of this five-year national action plan was supported by the establishment of the English Surveillance Programme for Antimicrobial Utilisation and Resistance (ESPAUR) focusing on enhancing the surveillance of antimicrobial resistance, monitoring antibiotic prescribing, and supporting
Acknowledgments: This work was supported by the George Moore Endowment for Data Science at Ulster University.
Funding: This work was supported by the George Moore Endowment for Data Science at Ulster University (MB); European Union INTERREG VA Programme (MOK, MB); and the Economic and Social Research Council.
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- Antibacterial drugs
- Antibiotics prescribing
- Antimicrobial resistance
- General practice
- Mixed-effects model
- Multilevel modelling
- Primary care