Koen B Pouwels1,2,3, F Christiaan K Dolk1,2, David R M Smith1, Timo Smieszek1,3, Julie V Robotham1. 1. Modelling and Economics Unit, National Infection Service, Public Health England, London NW9 5EQ, UK. 2. PharmacoTherapy, -Epidemiology & -Economics, Department of Pharmacy, University of Groningen, Groningen, The Netherlands. 3. MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College School of Public Health, London, UK.
Abstract
Objectives: Primary care practices in England differ in antibiotic prescribing rates, and, anecdotally, prescribers justify high prescribing rates based on their individual case mix. The aim of this paper was to explore to what extent factors such as patient comorbidities explain this variation in antibiotic prescribing. Methods: Primary care consultation and prescribing data recorded in The Health Improvement Network (THIN) database in 2013 were used. Boosted regression trees (BRTs) and negative binomial regression (NBR) models were used to evaluate associations between predictors and antibiotic prescribing rates. The following variables were considered as potential predictors: various infection-related consultation rates, proportions of patients with comorbidities, proportion of patients with inhaled/systemic corticosteroids or immunosuppressive drugs, and demographic traits. Results: The median antibiotic prescribing rate was 65.6 (IQR 57.4-74.0) per 100 registered patients among 348 English practices. In the BRT model, consultation rates had the largest total relative influence on antibiotic prescribing rate (53.5%), followed by steroid and immunosuppressive drugs (31.6%) and comorbidities (12.2%). Only 21% of the deviance could be explained by an NBR model considering only comorbidities and age and gender, whereas 57% of the deviance could be explained by the model considering all variables. Conclusions: The majority of practice-level variation in antibiotic prescribing cannot be explained by variation in prevalence of comorbidities. Factors such as high consultation rates for respiratory tract infections and high prescribing rates for corticosteroids could explain much of the variation, and as such may be considered in determining a practice's potential to reduce prescribing.
Objectives: Primary care practices in England differ in antibiotic prescribing rates, and, anecdotally, prescribers justify high prescribing rates based on their individual case mix. The aim of this paper was to explore to what extent factors such as patient comorbidities explain this variation in antibiotic prescribing. Methods: Primary care consultation and prescribing data recorded in The Health Improvement Network (THIN) database in 2013 were used. Boosted regression trees (BRTs) and negative binomial regression (NBR) models were used to evaluate associations between predictors and antibiotic prescribing rates. The following variables were considered as potential predictors: various infection-related consultation rates, proportions of patients with comorbidities, proportion of patients with inhaled/systemic corticosteroids or immunosuppressive drugs, and demographic traits. Results: The median antibiotic prescribing rate was 65.6 (IQR 57.4-74.0) per 100 registered patients among 348 English practices. In the BRT model, consultation rates had the largest total relative influence on antibiotic prescribing rate (53.5%), followed by steroid and immunosuppressive drugs (31.6%) and comorbidities (12.2%). Only 21% of the deviance could be explained by an NBR model considering only comorbidities and age and gender, whereas 57% of the deviance could be explained by the model considering all variables. Conclusions: The majority of practice-level variation in antibiotic prescribing cannot be explained by variation in prevalence of comorbidities. Factors such as high consultation rates for respiratory tract infections and high prescribing rates for corticosteroids could explain much of the variation, and as such may be considered in determining a practice's potential to reduce prescribing.
Authors: Katherine E Fleming-Dutra; Adam L Hersh; Daniel J Shapiro; Monina Bartoces; Eva A Enns; Thomas M File; Jonathan A Finkelstein; Jeffrey S Gerber; David Y Hyun; Jeffrey A Linder; Ruth Lynfield; David J Margolis; Larissa S May; Daniel Merenstein; Joshua P Metlay; Jason G Newland; Jay F Piccirillo; Rebecca M Roberts; Guillermo V Sanchez; Katie J Suda; Ann Thomas; Teri Moser Woo; Rachel M Zetts; Lauri A Hicks Journal: JAMA Date: 2016-05-03 Impact factor: 56.272
Authors: Paul Little; Beth Stuart; Nick Francis; Elaine Douglas; Sarah Tonkin-Crine; Sibyl Anthierens; Jochen W L Cals; Hasse Melbye; Miriam Santer; Michael Moore; Samuel Coenen; Chris Butler; Kerenza Hood; Mark Kelly; Maciek Godycki-Cwirko; Artur Mierzecki; Antoni Torres; Carl Llor; Melanie Davies; Mark Mullee; Gilly O'Reilly; Alike van der Velden; Adam W A Geraghty; Herman Goossens; Theo Verheij; Lucy Yardley Journal: Lancet Date: 2013-07-31 Impact factor: 79.321
Authors: Olga Tosas Auguet; Jason R Betley; Richard A Stabler; Amita Patel; Avgousta Ioannou; Helene Marbach; Pasco Hearn; Anna Aryee; Simon D Goldenberg; Jonathan A Otter; Nergish Desai; Tacim Karadag; Chris Grundy; Michael W Gaunt; Ben S Cooper; Jonathan D Edgeworth; Theodore Kypraios Journal: PLoS Med Date: 2016-01-26 Impact factor: 11.069
Authors: Koen B Pouwels; F Christiaan K Dolk; David R M Smith; Julie V Robotham; Timo Smieszek Journal: J Antimicrob Chemother Date: 2018-02-01 Impact factor: 5.790
Authors: Timo Smieszek; Koen B Pouwels; F Christiaan K Dolk; David R M Smith; Susan Hopkins; Mike Sharland; Alastair D Hay; Michael V Moore; Julie V Robotham Journal: J Antimicrob Chemother Date: 2018-02-01 Impact factor: 5.790
Authors: Paula Gomes Alves; Gail Hayward; Geraldine Leydon; Rebecca Barnes; Catherine Woods; Joseph Webb; Matthew Booker; Helen Ireton; Sue Latter; Paul Little; Michael Moore; Clare-Louise Nicholls; Fiona Stevenson Journal: BJGP Open Date: 2021-06-30
Authors: Martin C Gulliford; A Toby Prevost; Judith Charlton; Dorota Juszczyk; Jamie Soames; Lisa McDermott; Kirin Sultana; Mark Wright; Robin Fox; Alastair D Hay; Paul Little; Michael V Moore; Lucy Yardley; Mark Ashworth Journal: BMJ Date: 2019-02-12