Variation in antibiotic prescribing across English primary care from 2021 to 2024

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Although reductions in prescribing were observed during the coronavirus pandemic, patterns in the subsequent recovery period remain incompletely characterised. Understanding both national trends and regional variation in antibiotic use under the current commissioning structure may inform targeted quality improvement initiatives. Methods: A retrospective secondary analysis of publicly available NHS primary care prescribing data was undertaken using data obtained via OpenPrescribing. Monthly antibiotic prescribing data from January 2021 to December 2024 were analysed across 107 NHS commissioning areas. Prescribing intensity was defined as antibiotic prescription items per 1,000 registered patients per month. National trends were summarised using annual mean monthly prescribing rates. Regional variation was assessed descriptively using distribution metrics, including quartiles, standard deviation, maximum-to-minimum ratios, and annual ranking of commissioning areas. Persistence of extreme prescribing positions was evaluated across the four-year period. Results: National mean monthly antibiotic prescribing increased from 1.53 items per 1,000 registered patients in 2021 to 1.81 in 2023, before declining to 1.42 in 2024. The largest year-on-year change occurred between 2023 and 2024, with a reduction of 21.5%. Substantial regional variation was observed in all years. In 2023, prescribing ranged from 1.08 to 2.87 items per 1,000 registered patients per month, corresponding to a 2.67-fold difference between commissioning areas. Several areas appeared among the highest or lowest prescribing regions in all four years analysed, indicating persistence of extreme relative positions. Conclusions: Community antibiotic prescribing in English primary care between 2021 and 2024 demonstrated post-pandemic fluctuation and sustained regional variation across commissioning areas. Persistent differences in prescribing intensity suggest opportunities for regionally informed stewardship initiatives and performance benchmarking within the current healthcare administrative framework. Introduction Antimicrobial resistance is recognised as one of the most serious global public health challenges of the twenty-first century [ 1 ]. The emergence and spread of resistant organisms are strongly associated with the volume and appropriateness of antibiotic use across healthcare systems [ 2 ]. Inappropriate prescribing contributes to resistance development, avoidable adverse drug reactions, disruption of normal microbiota, and increased healthcare expenditure [ 3 ]. International and national policy frameworks therefore emphasise antimicrobial stewardship as a central strategy to preserve antibiotic effectiveness and improve patient safety. In England, the majority of antibiotic prescribing occurs within primary care, making general practice a central focus of stewardship interventions [ 4 ]. Over the past decade, national initiatives have included prescribing targets, quality premium incentives, audit and feedback programmes, public reporting through prescribing dashboards, and professional education campaigns [ 5 ]. These efforts have been associated with measurable reductions in overall antibiotic prescribing prior to the coronavirus pandemic [ 6 ]. Routine prescribing surveillance has become embedded within quality improvement processes and commissioning oversight, allowing benchmarking across practices and regions. The coronavirus pandemic substantially disrupted patterns of healthcare delivery. During 2020 and early 2021, reduced social mixing, altered respiratory virus transmission, reduced face-to-face consultations, and changes in patient health seeking behaviour were associated with marked reductions in antibiotic prescribing [ 6 ]. While these reductions were consistent with stewardship objectives, they occurred within an unusual healthcare environment characterised by restricted access, remote assessment, and broader public health measures. As healthcare systems transitioned toward recovery, consultation patterns shifted again, respiratory infections resurged, and primary care services faced workforce and demand pressures. It is important to examine prescribing behaviour in the period following the initial pandemic disruption to determine whether reduced antibiotic use has been sustained or whether prescribing intensity has returned to pre-pandemic trajectories. Beyond national averages, regional variation represents a critical dimension of health system performance [ 7 ]. Differences in antibiotic prescribing intensity across administrative regions may reflect variations in population demographics, age distribution, deprivation levels, comorbidity burden, local infection epidemiology, access to diagnostic services, and clinician decision making practices. Organisational culture and local stewardship implementation strategies may also contribute [ 7 ]. While some variation is expected in any large healthcare system, substantial and persistent disparities raise important questions regarding equity, consistency of care, and potential unwarranted variation. Recent organisational reforms in England have formalised commissioning arrangements [ 8 ]. These administrative units provide a meaningful analytical level for assessing prescribing behaviour, balancing population size with regional accountability. Examining variation at the commissioning area level enables comparison across regions with similar governance responsibilities while minimising instability associated with small practice-level denominators. Despite the availability of comprehensive national prescribing datasets, there remains limited contemporary analysis focusing specifically on the post-pandemic period within the current commissioning framework and examining persistence of regional variation across multiple years. Quantifying the magnitude of variation requires more than reporting national means. Distributional metrics such as quartiles, interquartile range, standard deviation, and maximum-to-minimum ratios provide a clearer understanding of dispersion and relative spread across regions [ 9 ]. Furthermore, examining persistence over multiple years allows differentiation between transient fluctuation and structurally embedded prescribing patterns. Regions that consistently appear among the highest or lowest prescribing areas may represent stable differences in practice, population characteristics, or stewardship implementation. Identifying such persistent extremes has practical relevance for benchmarking, targeted intervention, and policy prioritisation. Descriptive analyses of comprehensive administrative datasets provide valuable insight into health system performance [ 10 ]. When analysing complete national prescribing records, the objective is to characterise observed patterns across the entire healthcare system rather than draw inferences from a sampled subset. Transparent reporting of magnitude, distribution, and persistence supports evidence informed decision making and facilitates monitoring of stewardship progress. The present study aims to examine national trends and quantify regional variation in community antibiotic prescribing in England from January 2021 to December 2024. Using publicly available prescribing data aggregated across 107 National Health Service (NHS) commissioning areas, the study evaluates changes in prescribing intensity over time, characterises the distribution of prescribing rates across regions, identifies areas at the extremes of the distribution, and assesses the persistence of extreme prescribing ranks across multiple years. By focusing on the contemporary post-pandemic primary care context and the current commissioning structure, this analysis seeks to provide an updated and system-level assessment of antibiotic prescribing patterns within English primary care. Method A retrospective secondary analysis of publicly available primary care prescribing data in England was undertaken. Data were accessed through OpenPrescribing (Bennett Institute for Applied Data Science, University of Oxford) [ 11 ], which provides structured extracts of the NHS Business Services Authority English Prescribing Dataset. This dataset contains anonymised monthly prescribing records for all general practices in England, including prescription item counts, associated costs, and practice-level registered patient list sizes. It captures prescribing issued in primary care settings only and does not include hospital inpatient or outpatient prescribing. No patient-level identifiers are available within the dataset. Monthly prescribing data from January 2021 to December 2024 were extracted for analysis. This period was selected to examine prescribing patterns in the post-pandemic context using the current regional administrative structure and the most recent complete years of available data. Antibiotic prescribing was defined using British National Formulary section 5.1, Antibacterial drugs, which includes all systemic antibacterial agents prescribed in primary care [ 12 ]. The primary outcome measure was antibiotic prescribing intensity, defined as the number of antibiotic prescription items issued per 1,000 registered patients per month. Within NHS prescribing data, an item represents a single prescribed entry for an antibiotic product and does not correspond to the number of tablets dispensed, treatment duration, or prescription forms issued. For each commissioning area and month, prescribing intensity was calculated by dividing the total number of antibiotic prescription items by the corresponding registered patient list size and multiplying by 1,000. This standardisation enabled comparison across regions with differing population sizes. Prescribing rates were analysed at the level of NHS commissioning areas, with 107 areas included in the study. These represent regional healthcare administrative units in England. Aggregation at this level was selected to allow meaningful regional comparison while reducing instability associated with smaller practice-level denominators. Commissioning area boundaries were defined according to the NHS administrative structure in place during the study period [ 13 ], with practice-level prescribing data aggregated to the corresponding commissioning area for each month. For assessment of national trends, monthly prescribing rates were calculated for each commissioning area and averaged to obtain national monthly mean values. These monthly values were subsequently summarised as annual means for each year from 2021 to 2024. National mean prescribing rates were calculated as the unweighted average of commissioning area monthly rates to preserve comparability across regions. Inter-regional variation was assessed annually using descriptive distribution metrics, including mean, median, quartiles, interquartile range, standard deviation, minimum and maximum values, and the maximum-to-minimum ratio. The ratio of the highest to the lowest prescribing area was used as a measure of relative dispersion. Commissioning areas were ranked annually by prescribing intensity, and persistence of extreme prescribing positions was assessed by counting the number of years each commissioning area appeared within the highest and lowest ranked prescribing categories during the study period. As the dataset represents comprehensive national prescribing activity rather than a sample, analyses were descriptive in nature. The objective was to characterise the magnitude and pattern of prescribing variation rather than test predefined hypotheses. No inferential statistical testing or measures of statistical uncertainty were performed. All data extraction, aggregation, and calculations were performed using Microsoft Excel with formula-based processing. Derived measures were calculated using deterministic arithmetic operations, including rate standardisation, descriptive statistics, and ranking procedures. All analyses are reproducible from publicly available prescribing and population datasets. Results National prescribing trends National community antibiotic prescribing varied across the study period (Table 1 ). The mean monthly prescribing rate increased from 1.53 antibiotic prescription items per 1,000 registered patients in 2021 to 1.59 in 2022, representing an absolute increase of 0.06 items per 1,000 patients. Prescribing rose further to 1.81 in 2023, an increase of 0.22 compared with 2022, before declining to 1.42 items per 1,000 registered patients per month in 2024. Table 1 National mean monthly antibiotic prescribing rates (2021 to 2024) Year Mean Monthly Antibiotic Prescriptions per 1,000 Registered Patients 2021 1.53 2022 1.59 2023 1.81 2024 1.42 Year-on-year analysis (Table 2 ) demonstrated a 3.9% increase between 2021 and 2022 and a 13.8% increase between 2022 and 2023. The reduction observed in 2024 corresponded to a 21.5% decrease compared with 2023 and represented the largest year-on-year change during the study period. Overall, the 2024 mean prescribing rate was 0.11 items per 1,000 registered patients per month lower than in 2021, equivalent to a 7.2% net reduction across the four-year period. Table 2 Year-on-year change in national mean monthly antibiotic prescribing rates (2021 to 2024) Year Mean Rate Absolute Change % Change 2021 1.53 - - 2022 1.59 0.06 3.9 2023 1.81 0.22 13.8 2024 1.42 -0.39 -21.5 2021 to 2024 Overall change - -0.11 -7.2 Inter-regional distribution and dispersion Across the 107 NHS commissioning areas included in the analysis, prescribing rates demonstrated consistent and substantial inter-regional variation (Table 3 ). Median prescribing rates were 1.51 items per 1,000 registered patients per month in 2021, 1.59 in 2022, 1.81 in 2023, and 1.40 in 2024. Table 3 Distribution of annual mean monthly antibiotic prescribing rates per 1,000 registered patients across 107 NHS commissioning areas from 2021 to 2024 Metric 2021 2022 2023 2024 Mean 1.53 1.59 1.81 1.42 Median 1.51 1.59 1.81 1.40 First quartile (Q1) 1.37 1.43 1.58 1.23 Third quartile (Q3) 1.69 1.76 1.98 1.57 Interquartile range (IQR) 0.32 0.32 0.41 0.34 Standard deviation 0.24 0.24 0.30 0.24 Minimum 0.96 0.96 1.08 0.89 Maximum 2.16 2.17 2.87 2.15 Maximum-to-minimum ratio 2.26 2.25 2.67 2.41 Dispersion widened in 2023 relative to earlier years. The interquartile range increased from 0.32 in 2021 and 2022 to 0.41 in 2023, before narrowing to 0.34 in 2024. Standard deviation followed a similar pattern, rising from 0.24 in 2021 and 2022 to 0.30 in 2023 and returning to 0.24 in 2024. The overall range further illustrates the magnitude of variation. In 2021, prescribing rates ranged from 0.96 to 2.16 items per 1,000 registered patients per month. By 2023, the minimum rate had increased to 1.08 while the maximum rose to 2.87. The maximum-to-minimum ratio increased from 2.26 in 2021 to 2.67 in 2023, indicating that the highest-prescribing commissioning area issued more than two and a half times the number of antibiotic items per capita as the lowest-prescribing area. In 2024, despite the national decline, the ratio remained elevated at 2.41-fold. Commissioning areas at the extremes of prescribing Examination of commissioning areas at the upper and lower ends of the distribution in 2023 (Table 4 ) demonstrated clustering of high and low prescribing rates. NHS Southend recorded the highest prescribing rate (2.87 items per 1,000 registered patients per month), followed by NHS Castle Point and Rochford (2.58) and NHS Wigan Borough (2.57). Additional areas within the highest prescribing group included NHS Basildon and Brentwood, NHS Knowsley, NHS Southport and Formby, NHS Blackpool, NHS Oldham, NHS West Essex, NHS Kirklees, and NHS Birmingham and Solihull. Table 4 Highest and lowest prescribing NHS commissioning areas in 2023 (mean monthly antibiotic prescriptions per 1,000 registered patients) Highest prescribing areas Rate Lowest prescribing areas Rate NHS Southend 2.87 NHS Oxfordshire 1.08 NHS Castle Point and Rochford 2.58 NHS Bristol, North Somerset and South Gloucestershire 1.15 NHS Wigan Borough 2.57 NHS Brighton and Hove 1.16 NHS Basildon and Brentwood 2.35 NHS Southeast London 1.34 NHS Knowsley 2.33 NHS Somerset 1.34 NHS Southport and Formby 2.28 NHS Leicester City 1.36 NHS Blackpool 2.23 NHS Berkshire West 1.40 NHS Oldham 2.22 NHS Gloucestershire 1.41 NHS West Essex 2.21 NHS Vale of York 1.41 NHS Kirklees 2.21 NHS West Leicestershire 1.42 NHS Birmingham and Solihull 2.21 NHS Tameside and Glossop 1.42 At the lower end of the distribution, NHS Oxfordshire recorded the lowest prescribing rate (1.08), followed by NHS Bristol, North Somerset and South Gloucestershire (1.15) and NHS Brighton and Hove (1.16). Other areas within the lowest prescribing group included NHS Southeast London, NHS Somerset, NHS Leicester City, NHS Berkshire West, NHS Gloucestershire, NHS Vale of York, NHS West Leicestershire, and NHS Tameside and Glossop. The absolute difference between the highest and lowest commissioning areas in 2023 exceeded 1.8 items per 1,000 registered patients per month. Persistence of extreme prescribing ranks Analysis of rank persistence across 2021 to 2024 (Table 5 ) demonstrated repeated positioning of specific commissioning areas at the extremes of the prescribing distribution. NHS Blackpool, NHS Wigan Borough, NHS Southend, and NHS Southport and Formby appeared among the highest-prescribing areas in all four years analysed. Several additional areas, including NHS Oldham, NHS West Essex, NHS Basildon and Brentwood, NHS Castle Point and Rochford, and NHS Knowsley, appeared within the highest group in three of four years. Table 5 Persistence of extreme prescribing ranks across 2021 to 2024 (n = 107 NHS commissioning areas) Appeared in Top Five (number of years) Years Appeared in Bottom Five (number of years) Years NHS Blackpool 4 NHS Bristol, North Somerset and South Gloucestershire 4 NHS Wigan Borough 4 NHS Oxfordshire 4 NHS Southend 4 NHS Brighton and Hove 4 NHS Southport and Formby 4 NHS Berkshire West 4 NHS Oldham 3 NHS Southeast London 4 NHS West Essex 3 NHS Leicester City 3 NHS Basildon and Brentwood 3 NHS Gloucestershire 3 NHS Castle Point and Rochford 3 NHS Somerset 2 NHS Knowsley 3 NHS Portsmouth 2 NHS Northeast Lincolnshire 2 NHS West Leicestershire 2 NHS South Sefton 2 NHS Vale of York 2 NHS West Lancashire 2 Conversely, NHS Bristol, North Somerset and South Gloucestershire, NHS Oxfordshire, NHS Brighton and Hove, NHS Berkshire West, and NHS Southeast London appeared among the lowest-prescribing areas in all four years. Other commissioning areas, including NHS Leicester City and NHS Gloucestershire, were positioned within the lowest group in at least three years. Discussion This study demonstrates substantial regional variation in antibiotic prescribing intensity across England between 2021 and 2024 and shows that this variation persisted across consecutive years. While national mean prescribing fluctuated during the post-pandemic recovery period, the relative positioning of several commissioning areas at the upper and lower extremes remained stable. This persistence is a key finding and suggests that regional prescribing differences may reflect structurally embedded patterns rather than short-term epidemiological change [ 14 ]. The magnitude of variation observed is notable. In 2023, the highest prescribing commissioning area issued more than two and a half times the number of antibiotic items per capita compared with the lowest prescribing area. Differences of this scale are unlikely to be attributable solely to random fluctuation or seasonal variation. Even after national prescribing declined in 2024, the maximum-to-minimum ratio remained above 2.4. Although some variation is expected within a large and heterogeneous healthcare system, persistent two-fold or greater differences raise important questions regarding consistency in guideline implementation and underlying structural influences on prescribing behaviour across regions [ 15 ]. A two-fold or greater difference in prescribing intensity between commissioning areas represents a substantial divergence in population-level antibiotic exposure rather than a simple statistical contrast. Even modest differences in per capita prescribing can translate into large absolute differences in antibiotic courses issued across populations of several hundred thousand registered patients. In absolute terms, the 2023 difference between the highest and lowest prescribing commissioning areas corresponded to approximately 1.8 additional antibiotic items per 1,000 registered patients per month. Over the course of a year, this equates to more than 20 additional antibiotic prescriptions per 1,000 patients, which at commissioning population scale represents a considerable difference in cumulative antibiotic exposure. Such sustained exposure differences may have implications for antimicrobial resistance selection pressure, prescribing culture, and long-term stewardship outcomes [ 16 ]. Interpreting this variation requires consideration of multiple potential drivers. Population characteristics such as age distribution, deprivation, comorbidity burden, and infection incidence are likely to influence prescribing demand. Areas with higher levels of deprivation and chronic disease may reasonably experience higher consultation rates and antibiotic exposure [ 17 ]. However, ecological evidence from previous studies suggests that clinician level behaviours, local prescribing culture, peer norms, and access to diagnostic support also contribute meaningfully to prescribing variation. Persistent high prescribing regions may therefore reflect a combination of greater clinical need and established behavioural patterns within local professional communities [ 18 ]. The persistence analysis strengthens the interpretation that variation is not purely episodic. Commissioning areas that consistently appeared among the highest or lowest prescribing groups across four consecutive years are unlikely to reflect chance variation alone. Stability in relative ranking implies enduring differences in practice patterns or contextual factors. From a stewardship perspective, this finding is particularly relevant. Transient variation may resolve without intervention, whereas persistent divergence suggests the potential benefit of targeted and sustained quality improvement activity [ 19 ]. Although ranking approaches are inherently sensitive to small differences in absolute rates and may be influenced by regression toward the mean, the repeated appearance of several commissioning areas within the highest and lowest categories across four consecutive years suggests relative stability in prescribing position rather than random fluctuation. In a system comprising 107 commissioning areas, consistent placement at the extremes over multiple years is unlikely to occur purely by chance. This stability may reflect enduring structural, demographic, organisational, or behavioural influences on prescribing practice and warrants further investigation [ 20 ]. The post-pandemic trajectory observed in this study warrants further reflection. National prescribing increased between 2021 and 2023, coinciding with restoration of routine healthcare utilisation and resurgence of respiratory infections [ 21 ]. This rise may represent re-normalisation of consultation volumes rather than erosion of stewardship gains. The subsequent decline in 2024 suggests that prescribing behaviour may be stabilising within a new equilibrium. However, the distributional metrics indicate that although overall intensity shifted, the relative spread between regions remained wide. This pattern suggests that system-wide changes in demand do not necessarily reduce structural regional disparities. From a system perspective, the persistence of substantial regional variation under a unified national stewardship framework raises important questions regarding consistency of implementation and local accountability. Commissioning organisations are responsible for monitoring prescribing performance and supporting quality improvement within primary care. Sustained two-fold differences between commissioning areas suggest that stewardship policies may not be translating uniformly into practice across regions. Structured benchmarking, transparent reporting, and targeted feedback at commissioning area level may therefore play a critical role in reducing potentially unwarranted variation while preserving clinical autonomy [ 5 ]. In particular, commissioning areas that consistently appear at the upper end of the prescribing distribution may warrant focused review of local stewardship governance, workforce pressures, diagnostic access, and prescribing support mechanisms. It is important to emphasise that higher prescribing intensity does not automatically equate to inappropriate prescribing. Without patient-level data on diagnosis, clinical severity, and treatment indication, the analysis cannot determine appropriateness. Some degree of variation may be justified by legitimate differences in clinical need [ 22 ]. Nonetheless, the scale and persistence of observed differences support continued scrutiny and evaluation of regional prescribing patterns. The descriptive nature of this analysis reflects the comprehensive coverage of the dataset. Because the study included complete national prescribing records, the focus was on quantifying magnitude and distribution rather than estimating statistical uncertainty. This approach aligns with health services surveillance objectives, where the aim is to monitor system performance and identify areas for further investigation rather than test narrowly defined hypotheses [ 23 ]. Future research incorporating case mix adjustment, demographic modelling, or linkage with hospital admission data may provide additional insight into drivers of variation. Several limitations merit consideration. The analysis utilised ecological prescribing data and did not incorporate adjustment for demographic structure, deprivation, comorbidity burden, or consultation volume [ 23 ]. Observed regional differences may therefore reflect legitimate variation in population need as well as potential differences in prescribing behaviour [ 17 ]. The findings should be interpreted as differences in prescribing intensity rather than measures of prescribing quality or appropriateness. Aggregation at commissioning area level may conceal variation at practice-level. Furthermore, the persistence analysis was based on ordinal ranking rather than modelling of rank transitions, and small differences in absolute prescribing rates may influence placement within extreme categories. However, stability across multiple consecutive years suggests that observed persistence is unlikely to reflect short-term statistical fluctuation alone. In addition, national summary measures were derived using unweighted commissioning area averages. While this approach reflects regional comparability, population-weighted estimates may yield slightly different national values. The four-year observation window captures early post-pandemic recovery rather than long-term organisational characteristics. These findings should be considered within the context of existing national antimicrobial stewardship strategies in England, which include prescribing targets, audit and feedback mechanisms, and commissioning level oversight of primary care performance [ 5 ]. Commissioning organisations play a central role in translating national stewardship guidance into local practice. The persistence of substantial regional variation under a unified policy framework suggests that implementation and operationalisation of stewardship initiatives may differ across commissioning contexts. Understanding how national strategy interacts with local organisational structures and workforce pressures is therefore important when interpreting regional prescribing patterns. Taken together, the findings suggest that while national prescribing intensity fluctuated during post-pandemic recovery, regional prescribing disparities remained substantial and stable. Persistent variation of this magnitude indicates structural heterogeneity across commissioning areas that warrants further investigation and may represent opportunities for regionally tailored stewardship engagement. Future research incorporating age standardisation, socioeconomic indicators, and case mix adjustment would help clarify the extent to which observed regional disparities reflect underlying population characteristics versus modifiable prescribing practices [ 19 ]. Sustained regional disparities also underscore the potential value of commissioning area specific stewardship strategies. While national guidance provides a consistent framework, effective implementation may require adaptation to local demographic, organisational, and workforce contexts. Structured peer comparison within comparable commissioning groups, dissemination of practices from consistently lower prescribing regions, and strengthened audit and feedback mechanisms may support gradual convergence in prescribing behaviour over time [ 24 , 25 ]. Continued integration of prescribing surveillance into commissioning oversight processes will be important to ensure that variation is systematically monitored and addressed where appropriate. Conclusion Community antibiotic prescribing in English primary care between 2021 and 2024 demonstrated both temporal fluctuation and sustained regional disparity. Although national prescribing increased following pandemic related reductions and subsequently declined, substantial differences between commissioning areas persisted across consecutive years. In multiple years, the highest prescribing regions issued more than twice the number of antibiotic items per capita compared with the lowest prescribing regions. These persistent disparities suggest that regional prescribing patterns may reflect enduring structural, demographic, or behavioural factors rather than short-term epidemiological change alone. Continued surveillance, structured benchmarking, and regionally informed stewardship initiatives are likely to be important components of efforts to reduce potentially unwarranted variation while maintaining appropriate responsiveness to population need [ 5 , 19 ]. Further research incorporating case mix adjustment and clinical indication data would help clarify the drivers of observed disparities and inform targeted policy responses. Abbreviations NHS: National Health Service Declarations Acknowledgements The authors acknowledge the Bennett Institute for Applied Data Science, University of Oxford, for providing access to OpenPrescribing data. Authors’ contributions VL conceived and designed the study, conducted the data extraction and analysis, interpreted the findings, and drafted the manuscript. JL assisted with data extraction, data verification, and preliminary data processing. MSL provided methodological guidance, contributed to interpretation of findings, and critically revised the manuscript for important intellectual content. All authors read and approved the final manuscript. Funding This study was conducted without specific grant funding. Data availability The data analysed in this study are publicly available through OpenPrescribing (Bennett Institute for Applied Data Science, University of Oxford) and the NHS Business Services Authority English Prescribing Dataset. No additional datasets were generated. Ethics approval and consent to participate This study utilised publicly available, fully anonymised secondary data and did not require research ethics committee approval in accordance with UK Health Research Authority guidance. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Antimicrobial Resistance Collaborators. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet. 2022;399:629–55. 10.1016/S0140-6736(21)02724-0 . Goossens H, Ferech M, Vander Stichele R, Elseviers M, ESAC Project Group. Outpatient antibiotic use in Europe and association with resistance: a cross-national database study. Lancet. 2005;365:579–87. 10.1016/S0140-6736(05)17907-0 . Spellberg B, Bartlett JG, Gilbert DN. The future of antibiotics and resistance. N Engl J Med. 2013;368:299–302. 10.1056/NEJMp1215093 . Smieszek T, Pouwels KB, Dolk FCK, Smith DRM, Hopkins S, Sharland M, et al. Potential for reducing inappropriate antibiotic prescribing in English primary care. J Antimicrob Chemother. 2018;73(Suppl 2):ii36–43. 10.1093/jac/dkx500 . Ashiru-Oredope D, Cunningham N, Casale E, Muller-Pebody B, Hope R, Brown CS, et al. Reporting England's progress towards the ambitions in the UK action plan for antimicrobial resistance: the English surveillance programme for antimicrobial utilisation and resistance (ESPAUR). J Antimicrob Chemother. 2023;78:2387–91. 10.1093/jac/dkad248 . McCloskey AP, Malabar L, McCabe PG, Gitsham A, Jarman I. Antibiotic prescribing trends in primary care 2014–2022. Res Social Adm Pharm. 2023;19:1193–201. 10.1016/j.sapharm.2023.05.001 . Pouwels KB, Dolk FCK, Smith DRM, Smieszek T, Robotham JV. Explaining variation in antibiotic prescribing between general practices in the UK. J Antimicrob Chemother. 2018;73(Suppl 2):ii27–35. 10.1093/jac/dkx501 . NHS England. Integrated care systems: design framework. London: NHS England; 2021. Manikandan S. Measures of dispersion. J Pharmacol Pharmacother. 2011;2:315–6. 10.4103/0976-500X.85931 . Gliklich RE, Leavy MB, Dreyer NA, editors. Registries for evaluating patient outcomes: a user’s guide. 4th ed. Rockville (MD): Agency for Healthcare Research and Quality; 2020. OpenPrescribing. OpenPrescribing [Internet]. London: Bennett Institute for Applied Data Science, University of Oxford; [cited 2026 Mar 3]. Available from: https://openprescribing.net/ Joint Formulary Committee. British National Formulary. 91st ed. London: BMJ Group and Pharmaceutical; 2026. UK Parliament. Health and Care Act 2022 [Internet]. London: The Stationery Office. 2022 [cited 2026 Mar 3]. Available from: https://www.legislation.gov.uk/ukpga/2022/31/contents Brookes-Howell L, Hood K, Cooper L, Little P, Verheij T, Coenen S, et al. Understanding variation in primary medical care: a nine-country qualitative study of clinicians’ accounts of non-clinical factors shaping antibiotic prescribing decisions for lower respiratory tract infection. BMJ Open. 2012;2:e000796. 10.1136/bmjopen-2011-000796 . Kasse GE, Humphries J, Cosh SM, Islam MS. Factors contributing to variation in antibiotic prescribing among primary health care physicians: a systematic review. BMC Prim Care. 2024;25:8. 10.1186/s12875-023-02223-1 . Costelloe C, Metcalfe C, Lovering A, Mant D, Hay AD. Effect of antibiotic prescribing in primary care on antimicrobial resistance in individual patients: systematic review and meta-analysis. BMJ. 2010;340:c2096. 10.1136/bmj.c 2096. Pouwels KB, Hopkins S, Llewelyn MJ, Walker AS, McNulty CAM, Robotham JV. Duration of antibiotic treatment for common infections in English primary care: cross-sectional analysis and comparison with guidelines. BMJ. 2019;364:l440. 10.1136/bmj.l440 . Curtis HJ, Walker AJ, Mahtani KR, Goldacre B. Time trends and geographical variation in prescribing of antibiotics in England 1998–2017. J Antimicrob Chemother. 2019;74:242–50. 10.1093/jac/dky377 . Wennberg JE. Time to tackle unwarranted variations in practice. BMJ. 2011;342:d1513. 10.1136/bmj.d1513 . Fisher ES, Wennberg DE, Stukel TA, Gottlieb DJ, Lucas FL, Pinder EL. The implications of regional variations in Medicare spending. Part 1: content, quality, and accessibility of care. Ann Intern Med. 2003;138:273–87. 10.7326/0003-4819-138-4-200302180-00006 . Williams R, Jenkins DA, Ashcroft DM, Brown B, Campbell S, Carr MJ, et al. Diagnosis of physical and mental health conditions in primary care during the COVID-19 pandemic: a retrospective cohort study. Lancet Public Health. 2020;5:e543–50. 10.1016/S2468-2667(20)30201-2 . Gulliford MC, Dregan A, Moore MV, Ashworth M, Staa TP, McCann G, et al. Continued high rates of antibiotic prescribing to adults with respiratory tract infection: survey of 568 UK general practices. BMJ Open. 2014;4:e006245. 10.1136/bmjopen-2014-006245 . Morgenstern H. Ecologic studies in epidemiology: concepts, principles, and methods. Annu Rev Public Health. 1995;16:61–81. 10.1146/annurev.pu.16.050195.000425 . Ivers N, Yogasingam S, Lacroix M, Brown KA, Antony J, Soobiah C, et al. Audit and feedback: effects on professional practice. Cochrane Database Syst Rev. 2025;3:CD000259. 10.1002/14651858.CD000259.pub4 . Hallsworth M, Chadborn T, Sallis A, Sanders M, Berry D, Greaves F, et al. Provision of social norm feedback to high prescribers of antibiotics in general practice: a pragmatic national randomised controlled trial. Lancet. 2016;387:1743–52. 10.1016/S0140-6736(16)00215-4 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9071704","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":617825275,"identity":"8b4b1c0a-2494-4a0f-a71a-c8f604e8c546","order_by":0,"name":"Valerie Lee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYDACZjACAvbGhgMfKkAizA1EauE5fPDgjDMgEUYCWhhgWiTSkg/ztoFYBLSYs/Me/FxQsU1eviHH4ODMebXR/O1ALT8qtuHUYtnMlyw948xtww0Hzhgc+LjteO6Mw4wNjD1nbuPUYnCYx0Cat+024wbGHqAt247lNgC1MDO24dVi/Jv33237+c08Bod55xzLnU+EFjNp3obbiQ3H2BIO8zbU5G4grIUvzZrn2O3kDWeYDxyccexA7kagloN4/XL+7OHbPDW3befPf9j84UNNXe6884cPPvhRgVsLMApReIfB5AE86jG01OFXPApGwSgYBSMSAABRwWVYz6Q4DQAAAABJRU5ErkJggg==","orcid":"","institution":"Medical University of Łódź","correspondingAuthor":true,"prefix":"","firstName":"Valerie","middleName":"","lastName":"Lee","suffix":""},{"id":617825276,"identity":"fb5d30d7-99ca-4db6-86b5-5d3331cac95b","order_by":1,"name":"Julius Lee","email":"","orcid":"","institution":"Independent Research Contributor","correspondingAuthor":false,"prefix":"","firstName":"Julius","middleName":"","lastName":"Lee","suffix":""},{"id":617825277,"identity":"ddc62c59-a34f-4f84-b66c-c95127a4dd4d","order_by":2,"name":"Mun Seng Lee","email":"","orcid":"","institution":"Independent Medical Educator and Researcher","correspondingAuthor":false,"prefix":"","firstName":"Mun","middleName":"Seng","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2026-03-09 10:38:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9071704/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9071704/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106403373,"identity":"4bf5cbe1-7e30-4ef4-bba9-23560c6bfc4f","added_by":"auto","created_at":"2026-04-08 09:14:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":531217,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9071704/v1/9ba14141-a163-49c8-9646-0b6697c4543b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Variation in antibiotic prescribing across English primary care from 2021 to 2024","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAntimicrobial resistance is recognised as one of the most serious global public health challenges of the twenty-first century [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. The emergence and spread of resistant organisms are strongly associated with the volume and appropriateness of antibiotic use across healthcare systems [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]. Inappropriate prescribing contributes to resistance development, avoidable adverse drug reactions, disruption of normal microbiota, and increased healthcare expenditure [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]. International and national policy frameworks therefore emphasise antimicrobial stewardship as a central strategy to preserve antibiotic effectiveness and improve patient safety.\u003c/p\u003e \u003cp\u003eIn England, the majority of antibiotic prescribing occurs within primary care, making general practice a central focus of stewardship interventions [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]. Over the past decade, national initiatives have included prescribing targets, quality premium incentives, audit and feedback programmes, public reporting through prescribing dashboards, and professional education campaigns [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]. These efforts have been associated with measurable reductions in overall antibiotic prescribing prior to the coronavirus pandemic [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]. Routine prescribing surveillance has become embedded within quality improvement processes and commissioning oversight, allowing benchmarking across practices and regions.\u003c/p\u003e \u003cp\u003eThe coronavirus pandemic substantially disrupted patterns of healthcare delivery. During 2020 and early 2021, reduced social mixing, altered respiratory virus transmission, reduced face-to-face consultations, and changes in patient health seeking behaviour were associated with marked reductions in antibiotic prescribing [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]. While these reductions were consistent with stewardship objectives, they occurred within an unusual healthcare environment characterised by restricted access, remote assessment, and broader public health measures. As healthcare systems transitioned toward recovery, consultation patterns shifted again, respiratory infections resurged, and primary care services faced workforce and demand pressures. It is important to examine prescribing behaviour in the period following the initial pandemic disruption to determine whether reduced antibiotic use has been sustained or whether prescribing intensity has returned to pre-pandemic trajectories.\u003c/p\u003e \u003cp\u003eBeyond national averages, regional variation represents a critical dimension of health system performance [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]. Differences in antibiotic prescribing intensity across administrative regions may reflect variations in population demographics, age distribution, deprivation levels, comorbidity burden, local infection epidemiology, access to diagnostic services, and clinician decision making practices. Organisational culture and local stewardship implementation strategies may also contribute [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]. While some variation is expected in any large healthcare system, substantial and persistent disparities raise important questions regarding equity, consistency of care, and potential unwarranted variation.\u003c/p\u003e \u003cp\u003eRecent organisational reforms in England have formalised commissioning arrangements [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]. These administrative units provide a meaningful analytical level for assessing prescribing behaviour, balancing population size with regional accountability. Examining variation at the commissioning area level enables comparison across regions with similar governance responsibilities while minimising instability associated with small practice-level denominators. Despite the availability of comprehensive national prescribing datasets, there remains limited contemporary analysis focusing specifically on the post-pandemic period within the current commissioning framework and examining persistence of regional variation across multiple years.\u003c/p\u003e \u003cp\u003eQuantifying the magnitude of variation requires more than reporting national means. Distributional metrics such as quartiles, interquartile range, standard deviation, and maximum-to-minimum ratios provide a clearer understanding of dispersion and relative spread across regions [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. Furthermore, examining persistence over multiple years allows differentiation between transient fluctuation and structurally embedded prescribing patterns. Regions that consistently appear among the highest or lowest prescribing areas may represent stable differences in practice, population characteristics, or stewardship implementation. Identifying such persistent extremes has practical relevance for benchmarking, targeted intervention, and policy prioritisation.\u003c/p\u003e \u003cp\u003eDescriptive analyses of comprehensive administrative datasets provide valuable insight into health system performance [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. When analysing complete national prescribing records, the objective is to characterise observed patterns across the entire healthcare system rather than draw inferences from a sampled subset. Transparent reporting of magnitude, distribution, and persistence supports evidence informed decision making and facilitates monitoring of stewardship progress.\u003c/p\u003e \u003cp\u003eThe present study aims to examine national trends and quantify regional variation in community antibiotic prescribing in England from January 2021 to December 2024. Using publicly available prescribing data aggregated across 107 National Health Service (NHS) commissioning areas, the study evaluates changes in prescribing intensity over time, characterises the distribution of prescribing rates across regions, identifies areas at the extremes of the distribution, and assesses the persistence of extreme prescribing ranks across multiple years. By focusing on the contemporary post-pandemic primary care context and the current commissioning structure, this analysis seeks to provide an updated and system-level assessment of antibiotic prescribing patterns within English primary care.\u003c/p\u003e "},{"header":"Method","content":"\u003cp\u003eA retrospective secondary analysis of publicly available primary care prescribing data in England was undertaken. Data were accessed through OpenPrescribing (Bennett Institute for Applied Data Science, University of Oxford) [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e], which provides structured extracts of the NHS Business Services Authority English Prescribing Dataset. This dataset contains anonymised monthly prescribing records for all general practices in England, including prescription item counts, associated costs, and practice-level registered patient list sizes. It captures prescribing issued in primary care settings only and does not include hospital inpatient or outpatient prescribing. No patient-level identifiers are available within the dataset.\u003c/p\u003e\u003cp\u003eMonthly prescribing data from January 2021 to December 2024 were extracted for analysis. This period was selected to examine prescribing patterns in the post-pandemic context using the current regional administrative structure and the most recent complete years of available data. Antibiotic prescribing was defined using British National Formulary section 5.1, Antibacterial drugs, which includes all systemic antibacterial agents prescribed in primary care [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe primary outcome measure was antibiotic prescribing intensity, defined as the number of antibiotic prescription items issued per 1,000 registered patients per month. Within NHS prescribing data, an item represents a single prescribed entry for an antibiotic product and does not correspond to the number of tablets dispensed, treatment duration, or prescription forms issued. For each commissioning area and month, prescribing intensity was calculated by dividing the total number of antibiotic prescription items by the corresponding registered patient list size and multiplying by 1,000. This standardisation enabled comparison across regions with differing population sizes.\u003c/p\u003e\u003cp\u003ePrescribing rates were analysed at the level of NHS commissioning areas, with 107 areas included in the study. These represent regional healthcare administrative units in England. Aggregation at this level was selected to allow meaningful regional comparison while reducing instability associated with smaller practice-level denominators. Commissioning area boundaries were defined according to the NHS administrative structure in place during the study period [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e], with practice-level prescribing data aggregated to the corresponding commissioning area for each month. For assessment of national trends, monthly prescribing rates were calculated for each commissioning area and averaged to obtain national monthly mean values. These monthly values were subsequently summarised as annual means for each year from 2021 to 2024.\u003c/p\u003e\u003cp\u003eNational mean prescribing rates were calculated as the unweighted average of commissioning area monthly rates to preserve comparability across regions. Inter-regional variation was assessed annually using descriptive distribution metrics, including mean, median, quartiles, interquartile range, standard deviation, minimum and maximum values, and the maximum-to-minimum ratio. The ratio of the highest to the lowest prescribing area was used as a measure of relative dispersion. Commissioning areas were ranked annually by prescribing intensity, and persistence of extreme prescribing positions was assessed by counting the number of years each commissioning area appeared within the highest and lowest ranked prescribing categories during the study period.\u003c/p\u003e\u003cp\u003eAs the dataset represents comprehensive national prescribing activity rather than a sample, analyses were descriptive in nature. The objective was to characterise the magnitude and pattern of prescribing variation rather than test predefined hypotheses. No inferential statistical testing or measures of statistical uncertainty were performed. All data extraction, aggregation, and calculations were performed using Microsoft Excel with formula-based processing. Derived measures were calculated using deterministic arithmetic operations, including rate standardisation, descriptive statistics, and ranking procedures. All analyses are reproducible from publicly available prescribing and population datasets.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eNational prescribing trends\u003c/p\u003e \u003cp\u003eNational community antibiotic prescribing varied across the study period (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean monthly prescribing rate increased from 1.53 antibiotic prescription items per 1,000 registered patients in 2021 to 1.59 in 2022, representing an absolute increase of 0.06 items per 1,000 patients. Prescribing rose further to 1.81 in 2023, an increase of 0.22 compared with 2022, before declining to 1.42 items per 1,000 registered patients per month in 2024.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNational mean monthly antibiotic prescribing rates (2021 to 2024)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean Monthly Antibiotic Prescriptions per 1,000 Registered Patients\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eYear-on-year analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) demonstrated a 3.9% increase between 2021 and 2022 and a 13.8% increase between 2022 and 2023. The reduction observed in 2024 corresponded to a 21.5% decrease compared with 2023 and represented the largest year-on-year change during the study period. Overall, the 2024 mean prescribing rate was 0.11 items per 1,000 registered patients per month lower than in 2021, equivalent to a 7.2% net reduction across the four-year period.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eYear-on-year change in national mean monthly antibiotic prescribing rates (2021 to 2024)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean Rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbsolute Change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e% Change\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-21.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021 to 2024 Overall change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eInter-regional distribution and dispersion\u003c/p\u003e \u003cp\u003eAcross the 107 NHS commissioning areas included in the analysis, prescribing rates demonstrated consistent and substantial inter-regional variation (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Median prescribing rates were 1.51 items per 1,000 registered patients per month in 2021, 1.59 in 2022, 1.81 in 2023, and 1.40 in 2024.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of annual mean monthly antibiotic prescribing rates per 1,000 registered patients across 107 NHS commissioning areas from 2021 to 2024\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst quartile (Q1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThird quartile (Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterquartile range (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum-to-minimum ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDispersion widened in 2023 relative to earlier years. The interquartile range increased from 0.32 in 2021 and 2022 to 0.41 in 2023, before narrowing to 0.34 in 2024. Standard deviation followed a similar pattern, rising from 0.24 in 2021 and 2022 to 0.30 in 2023 and returning to 0.24 in 2024.\u003c/p\u003e \u003cp\u003eThe overall range further illustrates the magnitude of variation. In 2021, prescribing rates ranged from 0.96 to 2.16 items per 1,000 registered patients per month. By 2023, the minimum rate had increased to 1.08 while the maximum rose to 2.87. The maximum-to-minimum ratio increased from 2.26 in 2021 to 2.67 in 2023, indicating that the highest-prescribing commissioning area issued more than two and a half times the number of antibiotic items per capita as the lowest-prescribing area. In 2024, despite the national decline, the ratio remained elevated at 2.41-fold.\u003c/p\u003e \u003cp\u003eCommissioning areas at the extremes of prescribing\u003c/p\u003e \u003cp\u003eExamination of commissioning areas at the upper and lower ends of the distribution in 2023 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) demonstrated clustering of high and low prescribing rates. NHS Southend recorded the highest prescribing rate (2.87 items per 1,000 registered patients per month), followed by NHS Castle Point and Rochford (2.58) and NHS Wigan Borough (2.57). Additional areas within the highest prescribing group included NHS Basildon and Brentwood, NHS Knowsley, NHS Southport and Formby, NHS Blackpool, NHS Oldham, NHS West Essex, NHS Kirklees, and NHS Birmingham and Solihull.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHighest and lowest prescribing NHS commissioning areas in 2023 (mean monthly antibiotic prescriptions per 1,000 registered patients)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighest prescribing areas\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLowest prescribing areas\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHS Southend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNHS Oxfordshire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHS Castle Point and Rochford\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNHS Bristol, North Somerset and South Gloucestershire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHS Wigan Borough\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNHS Brighton and Hove\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHS Basildon and Brentwood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNHS Southeast London\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHS Knowsley\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNHS Somerset\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHS Southport and Formby\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNHS Leicester City\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHS Blackpool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNHS Berkshire West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHS Oldham\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNHS Gloucestershire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHS West Essex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNHS Vale of York\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHS Kirklees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNHS West Leicestershire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHS Birmingham and Solihull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNHS Tameside and Glossop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAt the lower end of the distribution, NHS Oxfordshire recorded the lowest prescribing rate (1.08), followed by NHS Bristol, North Somerset and South Gloucestershire (1.15) and NHS Brighton and Hove (1.16). Other areas within the lowest prescribing group included NHS Southeast London, NHS Somerset, NHS Leicester City, NHS Berkshire West, NHS Gloucestershire, NHS Vale of York, NHS West Leicestershire, and NHS Tameside and Glossop. The absolute difference between the highest and lowest commissioning areas in 2023 exceeded 1.8 items per 1,000 registered patients per month.\u003c/p\u003e \u003cp\u003ePersistence of extreme prescribing ranks\u003c/p\u003e \u003cp\u003eAnalysis of rank persistence across 2021 to 2024 (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) demonstrated repeated positioning of specific commissioning areas at the extremes of the prescribing distribution. NHS Blackpool, NHS Wigan Borough, NHS Southend, and NHS Southport and Formby appeared among the highest-prescribing areas in all four years analysed. Several additional areas, including NHS Oldham, NHS West Essex, NHS Basildon and Brentwood, NHS Castle Point and Rochford, and NHS Knowsley, appeared within the highest group in three of four years.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePersistence of extreme prescribing ranks across 2021 to 2024 (n\u0026thinsp;=\u0026thinsp;107 NHS commissioning areas)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAppeared in Top Five (number of years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYears\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAppeared in Bottom Five (number of years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYears\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHS Blackpool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNHS Bristol, North Somerset and South Gloucestershire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHS Wigan Borough\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNHS Oxfordshire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHS Southend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNHS Brighton and Hove\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHS Southport and Formby\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNHS Berkshire West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHS Oldham\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNHS Southeast London\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHS West Essex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNHS Leicester City\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHS Basildon and Brentwood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNHS Gloucestershire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHS Castle Point and Rochford\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNHS Somerset\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHS Knowsley\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNHS Portsmouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHS Northeast Lincolnshire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNHS West Leicestershire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHS South Sefton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNHS Vale of York\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHS West Lancashire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eConversely, NHS Bristol, North Somerset and South Gloucestershire, NHS Oxfordshire, NHS Brighton and Hove, NHS Berkshire West, and NHS Southeast London appeared among the lowest-prescribing areas in all four years. Other commissioning areas, including NHS Leicester City and NHS Gloucestershire, were positioned within the lowest group in at least three years.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates substantial regional variation in antibiotic prescribing intensity across England between 2021 and 2024 and shows that this variation persisted across consecutive years. While national mean prescribing fluctuated during the post-pandemic recovery period, the relative positioning of several commissioning areas at the upper and lower extremes remained stable. This persistence is a key finding and suggests that regional prescribing differences may reflect structurally embedded patterns rather than short-term epidemiological change [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe magnitude of variation observed is notable. In 2023, the highest prescribing commissioning area issued more than two and a half times the number of antibiotic items per capita compared with the lowest prescribing area. Differences of this scale are unlikely to be attributable solely to random fluctuation or seasonal variation. Even after national prescribing declined in 2024, the maximum-to-minimum ratio remained above 2.4. Although some variation is expected within a large and heterogeneous healthcare system, persistent two-fold or greater differences raise important questions regarding consistency in guideline implementation and underlying structural influences on prescribing behaviour across regions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA two-fold or greater difference in prescribing intensity between commissioning areas represents a substantial divergence in population-level antibiotic exposure rather than a simple statistical contrast. Even modest differences in per capita prescribing can translate into large absolute differences in antibiotic courses issued across populations of several hundred thousand registered patients. In absolute terms, the 2023 difference between the highest and lowest prescribing commissioning areas corresponded to approximately 1.8 additional antibiotic items per 1,000 registered patients per month. Over the course of a year, this equates to more than 20 additional antibiotic prescriptions per 1,000 patients, which at commissioning population scale represents a considerable difference in cumulative antibiotic exposure. Such sustained exposure differences may have implications for antimicrobial resistance selection pressure, prescribing culture, and long-term stewardship outcomes [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInterpreting this variation requires consideration of multiple potential drivers. Population characteristics such as age distribution, deprivation, comorbidity burden, and infection incidence are likely to influence prescribing demand. Areas with higher levels of deprivation and chronic disease may reasonably experience higher consultation rates and antibiotic exposure [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, ecological evidence from previous studies suggests that clinician level behaviours, local prescribing culture, peer norms, and access to diagnostic support also contribute meaningfully to prescribing variation. Persistent high prescribing regions may therefore reflect a combination of greater clinical need and established behavioural patterns within local professional communities [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe persistence analysis strengthens the interpretation that variation is not purely episodic. Commissioning areas that consistently appeared among the highest or lowest prescribing groups across four consecutive years are unlikely to reflect chance variation alone. Stability in relative ranking implies enduring differences in practice patterns or contextual factors. From a stewardship perspective, this finding is particularly relevant. Transient variation may resolve without intervention, whereas persistent divergence suggests the potential benefit of targeted and sustained quality improvement activity [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough ranking approaches are inherently sensitive to small differences in absolute rates and may be influenced by regression toward the mean, the repeated appearance of several commissioning areas within the highest and lowest categories across four consecutive years suggests relative stability in prescribing position rather than random fluctuation. In a system comprising 107 commissioning areas, consistent placement at the extremes over multiple years is unlikely to occur purely by chance. This stability may reflect enduring structural, demographic, organisational, or behavioural influences on prescribing practice and warrants further investigation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe post-pandemic trajectory observed in this study warrants further reflection. National prescribing increased between 2021 and 2023, coinciding with restoration of routine healthcare utilisation and resurgence of respiratory infections [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This rise may represent re-normalisation of consultation volumes rather than erosion of stewardship gains. The subsequent decline in 2024 suggests that prescribing behaviour may be stabilising within a new equilibrium. However, the distributional metrics indicate that although overall intensity shifted, the relative spread between regions remained wide. This pattern suggests that system-wide changes in demand do not necessarily reduce structural regional disparities.\u003c/p\u003e \u003cp\u003eFrom a system perspective, the persistence of substantial regional variation under a unified national stewardship framework raises important questions regarding consistency of implementation and local accountability. Commissioning organisations are responsible for monitoring prescribing performance and supporting quality improvement within primary care. Sustained two-fold differences between commissioning areas suggest that stewardship policies may not be translating uniformly into practice across regions. Structured benchmarking, transparent reporting, and targeted feedback at commissioning area level may therefore play a critical role in reducing potentially unwarranted variation while preserving clinical autonomy [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In particular, commissioning areas that consistently appear at the upper end of the prescribing distribution may warrant focused review of local stewardship governance, workforce pressures, diagnostic access, and prescribing support mechanisms.\u003c/p\u003e \u003cp\u003eIt is important to emphasise that higher prescribing intensity does not automatically equate to inappropriate prescribing. Without patient-level data on diagnosis, clinical severity, and treatment indication, the analysis cannot determine appropriateness. Some degree of variation may be justified by legitimate differences in clinical need [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Nonetheless, the scale and persistence of observed differences support continued scrutiny and evaluation of regional prescribing patterns.\u003c/p\u003e \u003cp\u003eThe descriptive nature of this analysis reflects the comprehensive coverage of the dataset. Because the study included complete national prescribing records, the focus was on quantifying magnitude and distribution rather than estimating statistical uncertainty. This approach aligns with health services surveillance objectives, where the aim is to monitor system performance and identify areas for further investigation rather than test narrowly defined hypotheses [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Future research incorporating case mix adjustment, demographic modelling, or linkage with hospital admission data may provide additional insight into drivers of variation.\u003c/p\u003e \u003cp\u003eSeveral limitations merit consideration. The analysis utilised ecological prescribing data and did not incorporate adjustment for demographic structure, deprivation, comorbidity burden, or consultation volume [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Observed regional differences may therefore reflect legitimate variation in population need as well as potential differences in prescribing behaviour [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The findings should be interpreted as differences in prescribing intensity rather than measures of prescribing quality or appropriateness. Aggregation at commissioning area level may conceal variation at practice-level. Furthermore, the persistence analysis was based on ordinal ranking rather than modelling of rank transitions, and small differences in absolute prescribing rates may influence placement within extreme categories. However, stability across multiple consecutive years suggests that observed persistence is unlikely to reflect short-term statistical fluctuation alone. In addition, national summary measures were derived using unweighted commissioning area averages. While this approach reflects regional comparability, population-weighted estimates may yield slightly different national values. The four-year observation window captures early post-pandemic recovery rather than long-term organisational characteristics.\u003c/p\u003e \u003cp\u003eThese findings should be considered within the context of existing national antimicrobial stewardship strategies in England, which include prescribing targets, audit and feedback mechanisms, and commissioning level oversight of primary care performance [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Commissioning organisations play a central role in translating national stewardship guidance into local practice. The persistence of substantial regional variation under a unified policy framework suggests that implementation and operationalisation of stewardship initiatives may differ across commissioning contexts. Understanding how national strategy interacts with local organisational structures and workforce pressures is therefore important when interpreting regional prescribing patterns.\u003c/p\u003e \u003cp\u003eTaken together, the findings suggest that while national prescribing intensity fluctuated during post-pandemic recovery, regional prescribing disparities remained substantial and stable. Persistent variation of this magnitude indicates structural heterogeneity across commissioning areas that warrants further investigation and may represent opportunities for regionally tailored stewardship engagement. Future research incorporating age standardisation, socioeconomic indicators, and case mix adjustment would help clarify the extent to which observed regional disparities reflect underlying population characteristics versus modifiable prescribing practices [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSustained regional disparities also underscore the potential value of commissioning area specific stewardship strategies. While national guidance provides a consistent framework, effective implementation may require adaptation to local demographic, organisational, and workforce contexts. Structured peer comparison within comparable commissioning groups, dissemination of practices from consistently lower prescribing regions, and strengthened audit and feedback mechanisms may support gradual convergence in prescribing behaviour over time [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Continued integration of prescribing surveillance into commissioning oversight processes will be important to ensure that variation is systematically monitored and addressed where appropriate.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eCommunity antibiotic prescribing in English primary care between 2021 and 2024 demonstrated both temporal fluctuation and sustained regional disparity. Although national prescribing increased following pandemic related reductions and subsequently declined, substantial differences between commissioning areas persisted across consecutive years. In multiple years, the highest prescribing regions issued more than twice the number of antibiotic items per capita compared with the lowest prescribing regions.\u003c/p\u003e \u003cp\u003eThese persistent disparities suggest that regional prescribing patterns may reflect enduring structural, demographic, or behavioural factors rather than short-term epidemiological change alone. Continued surveillance, structured benchmarking, and regionally informed stewardship initiatives are likely to be important components of efforts to reduce potentially unwarranted variation while maintaining appropriate responsiveness to population need [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Further research incorporating case mix adjustment and clinical indication data would help clarify the drivers of observed disparities and inform targeted policy responses.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNHS: National Health Service\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the Bennett Institute for Applied Data Science, University of Oxford, for providing access to OpenPrescribing data.\u003c/p\u003e\n\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eVL conceived and designed the study, conducted the data extraction and analysis, interpreted the findings, and drafted the manuscript. JL assisted with data extraction, data verification, and preliminary data processing. MSL provided methodological guidance, contributed to interpretation of findings, and critically revised the manuscript for important intellectual content. All authors read and approved the final manuscript.\u003c/p\u003e\n\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was conducted without specific grant funding.\u003c/p\u003e\n\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe data analysed in this study are publicly available through OpenPrescribing (Bennett Institute for Applied Data Science, University of Oxford) and the NHS Business Services Authority English Prescribing Dataset. No additional datasets were generated.\u003c/p\u003e\n\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study utilised publicly available, fully anonymised secondary data and did not require research ethics committee approval in accordance with UK Health Research Authority guidance.\u003c/p\u003e\n\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAntimicrobial Resistance Collaborators. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet. 2022;399:629\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(21)02724-0\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(21)02724-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoossens H, Ferech M, Vander Stichele R, Elseviers M, ESAC Project Group. Outpatient antibiotic use in Europe and association with resistance: a cross-national database study. Lancet. 2005;365:579\u0026ndash;87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(05)17907-0\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(05)17907-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpellberg B, Bartlett JG, Gilbert DN. The future of antibiotics and resistance. N Engl J Med. 2013;368:299\u0026ndash;302. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMp1215093\u003c/span\u003e\u003cspan address=\"10.1056/NEJMp1215093\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmieszek T, Pouwels KB, Dolk FCK, Smith DRM, Hopkins S, Sharland M, et al. Potential for reducing inappropriate antibiotic prescribing in English primary care. J Antimicrob Chemother. 2018;73(Suppl 2):ii36\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/jac/dkx500\u003c/span\u003e\u003cspan address=\"10.1093/jac/dkx500\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshiru-Oredope D, Cunningham N, Casale E, Muller-Pebody B, Hope R, Brown CS, et al. Reporting England's progress towards the ambitions in the UK action plan for antimicrobial resistance: the English surveillance programme for antimicrobial utilisation and resistance (ESPAUR). J Antimicrob Chemother. 2023;78:2387\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/jac/dkad248\u003c/span\u003e\u003cspan address=\"10.1093/jac/dkad248\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCloskey AP, Malabar L, McCabe PG, Gitsham A, Jarman I. Antibiotic prescribing trends in primary care 2014\u0026ndash;2022. Res Social Adm Pharm. 2023;19:1193\u0026ndash;201. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.sapharm.2023.05.001\u003c/span\u003e\u003cspan address=\"10.1016/j.sapharm.2023.05.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePouwels KB, Dolk FCK, Smith DRM, Smieszek T, Robotham JV. Explaining variation in antibiotic prescribing between general practices in the UK. J Antimicrob Chemother. 2018;73(Suppl 2):ii27\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/jac/dkx501\u003c/span\u003e\u003cspan address=\"10.1093/jac/dkx501\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNHS England. Integrated care systems: design framework. London: NHS England; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManikandan S. Measures of dispersion. J Pharmacol Pharmacother. 2011;2:315\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4103/0976-500X.85931\u003c/span\u003e\u003cspan address=\"10.4103/0976-500X.85931\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGliklich RE, Leavy MB, Dreyer NA, editors. Registries for evaluating patient outcomes: a user\u0026rsquo;s guide. 4th ed. Rockville (MD): Agency for Healthcare Research and Quality; 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOpenPrescribing. OpenPrescribing [Internet]. London: Bennett Institute for Applied Data Science, University of Oxford; [cited 2026 Mar 3]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://openprescribing.net/\u003c/span\u003e\u003cspan address=\"https://openprescribing.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoint Formulary Committee. British National Formulary. 91st ed. London: BMJ Group and Pharmaceutical; 2026.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUK Parliament. Health and Care Act 2022 [Internet]. London: The Stationery Office. 2022 [cited 2026 Mar 3]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.legislation.gov.uk/ukpga/2022/31/contents\u003c/span\u003e\u003cspan address=\"https://www.legislation.gov.uk/ukpga/2022/31/contents\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrookes-Howell L, Hood K, Cooper L, Little P, Verheij T, Coenen S, et al. Understanding variation in primary medical care: a nine-country qualitative study of clinicians\u0026rsquo; accounts of non-clinical factors shaping antibiotic prescribing decisions for lower respiratory tract infection. BMJ Open. 2012;2:e000796. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjopen-2011-000796\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2011-000796\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKasse GE, Humphries J, Cosh SM, Islam MS. Factors contributing to variation in antibiotic prescribing among primary health care physicians: a systematic review. BMC Prim Care. 2024;25:8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12875-023-02223-1\u003c/span\u003e\u003cspan address=\"10.1186/s12875-023-02223-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCostelloe C, Metcalfe C, Lovering A, Mant D, Hay AD. Effect of antibiotic prescribing in primary care on antimicrobial resistance in individual patients: systematic review and meta-analysis. BMJ. 2010;340:c2096. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmj.c\u003c/span\u003e\u003cspan address=\"10.1136/bmj.c\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e2096.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePouwels KB, Hopkins S, Llewelyn MJ, Walker AS, McNulty CAM, Robotham JV. Duration of antibiotic treatment for common infections in English primary care: cross-sectional analysis and comparison with guidelines. BMJ. 2019;364:l440. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmj.l440\u003c/span\u003e\u003cspan address=\"10.1136/bmj.l440\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCurtis HJ, Walker AJ, Mahtani KR, Goldacre B. Time trends and geographical variation in prescribing of antibiotics in England 1998\u0026ndash;2017. J Antimicrob Chemother. 2019;74:242\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/jac/dky377\u003c/span\u003e\u003cspan address=\"10.1093/jac/dky377\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWennberg JE. Time to tackle unwarranted variations in practice. BMJ. 2011;342:d1513. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmj.d1513\u003c/span\u003e\u003cspan address=\"10.1136/bmj.d1513\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFisher ES, Wennberg DE, Stukel TA, Gottlieb DJ, Lucas FL, Pinder EL. The implications of regional variations in Medicare spending. Part 1: content, quality, and accessibility of care. Ann Intern Med. 2003;138:273\u0026ndash;87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7326/0003-4819-138-4-200302180-00006\u003c/span\u003e\u003cspan address=\"10.7326/0003-4819-138-4-200302180-00006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliams R, Jenkins DA, Ashcroft DM, Brown B, Campbell S, Carr MJ, et al. Diagnosis of physical and mental health conditions in primary care during the COVID-19 pandemic: a retrospective cohort study. Lancet Public Health. 2020;5:e543\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S2468-2667(20)30201-2\u003c/span\u003e\u003cspan address=\"10.1016/S2468-2667(20)30201-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGulliford MC, Dregan A, Moore MV, Ashworth M, Staa TP, McCann G, et al. Continued high rates of antibiotic prescribing to adults with respiratory tract infection: survey of 568 UK general practices. BMJ Open. 2014;4:e006245. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjopen-2014-006245\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2014-006245\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorgenstern H. Ecologic studies in epidemiology: concepts, principles, and methods. Annu Rev Public Health. 1995;16:61\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1146/annurev.pu.16.050195.000425\u003c/span\u003e\u003cspan address=\"10.1146/annurev.pu.16.050195.000425\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIvers N, Yogasingam S, Lacroix M, Brown KA, Antony J, Soobiah C, et al. Audit and feedback: effects on professional practice. Cochrane Database Syst Rev. 2025;3:CD000259. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/14651858.CD000259.pub4\u003c/span\u003e\u003cspan address=\"10.1002/14651858.CD000259.pub4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHallsworth M, Chadborn T, Sallis A, Sanders M, Berry D, Greaves F, et al. Provision of social norm feedback to high prescribers of antibiotics in general practice: a pragmatic national randomised controlled trial. Lancet. 2016;387:1743\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(16)00215-4\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(16)00215-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-primary-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"famp","sideBox":"Learn more about [BMC Primary Care](https://bmcprimcare.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12875","title":"BMC Primary Care","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9071704/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9071704/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground:\u003c/p\u003e \u003cp\u003eAntimicrobial stewardship remains central to efforts to address antimicrobial resistance, with primary care accounting for the majority of antibiotic prescribing in England. Although reductions in prescribing were observed during the coronavirus pandemic, patterns in the subsequent recovery period remain incompletely characterised. Understanding both national trends and regional variation in antibiotic use under the current commissioning structure may inform targeted quality improvement initiatives.\u003c/p\u003e \u003cp\u003eMethods:\u003c/p\u003e \u003cp\u003eA retrospective secondary analysis of publicly available NHS primary care prescribing data was undertaken using data obtained via OpenPrescribing. Monthly antibiotic prescribing data from January 2021 to December 2024 were analysed across 107 NHS commissioning areas. Prescribing intensity was defined as antibiotic prescription items per 1,000 registered patients per month. National trends were summarised using annual mean monthly prescribing rates. Regional variation was assessed descriptively using distribution metrics, including quartiles, standard deviation, maximum-to-minimum ratios, and annual ranking of commissioning areas. Persistence of extreme prescribing positions was evaluated across the four-year period.\u003c/p\u003e \u003cp\u003eResults:\u003c/p\u003e \u003cp\u003eNational mean monthly antibiotic prescribing increased from 1.53 items per 1,000 registered patients in 2021 to 1.81 in 2023, before declining to 1.42 in 2024. The largest year-on-year change occurred between 2023 and 2024, with a reduction of 21.5%. Substantial regional variation was observed in all years. In 2023, prescribing ranged from 1.08 to 2.87 items per 1,000 registered patients per month, corresponding to a 2.67-fold difference between commissioning areas. Several areas appeared among the highest or lowest prescribing regions in all four years analysed, indicating persistence of extreme relative positions.\u003c/p\u003e \u003cp\u003eConclusions:\u003c/p\u003e \u003cp\u003eCommunity antibiotic prescribing in English primary care between 2021 and 2024 demonstrated post-pandemic fluctuation and sustained regional variation across commissioning areas. Persistent differences in prescribing intensity suggest opportunities for regionally informed stewardship initiatives and performance benchmarking within the current healthcare administrative framework.\u003c/p\u003e","manuscriptTitle":"Variation in antibiotic prescribing across English primary care from 2021 to 2024","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-06 14:37:01","doi":"10.21203/rs.3.rs-9071704/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-30T20:08:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-26T00:45:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-22T04:34:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-21T10:18:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"139710583342034207883453613856289595012","date":"2026-04-13T02:47:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52257052299910795599438525223852344438","date":"2026-04-10T10:04:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"251673061635330937856030954945681439808","date":"2026-04-09T00:19:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"100106233455624029774865181203175711843","date":"2026-04-01T20:47:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-01T16:04:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-11T06:04:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-10T07:23:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-10T07:23:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Primary Care","date":"2026-03-09T10:27:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-primary-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"famp","sideBox":"Learn more about [BMC Primary Care](https://bmcprimcare.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12875","title":"BMC Primary Care","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6107b052-ae25-47c3-93c5-a2c2d5b654b4","owner":[],"postedDate":"April 6th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-04-30T20:08:49+00:00","index":69,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-06T14:37:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-06 14:37:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9071704","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9071704","identity":"rs-9071704","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}