Association between paediatric antibiotic prescribing and socioeconomic deprivation: insights from a pilot project in West Yorkshire, United Kingdom

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In January 2025, the UK Health Security Agency revised the UK Access, Watch, Reserve (AWaRe) antibiotic list to guide prescribing of 90 antibiotics. This pilot study investigated relationships between socioeconomic deprivation and paediatric antibiotic prescribing in secondary care in the Mid Yorkshire Teaching NHS Trust region, UK. Methods Retrospective antibiotic prescribing data was obtained from the NHS Trust’s electronic prescribing system for patients aged 0–2 years prescribed systemic antibiotics between April 2022 and January 2025, the start of the Born and Bred in Wakefield (BaBi) Wakefield project. Demographic data retrieved from electronic clinical and management information system included ethnicity, admission and discharge date, ICD-10 diagnostic codes, and IMD decile, converted to quintile for statistical analysis. Quasi-Poisson count regression approach was used to explore the relationship between the rate of antibiotic prescription, socioeconomic deprivation, and region. Results A total of 780 patients and 2204 antibiotic prescriptions were identified from hospital prescribing report. Adjusted models identified four key findings. Firstly, length of stay (LOS) in hospital and number of diagnostic codes were highest in the most deprived group (Q1). Secondly, the number of unique antibiotics prescribed (adjusted per admission) was highest in the least deprived group(Q5) although this relationship was not statistically significant. Thirdly, the number of unique antibiotics (adjusted per LOS) was highest in Q5, and this was statistically significant (p = xxx). Finally, in contrast with other studies in the UK, ethnicity was not significantly associated with the use of systemic antibiotics. Conclusion Our findings suggest that children from more deprived areas with more comorbidities/ diagnosis received less antibiotics in secondary care settings compared with their peers from least deprived areas. The LOS and number of diagnostic codes also decreased from Q1 to Q5. Future prescribing trends among children aged 0-2years should account for contextual factors to ensure that children from the most deprived communities are not disproportionately exposed to less antibiotics despite of suffering more comorbidities. Antimicrobial stewardship Antimicrobial resistance Socioeconomic deprivation Index of multiple deprivation Paediatrics Infectious diseases and microbiology Health care intelligence and data analysis Secondary care Lower Layer Super Output Areas (LSOAs) Figures Figure 1 Introduction Inappropriate antibiotic use in all settings, including human and veterinary medicine, and the lack of novel therapies, have contributed to a global antimicrobial resistance (AMR) crisis. AMR has evolved from a silent pandemic to a global public health emergency resulting in increased mortality and longer hospitalisation, with negative impact on the economies of families, communities and countries [ 1 ]. The World Health Organisation (WHO) has declared AMR as one of the top ten threats to global health security [ 2 ]. It is indiscriminate of country border or income levels and causes about 9% of all global deaths [ 3 ]. AMR directly causes 1.3 million deaths annually, with a further 5 million associated deaths, 20% of which occur in children under five years of age [ 4 ]. This surpasses deaths from Human immunodeficiency virus (HIV), malaria and tuberculosis combined. By 2050, over 39 million people are projected to die from antibiotic-resistant infections [ 5 ]. The World Bank estimates that, without effective control, AMR could lead to US $ 3.4 trillion annual losses to gross domestic product by 2030 and an additional US $ 1 trillion health care costs by 2050, pushing 28 million people into extreme poverty [ 6 ]. Rational use of antibiotics is a critical measure for controlling AMR. The United Kingdom Health Security Agency (UKHSA) updated the UK Access, Watch, Reserve (AWaRe) antibiotic list in January 2025 to provide guidance on use of 90 antibiotics for healthcare professionals in primary and secondary care [ 7 ]. However, between 8.8% and 23.1% of antibiotic prescriptions in England are deemed inappropriate [ 8 ]. In England, higher antibiotic prescribing levels have been associated with Index of Multiple Deprivation (IMD) and certain geographical locations. Of the seven English regions, there is disproportionately high number of prescriptions per 100,000 population in North West (56.3%), and North East and Yorkshire (26.7%) [ 9 ]. Several studies have examined factors associated with high rates of antibiotic prescribing in the UK. A study conducted in England from 2014-18 investigated the relationship between primary care antibiotic prescription and area-level deprivation as well as region, after controlling for a range of other confounding variables, including rurality, ethnicity and health need [ 10 ]. A time series analysis in England from 2014–22 explored the link between primary care antibiotic prescriptions, locality and deprivation. [ 9 ]. A Welsh study from 2013–17 examined the association between primary care antibiotic prescribing and deprivation, controlling for common chronic conditions and other potential confounders [ 11 ]. While the three studies investigated antibiotic prescription patterns in primary care, none of them focused on the exposure to antibiotics among paediatric and neonatal patients, nor did they investigate antibiotic prescription in secondary care. To fill the gap in literature, our group aimed to explore the association between socioeconomic deprivation and exposure to antibiotics among children in secondary care settings in the UK. In this study, we aimed to identify relationships between paediatric antibiotic prescribing and IMD in the Mid Yorkshire Teaching NHS Trust region (Wakefield and North Kirklees Integrated Care Board Places, West Yorkshire, UK). Methods Using the Trust’s electronic prescribing system (Medchart®, Dedalus, Italy), patients aged 0–2 years who had been prescribed and administered antibiotics from 28 April 2022, the start of the Born and Bred in Wakefield (BaBi) Wakefield project, to 19 January 2025 were identified. BaBi Wakefield is a long-term research project that involves the collection of data during pregnancy about mothers and babies to provide a wider picture of the factors affecting local family’s health and wellbeing [ 12 ]. Only patients with a Wakefield (WF) postcode were considered, including WF postcodes in Kirklees, a metropolitan borough of West Yorkshire [1[ 3 ]. Antibiotics were defined following the British National Formulary Chap. 5 specification. Only intravenous and oral antibiotics were included in the main analysis as these were considered the targets for antimicrobial stewardship and main drivers of AMR. Antibiotics were then grouped according to the WHO AWaRe classification. Microbiology clinical results, including blood, urine, sputum and clinical swabs were collated from iLAB® (iLAB solutions LLC, Agilent technologies Inc, California, USA). Demographic data were retrieved from eCAMIS® (Electronic clinical and management information system, University Hospitals Southampton, UK), including ethnicity, admission and discharge date, ICD-10 (International Classification of Diseases; WHO, 2019) diagnostic codes, IMD decile, converted to quintile for statistical analysis. Using a Quasi-Poisson count regression approach, we tested the hypothesis that socioeconomic deprivation influences the rate of antibiotic prescriptions for patients aged 0–2 years in hospital. The sample population was divided into quintiles by socioeconomic status, with Q1 being the most deprived and Q5 the least deprived. Q4 and 5 were combined, allowing comparison of the most deprived 40% with the least deprived 40%. The distribution of Q3 was not consistent with the other quintiles, and so it was treated separately. The response variable captured the interaction between the number of drugs prescribed and the number of diagnoses, reflecting treatment complexity. This is referring to the multifaceted nature of delivering healthcare to patients, particularly when managing multiple medical conditions simultaneously. In the context of this study, treatment complexity specifically reflects the combined influence of the number of medications prescribed (unique antibiotics) and the number of medical conditions being treated (diagnoses). This combination is an important indicator of how intricate a patient's care regimen is. For example, a higher number of unique antibiotics prescribed suggests that more medications are being used, which may be due to the need to address multiple, possibly interrelated conditions. Similarly, a greater number of diagnoses typically indicates more health issues to manage, further complicating treatment decisions. When these two factors are considered together as an interaction, they represent the compounded intricacies involved in addressing a patient's healthcare needs. Treatment complexity also inherently considers: (i) medication interactions, where the potential for prescribed drugs to interact with each other requires careful monitoring and adjustments; (ii) resource utilisation, where the level of healthcare resources, coordination, and diverse expertise are needed to manage the patient's care effectively; and (iii) patient outcomes, representing the challenges in achieving desired health outcomes, as treatment complexity can increase the risk of complications or reduce treatment adherence. In this study, treatment complexity is quantified as the interaction between the number of unique drug names and the number of diagnoses, providing a meaningful measure of the challenges healthcare providers face in delivering appropriate care. To ensure the model estimated rates rather than raw counts, the logarithm of the number of hospital admissions was included as an offset. The analysis was conducted using R version 4.4.3 (R Foundation for Statistical Computing, Vienna, Austria). Approval for this project was given by the Trust’s research committee through an internal pump-priming grant to enable a National Institute for Health and Care Research grant application. Ethics approval was not required as this was considered a service improvement project with a view to further research. Results A total of 780 patients and 2204 antibiotic prescriptions were identified from the initial hospital prescribing report. Intravenous and oral antibiotics were grouped for further analysis (690 patients; 1907 prescriptions). The breakdown of population by IMD quintiles was Q1–313 (45%), Q2–154 (22%), Q3–109 (16%), Q4–88 (13%), and Q5–26 (4%). The average number of antibiotics per admitted patient, per IMD quintile, is shown in Table 1 . The total length of stay (LOS) for all admitted patients reduced on moving from Q1 to Q5 as reflected in Table 1 . The number of diagnostic codes also reduced from Q1 to Q5. The number of unique antibiotics (adjusted per admission) increased from Q1 to Q5, and this was not statistically significant. However, the number of unique antibiotics (adjusted per LOS) increased from Q1 to Q5, and this was statistically significant (p < 0.05). Table 1 IMD quintile vs number of admitted patients and average number of antibiotics per admitted patient in Wakefield [13]. IMD quintile Population IMD by LSOA# - Wakefield Total (%) Total number of admitted patients within IMD quintile (%) Average number of antibiotics per admitted patient Average length of stay* Average Number of diagnostic codes* Average number of unique antibiotics (adjusted per admission)* Average number of antibiotics per admitted patient* 1 (Most deprived) 30.00 313 (45) 2.64 5.94 13.28 1.17 2.77 2 28.30 154 (22) 3.05 3 13.30 109 (16) 2.58 4.4 10.42 1.14 2.58 4 18.40 88 (13) 2.95 4.4 9.21 1.11 2.90 5 (Least deprived) 10.00 26 (4) 2.73 *Average obtained by combining quintiles (1–2) and combining quintiles (4–5). # Lower Layer Super Output Areas (LSOAs) are small geographic areas created by The Office for National Statistics (ONS) for statistical analysis and reporting in England and Wales. Each LSOA contains approximately 1,000 to 3,000 residents, allowing for a detailed examination of local demographics, social conditions and economic factors. The total number of antibiotics prescribed did not significantly differ across IMD quintiles following the generation of both preliminary Poisson model coefficients and the alternative Quasi-Poisson or Negative Binomial model (p > 0.1). The number of unique antibiotics prescribed did not substantially differ across IMD quintiles and this was borne out by both the preliminary Poisson model coefficients and the alternative Quasi-Poisson model. The final Quasi-Poisson model, offset by the log transformed LOS, was re-estimated using the interaction of the number of unique antibiotics and the number of diagnoses as the response. Considering combined Q1 and Q2 as the reference, the model showed that the interaction between unique antibiotics and diagnoses was significant in the combined Q4 and Q5 (coefficient= -0.4261, p = 0.0096) while gender (p = 0.6897) and race (p = 0.5947) were not statistically significant. Discussion Our project identified that more patients were admitted to hospital from Q1 and Q2 (most deprived; 67% vs 58.3% in the general population) compared to 17% vs 28.4% from Q4 and Q5. The LOS and number of diagnostic codes decreased from Q1 to Q5 while there was a statistically significant increase in the use of antibiotics from Q1 to Q5. This suggests that more deprived children with more diagnoses (more comorbidity) received less antibiotics compared with those in Q4 and Q5. The longer LOS among the children from the lower quintiles could have played a role in the observed differences although the exact nature of the relationship remains unclear. The longer LOS should have theoretically exposed the children to more antibiotic courses, but the reverse appeared to be the case. It is possible that the more deprived children (who also appeared to have more comorbidities) were more extensively investigated resulting in longer LOS but less use of antibiotics as non-infectious diagnoses were uncovered during the course of the admission. We used the number of unique antibiotics prescribed to represent antibiotic consumption. It is possible that the use of other more standardised indices for measuring antibiotic consumption, such as the Defined Daily Dose (DDD), could have provided deeper insights. The DDD is defined as the assumed average maintenance dose per day for a drug used for its main indication in adults [14]. Further research is required to fully understand this preliminary data. We found that ethnicity was not significantly associated with the use of systemic antibiotics. This contrasts with reports from other studies in the UK that showed a strong link between ethnicity and antibiotic consumption [15–17]. In a recent scoping review, 32/58 studies (55%) included race/ethnicity and 22/58 (38%) showed an association between race/ethnicity and antibiotic use, particularly in acne and dental infections [18]. Despite concerted efforts by national governments and health systems to tackle AMR through antimicrobial stewardship (AMS), antibiotics continue to be prescribed needlessly for self-limiting conditions. Respiratory tract infections are responsible for 74.4% of antibiotic prescriptions in children [19], despite their marginal beneficial effects [20]. However, exposing children to antibiotics is not without its risks. Antibiotic use alters the diversity of the gut microbiome thus impairing immunity, colonisation resistance, and metabolic homeostasis [21]. Analysis of a birth cohort of 12,422 children born at full term found a notable attenuation of weight and height gain during the first 6 years of life after neonatal antibiotic exposure in boys with significantly higher body mass index in both boys and girls who were exposed to antibiotics after the neonatal period but during the first 6 years of life [22]. A systematic review and meta-analysis of 160 observational studies investigating 21 outcomes in 22,103,129 children showed that antibiotic exposure was associated with an increased risk of atopic dermatitis, food allergies, allergic rhinoconjunctivitis, asthma, obesity, juvenile idiopathic arthritis, psoriasis and neurodevelopment disorders [23]. Thus, appropriately limiting the exposure of children to antibiotics has both short- and long-term benefits. To deliver targeted antimicrobial stewardship (AMS) and tackle AMR locally, further research is needed to explore antibiotic prescription practice and use in local communities. The British National Formulary [24] defines AMS as an organizational or healthcare system-wide approach to fostering and monitoring the judicious use of antimicrobials to preserve their future effectiveness and prevent antimicrobial resistance. Addressing AMR through enhancing stewardship remains a national medicines optimization priority, led by NHS England. In Fig. 1 we propose a conceptual framework depicting core elements of a package of AMS strategies that be deployed to tackle antimicrobial resistance in local healthcare systems and communities. Figure 1 was adapted from the UK’s summary of 2024–2029 National Action Plan for tackling antimicrobial resistance in the UK [25]. The conceptual framework can be used to investigate how inputs (in this case the AMS strategies) can lead to intermediate outcomes and how outcomes can lead to anticipated longer-term impacts. The conceptual framework also outlines a program theory (see the upper part of Fig. 1 ) of how the AMS programme of work is expected to generate a change along with contextual factors that can influence change. It is important to emphasize that, as our pilot project investigated antimicrobial prescription in secondary care within the local healthcare system, we have selected six most relevant AMS strategies for inclusion in second column of Fig. 1 . These are namely: 1) Infection prevention, control and management; 2) Public engagement activities; 3) AMR Surveillance processes; 4) Workforce education and training; 5) Using AMR information for action and decision-making; and 6) Addressing health disparities and inequalities. We believe that the sustained implementation of these strategies would trigger intermediate outcomes shown in the third column of Fig. 1 and that these will in turn generate four anticipated long-term impacts of: 1) high-quality patient care, 2) reduced AMR-related morbidity and mortality, 3) progress towards UN sustainable development goal (SDG) -3 of improved health for people of all ages, and 3) progress towards SDG-10, of reduced inequities and disparities within countries. Our pilot was not without limitations. First, we were unable to utilise internationally recognised measures of antibiotic consumption due to the retrospective nature of the pilot. Second, we were unable to explore further some of the trends we identified such as the significant increase in the use of antibiotics from Q1 to Q5. Third, the study was restricted to quantitative, secondary data analysis. We did not have qualitative data to complement our analysis. This would have provided us with further insights into the contextual factors associated with antibiotic prescription and consumption in our community. However, our work represents an important first step towards understanding a complex issue and proposing context-specific strategies to mitigate this global threat. In conclusion, our findings support the need for further investigation into contextual drivers of antibiotic prescribing practices in more deprived populations in Mid Yorkshire. The next phase of this work will focus on exploring the interrelationships we have identified in this pilot such as the link between LOS, antibiotic use and IMD in local communities. Only by applying a social determinants of health (SDH) lens to understanding these drivers can targeted public health strategies and interventions in Fig. 1 be implemented to stem the tide of disability and death from AMR in Mid Yorkshire and similar contexts. While the strategies listed in Fig. 1 are not meant to be exhaustive, applying a SDH lens to implement them will accelerate the achievement of SDGs. Notable for AMR in our pilot study are: goal 3 of ensuring healthy lives and improving well-being, and goal 10 of reducing socioeconomic inequalities as well as disparities in age, sex, disabilities etc. in accessing quality healthcare services. We recommend the field-testing of Fig. 1 to determine its utility in different contexts. Declarations Conflict of Interest: None to declare. Funding: Research capacity building pump-priming funding was received from the Mid Yorkshire Teaching NHS Trust (MYTT) UK to support this work. Clinical Trial Number: Not applicable Ethics, Consent to Participate, and Consent to Publish declarations: not applicable. References Kariuki, Samuel. Global burden of antimicrobial resistance and forecasts to 2050. The Lancet, Volume 404, Issue 10459, 1172 – 1173 WHO. 2023. Antimicrobial Resistance: Briefing to WHO Member States. 22 March 2023. Accessed 30 March 2025. https://apps.who.int/gb/mspi/pdf_files/2023/03/Item1_22-03.pdf Antimicrobial Resistance Collaborators Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis Lancet. 2022; 399:629-655 What is antimicrobial resistance, a silent pandemic?. 2025. UN Regional Information Centre. Accessed 30 March 2025. https://unric.org/en/the-global-threat-of-antimicrobial-resistance-a-silent-pandemic/ Naghavi, Mohsen et al. Global burden of bacterial antimicrobial resistance 1990–2021: a systematic analysis with forecasts to 2050. The Lancet, Volume 404, Issue 10459, 1199 - 1226 WHO. 2024. Seventy-seventh World Health Assembly. A77/5 Provisional agenda item 11.8 11 April 2024. Antimicrobial resistance: accelerating national and global responses. https://apps.who.int/gb/ebwha/pdf_files/WHA77/A77_5-en.pdf GOV.UK. Antibiotic 'Access' list updated for the UK. 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Antimicrobial stewardship. Medicines guidance. BNF. NICE. Available from: https://bnf.nice.org.uk/medicines-guidance/antimicrobial-stewardship/ GOV.UK 2024. Confronting antimicrobial resistance 2024 to 2029. Available from: https://assets.publishing.service.gov.uk/media/664394d9993111924d9d3465/confronting-antimicrobial-resistance-2024-to-2029.pdf 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-6515666","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":463196074,"identity":"c3ce41ab-f94c-48f7-9c04-b128818d3335","order_by":0,"name":"Akaninyene Otu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYDACHijNxt7ARpIWAwY+ngNsKGYQ1iInkUCkFv6ew88eMFT8SWyTfHvs4Q+GO3L2hLRInG0zN2A4Y5DYJp2XbszD8MyYsMPOM5hJMLaBtOSYSTMwHE7sIaRD/jz7N4gWyTNmkj8YDtcT1GJwtgdqiwSPmQQPw+EEgg4zPHOmTCLhjLFxGw/QYTwGhw17DhDQIncmfZvEhwo52fntIIdVHJZnbyBkDQgkINxJjPJRMApGwSgYBQQBAN17NjIBCAm0AAAAAElFTkSuQmCC","orcid":"","institution":"Mid Yorkshire Teaching Hospital NHS Trust","correspondingAuthor":true,"prefix":"","firstName":"Akaninyene","middleName":"","lastName":"Otu","suffix":""},{"id":463196075,"identity":"52081eab-8ed8-49a6-97e2-438f4e8270fe","order_by":1,"name":"Vinesh Patel","email":"","orcid":"","institution":"Mid Yorkshire Teaching Hospital NHS Trust","correspondingAuthor":false,"prefix":"","firstName":"Vinesh","middleName":"","lastName":"Patel","suffix":""},{"id":463196076,"identity":"2fa71857-e5c3-4aba-8010-ede48f1dc4cc","order_by":2,"name":"Stuart Bond","email":"","orcid":"","institution":"Mid Yorkshire Teaching Hospital NHS Trust Aberford Rd","correspondingAuthor":false,"prefix":"","firstName":"Stuart","middleName":"","lastName":"Bond","suffix":""},{"id":463196077,"identity":"e6db5780-ac66-45c1-bb89-6ebf2a17bc30","order_by":3,"name":"Mamoon Aldeyab","email":"","orcid":"","institution":"Mid Yorkshire Teaching Hospital NHS Trust Aberford Rd","correspondingAuthor":false,"prefix":"","firstName":"Mamoon","middleName":"","lastName":"Aldeyab","suffix":""},{"id":463196078,"identity":"9611562a-fee9-48c5-9deb-a193f3a5c36d","order_by":4,"name":"Jade Lee-Milner","email":"","orcid":"","institution":"Mid Yorkshire Teaching Hospital NHS Trust Aberford Rd","correspondingAuthor":false,"prefix":"","firstName":"Jade","middleName":"","lastName":"Lee-Milner","suffix":""},{"id":463196079,"identity":"19bbaef6-0694-4987-9815-610739a8a0ba","order_by":5,"name":"William J. Lattyak","email":"","orcid":"","institution":"Scientific Computing Associates Corp","correspondingAuthor":false,"prefix":"","firstName":"William","middleName":"J.","lastName":"Lattyak","suffix":""},{"id":463196080,"identity":"2aa9c5a2-5b8f-4507-93e0-47762fa2291a","order_by":6,"name":"Sarah Chadwick","email":"","orcid":"","institution":"NHS West Yorkshire Integrated Care Board","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Chadwick","suffix":""},{"id":463196081,"identity":"c313cfae-bb85-4ee5-b9bd-9db47c84e5a3","order_by":7,"name":"Maria Marcolin","email":"","orcid":"","institution":"Calderdale and Huddersfield NHS Foundation Trust","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Marcolin","suffix":""},{"id":463196082,"identity":"810f9d14-2d34-43de-9331-d9ba7a0dd284","order_by":8,"name":"Joseph Spencer-Jones","email":"","orcid":"","institution":"Mid Yorkshire Teaching Hospital NHS Trust Aberford Rd","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"","lastName":"Spencer-Jones","suffix":""},{"id":463196083,"identity":"347b73ac-6381-43b0-afd7-30beaaf51c0e","order_by":9,"name":"Victoria Hemming","email":"","orcid":"","institution":"Mid Yorkshire Teaching Hospital NHS Trust","correspondingAuthor":false,"prefix":"","firstName":"Victoria","middleName":"","lastName":"Hemming","suffix":""},{"id":463196084,"identity":"c0c2d24c-e84d-4f23-ae94-384c11afcc71","order_by":10,"name":"Kathyrn Deakin","email":"","orcid":"","institution":"Mid Yorkshire Teaching Hospital NHS Trust","correspondingAuthor":false,"prefix":"","firstName":"Kathyrn","middleName":"","lastName":"Deakin","suffix":""},{"id":463196085,"identity":"09b9e265-3511-4c58-941a-48b5cb7a7e76","order_by":11,"name":"Bassey Ebenso","email":"","orcid":"","institution":"University of Leeds","correspondingAuthor":false,"prefix":"","firstName":"Bassey","middleName":"","lastName":"Ebenso","suffix":""}],"badges":[],"createdAt":"2025-04-23 22:23:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6515666/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6515666/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12982-025-01159-4","type":"published","date":"2025-12-29T15:58:06+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83645951,"identity":"7d6e8fc4-f820-40c0-b10d-c8cfd7d84ac5","added_by":"auto","created_at":"2025-05-30 05:29:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1616080,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual framework for understanding and evaluating antimicrobial stewardship programmes\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6515666/v1/7cabb4d860f7257aedb7424e.png"},{"id":99545423,"identity":"05dcb8d4-aa72-4962-b1f3-a1fd79a2eb7d","added_by":"auto","created_at":"2026-01-05 16:07:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2497975,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6515666/v1/ccfdae2f-2cc1-4168-b202-62f3feecd112.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between paediatric antibiotic prescribing and socioeconomic deprivation: insights from a pilot project in West Yorkshire, United Kingdom","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInappropriate antibiotic use in all settings, including human and veterinary medicine, and the lack of novel therapies, have contributed to a global antimicrobial resistance (AMR) crisis. AMR has evolved from a silent pandemic to a global public health emergency resulting in increased mortality and longer hospitalisation, with negative impact on the economies of families, communities and countries [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The World Health Organisation (WHO) has declared AMR as one of the top ten threats to global health security [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It is indiscriminate of country border or income levels and causes about 9% of all global deaths [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAMR directly causes 1.3\u0026nbsp;million deaths annually, with a further 5\u0026nbsp;million associated deaths, 20% of which occur in children under five years of age [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This surpasses deaths from Human immunodeficiency virus (HIV), malaria and tuberculosis combined. By 2050, over 39\u0026nbsp;million people are projected to die from antibiotic-resistant infections [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The World Bank estimates that, without effective control, AMR could lead to US\u003cspan\u003e$\u003c/span\u003e3.4 trillion annual losses to gross domestic product by 2030 and an additional US\u003cspan\u003e$\u003c/span\u003e1 trillion health care costs by 2050, pushing 28\u0026nbsp;million people into extreme poverty [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRational use of antibiotics is a critical measure for controlling AMR. The United Kingdom Health Security Agency (UKHSA) updated the UK Access, Watch, Reserve (AWaRe) antibiotic list in January 2025 to provide guidance on use of 90 antibiotics for healthcare professionals in primary and secondary care [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, between 8.8% and 23.1% of antibiotic prescriptions in England are deemed inappropriate [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In England, higher antibiotic prescribing levels have been associated with Index of Multiple Deprivation (IMD) and certain geographical locations. Of the seven English regions, there is disproportionately high number of prescriptions per 100,000 population in North West (56.3%), and North East and Yorkshire (26.7%) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral studies have examined factors associated with high rates of antibiotic prescribing in the UK. A study conducted in England from 2014-18 investigated the relationship between primary care antibiotic prescription and area-level deprivation as well as region, after controlling for a range of other confounding variables, including rurality, ethnicity and health need [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A time series analysis in England from 2014\u0026ndash;22 explored the link between primary care antibiotic prescriptions, locality and deprivation. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A Welsh study from 2013\u0026ndash;17 examined the association between primary care antibiotic prescribing and deprivation, controlling for common chronic conditions and other potential confounders [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. While the three studies investigated antibiotic prescription patterns in primary care, none of them focused on the exposure to antibiotics among paediatric and neonatal patients, nor did they investigate antibiotic prescription in secondary care. To fill the gap in literature, our group aimed to explore the association between socioeconomic deprivation and exposure to antibiotics among children in secondary care settings in the UK.\u003c/p\u003e \u003cp\u003eIn this study, we aimed to identify relationships between paediatric antibiotic prescribing and IMD in the Mid Yorkshire Teaching NHS Trust region (Wakefield and North Kirklees Integrated Care Board Places, West Yorkshire, UK).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eUsing the Trust\u0026rsquo;s electronic prescribing system (Medchart\u0026reg;, Dedalus, Italy), patients aged 0\u0026ndash;2 years who had been prescribed and administered antibiotics from 28 April 2022, the start of the Born and Bred in Wakefield (BaBi) Wakefield project, to 19 January 2025 were identified. BaBi Wakefield is a long-term research project that involves the collection of data during pregnancy about mothers and babies to provide a wider picture of the factors affecting local family\u0026rsquo;s health and wellbeing [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOnly patients with a Wakefield (WF) postcode were considered, including WF postcodes in Kirklees, a metropolitan borough of West Yorkshire [1[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Antibiotics were defined following the British National Formulary Chap.\u0026nbsp;5 specification. Only intravenous and oral antibiotics were included in the main analysis as these were considered the targets for antimicrobial stewardship and main drivers of AMR. Antibiotics were then grouped according to the WHO AWaRe classification. Microbiology clinical results, including blood, urine, sputum and clinical swabs were collated from iLAB\u0026reg; (iLAB solutions LLC, Agilent technologies Inc, California, USA). Demographic data were retrieved from eCAMIS\u0026reg; (Electronic clinical and management information system, University Hospitals Southampton, UK), including ethnicity, admission and discharge date, ICD-10 (International Classification of Diseases; WHO, 2019) diagnostic codes, IMD decile, converted to quintile for statistical analysis.\u003c/p\u003e \u003cp\u003eUsing a Quasi-Poisson count regression approach, we tested the hypothesis that socioeconomic deprivation influences the rate of antibiotic prescriptions for patients aged 0\u0026ndash;2 years in hospital. The sample population was divided into quintiles by socioeconomic status, with Q1 being the most deprived and Q5 the least deprived. Q4 and 5 were combined, allowing comparison of the most deprived 40% with the least deprived 40%. The distribution of Q3 was not consistent with the other quintiles, and so it was treated separately.\u003c/p\u003e \u003cp\u003eThe response variable captured the interaction between the number of drugs prescribed and the number of diagnoses, reflecting treatment complexity. This is referring to the multifaceted nature of delivering healthcare to patients, particularly when managing multiple medical conditions simultaneously. In the context of this study, treatment complexity specifically reflects the combined influence of the number of medications prescribed (unique antibiotics) and the number of medical conditions being treated (diagnoses). This combination is an important indicator of how intricate a patient's care regimen is.\u003c/p\u003e \u003cp\u003eFor example, a higher number of unique antibiotics prescribed suggests that more medications are being used, which may be due to the need to address multiple, possibly interrelated conditions. Similarly, a greater number of diagnoses typically indicates more health issues to manage, further complicating treatment decisions. When these two factors are considered together as an interaction, they represent the compounded intricacies involved in addressing a patient's healthcare needs.\u003c/p\u003e \u003cp\u003eTreatment complexity also inherently considers: (i) medication interactions, where the potential for prescribed drugs to interact with each other requires careful monitoring and adjustments; (ii) resource utilisation, where the level of healthcare resources, coordination, and diverse expertise are needed to manage the patient's care effectively; and (iii) patient outcomes, representing the challenges in achieving desired health outcomes, as treatment complexity can increase the risk of complications or reduce treatment adherence. In this study, treatment complexity is quantified as the interaction between the number of unique drug names and the number of diagnoses, providing a meaningful measure of the challenges healthcare providers face in delivering appropriate care.\u003c/p\u003e \u003cp\u003eTo ensure the model estimated rates rather than raw counts, the logarithm of the number of hospital admissions was included as an offset. The analysis was conducted using R version 4.4.3 (R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e \u003cp\u003eApproval for this project was given by the Trust\u0026rsquo;s research committee through an internal pump-priming grant to enable a National Institute for Health and Care Research grant application. Ethics approval was not required as this was considered a service improvement project with a view to further research.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 780 patients and 2204 antibiotic prescriptions were identified from the initial hospital prescribing report. Intravenous and oral antibiotics were grouped for further analysis (690 patients; 1907 prescriptions). The breakdown of population by IMD quintiles was Q1\u0026ndash;313 (45%), Q2\u0026ndash;154 (22%), Q3\u0026ndash;109 (16%), Q4\u0026ndash;88 (13%), and Q5\u0026ndash;26 (4%). The average number of antibiotics per admitted patient, per IMD quintile, is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe total length of stay (LOS) for all admitted patients reduced on moving from Q1 to Q5 as reflected in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The number of diagnostic codes also reduced from Q1 to Q5. The number of unique antibiotics (adjusted per admission) increased from Q1 to Q5, and this was not statistically significant. However, the number of unique antibiotics (adjusted per LOS) increased from Q1 to Q5, and this was statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\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\u003eIMD quintile vs number of admitted patients and average number of antibiotics per admitted patient in Wakefield [13].\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIMD quintile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation IMD by LSOA# - Wakefield Total (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal number of admitted patients within IMD quintile (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage number of antibiotics per admitted patient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAverage length of stay*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAverage Number of diagnostic codes*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAverage number of unique antibiotics (adjusted per admission)*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAverage number of antibiotics per admitted patient*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 (Most deprived)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e313 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e13.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e9.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 (Least deprived)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.73\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\u003e \u003cem\u003e*Average obtained by combining quintiles (1\u0026ndash;2) and combining quintiles (4\u0026ndash;5).\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e# Lower Layer Super Output Areas (LSOAs) are small geographic areas created by The Office for National Statistics (ONS) for statistical analysis and reporting in England and Wales. Each LSOA contains approximately 1,000 to 3,000 residents, allowing for a detailed examination of local demographics, social conditions and economic factors.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe total number of antibiotics prescribed did not significantly differ across IMD quintiles following the generation of both preliminary Poisson model coefficients and the alternative Quasi-Poisson or Negative Binomial model (p\u0026thinsp;\u0026gt;\u0026thinsp;0.1). The number of unique antibiotics prescribed did not substantially differ across IMD quintiles and this was borne out by both the preliminary Poisson model coefficients and the alternative Quasi-Poisson model.\u003c/p\u003e \u003cp\u003eThe final Quasi-Poisson model, offset by the log transformed LOS, was re-estimated using the interaction of the number of unique antibiotics and the number of diagnoses as the response. Considering combined Q1 and Q2 as the reference, the model showed that the interaction between unique antibiotics and diagnoses was significant in the combined Q4 and Q5 (coefficient= -0.4261, p\u0026thinsp;=\u0026thinsp;0.0096) while gender (p\u0026thinsp;=\u0026thinsp;0.6897) and race (p\u0026thinsp;=\u0026thinsp;0.5947) were not statistically significant.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur project identified that more patients were admitted to hospital from Q1 and Q2 (most deprived; 67% vs 58.3% in the general population) compared to 17% vs 28.4% from Q4 and Q5. The LOS and number of diagnostic codes decreased from Q1 to Q5 while there was a statistically significant increase in the use of antibiotics from Q1 to Q5. This suggests that more deprived children with more diagnoses (more comorbidity) received less antibiotics compared with those in Q4 and Q5. The longer LOS among the children from the lower quintiles could have played a role in the observed differences although the exact nature of the relationship remains unclear. The longer LOS should have theoretically exposed the children to more antibiotic courses, but the reverse appeared to be the case. It is possible that the more deprived children (who also appeared to have more comorbidities) were more extensively investigated resulting in longer LOS but less use of antibiotics as non-infectious diagnoses were uncovered during the course of the admission. We used the number of unique antibiotics prescribed to represent antibiotic consumption. It is possible that the use of other more standardised indices for measuring antibiotic consumption, such as the Defined Daily Dose (DDD), could have provided deeper insights. The DDD is defined as the assumed average maintenance dose per day for a drug used for its main indication in adults [14]. Further research is required to fully understand this preliminary data.\u003c/p\u003e \u003cp\u003eWe found that ethnicity was not significantly associated with the use of systemic antibiotics. This contrasts with reports from other studies in the UK that showed a strong link between ethnicity and antibiotic consumption [15\u0026ndash;17]. In a recent scoping review, 32/58 studies (55%) included race/ethnicity and 22/58 (38%) showed an association between race/ethnicity and antibiotic use, particularly in acne and dental infections [18].\u003c/p\u003e \u003cp\u003eDespite concerted efforts by national governments and health systems to tackle AMR through antimicrobial stewardship (AMS), antibiotics continue to be prescribed needlessly for self-limiting conditions. Respiratory tract infections are responsible for 74.4% of antibiotic prescriptions in children [19], despite their marginal beneficial effects [20]. However, exposing children to antibiotics is not without its risks. Antibiotic use alters the diversity of the gut microbiome thus impairing immunity, colonisation resistance, and metabolic homeostasis [21]. Analysis of a birth cohort of 12,422 children born at full term found a notable attenuation of weight and height gain during the first 6 years of life after neonatal antibiotic exposure in boys with significantly higher body mass index in both boys and girls who were exposed to antibiotics after the neonatal period but during the first 6 years of life [22]. A systematic review and meta-analysis of 160 observational studies investigating 21 outcomes in 22,103,129 children showed that antibiotic exposure was associated with an increased risk of atopic dermatitis, food allergies, allergic rhinoconjunctivitis, asthma, obesity, juvenile idiopathic arthritis, psoriasis and neurodevelopment disorders [23]. Thus, appropriately limiting the exposure of children to antibiotics has both short- and long-term benefits.\u003c/p\u003e \u003cp\u003eTo deliver targeted antimicrobial stewardship (AMS) and tackle AMR locally, further research is needed to explore antibiotic prescription practice and use in local communities. The British National Formulary [24] defines AMS as an organizational or healthcare system-wide approach to fostering and monitoring the judicious use of antimicrobials to preserve their future effectiveness and prevent antimicrobial resistance. Addressing AMR through enhancing stewardship remains a national medicines optimization priority, led by NHS England. In Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e we propose a conceptual framework depicting core elements of a package of AMS strategies that be deployed to tackle antimicrobial resistance in local healthcare systems and communities. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e was adapted from the UK\u0026rsquo;s summary of 2024\u0026ndash;2029 National Action Plan for tackling antimicrobial resistance in the UK [25]. The conceptual framework can be used to investigate how inputs (in this case the AMS strategies) can lead to intermediate outcomes and how outcomes can lead to anticipated longer-term impacts. The conceptual framework also outlines a program theory (see the upper part of Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) of how the AMS programme of work is expected to generate a change along with contextual factors that can influence change. It is important to emphasize that, as our pilot project investigated antimicrobial prescription in secondary care within the local healthcare system, we have selected six most relevant AMS strategies for inclusion in second column of Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. These are namely: 1) Infection prevention, control and management; 2) Public engagement activities; 3) AMR Surveillance processes; 4) Workforce education and training; 5) Using AMR information for action and decision-making; and 6) Addressing health disparities and inequalities. We believe that the sustained implementation of these strategies would trigger intermediate outcomes shown in the third column of Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and that these will in turn generate four anticipated long-term impacts of: 1) high-quality patient care, 2) reduced AMR-related morbidity and mortality, 3) progress towards UN sustainable development goal (SDG) -3 of improved health for people of all ages, and 3) progress towards SDG-10, of reduced inequities and disparities within countries.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOur pilot was not without limitations. First, we were unable to utilise internationally recognised measures of antibiotic consumption due to the retrospective nature of the pilot. Second, we were unable to explore further some of the trends we identified such as the significant increase in the use of antibiotics from Q1 to Q5. Third, the study was restricted to quantitative, secondary data analysis. We did not have qualitative data to complement our analysis. This would have provided us with further insights into the contextual factors associated with antibiotic prescription and consumption in our community. However, our work represents an important first step towards understanding a complex issue and proposing context-specific strategies to mitigate this global threat.\u003c/p\u003e \u003cp\u003eIn conclusion, our findings support the need for further investigation into contextual drivers of antibiotic prescribing practices in more deprived populations in Mid Yorkshire. The next phase of this work will focus on exploring the interrelationships we have identified in this pilot such as the link between LOS, antibiotic use and IMD in local communities. Only by applying a social determinants of health (SDH) lens to understanding these drivers can targeted public health strategies and interventions in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e be implemented to stem the tide of disability and death from AMR in Mid Yorkshire and similar contexts. While the strategies listed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e are not meant to be exhaustive, applying a SDH lens to implement them will accelerate the achievement of SDGs. Notable for AMR in our pilot study are: goal 3 of ensuring healthy lives and improving well-being, and goal 10 of reducing socioeconomic inequalities as well as disparities in age, sex, disabilities etc. in accessing quality healthcare services. We recommend the field-testing of Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e to determine its utility in different contexts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eConflict of Interest:\u003cem\u003e\u0026nbsp;\u003c/em\u003eNone to declare.\u003c/p\u003e\n\u003cp\u003eFunding:\u003cem\u003e\u0026nbsp;\u003c/em\u003eResearch capacity building pump-priming funding was received from the Mid Yorkshire Teaching NHS Trust (MYTT) UK to support this work.\u003c/p\u003e\n\u003cp\u003eClinical Trial Number: Not applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthics, Consent to Participate, and Consent to Publish declarations: not applicable.\u003c/p\u003e\n\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKariuki, Samuel. Global burden of antimicrobial resistance and forecasts to 2050. The Lancet, Volume 404, Issue 10459, 1172 \u0026ndash; 1173\u003c/li\u003e\n\u003cli\u003eWHO. 2023. Antimicrobial Resistance: Briefing to WHO Member States. 22 March 2023. Accessed 30 March 2025. https://apps.who.int/gb/mspi/pdf_files/2023/03/Item1_22-03.pdf\u003c/li\u003e\n\u003cli\u003eAntimicrobial Resistance Collaborators Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis Lancet. 2022; 399:629-655\u003c/li\u003e\n\u003cli\u003eWhat is antimicrobial resistance, a silent pandemic?. 2025. UN Regional Information Centre. Accessed 30 March 2025. https://unric.org/en/the-global-threat-of-antimicrobial-resistance-a-silent-pandemic/\u003c/li\u003e\n\u003cli\u003eNaghavi, Mohsen et al. Global burden of bacterial antimicrobial resistance 1990\u0026ndash;2021: a systematic analysis with forecasts to 2050. The Lancet, Volume 404, Issue 10459, 1199 - 1226\u003c/li\u003e\n\u003cli\u003eWHO. 2024. Seventy-seventh World Health Assembly. A77/5 Provisional agenda item 11.8 11 April 2024. Antimicrobial resistance: accelerating national and global responses. https://apps.who.int/gb/ebwha/pdf_files/WHA77/A77_5-en.pdf\u003c/li\u003e\n\u003cli\u003eGOV.UK. Antibiotic \u0026apos;Access\u0026apos; list updated for the UK. Available from: https://www.gov.uk/government/news/antibiotic-access-list-updated-for-the-uk \u003c/li\u003e\n\u003cli\u003eSmieszek T, Pouwels KB, Dolk FCK, et al. Potential for reducing inappropriate antibiotic prescribing in English primary care. J Antimicrob Chemother. 2018;73: ii36\u0026ndash;ii43. https://doi.org/10.1093/jac/dkx500.\u003c/li\u003e\n\u003cli\u003eMcCloskey AP, Malabar L, McCabe PG, Gitsham A, Jarman I. Antibiotic prescribing trends in primary care 2014-2022. Res Social Adm Pharm. 2023 Aug;19(8):1193-1201. doi: 10.1016/j.sapharm.2023.05.001. \u003c/li\u003e\n\u003cli\u003eThomson, K., Berry, R., Robinson, T. et al. An examination of trends in antibiotic prescribing in primary care and the association with area-level deprivation in England. BMC Public Health 20, 1148 (2020). https://doi.org/10.1186/s12889-020-09227-x\u003c/li\u003e\n\u003cli\u003eAdekanmbi et Al. 2020. Antibiotic use and deprivation: an analysis of Welsh primary care antibiotic prescribing data by socioeconomic status. Journal of Antimicrobial Chemotherapy, 2020. Volume 75, Issue 8, August 2020, Pages 2363\u0026ndash;2371, https://doi.org/10.1093/jac/dkaa168\u003c/li\u003e\n\u003cli\u003eBorn and Bred In (BaBi). Available from: https://www.babinetwork.co.uk/babi-sites/babi-wakefield\u003c/li\u003e\n\u003cli\u003eIndex of multiple deprivation 2019. Wakefield. Available from: https://research.mysociety.org/sites/imd2019/area/wmc_wakefield/lsoa/\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Defined Daily Dose (DDD). Definition and general considerations. Available from: https://www.who.int/tools/atc-ddd-toolkit/about-ddd\u003c/li\u003e\n\u003cli\u003eBarbieri JS, Shin DB, Wang S et al. Association of race/ethnicity and sex with differences in health care use and treatment for acne. JAMA Dermatol 2020; 156: 312\u0026ndash;9. https://doi.org/10.1001/jamadermatol. 2019.4818\u003c/li\u003e\n\u003cli\u003eOlesen SW, Grad YH. Racial/ethnic disparities in antimicrobial drug use, United States, 2014\u0026ndash;2015. Emerg Infect Dis 2018; 24: 2126\u0026ndash;8. https://doi.org/10.3201/eid2411.180762\u003c/li\u003e\n\u003cli\u003eOkunseri C, Zheng C, Steinmetz C et al. Trends and racial/ethnic disparities in antibiotic prescribing practices of dentists in the United States. J Public Health Dent 2018; 78: 109\u0026ndash;17. https://doi.org/10.1111/ jphd.12245\u003c/li\u003e\n\u003cli\u003eHarvey EJ, De Br\u0026uacute;n C, Casale E, Finistrella V, Ashiru-Oredope D, Influence of factors commonly known to be associated with health inequalities on antibiotic use in high-income countries: a systematic scoping review, \u003cem\u003eJournal of Antimicrobial Chemotherapy\u003c/em\u003e, Volume 78, Issue 4, April 2023, Pages 861\u0026ndash;870, https://doi.org/10.1093/jac/dkad034\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Brien K, Bellis TW, Kelson M, et al. Clinical predictors of antibiotic prescribing for acutely ill children in primary care: an observational study. Br J Gen Pract 2015; DOI: https://doi.org/10.3399/bjgp15X686497.\u003c/li\u003e\n\u003cli\u003eButler CC, Hood K, Verheij T, et al. Variation in antibiotic prescribing and its impact on recovery in patients with acute cough in primary care: prospective study in 13 countries. BMJ 2009; 338: b2242.\u003c/li\u003e\n\u003cli\u003eLathakumari RH, Vajravelu LK, Satheesan A, Ravi S, Thulukanam J, Antibiotics and the gut microbiome: Understanding the impact on human health, Medicine in Microecology (2024), doi: https://doi.org/10.1016/j.medmic.2024.100106.\u003c/li\u003e\n\u003cli\u003eUzan-Yulzari A, Turta O, Belogolovski A, Ziv O, Kunz C, Perschbacher S, Neuman H, Pasolli E, Oz A, Ben-Amram H, Kumar H, Ollila H, Kaljonen A, Isolauri E, Salminen S, Lagstr\u0026ouml;m H, Segata N, Sharon I, Louzoun Y, Ensenauer R, Rautava S, Koren O. Neonatal antibiotic exposure impairs child growth during the first six years of life by perturbing intestinal microbial colonization. Nat Commun. 2021 Jan 26;12(1):443. doi: 10.1038/s41467-020-20495-4.\u003c/li\u003e\n\u003cli\u003eDuong QA, Pittet LF, Curtis N, Zimmermann P. Antibiotic exposure and adverse long-term health outcomes in children: A systematic review and meta-analysis. J Infect. 2022 Sep;85(3):213-300. doi: 10.1016/j.jinf.2022.01.005. Epub 2022 Jan 10. Erratum in: J Infect. 2023 Jan;86(1):118. doi: 10.1016/j.jinf.2022.10.035.\u003c/li\u003e\n\u003cli\u003eAntimicrobial stewardship. Medicines guidance. BNF. NICE. Available from: https://bnf.nice.org.uk/medicines-guidance/antimicrobial-stewardship/\u003c/li\u003e\n\u003cli\u003eGOV.UK 2024. Confronting antimicrobial resistance 2024 to 2029. Available from: https://assets.publishing.service.gov.uk/media/664394d9993111924d9d3465/confronting-antimicrobial-resistance-2024-to-2029.pdf\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Antimicrobial stewardship, Antimicrobial resistance, Socioeconomic deprivation, Index of multiple deprivation, Paediatrics, Infectious diseases and microbiology, Health care intelligence and data analysis, Secondary care, Lower Layer Super Output Areas (LSOAs)","lastPublishedDoi":"10.21203/rs.3.rs-6515666/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6515666/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eInappropriate antibiotic use in settings including human and veterinary medicine and a lack of novel therapies have contributed to a global antimicrobial resistance (AMR) crisis. In January 2025, the UK Health Security Agency revised the UK Access, Watch, Reserve (AWaRe) antibiotic list to guide prescribing of 90 antibiotics. This pilot study investigated relationships between socioeconomic deprivation and paediatric antibiotic prescribing in secondary care in the Mid Yorkshire Teaching NHS Trust region, UK.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eRetrospective antibiotic prescribing data was obtained from the NHS Trust\u0026rsquo;s electronic prescribing system for patients aged 0\u0026ndash;2 years prescribed systemic antibiotics between April 2022 and January 2025, the start of the Born and Bred in Wakefield (BaBi) Wakefield project. Demographic data retrieved from electronic clinical and management information system included ethnicity, admission and discharge date, ICD-10 diagnostic codes, and IMD decile, converted to quintile for statistical analysis. Quasi-Poisson count regression approach was used to explore the relationship between the rate of antibiotic prescription, socioeconomic deprivation, and region.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA total of 780 patients and 2204 antibiotic prescriptions were identified from hospital prescribing report. Adjusted models identified four key findings. Firstly, length of stay (LOS) in hospital and number of diagnostic codes were highest in the most deprived group (Q1). Secondly, the number of unique antibiotics prescribed (adjusted per admission) was highest in the least deprived group(Q5) although this relationship was not statistically significant. Thirdly, the number of unique antibiotics (adjusted per LOS) was highest in Q5, and this was statistically significant (p\u0026thinsp;=\u0026thinsp;xxx). Finally, in contrast with other studies in the UK, ethnicity was not significantly associated with the use of systemic antibiotics.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOur findings suggest that children from more deprived areas with more comorbidities/ diagnosis received less antibiotics in secondary care settings compared with their peers from least deprived areas. The LOS and number of diagnostic codes also decreased from Q1 to Q5. Future prescribing trends among children aged 0-2years should account for contextual factors to ensure that children from the most deprived communities are not disproportionately exposed to less antibiotics despite of suffering more comorbidities.\u003c/p\u003e","manuscriptTitle":"Association between paediatric antibiotic prescribing and socioeconomic deprivation: insights from a pilot project in West Yorkshire, United Kingdom","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-30 05:29:30","doi":"10.21203/rs.3.rs-6515666/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-21T07:17:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-08T09:47:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176412363793614644236963056405713744338","date":"2025-07-08T07:39:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-13T08:14:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112808739867715498888060524496520132161","date":"2025-06-06T06:36:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-27T16:39:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-08T13:25:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-08T13:20:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2025-04-23T22:10:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"27121171-fe7e-4d04-a63d-aabfc585a1cf","owner":[],"postedDate":"May 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-05T16:03:36+00:00","versionOfRecord":{"articleIdentity":"rs-6515666","link":"https://doi.org/10.1186/s12982-025-01159-4","journal":{"identity":"discover-public-health","isVorOnly":false,"title":"Discover Public Health"},"publishedOn":"2025-12-29 15:58:06","publishedOnDateReadable":"December 29th, 2025"},"versionCreatedAt":"2025-05-30 05:29:30","video":"","vorDoi":"10.1186/s12982-025-01159-4","vorDoiUrl":"https://doi.org/10.1186/s12982-025-01159-4","workflowStages":[]},"version":"v1","identity":"rs-6515666","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6515666","identity":"rs-6515666","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}