Development of AWaRe antibiotic quality indicators for optimal use

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Profile Elisa Funiciello , View ORCID Profile Sumanth Gaundra , View ORCID Profile Brian Godman , View ORCID Profile Benedikt Huttner , View ORCID Profile Yara Mohsen Khalaf , View ORCID Profile Guilia Lorenzetti , View ORCID Profile Marc Mendelson , View ORCID Profile Catrin E Moore , View ORCID Profile Claudia GS Osorio-de-Castro , View ORCID Profile Zikria Saleem , View ORCID Profile Jeroen Schouten , View ORCID Profile Elizabeth Tayler , View ORCID Profile Evelyn Wesangula , AWaRe QIs Delphi Panel , View ORCID Profile Stephen M Campbell , View ORCID Profile Michael Sharland doi: https://doi.org/10.1101/2025.10.24.25338539 Annie Heath 1 Antibiotic Policy Group, Institute for Infection and Immunity, City St. George’s University of London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Annie Heath For correspondence: aheath{at}citystgeorges.ac.uk Jan Goelen 1 Antibiotic Policy Group, Institute for Infection and Immunity, City St. George’s University of London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jan Goelen Pem Chuki 2 Independent Consultant , Bhutan Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Pem Chuki Aislinn Cook 1 Antibiotic Policy Group, Institute for Infection and Immunity, City St. George’s University of London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Aislinn Cook Filip Djukic 1 Antibiotic Policy Group, Institute for Infection and Immunity, City St. George’s University of London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Filip Djukic Nga Thi Thuy Do 3 Oxford University Clinical Research Unit , Hanoi, Vietnam Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nga Thi Thuy Do Elisa Funiciello 1 Antibiotic Policy Group, Institute for Infection and Immunity, City St. George’s University of London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Elisa Funiciello Sumanth Gaundra 4 Washington University School of Medicine , Saint Louis, MO, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sumanth Gaundra Brian Godman 5 School of Pharmacy, Sefako Makgatho Health Sciences University , Ga-Rankuwa, Pretoria, South Africa Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Brian Godman Benedikt Huttner 6 Antimicrobial Resistance Department, Health Systems Division, World Health Organization , Geneva, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Benedikt Huttner Yara Mohsen Khalaf 7 Department of Epidemiology, High Institute of Public Health , Alexandria, Egypt Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yara Mohsen Khalaf Guilia Lorenzetti 1 Antibiotic Policy Group, Institute for Infection and Immunity, City St. George’s University of London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Guilia Lorenzetti Marc Mendelson 8 Division of Infectious Diseases and HIV Medicine, Department of Medicine, Groote Schuur Hospital, University of Cape Town , Cape Town, South Africa Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marc Mendelson Catrin E Moore 1 Antibiotic Policy Group, Institute for Infection and Immunity, City St. George’s University of London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Catrin E Moore Claudia GS Osorio-de-Castro 9 Sergio Arouca National School of Public Health , Rio De Janeiro, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Claudia GS Osorio-de-Castro Zikria Saleem 10 Department of Pharmacy Practice, College of Pharmacy, Qassim University , Buraydah, Qassim, Saudi Arabia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Zikria Saleem Jeroen Schouten 11 Radboudumc Community of Infectious Diseases , Radboudumc, Nijmegen, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jeroen Schouten Elizabeth Tayler 1 Antibiotic Policy Group, Institute for Infection and Immunity, City St. George’s University of London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Elizabeth Tayler Evelyn Wesangula 12 East Central and Southern Africa Health Community , Arusha, United Republic of Tanzania Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Evelyn Wesangula Stephen M Campbell 13 School of Health Sciences, University of Manchester , Manchester, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Stephen M Campbell Michael Sharland 1 Antibiotic Policy Group, Institute for Infection and Immunity, City St. George’s University of London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Michael Sharland Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background The World Health Organization (WHO) AWaRe (Access/Watch/Reserve) book gives detailed guidance on the optimal use of antibiotics across primary care and hospitals for adults and children with the aim of improving the quality of use and reducing antimicrobial resistance. Objectives To develop model sets of appropriate and feasible quality indicators based on the WHO AWaRe system, that are widely implementable across primary care, hospital, and the health care system settings to support optimal antibiotic use. Methods Indicators from a scoping review were revised to focus on clinical infections in the AWaRe book. They were assessed using consensus techniques through two rounds each of the Global Delphi Technique and RAND/UCLA Appropriateness Method, evaluating appropriateness and feasibility at national and global levels respectively. In Round 1 of each method, panellists rated clarity and suggested revisions or new indicator. Round 2 results are reported. Findings The AWaRe QIs covered common primary and hospital infections listed in the AWaRe book and general patient and population level indicators. There were 102 quality indicators (Primary Care: 46; Hospital: 39; General: 17) included in Round 2 of the Delphi Technique and 136 indicators (Primary Care: 56; Hospital: 60; General: 20) in Round 2 of the RAND/UCLA method, which are presented as model sets of indicators. Conclusion These AWaRe model quality indicators can be locally adapted to support optimal use of antibiotics and inform global and national antimicrobial stewardship programs. Introduction The AWaRe (Access, Watch, Reserve) system was developed by the World Health Organization (WHO) and includes the classification of antibiotics based on their spectrum of activity and resistance potential ( 1 ). The AWaRe system ( Figure 1 ) is aligned with and complements the WHO Model List of Essential Medicines (EML), a tool developed by the WHO that prioritises the use of specific antibiotics ( 2 ). It facilitates and promotes appropriate antibiotic use ( 3 , 4 ) and underpins the recent United Nations General Assembly (UNGA) target that at least 70% of all human use of antibiotics globally should be Access antibiotics ( 5 ). The 2022 AWaRe antibiotic book provides empiric treatment guidance for 34 common infections in primary and hospital care for children and adults based on the AWaRe system ( 6 , 7 ). The AWaRe book can be a valuable guideline given concerns with the robustness of antibiotic guidelines from a number of low-and-middle income countries (LMICs) ( 8 ). Improving appropriate antibiotic use requires insight into current patterns of use at regional, national, and healthcare facility levels through measurement and comparison to agreed standards through validated quality indicators (QI)( 9 ). Download figure Open in new tab Figure 1: Overview of the WHO AWaRe Groups ( 7 ). QIs are clearly defined, quantifiable scores based on evidence, which should be tracked over time and demonstrates that greater adherence is associated with better quality of care ( 10 , 11 ). The WHO and other organisations have developed antibiotic stewardship toolkits for hospitals and ambulatory care to improve antibiotic use ( 12 , 13 ). However, there are currently no agreed lists of QIs reflecting recommendations in WHO AWaRe book guidance to assess and optimise the appropriate use of antibiotics. This is especially important for the primary care sector, where over 90% of antibiotics are dispensed in most countries ( 14 , 15 ). As part of the Antibiotic Data to Inform Local Action (ADILA) project (Wellcome Trust grant number: 222051/Z/20/Z), we conducted an initial scoping review that identified 773 existing quality indicators and treatment recommendations on systemic antibiotic use ( 16 ). The review found that for infection-specific indicators, approximately 50% focused on respiratory tract infections; targeted treatment guided by microbiological results was rare, and there were no indicators for many clinically important infections (e.g. osteoarticular and intra-abdominal infections). The review highlighted that only 8 of 773 (1%) indicators were based on the AWaRe system directly although 445 (57.6%) indicators indirectly referred to the guidance provided in the AWaRe Antibiotic book ( 16 ). The review demonstrated the need to develop globally applicable AWaRe indicators to inform antimicrobial stewardship (AMS) in both primary and hospital settings, for adults and children, with a focus on lower-resource settings. As part of the ADILA project, this paper aimed to develop model sets of appropriate and feasible QIs, based on the WHO AWaRe book guidelines for common infections in primary and hospital care, and general QIs for appropriate antibiotic use, that can be adapted locally as part of AMS activities within individual countries or settings. Methods Delphi and RAND/UCLA Appropriateness Techniques The indicators identified in the initial scope ( 16 ) were reviewed to focus specifically on the clinical infections included in the 2022 WHO AWaRe antibiotic book on empiric antibiotic treatment and management of common infections ( 6 ). The general QIs were based on the antibiotic prescribing and dispensing guidance given in the AWaRe antibiotic book on the appropriate use of antibiotics across all settings. Two distinct consensus methodology processes were conducted, each with a different focus and panel composition. The first was a two-round Delphi Technique which engaged a panel of over 100 international experts in AMR and AMS from all WHO regions (Supplementary Box 1) ( 17 ). This process assessed the appropriateness and feasibility of the indicators within national and local health system contexts ( Figure 2 ). Download figure Open in new tab Figure 2: Delphi Technique and RAND/UCLA Method Processes. Following this, a separate RAND/UCLA Appropriateness Method was conducted with a panel of 12 global experts in AMR/AMS from all WHO regions (PC, NTTD, SG, BH, YMK, FCL, MM, CGSOC, ZS, JS, ET, EW) (Supplementary Box 1) ( 11 , 18 ). This second process focused specifically on evaluating the appropriateness and feasibility of the indicators in a global context, considering their potential for broader international application and comparability ( Figure 2 ) ( 19 ). In Round 1 of each method, the panellists rated indicators on separate 9-point integer scales for the clarity and appropriateness of each indicator, and appropriateness and current feasibility in Round 2 of each method ( Figure 3 and Supplementary Box 1). During the first round, panellists were encouraged to provide comments to improve the wording of the indicators. Any newly worded indicators were included in the second round for rating, in addition to the originally worded indicators. Revised indicators that more clearly captured the intended concept were brought into the RAND/UCLA method for rating. This ensured that feedback from the Delphi panel was reflected in more precise and refined versions in the RAND/UCLA method. Download figure Open in new tab Figure 3: Definition of Scales Used in Delphi Technique and RAND/UCLA Methods Both rounds of the Delphi Technique and Round 1 of the RAND/UCLA method were conducted online with no interaction between panellists. In Round 2 of the RAND/UCLA method, the panellists met virtually for a 4-hour face-to-face interactive meeting to discuss, and in some cases reword, the indicators for greater clarity and to rate each indicator for appropriateness and feasibility. Any newly worded indicators during the RAND/UCLA meeting were rated anonymously immediately during the meeting using Mentimeter. All other indicators were rated individually on the panellists’ ratings MS Excel® file during the meeting. All developed indicators and all ratings from the Delphi and RAND/UCLA Round 2 processes are included to ensure transparency. Indicators were evaluated to characterise their appropriateness and feasibility, but were not ranked, prioritised, or excluded as the ratings are intended to inform implementation and use within AMS programmes. Detailed outline of the Delphi Technique and RAND/UCLA method can be found in the Supplementary materials. This paper presents all developed indicators and all ratings from the Delphi and RAND/UCLA Round 2 processes to ensure transparency. Indicators were evaluated to characterise their appropriateness and feasibility, but were not ranked, prioritised, or excluded as the ratings are intended to inform implementation and use within AMS programmes. Results Delphi and RAND/UCLA Appropriateness Techniques Building on the QIs brought forward from the scoping review ( 16 ), 52 indicators were rated during Delphi Round 1. An additional 83 QIs were suggested by panellists and were added to the list in Round 2 ( Figure 4 ). This resulted in 102 indicators at the end of the Delphi Round 2. Download figure Open in new tab Figure 4: Delphi and RAND/UCLA flowchart showing the number of quality indicators (QIs) rated per round and the resulting ratings. Based on the results and suggestions from the panellists in Delphi Round 2, 16 indicators were added to the RAND/UCLA Method ( Figure 4 ). In Round 1 of the RAND/UCLA method, 114 indicators were rated. One new primary care QI was suggested for inclusion in Round 2 resulting in 115 QIs to be rated in RAND/UCLA Round 2 ( Figure 4 ). During the discussion section of Round 2, the panellists added and rated 21 indicators. The final list of indicators after RAND/UCLA Round 2 included 136 QIs. Primary Care The primary care indicators included the most common conditions managed in primary care as outlined in the WHO AWaRe antibiotic book ( Figure 5 ). These indicators assess the proportion of patients with specific infections who receive antibiotic management aligned with AWaRe recommendations, including appropriate antibiotic choice, dose, and duration of therapy. In addition to condition-specific measures, the primary care indicator set includes broader prescribing indicators that provide an overview of antibiotic use patterns, such as the proportion of patients with a specific infection receiving oral antibiotics and the distribution of Access and Watch antibiotic use. Together, these indicators enable assessment of both infection-specific prescribing practices and overall antibiotic use in primary care settings. ( Figure 5 & Table 1 ). Download figure Open in new tab Figure 5: Infection categories and indicator themes covered by the AWaRe quality indicators. View this table: View inline View popup Table 1: Total List of Primary Care/Outpatient model Quality Indicators Rated in the Delphi Technique and RAND/UCLA Panel. Round 2 of the Delphi Technique resulted in ratings for 46 Primary Care indicators, which were all (100%) rated appropriate ( Supplementary Table 1) while 43 (93.5%) were rated feasible (Supplementary Table 4) in a local context. The final list of indicators after Round 2 of the RAND/UCLA method resulted in ratings for 56 Primary Care indicators. Of these 56 indicators, 53 (94.6%) were rated appropriate (Supplementary Table 1) and 22 (39.3%) were rated feasible (Supplementary Table 4) in a global context. Hospital Facility The hospital indicators set covered eight common infections managed in hospital settings, as well as surgical prophylaxis ( Figure 5 ). These indicators follow a similar structure to the primary care indicators, assessing the proportion of patients with specific hospital-treated infections who receive antibiotic treatment aligned with AWaRe book guidance, including recommended antibiotic choice, dose, and duration of therapy. In addition, the hospital indicators include broader measures of antibiotic use, such as the proportion of patients receiving intravenous antibiotics and the distribution of antibiotic use across AWaRe groups ( Figure 5 & Table 2 ). These complementary indicators allow facilities to obtain a more granular understanding of hospital antibiotic prescribing patterns, supporting interpretation of infection-specific practices alongside patterns of overall antibiotic use. View this table: View inline View popup Table 2: Total List of model Hospital Setting Quality Indicators Rated in the Delphi Technique and RAND/UCLA Panel. Round 2 of the Delphi Technique resulted in the rating of 39 hospital facility indicators, which were all rated appropriate (Supplementary Table 2) and feasible (Supplementary Table 5) on a local level. Round 2 of the RAND/UCLA method resulted in the rating of 60 indicators, with 59 (98.3%) rated appropriate (Supplementary Table 2) and 45 (75%) rated feasible in a global context (Supplementary Table 5). General Quality Indicators The general indicators covered patient level indicators including, detailed documentation of empiric treatment plans in medical records (G1 in Table 1 ), as well as population level metrics such as Access and Watch antibiotic use measured in DIDs in Primary Care (G12 in Table 3 ) aiming to provide insight into overall facility or national prescribing. The indicators can be calculated using facility, sub-national, or national level data using. They are designed to allow facilities, national programmes, and AMS committees to define the numerator and denominator with their routine data systems. For example, indicator G16 “Percentage of total oral Access antibiotic use” could be measured at department, facility, or national level ( Table 3 ). View this table: View inline View popup Table 3: Total List of General Quality Indicators Rated in the Delphi Technique and RAND/UCLA Method. Round 2 of the Delphi Technique resulted in the rating of 17 general indicators. All of the indicators were rated appropriate (Supplementary Table 3) and feasible (Supplementary Table 6) in a local context. The RAND/UCLA panellists rated 20 general QIs. Nineteen (95.0%) of the general QIs were rated appropriate and 5 (25.0%) were rated feasible on a global level. Discussion This study used established consensus methods to develop model sets of quality indicators reflecting WHO AWaRe recommendations that can be adapted in national and sub-national AMS activities to support more appropriate antibiotic use. The indicators were designed to be practical, clinically meaningful measures of prescribing quality, reflecting core stewardship principles such as appropriate antibiotic choice, dose, route, and duration, while remaining flexible enough to be implemented using routinely collected data across diverse health system contexts. The iterative process of using both the Delphi Technique and the RAND/UCLA method enabled broad expert input while systematically assessing both clinical relevance and practical implementation in local and global contexts. The indicators were highly rated in the Delphi Technique on appropriateness and feasibility in the context of national and local health systems with 102 (100%) of the indicators rated appropriate and 99 (97.1%) currently feasible, in a local context. The RAND/UCLA panellists rated appropriateness and feasibility in a global context and rated 131 indicators (96.3%) appropriate and 72 (52.9%) currently feasible, in a global context. One challenge that can hinder the application of quality indicators is the feasibility of collecting accurate and reliable data on the numerator and denominator in sufficient numbers of cases, to enable a measurable outcome evaluation over time ( 9 , 20 ). It is important that any difference in data outcomes between facilities is a genuine difference in quality of care, rather than a function of data quality and availability. Feedback from the Delphi Technique and the RAND/UCLA panellists highlighted several factors that currently limit the feasibility of some indicators, despite their perceived appropriateness. A substantial proportion of indicators received equivocal feasibility ratings (median4-6) across both consensus methods, reflecting uncertainty about their routine implementation rather than disagreement about their relevance to improving antibiotic use. These feasibility concerns were largely due to contextual constraints, including variability in data availability, inconsistent clinical documentation or diagnostic capacity, and human and analytical resources required to routinely measure the vast number of indicators. As the indicators were aligned to the AWaRe antibiotic book, they focus predominantly on empiric prescribing, which accounts for the majority of antibiotic use in the primary care settings ( 14 , 15 ). Empiric prescribing based on the AWaRe book may serve as a starting point for local adaptation based on local resistance patterns ( 8 ). Encouragingly, one study that compared 80 national treatment guidelines of paediatric infections in primary care with the AWaRe Book, found that most first line recommendations were closely aligned to AWaRe Book recommendations ( 21 ). In another study assessing the alignment of adult STGs in 24 countries to the AWaRe book found that many first-line antibiotic treatment recommendations showed moderate alignment with the AWaRe book ( 22 ). The indicators reported here are therefore presented as model indicators, without accompanying targets. They were deemed appropriate in that they are reasonable and pertinent for supporting quality antibiotic care. However, variation in local disease burden, health system capacity, and stewardship priorities means that not all indicators will be equally informative or effective in every setting. This variability underscores important implementation challenges, including the feasibility of routine data capture, prioritisation of local indicators, and technical capacity. Our study used QIs identified in a narrative review ( 16 ), refined them as antibiotic treatment indicators aligned with the AWaRe antibiotic book and developed model indicators for use in primary care and hospital care settings in both higher-income and LMICs. However, the emphasis is on improving stewardship interventions in LMICs, especially the primary care settings ( 23 , 24 ). Previous studies have developed a broad range of QIs using similar methodologies; however, many of these are more HIC based ( 25 ). The Driving Reinvestment in Research and Development and Responsible Antibiotic Use (DRIVE-AB) project aimed to identify QIs through a similar method ( 25 , 26 ); however, their indicators were more tailored to high-income countries and placed greater emphasis on microbiology-driven prescribing ( 25 ). This can make some of their indicators less feasible in many LMIC settings, where diagnostic capacity and data quality may be limited. The European Surveillance of Antimicrobial Consumption (ESAC) developed QIs; however, they were focused on applicability in Europe ( 27 ). ESAC indicators focused on antibiotic prescribing based on national data on antimicrobial use ( 27 ), while these AWaRe QIs focus on disease specific patient level data with additional general indicators. The Delphi Technique and RAND/UCLA panels were selected for regional and gender balance and included diverse health professionals. However, some specialties may not have been represented, and panellists may have had broad subject knowledge but not direct experience with data collection at facility level. In addition, the AWaRe QIs did not include sexually transmitted infections, as antibiotic use for these conditions is often guided by syndromic management approaches, local resistance patterns, and evolving treatment guidelines, which limited the development of consistent and comparable QIs. The strengths of this study include a level of consensus within panels that reflected regional WHO office and gender balance, and also feasibility of collecting data currently, which were included as important attributes for the indicators ( 11 , 19 ) in parallel with appropriateness. Both methods followed established standards for conducting and reporting consensus studies, ensuring anonymity of ratings, iteration, controlled feedback, statistical stability, with no pressure to force agreement ( 17 ). The next step is to evaluate the practical application of the QIs in clinical settings across different countries, including how they can be implemented using routine data, their measurability in practice, and how useful they are for informing decision-making by AMS committees ( 28 ). Addressing the feasibility challenges identified through this work, including strengthening data availability and quality, improving diagnostic capability, and building stewardship capacity, will be essential to enable broader and more consistent use of these indicators. In particular, even where data are available there are limitations related to data quality, incomplete or inconsistent reporting, and lack of standardisation ( 29 ), which limit the reliable application and comparability of the QIs. Strengthening routine data systems and expanding the use of electronic and digital data infrastructure, especially among LMICs, will be critical to support accurate measurement and sustainable implementation. Widespread use of these indicators could lead to improved ( 1 ) prescribing quality and outcomes for patients, ( 2 ) enhanced monitoring of antibiotic use stratified by AWaRe group and ( 3 ) identification of priorities for interventions and policies both globally and locally. For example, a focus on primary care minor respiratory tract infections could be prioritised, which is commonly associated with antibiotic overuse in HIC and LMICs ( 23 , 29 , 30 ). Optimising the use of antibiotics is a key global priority for better patient care and to reduce the burden of AMR. Achieving this means promoting better care practices and using measures including QIs to help implement the AWaRe system ( 31 ), with its use growing across LMICs to monitor and improve future antibiotic use ( 32 ). Standardisation of AWaRe based QIs allows sharing of findings and experiences from testing between and within countries to foster a culture of individual and collective transferable learning and quality improvement within and between countries ( 33 , 34 ) Conclusion This study has identified appropriate and feasible sets of model quality indicators that are actionable and adaptable within national multidisciplinary AMS strategies across the human health sector. These AWaRe quality indicators can help provide data for standardised global reporting of antibiotics, optimise antibiotic use, identify infection specific prescribing patterns and help assess clinical performance. Data Availability All data produced in the present work are contained in the manuscript and supplementary Author Contributions MS, SMC, AC conceptualised the study EF, GL conducted narrative review to establish the initial list of quality indicators AC, JG, BG, CEM, SMC, MS adapted indicators before and after panel ratings OOO, MZM, JM, IDO, PCW, GPR, PT, MSB, HFLW, FIA, SJC, CT, LB, FK, HBK, NRP, CLA, SMSY, RXN, MAH, RS, LL, JEEA, CP, RC, PL, NAF, SJH, RA, PT, LG, AJB, MEDK, AD, GJP, ZNS, MHO, CSJT, EI, NS, ZUM, FD, DD, CWO, LS, AC, RP, SYE, NK, DK, NN, AR, JF, AF, L-E, BI, CE, GLH, APMP, LYH, XTT, FB, CT, EJN, DL, SA, HHC, SH, LIC, MNO, VV, SFM, TTPN, SSYW, DD, ABAR, YJ, GAMY, FK, ML, MS, JE, ALG, MP, KMF, MSF, RC, MBYN, AV, EAA, VZ, SP, MC, BN, ABL, ON, IK, HO, DA, HS, KR, TA, SBB, YU, APP, VTLH, MM, HZS, JS, DK, MH, SFC, YX, JTO, NAS, JA, TK, SA, MA, SMF, AM, RA, CW, EDA, LB, RADS, AYM rated indicators as part of the Delphi Panel PC, CO, NTTD, ET, EW, JAS, BH, YMK, ZS, SG, FCL, MM rated indicators as part of the RAND/UCLA Panel JG managed the panellists and data AH, FD, JG analysed the indicators and consensus ratings AH, JG, SMC wrote the first draft of the manuscript All authors critically reviewed and revised the manuscript View this table: View inline View popup Acknowledgements Thank you to Baboucarr Njie for providing data management support. AC is also affiliated with Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, England, UK. SMC are also affiliated with School of Pharmacy, Sefako Makgatho Health Sciences University, Ga-Rankuwa, Pretoria, South Africa. BG is also affiliated with Strathclyde Institute of Pharmacy and Biomedical Sciences, Strathclyde University, Glasgow, UK and Institute for Infection and Immunity, City St. George’s, University of London, London, UK. This work was done as part of the ADILA project (Wellcome Trust Grant Number 222051/Z/20/Z) and supported by Fleming Fund TACE Asia and Africa which is funded by the Department of Health and Social Care (DHSC)’s Fleming Fund using UK aid. Ethical approval for this study was obtained from St. George’s, University of London Research Ethics Committee under the ADILA project (REC reference: 2022.0113) This study is an independent academic exercise and not a WHO-endorsed effort. An ERC review was not required as the WHO-affiliated participants in this study are experts or organisational representatives commenting in their professional role on technical matters and who reviewed and commented on draft findings. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of WHO or any of the institutions mentioned. Footnotes ↵ ‡ AWaRe QIs Delphi Panel (see full list of authors at end of manuscript) ↵ § RAND/UCLA Panel Improved the clarity and focus of the Results and Discussion sections. Specifically, the Results aligns more closely with the Methods, and the Discussion focuses on synthesising findings, anticipated implementation challenges, and next steps. Reduced the emphasis on procedural methods by moving detailed descriptions of the Delphi and RAND/UCLA processes to the Supplementary Material. 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Share Development of AWaRe antibiotic quality indicators for optimal use Annie Heath , Jan Goelen , Pem Chuki , Aislinn Cook , Filip Djukic , Nga Thi Thuy Do , Elisa Funiciello , Sumanth Gaundra , Brian Godman , Benedikt Huttner , Yara Mohsen Khalaf , Guilia Lorenzetti , Marc Mendelson , Catrin E Moore , Claudia GS Osorio-de-Castro , Zikria Saleem , Jeroen Schouten , Elizabeth Tayler , Evelyn Wesangula , AWaRe QIs Delphi Panel , Stephen M Campbell , Michael Sharland medRxiv 2025.10.24.25338539; doi: https://doi.org/10.1101/2025.10.24.25338539 Share This Article: Copy Citation Tools Development of AWaRe antibiotic quality indicators for optimal use Annie Heath , Jan Goelen , Pem Chuki , Aislinn Cook , Filip Djukic , Nga Thi Thuy Do , Elisa Funiciello , Sumanth Gaundra , Brian Godman , Benedikt Huttner , Yara Mohsen Khalaf , Guilia Lorenzetti , Marc Mendelson , Catrin E Moore , Claudia GS Osorio-de-Castro , Zikria Saleem , Jeroen Schouten , Elizabeth Tayler , Evelyn Wesangula , AWaRe QIs Delphi Panel , Stephen M Campbell , Michael Sharland medRxiv 2025.10.24.25338539; doi: https://doi.org/10.1101/2025.10.24.25338539 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Health Systems and Quality Improvement Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4434) Dentistry and Oral Medicine (444) Dermatology (382) Emergency Medicine (608) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1509) Epidemiology (15227) Forensic Medicine (30) Gastroenterology (1124) Genetic and Genomic Medicine (6595) Geriatric Medicine (668) Health Economics (997) Health Informatics (4534) Health Policy (1368) Health Systems and Quality Improvement (1613) Hematology (540) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15916) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (146) Nephrology (667) Neurology (6599) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1144) Occupational and Environmental Health (957) Oncology (3332) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (663) Pediatrics (1692) Pharmacology and Therapeutics (691) Primary Care Research (711) Psychiatry and Clinical Psychology (5447) Public and Global Health (9230) Radiology and Imaging (2198) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (712) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a000678fda0df047',t:'MTc3OTUwMDk3MQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();