---
title: "Estimating WHO AWaRe Antibiotic Use Levels Based on Infection and Resistance Burden"
id: "the-lancet-public-health-3-estimating-optimal-levels-of-who-access-watch-reserve-aware-antibiotic-use-in"
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content_type: "clinical_feed_article"
specialty: "Infectious Disease"
source_name: "The Lancet Public Health"
source_url: "https://www.thelancet.com/journals/lanpub/article/PIIS2468-2667(26)00103-9/fulltext?rss=yes"
doi: "10.1016/S2468-2667(26)00103-9"
published_at: "2026-07-26T12:32:44.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Estimating WHO AWaRe Antibiotic Use Levels Based on Infection and Resistance Burden
## Provenance & Clinical Metadata
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- **Specialty:** [Infectious Disease](https://medichelpline.com/clinical-feed/infectious-disease.md)
- **Primary Source:** The Lancet Public Health
- **Source URL:** [Original Journal Publication](https://www.thelancet.com/journals/lanpub/article/PIIS2468-2667(26)00103-9/fulltext?rss=yes)
- **DOI:** [10.1016/S2468-2667(26)00103-9](https://doi.org/10.1016%2FS2468-2667(26)00103-9)
- **Published At:** 2026-07-26T12:32:44.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- The study addresses the need for appropriate access to **antibiotics** as emphasized by the 2024 UN General Assembly, targeting 70% of use from the **Access** group of the WHO **AWaRe** system. - It applies a framework to estimate optimal national antibiotic use for **186 countries, territories, and areas (CTAs)** based on disease burden and antibiotic resistance. - Data from global datasets were used to categorize CTAs into four peer clusters, allowing benchmarking against regions with low antibiotic use and mortality. - Findings indicate a need for an estimated **43 billion defined daily doses (DDD)** of antibiotics, with over 77% ideally from the **Access** group. - Significant overuse of antibiotics was observed, particularly in high-income settings, where 87% exceeded optimal use levels. - This research emphasizes the importance of comparing actual antibiotic use against estimated optimal levels to inform national health policies regarding antibiotic access.
## Clinical Analysis & Structured Key Points
Skip to Main Content Skip to Main Menu Submit Article Log in Register This journal Journals Publish Clinical Global health Multimedia Events About Advanced search ARTICLESVolume 11, Issue 8E476-E486August 2026Open Access Download Full Issue Estimating optimal levels of WHO Access, Watch, Reserve (AWaRe) antibiotic use in 186 countries, territories, and areas on the basis of clinical infection and resistance burden Aislinn Cook, MSca,b Send email to aicook@citystgeorges.ac.uk ∙ Prof Ben Cooper, PhDc ∙ Mike Thorna ∙ Nam Nguyen, PhDb ∙ Cherry Lim, DPhild ∙ Myo Maung Maung Swe, DPhild ∙ et al. Show more Affiliations & Notes Article Info Linked Articles (1) Download PDF Cite Share Set Alert Get Rights Reprints Previous article Next article Show Outline Summary Background Ensuring appropriate access to essential antibiotics is a crucial public health goal. The 2024 UN General Assembly agreed that 70% of global antibiotic use should be from the Access group of the WHO Access, Watch, Reserve (AWaRe) system. A standard method to estimate optimal national-level antibiotic use based on burden of disease, resistance, and local context is needed to inform national policies. We aimed to develop and apply a burden-adjusted framework for estimating expected optimal national levels of AWaRe antibiotic use, in total and by AWaRe group. Methods We used data from multiple global sources—including datasets from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021; the Global Research on Antimicrobial Resistance project; and the World Bank—to cluster 186 countries, territories, and areas (CTAs) into four peer groups on the basis of sociodemographic factors, infection burden, and resistance incidence using a latent class model. Within each cluster, we identified benchmark CTAs with low antibiotic use and low infection mortality. For each CTA, we used the infection burden to estimate the optimal total defined daily doses (DDD) per 1000 inhabitants per day (DID) for 2019. We then estimated optimal Reserve DID on the basis of relevant resistance burdens, optimal Watch DID from the number of infections requiring Watch antibiotics as defined in the WHO AWaRe antibiotic book, and optimal Access DID as the remaining volume after accounting for Watch and Reserve antibiotic needs. Where CTA-level data on actual antibiotic use in 2019 were available in the IQVIA MIDAS database, estimated optimal levels were compared with actual levels, in total and by AWaRe group. Findings We estimated that, in 2019, 43·0 billion DDD (95% CI 35·4 billion–57·7 billion) of antibiotics were needed in 186 CTAs, of which 77% (95% CI 71–83) would optimally be from the Access group. CTAs in lower-income clusters required more Watch and Reserve antibiotics than higher-income CTAs: at optimal use levels, 81·7% (80·3–82·9) of global Watch antibiotic need and 80·7% (95% CI 68·5–88·9) of global Reserve antibiotic need would arise from the two lowest-income clusters. Among 67 CTAs with actual antibiotic use data available, 48 (72%) used higher total antibiotic volumes than were estimated optimal. Overuse was most frequent in high-income settings: 33 (87%) of 38 CTAs in the highest-income cluster exceeded the estimated optimal total DID. 66 (99%) of 67 CTAs used more Watch antibiotics than optimal, whereas 36 (54%) used lower volumes of Reserve antibiotics and 28 (42%) used lower volumes of Access antibiotics than were estimated optimal. Interpretation We present estimates for optimal AWaRe antibiotic use for 186 CTAs. After accounting for CTA-specific needs, the UN General Assembly's target of 70% of global antibiotic use being from the Access group seems globally appropriate. Benchmarking the use of AWaRe antibiotics enables estimates of their underuse and overuse in individual CTAs, helping to inform national policies. Funding The Antibiotic Data to Inform Local Action (ADILA) Project, funded by the Wellcome Trust. Introduction Ensuring equitable, appropriate access to effective essential antibiotics is central to the Sustainable Development Goals.1 In 2017, WHO introduced the Access, Watch, Reserve (AWaRe) system, which groups more than 250 antibiotics on the basis of effectiveness, safety, and selection for resistance.2,3 Access antibiotics, such as amoxicillin, are generally first-line or second-line antibiotics for common infections and have lower potential for resistance; Watch antibiotics have higher potential for resistance and should be used for particular specific infections; and Reserve antibiotics are last-resort antibiotics for the treatment of multidrug-resistant (MDR) infections. Only 42 antibiotics were included in the 2025 update of the WHO Model List of Essential Medicines (appendix p 8). The WHO AWaRe antibiotic book provides treatment guidance for the 35 most common infections in primary care and hospital settings, including recommendations for the choice of antibiotic from the list (if indicated) and its dose and duration.4,5 Research in context Evidence before this study At the 2024 UN General Assembly High-level Meeting on antimicrobial resistance (AMR), member states agreed that the WHO Access, Watch, Reserve (AWaRe) system should underpin global antibiotic surveillance and that 70% of global use should comprise antibiotics from the AWaRe Access group, accounting for national contexts. However, no method for deriving country-level targets was recommended. Several studies and surveillance systems have reported observed antibiotic use. The WHO Global Antimicrobial Resistance and Use Surveillance System antimicrobial use component (GLASS-AMU) reported actual medicine-level antibiotic use from 2015 to 2022 for 60 countries, territories, and areas (CTAs). The Global Research on Antimicrobial Resistance (GRAM) project modelled estimated total antibiotic use from 2000 to 2018 for 204 CTAs but was able to estimate AWaRe antibiotic use for only 76 CTAs for which data from the commercial IQVIA MIDAS database were available. A study published in 2024 reported changes in antibiotic use from 2016 to 2023 for 67 CTAs, and a 2025 study reported trends in antibiotic use from 2010 to 2021 for 74 CTAs with data in the MIDAS database. However, there are very few estimates of what levels of antibiotic use could be considered as optimal, both overall and by AWaRe group. We searched PubMed for studies published in English from Jan 1, 2015 to March 30, 2026, using the search string “estimates global antibiotic use”. We identified three studies that estimated optimal levels of antibiotic use, but none that included estimates for all AWaRe groups. One 2024 study estimated expected total antibiotic use on the basis of infection burden using guidance from the WHO AWaRe antibiotic book. This study developed a framework for estimating the required use of Watch group antibiotics, but did not provide comprehensive estimates for the Access and Reserve groups. A study published in 2025 estimated antibiotic needs for chronic obstructive pulmonary disease and pneumonia in 20 CTAs using disease burden and bacterial aetiology. These estimates focused only on penicillins and cephalosporins and did not provide population-based standardised measures to enable cross-national comparison among CTAs. Another 2025 publication used estimates from the GRAM project and IQVIA sales data to estimate the gap in Reserve antibiotic treatment courses for use in carbapenem-resistant, Gram-negative infections in eight countries. Although these studies estimated expected antibiotic use for specific antibiotics or infections, we found no reported method to estimate optimal national levels for total antibiotic use and use by AWaRe group. Added value of this study We developed a standard method for deriving optimal ranges of total and AWaRe antibiotic use in defined daily doses per 1000 inhabitants per day (DID), accounting for national-level infection and antibiotic resistance burden, population sociodemographics, national income, health system infrastructure, and health-care access using a benchmarking approach. Our study provides, to our knowledge, the first comprehensive estimates of optimal total antibiotic use, disaggregated by AWaRe group, in DID for a 2019 baseline for 186 CTAs, and compares actual AWaRe antibiotic use to expected optimal use for 67 CTAs using the MIDAS dataset. Our findings could inform national and global evidence-based antibiotic policy development and implementation. Implications of all the available evidence The UN General Assembly AMR commitments provide a clear direction for global target-setting for antibiotic use. We present a method to estimate CTA-specific optimal antibiotic use for both total and AWaRe antibiotics for 186 CTAs. Estimating optimal use with an agreed standard method across CTAs would enable benchmarking and comparison among peer groups, assisting with target-setting and shared learning across National Action Plans from different policy initiatives and outcomes. Global human antibiotic use is now driven largely by increased use in middle-income countries, particularly of antibiotics from the Watch group.6 High-income countries generally still have higher rates of antibiotic use per capita than countries from lower-income groups,7,8 and, in many low-income countries, access to essential antibiotics from all AWaRe groups remains limited, from oral amoxicillin (in the Access group) to new antibiotics from the Reserve group.9,10 The 2024 UN General Assembly High-level Meeting on antimicrobial resistance (AMR) endorsed two key commitments on antibiotic use: that the WHO AWaRe system should underpin global surveillance, and that, by 2030, at least 70% of global human antibiotic use should comprise antibiotics from the Access group.11 Although this usage target provides an important global benchmark, it does not account for large differences between countries in terms of infection burden, antibiotic resistance, population demographics, and health-care provision.12–14 Countries with very different absolute levels of antibiotic use can meet the 70% Access target, obscuring both excessive use and crucial gaps in access (appendix p 9). To address these limitations, complementary metrics are required that estimate optimal antibiotic volumes, expressed as defined daily doses (DDD) per 1000 inhabitants per day (DID), for both total use and use across AWaRe groups, while accounting for local epidemiological contexts and prevalence of antibiotic resistance. We aimed to develop and apply a burden-adjusted framework for estimating expected optimal national levels of AWaRe antibiotic use—on the basis of infection burden, antibiotic resistance, and sociodemographic characteristics—for 186 countries, territories, and areas (CTAs). Comparing these optimal levels with actual use levels could help to inform national antibiotic policies and identify access gaps. Methods Overview For 186 CTAs and at the CTA level, we estimated optimal ranges of antibiotic use volumes, expressed as DID, for total AWaRe group antibiotics for 2019. Estimates were made on the basis of CTA-specific infection burden, antibiotic resistance burden, and sociodemographic factors such as age groups, national income, and health system factors that could influence infection risk, poor infection outcomes, and health-care access. We defined optimal levels as the estimated expected AWaRe antibiotic use on the basis of infection burden and AMR accounting for wider sociodemographic factors. We used two complementary analytical approaches, described in brief here and in full in the appendix (pp 10–49). First, we applied a multivariate ecological regression model to estimate expected levels of AWaRe antibiotic use given CTA-level infection burden, antibiotic resistance burden, and population risk factors to predict expected AWaRe-specific antibiotic use volumes for each CTA with covariates. Second, we developed a benchmarking approach to estimate expected optimal levels of AWaRe antibiotic use based on comparisons with peer CTAs. CTAs were clustered into peer groups with similar characteristics using a latent class model. Within each cluster, benchmark CTAs were selected on the basis of total DID, infection mortality, and percentage of Access antibiotic use (ie, percentage of total antibiotics used that were from the Access group). Optimal total antibiotic levels were estimated for CTAs within each cluster given their respective infection burden, after which optimal levels of Reserve antibiotics were estimated from relevant antibiotic resistance burdens, Watch antibiotics from the burden of infections requiring Watch antibiotics, and Access antibiotics as the residual volume after accounting for Watch and Reserve antibiotic needs (figure 1). Figure 1 Schema for deriving estimates of optimal AWaRe antibiotic use Show full captionFigure viewer The ADILA Project, under which this study was conducted, received ethical approval from the research ethics committee of City St George's, University of London (London, UK; reference 2202.0113). This study is reported in accordance with the GATHER recommendations for health estimates; a completed GATHER checklist is provided in the appendix (p 7). Data sources We combined data from multiple sources, with full details in the appendix (pp 10–18). We used data from 2019, as this was the baseline year specified in the UN General Assembly declaration and to avoid disruption from the COVID-19 pandemic. CTA-level antibiotic sales data were obtained from the IQVIA MIDAS Quarterly Sales Database for 72 CTAs, covering hospital and retail sectors. Sales volumes were converted from kg to DDD using the 2025 WHO Anatomical Therapeutic Chemical/DDD method15 and expressed as DID using World Bank population estimates or alternative sources.16,17 Infection incidence and mortality estimates for 2019 for conditions for which antibiotics are recommended in the WHO AWaRe antibiotic book were taken from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 202118,19 where available: cellulitis, upper respiratory tract infections, otitis media, lower respiratory tract infections (excluding hospital-acquired pneumonia), MDR and extensively drug-resistant tuberculosis, typhoid and paratyphoid, diarrhoea, urinary tract infections, chlamydia, syphilis, gonorrhoea, trichomoniasis, and sepsis. We approximated the incidence of cholera, dysentery, and hospital infections not included in GBD 2021 (hospital-acquired pneumonia and intra-abdominal infections; appendix p 11). We used data from the Global Research on Antimicrobial Resistance (GRAM) project20 to estimate the incidence of infections caused by antibiotic-resistant pathogens that might require escalating among AWaRe groups (eg, where carbapenem resistance is common, sepsis treatment might more frequently require a Reserve antibiotic). We included other covariates that might be associated with antibiotic use at a population level (appendix pp 12–18). Missing sector-level data from MIDAS and missing covariates were imputed with multiple imputation by chained equations (appendix pp 18–20). To avoid fitting models on data from CTAs with low, potentially unsafe levels of antibiotic use, or those where reported use levels were implausibly low, we excluded five CTAs (China, Indonesia, Malaysia, the Philippines, and Venezuela) where the majority (>50%) of imputed total DID were less than 9·7, the lowest value observed in a high-income CTA (the Netherlands), leaving 67 CTAs for further analysis. Statistical analysis To quantify how national factors potentially associated with antibiotic need relate to antibiotic use volumes across AWaRe groups, we initially applied a Bayesian multivariate ecological regression model with a gamma distribution and a log-link function to CTA-level data from 67 CTAs in MIDAS (figure 1 component 1). Covariates including infectious disease burden, antibiotic resistance, and other covariates were selected a priori and specified separately for Access, Watch, and Reserve antibiotics. We used normal priors for the intercepts, deriving mean and SD from the GRAM project estimates of antibiotic use in 2018.7 For the covariates, we used horseshoe priors with a parameter ratio of 0·8. Models were fitted separately to each of 1000 imputed datasets, and posterior draws were pooled to propagate uncertainty arising from both missing data and model estimation (appendix pp 21–22). As a separate analysis, we also developed an alternative benchmarking approach to estimate optimal national antibiotic use volumes by AWaRe group (figure 1 component 2). The 186 CTAs with complete covariates were grouped into four peer clusters using latent class modelling of 40 sociodemographic and clinical covariates (excluding antibiotic use; figure 1, appendix pp 23–26).21,22 The four clusters were ordered by median gross national income per capita, from lowest (cluster 1) to highest (cluster 4) income. Within each cluster, a benchmark CTA with actual antibiotic use data available in MIDAS was then identified as best-in-cluster in terms of antibiotic use levels, selected on the basis of minimising total antibiotic use and infection mortality while maximising the percentage of Access antibiotic use (appendix pp 27–34). If no suitable benchmark CTA could be identified within a cluster owing to high mortality, a low percentage of Access antibiotic use, or high total antibiotic use, we used the benchmark CTA from the next highest cluster (appendix pp 27–28); in these cases, estimates represent a minimum optimal range of antibiotic use required for that cluster. We used the benchmark CTAs to estimate optimal total DID for other CTAs in the same cluster, assuming their respective infection burdens would be treated according to the prescribing practices of the benchmark CTA. For each benchmark CTA, we calculated the ratio of total antibiotic use to total infection incidence. This ratio was applied to the infection burden of the remaining CTAs within the same cluster to calculate the expected total DID (appendix p 38). For our primary analyses, we adjusted total case counts for urinary tract infection and cellulitis to account for potential underdiagnosis of infections requiring antibiotics in GBD 2021 (appendix pp 35–37). Expected Reserve DID was estimated using ratios of Reserve antibiotics to resistant infections derived from the benchmark CTA in the highest-income cluster, assuming that this CTA has no issues with accessing antibiotics (figure 1). Ratios were calculated separately by pathogen group and multiplied by the corresponding resistant-infection burdens in each CTA, then summed to estimate total Reserve antibiotic requirements (appendix pp 38–39). Expected Watch DID was estimated from case counts of infections for which Watch antibiotics are recommended in WHO guidelines: typhoid, dysentery, lower respiratory tract infections, urinary tract infections, MDR tuberculosis, sepsis, hospital-acquired pneumonia, intra-abdominal infections, and necrotising fasciitis (appendix pp 40–44). Given diagnostic uncertainty, for CTAs with a typhoid burden of more than 50 cases per 100 000 population, we inflated the typhoid case count to account for suspected cases that might require antibiotics (appendix pp 41–42). We calculated the proportion of urinary tract infections and lower respiratory tract infections for which Watch antibiotics are recommended (ie, upper urinary tract infections and severe lower respiratory tract infections) using estimates from the literature (appendix pp 42–43). For each infection, we multiplied the case count by the maximum DDD recommended to treat a case, then summed these estimates to calculate total Watch antibiotic needs (appendix pp 40–41). Expected Access DID was calculated as the residual volume after subtracting estimated Watch and Reserve antibiotic requirements from expected total antibiotic use. We assumed that op
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