PREreview of Benchmarking AWaRe: estimating optimal levels of AWaRe antibiotic use in 186 countries, territories and areas based on clinical infection and resistance burden

This study presents a timely and policy-relevant effort to estimate “optimal” antibiotic use across 186 countries using the AWaRe framework. Its central contribution—moving beyond descriptions of current antibiotic consumption toward defining need-based targets grounded in infection burden and resistance patterns—addresses an important gap in the literature and aligns well with global policy priorities such as the UNGA 70% Access target. At the same time, the concept of “optimal” use, while useful, relies heavily on modeling choices and assumptions that would benefit from clearer qualification throughout the manuscript.

The abstract succeeds in highlighting key findings, including the estimate of 43 billion DDDs and 76% Access use, and conveys the potential policy relevance of the work. However, it tends to oversimplify the underlying methodology, providing limited insight into how these estimates are derived or how sensitive they are to assumptions. This is particularly important given that one of the initial analytical approaches—the ecological regression model—did not yield meaningful results and was not used in the final analysis. Explicitly acknowledging this and clarifying the reliance on the benchmarking approach would improve transparency.

The study is methodologically innovative, especially in its use of a benchmarking framework combined with clustering of “peer” countries. This approach offers a creative way to account for heterogeneity across settings and provides a potentially useful tool for policy comparison and target setting. However, it also depends on strong assumptions about comparability between countries and on modeled infection and resistance data, particularly in low-income settings where empirical data are sparse. These constraints do not undermine the value of the approach but do suggest that results should be interpreted with appropriate caution.

The figures reflect a clear effort to communicate complex results in a policy-relevant way. For example, Figure 4 effectively highlights discrepancies between observed and estimated optimal use, particularly the overuse of Watch antibiotics. In contrast, other figures (e.g., Figures 1–3) are visually dense and sometimes difficult to interpret, and in some cases include analytical components that were not ultimately used. Simplifying these visuals and more clearly representing uncertainty would enhance their accessibility and impact, especially for non-specialist audiences.

The manuscript demonstrates technical rigor and comprehensive data integration, drawing on multiple large-scale datasets to produce global estimates. At the same time, the extensive use of technical nomenclature and abbreviations (e.g., DDD, DID, CTAs, ABU, ABR) can reduce readability and make it harder to follow the main arguments. Streamlining terminology and improving figure labeling would help make the findings more accessible without compromising rigor.

Overall, the study provides important and potentially impactful insights into global patterns of antibiotic use, including evidence of both overuse and underuse across settings. Its benchmarking framework is a valuable step toward more context-sensitive antibiotic policy. However, greater clarity around key assumptions, more explicit communication of limitations, and a more cautious interpretation of “optimal” targets would strengthen the credibility and usability of the findings.

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they did not use generative AI to come up with new ideas for their review.