In a recent study published in The Lancet Public Health, Aislinn Cook and her colleagues present modelled estimates of optimal antibiotic use across 186 countries, territories, and areas (CTAs). The research utilizes data from the WHO AWaRe classification, which categorizes antibiotics into three groups: Access, Watch, and Reserve. By analyzing the disease burden and levels of antibiotic resistance, the authors establish benchmarks to help CTAs compare their antibiotic practices effectively.
The ideal strategy aims for at least 70% of total antibiotic use to come from the Access group by 2030, as outlined in the 2024 UN General Assembly target. This benchmarking approach enables CTAs to contextualize their antibiotic use against a relevant reference, thereby informing better practices.
The study highlights significant disparities in antibiotic use across different income settings. While estimates of optimal total antibiotic use align closely with actual usage data, there exists a problematic skew in global distribution. Overuse is predominantly observed in high-income regions, whereas low-income countries often face issues with underuse of necessary antibiotics. This imbalance poses a critical threat to successful efforts in combating antimicrobial resistance (AMR).
Cook and colleagues argue that merely assessing the overall use of antibiotics does not sufficiently capture the essence of antibiotic stewardship. They advocate for a model that adjusts total antibiotic use according to the incidence of infectious diseases, which provides a clearer picture of potential overuse or underuse.
In their findings, the authors point out that many low-income settings experience unmet needs for Watch and Reserve antibiotics, which can exacerbate issues related to AMR. For instance, a large portion of Watch antibiotic utilization in rural sub-Saharan Africa could be better managed through either optimal Access antibiotic use or the complete avoidance of antibiotic prescriptions.
Conversely, while high-income regions may exhibit rigorous regulation of antibiotic distribution, this does not always apply in lower-income contexts. These areas often have a significant prevalence of substandard antibiotics being used informally, making it challenging to gather accurate data on availability and actual use.
The authors found no clear correlation between demographic or clinical factors and antibiotic use by AWaRe category, suggesting that effective antibiotic stewardship largely hinges on changes in community behavior. Their previous studies demonstrate that targeted behavioral interventions can significantly reduce the use of Watch antibiotics by influencing both healthcare providers and community members. However, for such strategies to be effective long-term, they must be integrated into existing health systems. This also demands sustained support from policymakers, regulatory bodies, and community leaders.
To combat the inequities described, many CTAs have established national action plans to address antimicrobial resistance, in line with WHO recommendations. Integrating benchmarking into these action plans can help structure activities and timelines, ensuring that antibiotic use aligns with actual healthcare needs. Proposed actions might include training on diagnostics, prescribing practices, and improving access to affordable antibiotics, particularly from the Watch and Reserve categories.
Despite the study’s robust framework, significant challenges remain, particularly regarding data quality in lower-income settings. The authors emphasize that data were only available for 67 CTAs, none of which were classified in the lowest-income bracket. Consequently, the benchmarking process necessitates using references from higher income categories, complicating comparisons.
To successfully tackle these disparities, improving the availability and reliability of data concerning disease burden, antibiotic resistance, and usage patterns in resource-constrained areas is essential. Action is urgently needed in regions facing the greatest challenges, as improved data collection can lead to more effective interventions against AMR.