Intersectional indicators in surveillance of antimicrobial resistance and use

Why is it important?

Collecting and disaggregating data by sex, age, and other variables can provide essential insights into the drivers of health inequities. These indicators can make the inequitable burden of antimicrobial resistance (AMR) within and between groups visible and can inform the design of interventions that address the root causes of AMR.Aggregated datasets (where there is no disaggregation of data) mask significant differences between groups.

The Global Antimicrobial Resistance and Use Surveillance System (GLASS) now encourages the reporting of AMR data disaggregated by sex and age, yet many countries are still not reporting disaggregated data. There will be around 39 million deaths due to drug-resistant infections between 2025 and 2050. If we don’t disaggregate data at the global level, we will not know who is most affected (Naghavi et al., 2024).Data disaggregated by variables such as age, sex, and location are an essential first step of more in-depth intersectional gender analysis, but they are only astarting point in understanding intersectional health inequities (Batheja et al.,2025).

For guidance on how to conduct an equity analysis using surveillance data see Gender, Equity, and Antimicrobial Resistance: Guidance on analysing bacteriology laboratory and antimicrobial use data. Collecting variables that relate to multiple axes of inequity (including age, gender, disability status, ethnicity, and social class, for example) facilitates an intersectional approach, which investigates how different social inequities andpower relations interact dynamically to create unique experiences (WHO, 2020).

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