The aim of this tracker is to provide an evidence-base for analysis of global and local trends, including comparisons across countries, locations, and human and animal sectors over time. Analysis of antimicrobial use is a key element for identifying areas at risk from AMR and can inform optimisation of antimicrobial consumption and use. The database therefore represents a powerful resource for analysis to highlight the latest antimicrobial use data and trends, as well as gaps in the existing evidence in order to influence surveillance, research and policy. The database can be filtered by author, title, journal, year of publication, location, and population.


Antimicrobial use (AMU) has been identified as one of the key drivers of antimicrobial resistance (AMR), a phenomenon which is causing increasing concern around the world, particularly in LMICs (Laxminarayan et al 2012). Antimicrobials are commonly used in human and animal health to treat infection, and in animal production to enhance growth (Van Boeckel et al 2015; Hao Van 2020). Extensive use of antimicrobials potentiates AMR by exerting a selection pressure on bacteria to select for resistant genes. Consequently, efforts to contain antimicrobial resistance have focussed on reducing antimicrobial consumption and ‘rationalising’ use. The surveillance of antimicrobial consumption and use is a key part of global plans to tackle AMR (WHO 2015), however limited synthesis of the existing data precludes systematic analysis, impeding efforts to develop targeted interventions in AMU. 

Current estimates of antiicrobial use and resistance often utilise macro-level consumption data from national sales and imports and point prevalence surveys from hospitals. The European Surveillance of Antimicrobial Consumption (ESAC) is an example of a large-scale surveillance system using national sales or reimbursement data to track consumption of antimicrobials over time, enabling cross-country comparisons and informing public policy across Europe. This system complements the European Antimicrobial Resistance Surveillance System (EARSS). The majority of LMICs have not historically collected antimicrobial consumption data, but a system is now in place for this for animal antibiotic consumption data through the OIE, and the WHO is supporting capacity in IQVIA and is expanding their network of consumption data collection across Africa and Asia through the Fleming Fund (OIE 2016, WHO 2018).

Hospital-level point prevalence surveys are also useful for enabling benchmarking comparisons of antimicrobial use across healthcare facilities, quantifying the selection pressure on microbial populations, and assessing the impact AMR on morbidity and mortality. However, in many LMICs, antimicrobials can be purchased over the counter, and there is evidence of increasing consumption of antimicrobials in formal and informal community settings; including private and public drug shops, clinics, pharmacies, and long-term care facilities (Laxminarayan et al 2012; Kotwani et al 2011). In addition, in the context of a changing global food landscape, animal and plant production are key sites of interest in a One Health approach to the surveillance of AMU and control of AMR (Van Boeckel et al 2015). 

Surveillance of antimicrobial use in the community beyond hospitals is more challenging due to the diversity of actors, facilities, and metrics involved (Chandy 2013). Nevertheless, an increasing number of studies have sought to investigate antimicrobial use at the household and community level, using a range of methods including simulated client visits, audits, outlet and community surveys (Holloway et al 2009). These studies offer a more granular analysis of the use of antimicrobials in different settings, helping to illuminate trends in availability, access, consumption and use of antimicrobials. Such data further enables critical consideration of characterisations of antimicrobial use as ‘rational’ or ‘irrational’, as well as the development of localised surveillance and intervention strategies. 

Scope of database

The database includes human and animal studies conducted in non-hospital settings that describe empirical data measuring antimicrobial use. Studies that describe reasons for antimicrobial use are also included as long as they also provide a measure of use. In order to provide different levels of data for analysis, the tracker will include national level studies, for example using sales data, as well as end-user studies, for example, outlet and household surveys. The tracker does not include hospital-based point prevalence surveys.