Antimicrobial resistance (AMR) is a global health crisis [1]. The report by Lord Jim O’Neill estimates that 700,000 global deaths could be attributable to AMR in 2015, and projects that the annual death toll could reach 10 million by 2050 [1]. However, data of AMR surveillance from low- and middle-income countries (LMICs) is still limited [1,2], and data of mortality associated with AMR is rarely available. A recent study estimated that 19,000 deaths are attributable to antibiotic-resistant infection in Thailand annually, using routinely available microbiological and hospital databases [3]. The study also proposed that hospitals in LMICs should utilize routinely available microbiological and hospital admission databases to generate reports on AMR surveillance systematically [3].

When reporting AMR surveillance results, it is generally recommended that (a) duplicate results of bacterial isolates are removed, and (b) reports are stratified by infection origin (community-acquired or hospital-acquired), if possible [2]. For de-duplication, in short, only the first isolate of a species per patient per specimen type per surveyed organism per evaluation period should be included in the report. For infection origin, specimen collection date relative to hospital admission date can be used as a proxy for community-origin or hospital-origin infections. Community-origin infection can be defined for patients cared for at outpatient clinics, or patients in hospital for less than or equal to 2 calendar days when the culture-positive specimen was taken. Hospital-origin infection could be defined for patients admitted for >2 calendar days when the culture-positive specimen was taken, or admitted to the healthcare facility for <2 calendar days but transferred from another health-care facility where he or she was admitted for ≥2 calendar days.

Reports on AMR surveillance from LMICs are limited due to multiple reasons. First, some hospitals in LMICs are recording microbiological data solely in paper forms. Secondly, hospitals with data recorded electronically often store microbiology data and hospital admission data in separate and unlinked databases. Thirdly, data cleaning, merging, de-duplication and analysis are complicated and time-consuming. Well-trained epidemiologists or statisticians who can use statistical programmes such as R, SAS, SPSS and STATA are usually required to perform data cleaning, merging, and analysis. These experts and resources often are unavailable in LMICs.

AutoMated tool for Antimicrobial resistance Surveillance System (AMASS) was developed as an offline, open-access and easy-to-use application that allows a hospital to perform data analysis independently and generate isolate-based and sample-based surveillance reports stratified by infection origin from routinely collected electronic databases. The application was built in R, which is a free software environment. The application has been placed within a user-friendly interface that only requires the user to double-click on the application icon.

AMASS performs data analysis and generates reports automatically. The raw data files required are hospital admission and microbiology databases. The application cleans the raw microbiology database and produces isolate-based and sample-based surveillance reports. The application then merges the microbiology and hospital admission datasets, analyzes the merged data, and produces an AMR surveillance report. The final step is a statistical analysis to estimate all-cause mortality of patients with AMR infection and mortality attributable to AMR, which are automatically added into the surveillance report.

Please details on how to use AMASS to generate AMR surveillance report, please refer the document and tutorial below:

How to use AMASS

AMASS Tutorial


[1] O'Neill J. (2014) Antimicrobial resistance: tackling a crisis for the health and wealth of nations. Review on antimicrobial resistance.
[2] World Health Organization (2018) Global Antimicrobial Resistance Surveillance System (GLASS) Report. Early implementation 2016-2017.
[3] Lim C., et al. (2016) Epidemiology and burden of multidrug−resistant bacterial infection in a developing country. Elife 5: e18082.