4.4 Interpretation based on choice of comparator
The interpretation of deaths due to AMR (whether referring to excess mortality, population attributable mortality or deaths due to AMR) has two components based on the comparator used. (For the definitions of the two comparators and their underlying assumptions refer to Section 3.5.)
Using the antimicrobial-susceptible infection counterfactual, this estimate reflects the number of preventable deaths assuming that:
- AMR infections could be avoided
- susceptible infections of the same bacterial species could take over the same ecological niche.
Using the target AMR infection-free counterfactual, this estimate reflects the number of preventable deaths assuming that:
- AMR infections could be avoided
- preventing the AMR infection would not increase the risk of susceptible infections caused by the same pathogen.
Activity 6: Contextualising the scale of AMR burden and reflecting on burden of AMR for your country
The following two tasks aim to help you contextualise AMR burden estimates. You will be using two open access platforms to take a close look at the existing estimates for your country.
The first platform is the WHO’s Global Antimicrobial Resistance and Use Surveillance System (GLASS) surveillance dashboard. Launched in 2015, WHO GLASS is the first global collaborative effort to standardise AMR surveillance. The WHO has provided further details of WHO GLASS [Tip: hold Ctrl and click a link to open it in a new tab. (Hide tip)] (WHO, n.d. 3).
The second platform is the Measuring Infectious Causes and Resistance Outcomes for Burden Estimation (MICROBE) interactive visualisation tool. This tool is one of the outputs from the Global Research on Antimicrobial Resistance (GRAM) project, which aims to understand the threat using modelling to estimate the burden of AMR. The University of Washington’s Institute for Health Metrics and Evaluation has provided further details of the GRAM project (IHME, n.d.). A key publication from the GRAM project is ‘Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis’ (AMRC, 2022).
Task 2
Go to the MICROBE tool.
Select your country from the drop-down menu and complete Table 3 using the information for your country. Explore the dashboard by reading the descriptions for each of the presented estimates and by going through the ‘Sepsis’, ‘Bacteria’ and ‘Resistance’ tabs.
| Country | |
| Estimated number of sepsis deaths | |
| Number of sepsis deaths related to bacterial infections | |
| Number of excess deaths due to AMR infection compared to if the exposed population had an antimicrobial-susceptible infection | |
| Number of excess deaths due to AMR infection compared to if the exposed population had not had any bacterial infection |
(Optional) Fill in the table for another country and reflect on why there might be differences between this country and your own.
Discussion
The results that you recorded in Table 3 will depend on the country that you have selected. Table 4 shows the data for Timor-Leste as an example.
| Country | Timor-Leste |
| Estimated number of sepsis deaths | It is estimated that in 2021, 3423 people in Timor-Leste died with sepsis as an immediate or intermediate cause of death |
| Number of sepsis deaths related to bacterial infections | The number of bacterial infections in the sepsis deaths is 1851 |
| Number of excess deaths due to AMR infection compared to if the exposed population had an antimicrobial-susceptible infection | 192 deaths |
| Number of excess deaths due to AMR infection compared to if the exposed population had not had any bacterial infection | 840 deaths |
Now that you have learned about how the burden of AMR can be estimated, the next sections of the course will show how these techniques can be (a) linked to economics, and (b) used to inform decision-making.
4.3 Estimating mortality and number of deaths due to AMR
