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Toolbox  –  Measure

Burden of antibiotic resistance

Data on the burden of antibiotic resistance is important to be able to communicate the urgency to address the issue, prioritize actions and determine the effects of interventions.

Potential indicators of burden

Here are some examples of relevant indicators for measuring the burden of antibiotic resistance and related infections.

Humans:

  • Excess mortality or morbidity (for a resistant infection as compared to a non-resistant infection)
  • Excess hospital, healthcare provider or payer costs
  • Economic impact (change in Gross Domestic Product)

Animals:

  • Farm productivity or yield
  • Farm costs, income or profit
  • Economic impact (change in Gross Domestic Product)

Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis

In January 2022 the most comprehensive data to date on the global health burden of antibiotic resistance was published in the Lancet. An estimated 1.27 million deaths were found to be a direct result of antibiotic resistant bacterial infections in 2019.

Based on 471 million individual records, 7585 study-location-years, 23 pathogens, and 88 pathogen–drug combinations in 204 countries and territories in 2019, the study provides compelling evidence that antibiotic resistance is not a future threat – it is already a leading cause of death globally. These are high quality estimates, however, serious data gaps still remains on infectious diseases and resistance especially in low and middle income countries.

This is the first time mortality and morbidity estimates are related to data within the Global Burden of Disease program. This means that the antibiotic resistance burden can be compared with other causes of deaths. The Global Burden of Disease is a global research program that quantifies health loss from different diseases, injuries, and risk factors.

Potential long term consequences of antibiotic resistance

When estimating the burden of resistance or the impact of related policies, it is important to consider the long-term consequences of antibiotic resistance. The flowchart below provides an overview of long-term burden of resistance if no interventions or effective antibiotics were to be added to the system.

Figure 1. Overview of drivers and consequences associated with antibiotic resistant bacteria and antibiotic use from the perspective of society.

The resources below have been divided into the following tables:

  • Tools and methodology
  • Data and reports

The burden of antibiotic resistance is closely linked to the overall occurrence of bacterial infections. For information on measuring infections, see MEASURE – Infections.

Selected Resources

Tools and methodology

Resources Description
GLASS method for estimating attributable mortality of antimicrobial resistant bloodstream infections Protocol. Master template protocol from WHO GLASS for estimating in-hospital mortality attributable to resistant blood stream infections. Focuses on ESBL E. coli and MRSA infections of both community and hospital-origin. Can be expanded to other bacteria as well. Can also support tracking of progress towards the sustainable development goals and specifically the antimicrobial resistance indicator.
Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area in 2015: a population-level modelling analysis Journal article with methodology from ECDC reporting on the burden of antibiotic resistance in EU/EEA. It also includes estimates of morbidity in DALYs. 
LiST: The Lives Saved Tool Tool: The Lives Saved Tool (LiST) is a PC tool that estimates the impact of scaling up health and nutrition interventions on maternal, newborn, and child health.
Estimating the burden of antimicrobial resistance: a systematic literature review Review with recommendations discussing alternative methodologies for estimating the burden of antibiotic resistance in the current evidence base.
Economic analysis of animal diseases Guidelines on how to conduct analysis on economic impact of animal diseases (but not specifically on antibiotic resistance).

Data and reports

Resources Description
Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis Report from the Global Research on AntiMicrobial resistance (GRAM) project that details the burden of antibiotic resistance globally as well as broken down into different regions and countries. It presents data on 23 pathogens and 88 pathogen–drug combinations, and what bacterial diseases are most affected by resistance.

See the webinar dedicated to the launch of the GRAM study report here.

Clinical and economic impact of antibiotic resistance in developing countries: A systematic review and meta-analysis Review that collates studies estimating the health and cost burden of antibiotic resistance in developing countries.
When the Drugs Don’t Work – Antibiotic Resistance as a Global Development Problem (PDF 6,1MB) Report from ReAct that describes the negative impact of antibiotic resistance on global and national efforts to eradicate poverty, spur economic growth, reduce inequality, improve global public health, reduce hunger and protect the environment (Sustainable development goals 1, 2, 3, 5, 6, 8, 10, 12, 14 and 15).
Antibiotic resistance threats in the United States, 2019 Report that describes the situation for 18 pathogens, trends over time and treatment options in an easy to access format.
Drug-resistant infections: A Threat to our Economic Future, 2017 Report from the World Bank that estimates the potential burden of antimicrobial resistance by 2050. See “Part II. Economic Impact of AMR” for estimates of the burden of antibiotic resistance on health and agricultural sectors in low-income countries.
Antibiotic resistance – consequences for animal health, welfare, and food production Review that describes the negative effects of antibiotic resistance on animal health, animal welfare and different socio-economic consequences if infectious diseases in animals cannot be treated.
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Antimicrobial Resistance Collaborators. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. (2022) http://doi.org/10.1016/S0140-6736(21)02724-0.
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World Health Organization - WHO. GLASS method for estimating attributable mortality of antimicrobial resistant bloodstream infections. Preprint at https://www.who.int/publications/i/item/9789240000650 (2020).
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Centers for Disease Control and Prevention - CDC. Antibiotic Resistance Threats in the United States 2019. Preprint at https://www.cdc.gov/drugresistance/biggest-threats.html?CDC_AA_refVal=https%3A%2F%2Fwww.cdc.gov%2Fdrugresistance%2Fbiggest_threats.html (2019).
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World Bank. Drug-Resistant Infections: A Threat to Our Economic Future. http://www.worldbank.org/en/topic/health/publication/drug-resistant-infections-a-threat-to-our-economic-future (2017).
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McLeod, A. et al. Economic analysis of animal diseases. (2016).
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Cassini, A. et al. Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area in 2015: a population-level modelling analysis. The Lancet Infectious Diseases 19, 56–66 (2019).
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ReAct - Action on antibiotic resistance. When the Drugs Don’t Work - Antibiotic Resistance as a Global Development Problem. https://www.reactgroup.org/wp-content/uploads/2019/02/When-the-Drugs-Don%E2%80%99t-Work-Antibiotic-Resistance-as-a-Global-Development-Problem-Feb-2019.pdf (2019).
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Naylor, N. R. et al. Estimating the burden of antimicrobial resistance: a systematic literature review. Antimicrobial Resistance & Infection Control 7, 58 (2018).
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LiST: The Lives Saved Tool. Johns Hopkins Bloomberg School of Public Health https://www.livessavedtool.org/.
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Founou, R. C., Founou, L. L. & Essack, S. Y. Clinical and economic impact of antibiotic resistance in developing countries: A systematic review and meta-analysis. PLOS ONE 12, e0189621 (2017).
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Bengtsson, B. & Greko, C. Antibiotic resistance—consequences for animal health, welfare, and food production. Upsala Journal of Medical Sciences 119, 96–102 (2014).