Each year, an estimated 7·7 million deaths are attributed to bacterial infections, of which 4.95 million are associated with drug-resistant pathogens, and 1·27 million are caused by bacterial pathogens resistant to the antibiotics available. Access to effective antibiotics when indicated prolongs life, reduces disability, reduces health-care expenses, and enables access to other life-saving medical innovations. Antimicrobial resistance undoes these benefits and is a major barrier to attainment of the Sustainable Development Goals, including targets for newborn survival, progress on healthy ageing, and alleviation of poverty. Adverse consequences from antimicrobial resistance are seen across the human life course in both health-care-associated and community-associated infections, as well as in animals and the food chain. The small set of effective antibiotics has narrowed, especially in resource-poor settings, and people who are very young, very old, and severely ill are particularly susceptible to resistant infections. This paper, the first in a Series on the challenge of antimicrobial resistance, considers the global scope of the problem and how it should be measured. Robust and actionable data are needed to drive changes and inform effective interventions to contain resistance. Surveillance must cover all geographical regions, minimise biases towards hospital-derived data, and include non-human niches.

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The increase of multidrug resistance and resistance to last-line antibiotics is a major global public health threat. Although surveillance programs provide useful current and historical information on the scale of the problem, the future emergence and spread of antibiotic resistance is uncertain, and quantifying this uncertainty is crucial for guiding decisions about investment in antibiotics and resistance control strategies. Mathematical and statistical models capable of projecting future rates are challenged by the paucity of data and the complexity of the emergence and spread of resistance, but experts have relevant knowledge. We use the Classical Model of structured expert judgment to elicit projections with uncertainty bounds of resistance rates through 2026 for nine pathogen-antibiotic pairs in four European countries and empirically validate the assessments against data on a set of calibration questions. The performance-weighted combination of experts in France, Spain, and the United Kingdom projected that resistance for five pairs on the World Health Organization’s priority pathogens list (E. coli and K. pneumoniae resistant to third-generation cephalosporins and carbapenems and MRSA) would remain below 50% in 2026. In Italy, although upper bounds of 90% credible ranges exceed 50% resistance for some pairs, the medians suggest Italy will sustain or improve its current rates. We compare these expert projections to statistical forecasts based on historical data from the European Antimicrobial Resistance Surveillance Network (EARS-Net). Results from the statistical models differ from each other and from the judgmental forecasts in many cases. The judgmental forecasts include information from the experts about the impact of current and future shifts in infection control, antibiotic usage, and other factors that cannot be easily captured in statistical forecasts, demonstrating the potential of structured expert judgment as a tool for better understanding the uncertainty about future antibiotic resistance.@en