Quantifying uncertainty about future antimicrobial resistance
Comparing structured expert judgment and statistical forecasting methods
Abigail R. Colson (Center for Disease Dynamics, Economics and Policy, University of Strathclyde)
Itamar Megiddo (University of Strathclyde, Center for Disease Dynamics, Economics and Policy)
Gerardo Alvarez-Uria (Rural Development Trust Hospital)
Sumanth Gandra (University of Strathclyde, Center for Disease Dynamics, Economics and Policy)
Tim Bedford (University of Strathclyde)
Alec Morton (University of Strathclyde)
Roger M. Cooke (Resources for the Future, TU Delft - Applied Probability)
Ramanan Laxminarayan (Center for Disease Dynamics, Economics and Policy, University of Strathclyde, Princeton University)
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Abstract
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.
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