Shrestha et al. calculated these three variables to produce AC/AM values for the USA and Thailand, across six key AMR bacterial pathogens and the antimicrobials implicated in the development of their resistance. One key observation from this process was that indirect costs (loss of economic productivity) constitute the bulk (>80%) of AMR costs, implying that increased effort to reduce AMR related mortality would be cost effective. Furthermore, the resistance modulating factors showed a significant range (0.37-0.62) across the 6 bacterial pathogens i.e. AMR development via antimicrobial exposure has been more important in some pathogen than others. This implies that proposals looking at antimicrobial usage should focus on specific pathogens that are highly dependent on antimicrobial driven AMR. Additionally, the anti-microbial specific AC/AM values generally vastly exceeded their associated price tag, confirming that the cost of antimicrobials goes very far beyond their actual purchase price.

Shrestha et al. were successful in gathering the required data to produce an estimate of the AC/AM value and capture the significant cost associated with AMR resulting from antimicrobial use. However, this method is in clear need of refinement, potentially via the use of a more statistically rigorous methods⁹ for estimating the resistance modulating factor, although this is complicated by highly variable data and limited coverage of times frames¹. Furthermore, empirical input values must have their ranges refined to avoid over-sensitivity of the method to said inputs. Perhaps the most important improvement will be to be increase the amount and quality of data sources as ultimately, the reliability of this method comes down to the quality of the incoming data and assumptions. Nonetheless, Shrestha et al. have laid down a very solid foundation for the subsequent expansion of methods for estimation of AMR costs associated with antimicrobial use. In turn, they have contributed significantly towards the development of more comprehensive and accurate economic evaluations of AMR associated policy and products.

References

1. Shrestha, P., Cooper, B. S., Coast, J., Oppong, R., Do, N. T. T., Podha, T., Guerin, P. J., Wertheim, H. F. and Lubell, Y. (2017) ‘Enumerating the Economic Cost of Antimicrobial Resistance Per Antibiotic Consumed to Inform the Evaluation of Interventions Affecting their Use’, bioRxiv.

4. Coast, J., Smith, R. D. and Millar, M. R. (1996) ‘Superbugs: Should antimicrobial resistance be included as a cost in economic evaluation?’, Health Economics. Wiley Subscription Services, Inc., A Wiley Company, 5(3), pp. 217–226.

6. Michaelidis, C. I., Fine, M. J., Lin, C. J., Linder, J. A., Nowalk, M. P., Shields, R. K., Zimmerman, R. K. and Smith, K. J. (2016) ‘The hidden societal cost of antibiotic resistance per antibiotic prescribed in the United States: an exploratory analysis’, BMC Infectious Diseases. London: BioMed Central, 16, p. 655.

8. Oppong, R., Smith, R. D., Little, P., Verheij, T., Butler, C. C., Goossens, H., Coenen, S., Moore, M. and Coast, J. (2016) ‘Cost effectiveness of amoxicillin for lower respiratory tract infections in primary care: an economic evaluation accounting for the cost of antimicrobial resistance’, British Journal of General Practice, 66(650), pp. 633–639.

9. Kaier, K., Hagist, C., Frank, U., Conrad, A. and Meyer, E. (2009) ‘Two time-series analyses of the impact of antibiotic consumption and alcohol-based hand disinfection on the incidences of nosocomial methicillin-resistant Staphylococcus aureus infection and Clostridium difficile infection.’, Infection control and hospital epidemiology. United States, 30(4), pp. 346–353.