Quantifying the Economic and Clinical Value of Reducing Antimicrobial Resistance in Gram-negative Pathogens Causing Hospital-Acquired Infections in Australia
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Abstract
Introduction
Antimicrobial resistance (AMR) is a global public health challenge requiring a global
response to which Australia has issued a National Antimicrobial Resistance Strategy.
The necessity for continued-development of new effective antimicrobials is required
to tackle this immediate health threat is clear, but current market conditions may
undervalue antimicrobials. We aimed to estimate the health-economic benefits of reducing
AMR levels for drug-resistant gram-negative pathogens in Australia, to inform health
policy decision-making.
Methods
A published and validated-dynamic health economic model was adapted to the Australian
setting. Over a 10-year time horizon, the model estimates the clinical and economic
outcomes associated with reducing current AMR levels, by up to 95%, of three gram-negative
pathogens in three hospital-acquired infections, from the perspective of healthcare
payers. A willingness-to-pay threshold of AUD$15,000—$45,000 per quality-adjusted
life-year (QALY) gained and a 5% discount rate (for costs and benefits) were applied.
Results
Over ten years, reducing AMR for gram-negative pathogens in Australia is associated
with up to 10,251 life-years and 8924 QALYs gained, 9041 bed-days saved and 6644 defined-daily
doses of antibiotics avoided. The resulting savings are estimated to be $10.5 million
in hospitalisation costs, and the monetary benefit at up to $412.1 million.
Discussion
Our results demonstrate the clinical and economic value of reducing AMR impact in
Australia. Of note, since our analysis only considered a limited number of pathogens
in the hospital setting only and for a limited number of infection types, the benefits
of counteracting AMR are likely to extend well beyond the ones demonstrated here.
Conclusion
These estimates demonstrate the consequences of failure to combat AMR in the Australian
context. The benefits in mortality and health system costs justify consideration of
innovative reimbursement schemes to encourage the development and commercialisation
of new effective antimicrobials.
Supplementary Information
The online version contains supplementary material available at 10.1007/s40121-023-00835-9.
Summary Background Antimicrobial resistance (AMR) poses a major threat to human health around the world. Previous publications have estimated the effect of AMR on incidence, deaths, hospital length of stay, and health-care costs for specific pathogen–drug combinations in select locations. To our knowledge, this study presents the most comprehensive estimates of AMR burden to date. Methods We estimated deaths and disability-adjusted life-years (DALYs) attributable to and associated with bacterial AMR for 23 pathogens and 88 pathogen–drug combinations in 204 countries and territories in 2019. We obtained data from systematic literature reviews, hospital systems, surveillance systems, and other sources, covering 471 million individual records or isolates and 7585 study-location-years. We used predictive statistical modelling to produce estimates of AMR burden for all locations, including for locations with no data. Our approach can be divided into five broad components: number of deaths where infection played a role, proportion of infectious deaths attributable to a given infectious syndrome, proportion of infectious syndrome deaths attributable to a given pathogen, the percentage of a given pathogen resistant to an antibiotic of interest, and the excess risk of death or duration of an infection associated with this resistance. Using these components, we estimated disease burden based on two counterfactuals: deaths attributable to AMR (based on an alternative scenario in which all drug-resistant infections were replaced by drug-susceptible infections), and deaths associated with AMR (based on an alternative scenario in which all drug-resistant infections were replaced by no infection). We generated 95% uncertainty intervals (UIs) for final estimates as the 25th and 975th ordered values across 1000 posterior draws, and models were cross-validated for out-of-sample predictive validity. We present final estimates aggregated to the global and regional level. Findings On the basis of our predictive statistical models, there were an estimated 4·95 million (3·62–6·57) deaths associated with bacterial AMR in 2019, including 1·27 million (95% UI 0·911–1·71) deaths attributable to bacterial AMR. At the regional level, we estimated the all-age death rate attributable to resistance to be highest in western sub-Saharan Africa, at 27·3 deaths per 100 000 (20·9–35·3), and lowest in Australasia, at 6·5 deaths (4·3–9·4) per 100 000. Lower respiratory infections accounted for more than 1·5 million deaths associated with resistance in 2019, making it the most burdensome infectious syndrome. The six leading pathogens for deaths associated with resistance (Escherichia coli, followed by Staphylococcus aureus, Klebsiella pneumoniae, Streptococcus pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa) were responsible for 929 000 (660 000–1 270 000) deaths attributable to AMR and 3·57 million (2·62–4·78) deaths associated with AMR in 2019. One pathogen–drug combination, meticillin-resistant S aureus, caused more than 100 000 deaths attributable to AMR in 2019, while six more each caused 50 000–100 000 deaths: multidrug-resistant excluding extensively drug-resistant tuberculosis, third-generation cephalosporin-resistant E coli, carbapenem-resistant A baumannii, fluoroquinolone-resistant E coli, carbapenem-resistant K pneumoniae, and third-generation cephalosporin-resistant K pneumoniae. Interpretation To our knowledge, this study provides the first comprehensive assessment of the global burden of AMR, as well as an evaluation of the availability of data. AMR is a leading cause of death around the world, with the highest burdens in low-resource settings. Understanding the burden of AMR and the leading pathogen–drug combinations contributing to it is crucial to making informed and location-specific policy decisions, particularly about infection prevention and control programmes, access to essential antibiotics, and research and development of new vaccines and antibiotics. There are serious data gaps in many low-income settings, emphasising the need to expand microbiology laboratory capacity and data collection systems to improve our understanding of this important human health threat. Funding Bill & Melinda Gates Foundation, Wellcome Trust, and Department of Health and Social Care using UK aid funding managed by the Fleming Fund.
Decades after the first patients were treated with antibiotics, bacterial infections have again become a threat because of the rapid emergence of resistant bacteria-a crisis attributed to abuse of these medications and a lack of new drug development.
[3
]GRID grid.1003.2, ISNI 0000 0000 9320 7537, Centre for Superbug Solutions, Institute for Molecular Bioscience, , The University of Queensland, ; St Lucia, QLD 4072 Australia
[4
]GRID grid.476921.f, Centre for Infectious Diseases and Microbiology, , Westmead Institute, WestmeadHospital/University of Sydney, ; Sydney, NSW 2145 Australia
[5
]GRID grid.1010.0, ISNI 0000 0004 1936 7304, Adelaide Medical School and School of Biological Sciences, , University of Adelaide, ; Adelaide, SA Australia
[6
]European Committee on Antimicrobial Susceptibility Testing (EUCAST), Basel, Switzerland
[7
]GRID grid.467667.2, ISNI 0000 0001 2019 1105, Australian Commission on Safety and Quality in Health Care, ; Sydney, Australia
[8
]GRID grid.1025.6, ISNI 0000 0004 0436 6763, Antimicrobial Resistance and Infectious Diseases (AMRID) Research Laboratory, , Murdoch University, ; Perth, WA Australia
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