The global economic burden of antibiotic-resistant infections and the potential impact of bacterial vaccines: a modelling study

Abstract

Introduction: Antibiotic resistance (ABR) may increase hospital costs, utility loss and mortality risk per patient. Understanding these losses at national, regional and global scales is necessary for efficiently tackling ABR. Our aim is to estimate the global economic burden of antibiotic-resistant infections and the potential for bacterial vaccines to mitigate this burden.

Methods: We take healthcare system and labour productivity perspectives. Hospital cost-per-case and length-of-stay estimates were calculated through meta-analyses and reviewing published systematic reviews. Unit labour productivity losses were estimated through a human capital approach. Modelled estimates were used where secondary data were missing. Death and incidence data were combined with unit cost data to estimate the economic burden associated with ABR in 2019, and the potential costs averted (in 2019 US$) based on uptake scenarios of vaccines that currently exist or are likely to be developed.

Results: Multidrug-resistant tuberculosis had the highest mean hospital cost attributable to ABR per patient, the range was US$3000 in lower-income settings to US$41 000 in high-income settings, with carbapenem-resistant infections associated with a high cost-per-case of US$3000-US$7000 depending on syndrome. ABR was associated with a median value of US$693 billion (IQR: US$627 bn-US$768 bn) in hospital costs globally, with US$207 bn (IQR: US$186 bn-US$229 bn) potentially avertable by vaccines. Productivity losses were quantified at almost US$194 billion, with US$76 bn avertable by vaccines.

Conclusions: The economic burden of ABR is associated with high levels of hospital bed-days occupied, hospital spending and labour productivity losses globally and should, therefore, remain high on national and international policy agendas. Vaccines against Staphylococcus aureus, Escherichia coli and Klebsiella pneumoniae would avert a substantial portion of the economic burden associated with ABR. More robust evidence, particularly in low-income countries, on the hospital costs, associated with and attributable to ABR, is needed.

Keywords: Global Health; Health economics; Vaccines.

Figure 1. Model processes for estimating the economic burden of drug-resistant infections. ABR, antibiotic resistance; ILO, International Labour Organization; LoS, length of hospital stay.

Figure 2. Hospital cost-per-case associated with antibiotic resistance from the meta-analyses. Median values, by country, of statistically significant cost-per-case estimates from the meta-analyses. Costs presented are in US$. (A) represents cost estimates based on LoS, combined with WHO-CHOICE, while (B) represents averaged values based on LoS and cost estimates from the literature. LoS, length of hospital stay.

Figure 3. Global averages of hospital cost-per-case associated with antibiotic resistance. Exposure groups are defined by antibiotic (which they are resistant to) and bacteria. Colours represent the antibiotic class resistance (as defined by the exposure group legend). (A) Shows results for bacteria, (B) for mycobacteria. 3g, third generation; BSI, bloodstream infection; CNS, central nervous system; IAI, intra-abdominal infections; MDR, multidrug resistant; (p-)T & iNTS, (para-)/Typhoid/iNTS; RTI, respiratory tract infection; TB, tuberculosis; UTI, urinary tract infections.

Figure 4. Regional costs in 2019 associated with antibiotic resistance. Log scaled costs in 2019 US$, mn=a million. Where the point on the y-axis represents hospital costs. Circles are sized by productivity loss due to deaths, and colour indicates WHO region. AFR, African Region; AMR, Region of the Americas; EMR, Eastern Mediterranean Region; EUR, European Region; SEAR, South-East Asia Region; WPR, Western Pacific Region.

Figure 5. Potential vaccine avertable burden associated with ABR in 2019. (A) Hospital cost burden. Panels by WHO region. The primary y-axis relates to the bar plots, which represent median total hospital cost averted, while corresponding error bars represent the IQR of hospital cost ($), where B=billions. The secondary axis is related to the plotted points (total averted bed days), where M=millions. (B) Productivity Loss Burden–Costs ($). Point estimates are results in base case scenario, with the corresponding line representing min and max values across scenarios (trend adjusted vs non-trend adjusted). (C) Productivity loss burden–working-life-years-lost. ABR, antibiotic resistance; AFR, African Region; AMR, Region of the Americas; EMR, Eastern Mediterranean Region; EUR, European Region; SEAR, South-East Asia Region; WPR, Western Pacific Region.