Abstract
BackgroundValuation of the economic cost of antimicrobial resistance (AMR) is important for decision making and should be estimated accurately. Highly variable or erroneous estimates may alarm policy makers and hospital administrators to act, but they also create confusion as to what the most reliable estimates are and how these should be assessed. This study aimed to assess the quality of methods used in studies that quantify the costs of AMR and to determine the best available evidence of the incremental cost of these infections.MethodsIn this systematic review, we searched PubMed, Embase, Cinahl, Cochrane databases and grey literature sources published between January 2012 and October 2016. Articles reporting the additional burden of Enterococcus spp., Escherichia coli (E. coli), Klebsiella pneumoniae (K. pneumoniae), Pseudomonas aeruginosa (P. aeruginosa) and Staphylococcus aureus (S. aureus) resistant versus susceptible infections were sourced. The included studies were broadly classified as reporting oncosts from the healthcare/hospital/hospital charges perspective or societal perspective. Risk of bias was assessed based on three methodological components: (1) adjustment for length of stay prior to infection onset and consideration of time-dependent bias, (2) adjustment for comorbidities or severity of disease, and (3) adjustment for inappropriate antibiotic therapy.ResultsOf 1094 identified studies, we identified 12 peer-reviewed articles and two reports that quantified the economic burden of clinically important resistant infections. Two studies used multi-state modelling to account for the timing of infection minimising the risk of time dependent bias and these were considered to generate the best available cost estimates. Studies report an additional CHF 9473 per extended-spectrum beta-lactamases -resistant Enterobacteriaceae bloodstream infections (BSI); additional €3200 per third-generation cephalosporin resistant Enterobacteriaceae BSI; and additional €1600 per methicillin-resistant S. aureus (MRSA) BSI. The remaining studies either partially adjusted or did not consider the timing of infection in their analysis.ConclusionsImplementation of AMR policy and decision-making should be guided only by reliable, unbiased estimates of effect size. Generating these estimates requires a thorough understanding of important biases and their impact on measured outcomes. This will ensure that researchers, clinicians, and other key decision makers concerned with increasing public health threat of AMR are accurately guided by the best available evidence.
Highlights
Valuation of the economic cost of antimicrobial resistance (AMR) is important for decision making and should be estimated accurately
Published articles reporting the economic burden of the following infections: Enterococcus spp., E. coli, K. pneumoniae, P. aeruginosa and S. aureus compared to susceptible infections, were searched in Pubmed, Embase, Cinahl and Cochrane databases from January 2012 until October 2016
Studies were eligible for inclusion if they: reported empirical or primary evidence about the economic impact of resistant versus susceptible infections, or reported about models of this impact; pertained to community or healthcare acquired Enterococcus spp., Escherichia coli (E. coli), Klebsiella pneumonia (K. pneumoniae), Pseudomonas aeruginosa (P. aeruginosa), or Staphylococcus aureus (S. aureus); reported the costs of resistant infections compared to susceptible infections; reported the control group as the susceptible strain of the organism; were published between 2012 and last date of database searching (11th October 2016); were conducted in adult populations
Summary
Valuation of the economic cost of antimicrobial resistance (AMR) is important for decision making and should be estimated accurately. Quantifying the burden of antimicrobial resistance (AMR) is challenging and encompasses various methodologies that aim to measure the impact on the patient, their use of the healthcare system and/ or contribution to society [1,2,3,4]. Assessing the quality of the available studies is essential to equip clinicians and policy-makers with the right tools to ensuring that decisions are based on well-designed studies which generate reliable, detailed and actionable measures [5]. Inconsistency in economic studies examining the burden of AMR have been reported [6, 8,9,10] and demonstrate the importance of a thorough analysis of the methodologies used to generate these estimates [11]. Determining the most likely causes of heterogeneity in cost data, may require an analysis of both the clinical differences in participant characteristics as well as variability produced by differences in the methodology and the overall approaches used [12, 13]
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