Estimating the burden of antimicrobial resistance: a systematic literature review

Abstract

Background: Accurate estimates of the burden of antimicrobial resistance (AMR) are needed to establish the magnitude of this global threat in terms of both health and cost, and to paramaterise cost-effectiveness evaluations of interventions aiming to tackle the problem. This review aimed to establish the alternative methodologies used in estimating AMR burden in order to appraise the current evidence base.

Methods: MEDLINE, EMBASE, Scopus, EconLit, PubMed and grey literature were searched. English language studies evaluating the impact of AMR (from any microbe) on patient, payer/provider and economic burden published between January 2013 and December 2015 were included. Independent screening of title/abstracts followed by full texts was performed using pre-specified criteria. A study quality score (from zero to one) was derived using Newcastle-Ottawa and Philips checklists. Extracted study data were used to compare study method and resulting burden estimate, according to perspective. Monetary costs were converted into 2013 USD.

Results: Out of 5187 unique retrievals, 214 studies were included. One hundred eighty-seven studies estimated patient health, 75 studies estimated payer/provider and 11 studies estimated economic burden. 64% of included studies were single centre. The majority of studies estimating patient or provider/payer burden used regression techniques. 48% of studies estimating mortality burden found a significant impact from resistance, excess healthcare system costs ranged from non-significance to $1 billion per year, whilst economic burden ranged from $21,832 per case to over $3 trillion in GDP loss. Median quality scores (interquartile range) for patient, payer/provider and economic burden studies were 0.67 (0.56-0.67), 0.56 (0.46-0.67) and 0.53 (0.44-0.60) respectively.

Conclusions: This study highlights what methodological assumptions and biases can occur dependent on chosen outcome and perspective. Currently, there is considerable variability in burden estimates, which can lead in-turn to inaccurate intervention evaluations and poor policy/investment decisions. Future research should utilise the recommendations presented in this review.

Trial registration: This systematic review is registered with PROSPERO (PROSPERO CRD42016037510).

Keywords: Antibiotic resistance; Antimicrobial resistance; Burden; Cost.

Fig. 1

PRISMA Diagram of Article Retrieval & Inclusion

Fig. 2

Odds ratios of Mortality Outcomes for Resistant Infections. Results presented are from studies utilising regression techniques, where 1.0 represents the point at which exposure does not affect the odds of the outcome occurring. The box point represents the reported OR value, with horizontal lines representing the reported 95% Confidence Interval. Results have not been adjusted or adapted to represent sample size, and are presented grouped by genera. a Gram-positive Bacteria. b Gram-negative Bacteria. [, , , –103]

Fig. 3

Hazard Ratios of Mortality Outcomes for Resistant Infections. Results presented are from studies utilising Cox proportional hazards regression techniques, where 1.0 represents the point at which exposure and control experience the same event rate at any point in time. The box point represents the reported HR value, with horizontal lines representing the reported 95% Confidence Interval. Results have not been adjusted or adapted to represent sample size, and are presented grouped by genera. a Gram-positive Bacteria. b Gram-negative Bacteria. [, , –121]

Fig. 4

Estimates of Excess Length of Stay of Hospital/ICU Stay Caused by Antimicrobial Resistance. (i) - (iii) denote different methods used in a single study [–51, 58, 59, 76, 122, 123, 132 ,133]

Fig. 5

Histograms of Quality Assessment Scores by Study Perspective