Nga Do Thi Thuy’s research while affiliated with Oxford University Clinical Research Unit and other places

Objective: To establish a hospital-based surveillance network with national coverage for antimicrobial resistance (AMR) and antibiotic consumption in Viet Nam. Methods: A 16-hospital network (Viet Nam Resistance: VINARES) was established and consisted of national and provincial-level hospitals across the country. Antimicrobial susceptibility testing results from routine clinical diagnostic specimens and antibiotic consumption data in Defined Daily Dose per 1000 bed days (DDD/1000 patient-days) were prospectively collected and analysed between October 2012 and September 2013. Results: Data from a total of 24 732 de-duplicated clinical isolates were reported. The most common bacteria were: Escherichia coli (4437 isolates, 18%), Klebsiella spp. (3290 isolates, 13%) and Acinetobacter spp. (2895 isolates, 12%). The hospital average antibiotic consumption was 918 DDD/1000 patient-days. Third-generation cephalosporins were the most frequently used antibiotic class (223 DDD/1000 patient-days, 24%), followed by fluoroquinolones (151 DDD/1000 patient-days, 16%) and second-generation cephalosporins (112 DDD/1000 patient-days, 12%). Proportions of antibiotic resistance were high: 1098/1580 (69%) Staphylococcus aureus isolates were methicillin-resistant (MRSA); 115/344 isolates (33%) and 90/358 (25%) Streptococcus pneumoniae had reduced susceptibility to penicillin and ceftriaxone, respectively. A total of 180/2977 (6%) E. coli and 242/1526 (16%) Klebsiella pneumoniae were resistant to imipenem, respectively; 602/1826 (33%) Pseudomonas aeruginosa were resistant to ceftazidime and 578/1765 (33%) to imipenem. Of Acinetobacter spp. 1495/2138 (70%) were resistant to carbapenems and 2/333 (1%) to colistin. Conclusions: These data are valuable in providing a baseline for AMR among common bacterial pathogens in Vietnamese hospitals and to assess the impact of interventions.

Background Antimicrobial resistance (AMR) poses a colossal threat to global health and incurs high economic costs to society. Economic evaluations of antimicrobials and interventions such as diagnostics and vaccines that affect their consumption rarely include the costs of AMR, resulting in sub-optimal policy recommendations. We estimate the economic cost of AMR per antibiotic consumed, stratified by drug class and national income level. Methods The model is comprised of three components: correlation coefficients between human antibiotic consumption and subsequent resistance; the economic costs of AMR for five key pathogens; and consumption data for antibiotic classes driving resistance in these organisms. These were used to calculate the economic cost of AMR per antibiotic consumed for different drug classes, using data from Thailand and the United States (US) to represent low/middle and high-income countries. Results The correlation coefficients between consumption of antibiotics that drive resistance in S. aureus, E. coli, K. pneumoniae, A. baumanii, and P. aeruginosa and resistance rates were 0.37, 0.27, 0.35, 0.45, and 0.52, respectively. The total economic cost of AMR due to resistance in these five pathogens was $0.5 billion and$2.9 billion in Thailand and the US, respectively. The cost of AMR associated with the consumption of one standard unit (SU) of antibiotics ranged from $0.1 for macrolides to$0.7 for quinolones, cephalosporins and broad-spectrum penicillins in the Thai context. In the US context, the cost of AMR per SU of antibiotic consumed ranged from $0.1 for carbapenems to$0.6 for quinolones, cephalosporins and broad spectrum penicillins. Conclusion The economic costs of AMR per antibiotic consumed were considerable, often exceeding their purchase cost. Differences between Thailand and the US were apparent, corresponding with variation in the overall burden of AMR and relative prevalence of different pathogens. Notwithstanding their limitations, use of these estimates in economic evaluations can make better-informed policy recommendations regarding interventions that affect antimicrobial consumption and those aimed specifically at reducing the burden of AMR. Electronic supplementary material The online version of this article (10.1186/s13756-018-0384-3) contains supplementary material, which is available to authorized users.