Antimicrobial susceptibility testing results from 13 hospitals in Viet Nam: VINARES 2016-2017.

OBJECTIVE: To analyse data from 2016-17 from a hospital-based antimicrobial resistance surveillance with national coverage in a network of hospitals Viet Nam. METHODS: We analysed data from 13 hospitals, 3 less than the dataset from the 2012-13 period. Identification and antimicrobial susceptibility testing data from the clinical microbiology laboratories from samples sent in for routine diagnostics were used. Clinical and Laboratory Standards Institute 2018 guidelines were used for antimicrobial susceptibility testing interpretation. WHONET was used for data entry, management and analysis. RESULTS: 42,553 deduplicated isolates were included in this analysis; including 30,222 (71%) Gram-negative and 12,331 (29%) Gram-positive bacteria. 8,793 (21%) were from ICUs and 7,439 (18%) isolates were from invasive infections. Escherichia coli and Staphylococcus aureus were the most frequently detected species with 9,092 (21%) and 4,833 isolates (11%), respectively; followed by Klebsiella pneumoniae (3,858 isolates - 9.1%) and Acinetobacter baumannii (3,870 isolates - 9%). Bacteria were mainly isolated from sputum (8,798 isolates - 21%), blood (7,118 isolates - 17%) and urine (5,202 isolates - 12%). Among Gram-positives 3,302/4,515 isolates (73%) of S. aureus were MRSA; 99/290 (34%) of Enterococcus faecium were resistant to vancomycin; and 58% (663/1,136) of Streptococcus pneumoniae proportion were reduced susceptible to penicillin. Among Gram-negatives 59% (4,085/6,953) and 40% (1,186/2,958) of E. coli and K. pneumoniae produced ESBL and 29% (376/1,298) and 11% (961/8,830) were resistant to carbapenems, respectively. 79% (2855/3622) and 45% (1,514/3,376) of Acinetobacter spp. and Pseudomonas aeruginosa were carbapenem resistant, respectively. 88% (804/911) of Haemophilus influenzae were ampicillin resistant and 18/253 (7%) of Salmonella spp. and 7/46 (15%) of Shigella spp. were resistant to fluoroquinolones. The number of isolates from which data were submitted in the 2016-2017 period was twice as high as in 2012-2013. AMR proportions were higher in 2016-2017 for most pathogen-antimicrobial combinations of interest including imipenem-resistant A. baumannii, P. aeruginosa and Enterobacterales. CONCLUSIONS: The data show alarmingly high and increasing resistant proportions among important organisms in Viet Nam. AMR proportions varied across hospital types and should be interpreted with caution because existing sampling bias and missing information on whether isolates were community or hospital acquired. Affordable and scalable ways to adopt a sample- or case-based approach across the network should be explored and clinical data should be integrated to help provide more accurate inferences of the surveillance data.

Antimicrobial susceptibility testing and antibiotic consumption results from 16 hospitals in Viet Nam: The VINARES project 2012-2013.

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.

Enumerating the economic cost of antimicrobial resistance per antibiotic consumed to inform the evaluation of interventions affecting their use.

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.