On January 29, 2018, the first report from the WHO Global Antimicrobial Resistance Surveillance System (GLASS) was released to the public domain. The release was preceded by a technical webinar to provide background information, highlight some technical features of the system and report, and provide opportunity to discuss the GLASS report.
The 164 page report provides a wealth of information on the surveillance systems in the reporting countries as well as surveillance data. The downside of such an abundance is that it may be difficult to get a global overview and appreciate the differences and difficulties between countries and with reporting to a global system.
In this article, we present four take-aways from the report to provide some overview and help with reading and interpreting the report
The Global Antimicrobial Resistance Surveillance System was launched by WHO in 2015 in an effort to standardize surveillance of antimicrobial resistance. Borne out of the WHO Global Action Plan, it aims to strengthen country-level surveillance systems and integrate data for analysis and sharing. One important aspect of the GLASS monitoring system is the shift from only reporting data from individual isolates towards including epidemiological, clinical and population level data.
In December 2017, 50 countries were enrolled in GLASS, covering 30% of the world population. Of these, 24 are high income countries, 20 middle-income countries and 6 low-income countries.
In it’s early implementation phase, GLASS collects data on surveillance systems and eight key pathogens from blood, urine, stool and urethral/cervical swabs:
- Acinetobacter baumannii
- Escherichia coli
- Klebsiella pneumoniae
- Neisseria gonorrhoeae
- Salmonella spp.
- Shigella spp.
- Staphylococcus aureus
- Streptococcus pneumoniae
Isolates are consecutive, with duplicate findings from single patients removed. Rates of resistance are calculated based on how many isolates were tested, but five countries also reported incidence, i.e. the amount of resistant isolates in relation to the population in the uptake area of the surveillance site.
Take away 1: Summary of results
40 countries provided information of their surveillance systems for the report (20 high-income countries, 15 middle-income countries and 5 low-income countries), and 22 of these also reported data. Data on a total of 507,746 isolates from 7239 surveillance sites were reported to GLASS (to be compared with >190,000 isolates in EARS-Net). But for most countries, including high income countries, data was incomplete with regard to GLASS standards.
Most countries had established a surveillance structure in line with GLASS requirements: a National Coordination Centre that coordinates data collection from surveillance sites, National Focal Point and National Reference Laboratory and a National AMR Surveillance plan.
Another interesting finding is that only 20 high income countries out of 78 had joined GLASS in time for this report. Even further, some high income countries have shared very scarce data. For example, the US had submitted no data, and Canada only submitted data on Salmonella spp. (3727 isolates in total) although these countries have had national surveillance systems for several years. This indicates that creating or modifying systems for reporting into GLASS takes time.
Take away 2: Large differences between countries
Of the reported data, most isolates were E. coli from urine samples. As expected, resistance rates showed great variability between countries: some countries reported very high rates of resistance in many pathogen-antibiotic combinations (up to 100%), whereas others reported very low rates. As an example, compare Germany with the Philippines. These two countries display an important trend: high income countries tend to have lower rates of resistance than low and middle income countries. The reasons for these differences are not to be found within the GLASS system, but may be related to infection prevention and control systems, antibiotic use and socioeconomic status.
Take away 3: Care must be taken when reading and interpreting the data
The data is aggregated on country level, so regional differences within a country are not reflected. Also, the number of surveillance sites and isolates tested will affect the reliability of the data, as can be seen in e.g. Zambia.
At face value, resistance in E. coli is abundant with the exception of carbapenems. Cefepime resistance is around 45% in blood cultures and the isolates are all resistant to co-trimoxazole and ampicillin in urine cultures. However, the error bars indicate that the data is highly variable. Taking a closer look at the data reveals that the surveillance system of Zambia consists of one surveillance site, and that the graph is based on 22 isolated from blood and 34 isolates from urine, all with hospital origin. Therefore care needs to be taken when interpreting the data: depending on how and where the samples were collected, different bias or skew in the data may occur and should be identified on a case by case basis.
Take away 4: Good start, but work is still needed and in progress on all levels
GLASS is still in a relatively early development and implementation phase, but has come to a good start. The GLASS team also have many good ideas on how the system can be further developed and expanded upon, e.g. by importing data from the EARS-Net, CEASAR and ReLAVRA surveillance systems, adding information on genetics, antibiotic consumption etc.
Unfortunately, the data is not readily available for the less technical reader and it is difficult to get a clear overview of the global situation. As similar data has been made visually available by other actors, such as ECDC and CDDEP, hopefully the GLASS data will be visualized in a more overseeable manner in future versions.
It is encouraging that many LMICs have enrolled into GLASS and have started collecting data in spite of severe challenges regarding capacity and resources. Every effort should be made to support these countries in this work. Also, high income countries should lead by example, i.e. by enrolling and reporting data to GLASS in order to show that data generation and sharing is achievable.
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