Global Antimicrobial Resistance Surveillance System
(GLASS)
Guide to preparing aggregated
antimicrobial resistance data files
Global Antimicrobial Resistance Surveillance System
(GLASS)
Guide to preparing aggregated
antimicrobial resistance data files
WHO/DGO/AMR/2016.6
© World Health Organization 2016
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Financial Support
The Governments of Germany, Japan, the Netherlands, the Republic of Korea, Sweden, the United Kingdom and United States of America.
Acronyms
AST antimicrobial susceptibility testing
CAESAR Central Asian and Eastern European Surveillance of Antimicrobial Resistance CLSI Clinical and Laboratory Standards Institute
CSV comma-separated values
CV coded value
EUCAST European Committee on Antimicrobial Susceptibility Testing GLASS Global Antimicrobial Resistance Surveillance System
TSV tab separated values WHO World Health Organization
WHONET software for management and analysis of microbiology laboratory test results
Table of contents
Aggregated data files specifications ... 1
GLASS data file format ... 1
Two files to submit to GLASS ... 1
RIS file specifications ... 2
Sample file specifications ... 11
Removal of duplicate results ... 12
How to generate GLASS data files ... 13
Validation of data ... 14
References ... 19
Annex 1. Coded values ... 20
1
Introduction
This document has been developed for national GLASS focal points and national AMR surveillance data managers. It provides instructions and explanatory information on how to prepare aggregated national AMR data files for submitting the data to GLASS. Detailed information on the GLASS methodology and implementation roadmap is available in the GLASS Manual for early implementation (1). Detailed information on how to upload the aggregated data into the GLASS IT platform is available in the GLASS Guide to uploading aggregated AMR data (2).
Aggregated data files specifications
GLASS has developed a secure database with web-interface which allows electronic submission of AMR data aggregated at a national level from the countries enrolled in GLASS. More information on this and other functions of the GLASS IT platform is available in separate documents (2-4).
A simple text-based data file format has been chosen for GLASS data providers to submit AMR data to the GLASS IT platform in a standardized way.
GLASS data file format
The GLASS IT platform accepts tab-separated (tab-delimited) values files which are simple text files for storing data in a tabular structure. Each record in the database is one line of the text file. Each field value of a record is separated from the next by a tab stop character. This format is widely supported, so it is often used to move tabular data between different computer programs.
Files with both .txt and .csv extensions will be accepted in the IT platform, but .txt is preferable as it could be easily saved in a tab-delimited format in Microsoft Excel. The .csv extension is usually used for the comma-separated values (CSV) format, which often causes difficulties because of the need to escape commas – they are very common in text data. It is possible to create a tab-delimited .csv file, but note that .csv files saved in Microsoft Excel are comma-separated1.
NB: GLASS IT platform accepts tab-delimited text files only (*.txt, *.csv)
Two files to submit to GLASS
Currently two types of data files are expected to be submitted to GLASS:
1. RIS file with susceptibility testing results. These are data (aggregated from all participating national surveillance sites submissions) on the number of resistant, intermediate, susceptible (and
1 If you need to save a tab delimited .csv file, you can use other data management tools such as e.g. CSVed (http://csved.sjfrancke.nl/).
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other interpretations of AST results defined below) isolates detected in GLASS priority specimens, stratified by gender, infection origin, and age.
2. Sample file with “sample statistics”. These are the numbers of patients from whom specimens have been taken, stratified by the same variables as in the RIS file.
Both RIS and Sample files are generated from the same source database.
RIS file specifications
RIS file: overview
The RIS file variables are shown in the table below:
Variable ID Variables in RIS file Type of variable Example
R1 COUNTRY Coded value* AFG
R2 YEAR Coded value 2015
R3 SPECIMEN Coded value BLOOD
R4 PATHOGEN Coded value ACISPP
R5 GENDER Coded value M
R6 ORIGIN Coded value HO
R7 AGEGROUP Coded value 01<04
R8 ANTIBIOTIC Coded value AMK
R9 RESISTANT Integer (≥0) 15
R10 INTERMEDIATE Integer (≥0) 10
R11 NONSUSCEPTIBLE Integer (≥0) 5
R12 SUSCEPTIBLE Integer (≥0) 30
R13 UNKNOWN_NO_AST Integer (≥0) 5
R14 UNKNOWN_NO_BREAKPOINTS Integer (≥0) 0
R15 BATCHID Coded value DS1
* The coded values lists for all CV variables are provided in the Annex 1
NB: All the variables in the RIS file are mandatory. The order of the variables is also very important. If any of the variables is missing in the submitted file, or the order of the
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variables differs from the required order (as shown in the table), the file will not be accepted by the GLASS IT platform.
A fragment of a RIS file opened in Microsoft Excel is shown on the screenshot below:
Variable COUNTRY
COUNTRY is a mandatory coded value variable with three-letter country codes based on ISO 3166-1 (e.g. AFG = Afghanistan). The list of country codes with both full and short country names is available in Annex 1.
Variable YEAR
YEAR is a mandatory coded value variable. The list of allowed values currently covers a period from 2009 to 2016 and will be extended in the future. The value in the RIS file shows the year represented by the data submission, typically using specimen collection date in the source database.
Variable SPECIMEN
SPECIMEN is a mandatory coded value variable. The coded value list for the four GLASS priority specimens (blood, urine, faeces, and urethral and cervical swabs) chosen for early implementation is available in Annex 1. While the GLASS Manual (1) specifically mentions urethral and cervical swabs, handling them together as “GENITAL” in the aggregated data submissions simplifies data management and queries.
Variable PATHOGEN
PATHOGEN is a mandatory coded value variable. The coded value list for the 8 GLASS priority pathogens chosen for early implementation (see the GLASS Manual (1) for more details) is available in Annex 1.
Variable GENDER
GENDER is a mandatory coded value variable. The coded value list is available in Annex 1. Please use the SUM value when the data are not stratified by gender.
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Variable ORIGIN
ORIGIN is a mandatory coded value variable. The coded value list is available in Annex 1.
Please note that patients are considered to be of “hospital origin” if they had been hospitalized for >
2 calendar days when the specimen was taken. This includes the following:
patient admitted to a health care facility for > 2 calendar days; or
patient admitted to a health care facility for < 2 calendar days but transferred from another health care facility where admitted for ≥ 2 calendar days
Patients are considered to be of “community origin” if they were being cared for at an outpatient clinic when the specimen was taken or hospitalized for ≤ 2 calendar days when the specimen was taken.
If the data on the patient origin are not entered directly at the surveillance site using the case definitions above, the variable ORIGIN could be calculated using the variables with the data on the date of admission, data of sample, and patient location (outpatient vs. inpatient facilities).
Please use the SUM value when the data are not stratified by patient origin.
Variable AGEGROUP
AGEGROUP is a mandatory coded value variable. The coded value list is available in Annex 1. Please note that the sign “<” is used in the AGEGROUP codes instead of the sign “-“: this is to avoid re- formatting issues in Microsoft Excel. When data are not stratified by age, use SUM= all age groups +UNK
Variable ANTIBIOTIC
ANTIBIOTIC is a mandatory coded value variable. The coded value list is available in Annex 1.
Numeric variables in the RIS file: overview
The numeric variables in the RIS file include AST interpretation results, based on definitions and standards used in the reporting country. They also include data on the identified pathogens (isolates) where AST was not performed or could not be interpreted.
Variable RESISTANT
RESISTANT is a mandatory integer (0) variable representing the number of isolates resistant to a specific antibiotic. This includes AST results interpreted as Resistant (R), according to EUCAST (6), CLSI (7), or national definitions.
Variable INTERMEDIATE
INTERMEDIATE is a mandatory integer (0) variable representing the number of isolates with intermediate susceptibility to a specific antibiotic. This includes AST results interpreted as
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Intermediate (I) according to EUCAST (6), CLSI (7), or national definitions or Susceptible dose- dependent (SDD) according to CLSI (7).
Variable NONSUSCEPTIBLE
NONSUSCEPTIBLE is a mandatory integer (0) variable representing the number of isolates non- susceptible to a specific antibiotic. This includes AST results interpreted as Non-susceptible (NS), according to EUCAST (6), CLSI (7), or national definitions.
Variable SUSCEPTIBLE
SUSCEPTIBLE is a mandatory integer (0) variable representing the number of isolates susceptible to a specific antibiotic. This includes AST results interpreted as Susceptible (S), according to EUCAST (6), CLSI (7), or national definitions.
Variable UNKNOWN_NO_AST
UNKNOWN_NO_AST is a mandatory integer (0) variable representing the number of isolates with AST results not reported (not performed) for a specific antibiotic.
How to calculate the number of AST not reported: an example
1000 S.aureus were isolated from blood, but only 100 isolates were tested for Cefoxitin. In this case 1000-100=900 isolates should be reported as UNKNOWN_NO_AST for this antibiotic.
If the total number of isolates is not known, the highest number of tests for specific antibiotic should be used instead. In the table below the highest number of tests was performed for S.aureus to Clindamycin (1000 isolates):
Pathogen-antibiotic combination
R I S R+I+S Unknown* Total number of
isolates
S.aureus to Cefoxitin 10 0 90 100 ? ?
S.aureus to Oxacillin 500 0 100 600 ? ?
S.aureus to Clindamycin 10 0 990 1000 ? ?
S.aureus to Vancomycin 0 0 500 500 ? ?
* UNKNOWN_NO_AST in the RIS file
Then for the GLASS reporting purposes the Unknown (UNKNOWN_NO_AST) value should be substituted as follows:
6 Pathogen-antibiotic
combination
R I S R+I+S Unknown* Highest number of
tested isolates
S.aureus to Cefoxitin 10 0 90 100 900
1000
S.aureus to Oxacillin 500 0 100 600 400
S.aureus to Clindamycin 10 0 990 1000 0
S.aureus to Vancomycin 0 0 500 500 500
* Highest number of tested isolates minus (R+I+S)
Variable UNKNOWN_NO_BREAKPOINTS
UNKNOWN_NO_BREAKPOINTS is a mandatory integer (0) variable representing the number of isolates with AST performed but no interpretation of results available for a specific antibiotic.
Variable BATCHID
BATCHID is a mandatory coded value variable. It is introduced to distinguish sub-sets of national aggregated data provided by a country where for some reasons it is not possible to aggregate national data in the same way or when dividing the national data set has an important added value.
This may be needed, for example, if the country has several different surveillance systems or there is a need to report data from different parts of the country separately. This may also be needed if, for example, the sample statistics (needed for generating a Sample file) are missing in a big part of the country.
An example of country A:
Data set RIS file Sample file Comments
Data Set 1 from surveillance sites A, B, F (BATCHID=DS1)
Available Available Data will be used to calculate both proportions and AMR rates per 1000 sampled patients for the Data Set 1
Data Set 2 from surveillance sites C, D, E, G (BATCHID=DS2)
Available Not available Data will be used in reports displaying proportions only (%) for the Data Set 2
National data set (total) Data Set1 + Data Set 2
ND* Data will be used in reports displaying proportions only (%) for the country A
*in the automatically produced reports, it will be displayed as ND = no data
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Countries are free to choose up to 5 data sets (Data Set 1, Data Set 2, Data Set 3, Data Set 4, and Data Set 5). CAESAR coded value is dedicated for CAESAR network data, WHONET coded value is dedicated to the data batches aggregated and generated using WHONET software.
RIS file organization
Full (3D) stratification: “green” aggregation level
Ideally, the AMR data for each Specimen-Pathogen-Antibiotic2 will be stratified by all 3 variables currently used by GLASS
1. AGEGROUP 2. GENDER 3. ORIGIN
Below is an example of a subset of AMR data provided by country ABC for the year 2015 on number of Acinetobacter spp. resistant to Amikacin isolated from blood in infants (i.e. <1 years old)3:
GENDER ORIGIN
Male (M)
Female (F)
Other (O)
Unknown (UNK)
Community origin (CO)
1 2 0 3
Hospital origin (HO)
4 5 0 6
Unknown (UNK)
0 0 0 9
2 From the same batch/data set (BATCHID)
3 Specifying the age group here we already started stratifying by age
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The corresponding fragment of the RIS file will be as follows:
COUNTRY YEAR SPECIMEN PATHOGEN GENDER ORIGIN AGEGROUP ANTIBIOTIC RESISTANT INTERMEDIATE NONSUSCEPTIBLE SUSCEPTIBLE UNKNOWN_NO_AST UNKNOWN_NO_BREAKPOINTS BATCHID
ABC 2015 BLOOD ACISPP F CO <1 AMK 2 0 0 0 0 0 DS1
ABC 2015 BLOOD ACISPP M CO <1 AMK 1 0 0 0 0 0 DS1
ABC 2015 BLOOD ACISPP UNK CO <1 AMK 3 0 0 0 0 0 DS1
ABC 2015 BLOOD ACISPP F HO <1 AMK 5 0 0 0 0 0 DS1
ABC 2015 BLOOD ACISPP M HO <1 AMK 4 0 0 0 0 0 DS1
ABC 2015 BLOOD ACISPP UNK HO <1 AMK 6 0 0 0 0 0 DS1
ABC 2015 BLOOD ACISPP UNK UNK <1 AMK 9 0 0 0 0 0 DS1
Partial (2D) stratification: “yellow-orange” aggregation level
Now shown is the same subset of data, but in the situation when stratification by all 3 variables is not possible for any reason. The values for missing stratifiers will be SUM which are subtotals for the specific strata in the existing stratifying variable
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COUNTRY YEAR SPECIMEN PATHOGEN GENDER ORIGIN AGEGROUP ANTIBIOTIC RESISTANT INTERMEDIATE NONSUSCEPTIBLE SUSCEPTIBLE UNKNOWN_NO_AST UNKNOWN_NO_BREAKPOINTS BATCHID
ABC 2015 BLOOD ACISPP F SUM SUM AMK 5 0 0 0 0 0 DS1
ABC 2015 BLOOD ACISPP M SUM SUM AMK 7 0 0 0 0 0 DS1
ABC 2015 BLOOD ACISPP UNK SUM SUM AMK 18 0 0 0 0 0 DS1
ABC 2015 BLOOD ACISPP SUM CO SUM AMK 6 0 0 0 0 0 DS1
ABC 2015 BLOOD ACISPP SUM HO SUM AMK 15 0 0 0 0 0 DS1
ABC 2015 BLOOD ACISPP SUM UNK SUM AMK 9 0 0 0 0 0 DS1
ABC 2015 BLOOD ACISPP SUM SUM <1 AMK 30 0 0 0 0 0 DS1
ABC 2015 BLOOD ACISPP SUM SUM 01<04 AMK 0 0 0 0 0 0 DS1
ABC 2015 BLOOD ACISPP SUM SUM 15<24 AMK 0 0 0 0 0 0 DS1
ABC 2015 BLOOD ACISPP SUM SUM 25<34 AMK 0 0 0 0 0 0 DS1
ABC 2015 BLOOD ACISPP SUM SUM 35<44 AMK 0 0 0 0 0 0 DS1
ABC 2015 BLOOD ACISPP SUM SUM 45<54 AMK 0 0 0 0 0 0 DS1
ABC 2015 BLOOD ACISPP SUM SUM 55<64 AMK 0 0 0 0 0 0 DS1
ABC 2015 BLOOD ACISPP SUM SUM 65<74 AMK 0 0 0 0 0 0 DS1
ABC 2015 BLOOD ACISPP SUM SUM 75<84 AMK 0 0 0 0 0 0 DS1
ABC 2015 BLOOD ACISPP SUM SUM 85< AMK 0 0 0 0 0 0 DS1
If these data reported correctly, subtotal by GENDER = subtotal by AGEGROUP = subtotal by ORIGIN.
Subtotal by GENDER = 5+7+18=30
Subtotal by Origin = 6+15+9=30
Subtotal by AGEGROUP = 30
10 No stratification: “blue” aggregation level
When no stratification is possible, only subtotals will be provided:
COUNTRY YEAR SPECIMEN PATHOGEN GENDER ORIGIN AGEGROUP ANTIBIOTIC RESISTANT INTERMEDIATE NONSUSCEPTIBLE SUSCEPTIBLE UNKNOWN_NO_AST UNKNOWN_NO_BREAKPOINTS BATCHID
ABC 2015 BLOOD ACISPP SUM SUM SUM AMK 30 0 0 0 0 0 DS1
The data used to illustrate the three aggregation levels are summarized in the table below:
GENDER ORIGIN
Male (M)
Female (F)
Other (O)
Unknown (UNK)
Subtotal by ORIGIN
(sum in the row)
Community origin (CO)
1 2 0 3 6
Hospital origin (HO)
4 5 0 6 15
Unknown (UNK)
0 0 0 9 9
Subtotal by GENDER (sum in the column)
5 7 0 18 30
(Total)
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Sample file specifications
Sample file: overview
The Sample file variables are shown in the table below:
Variable ID Variables in Sample file Type of variable Example
S1 COUNTRY Coded value* AFG
S2 YEAR Coded value 2015
S3 SPECIMEN Coded value BLOOD
S4 GENDER Coded value M
S5 ORIGIN Coded value HO
S6 AGEGROUP Coded value 01<04
S7 NUMSAMPLEDPATIENTS Integer (≥0) 1000
S8 BATCHID Coded value DS1
* The coded values lists for all CV variables are provided in the Annex 1
NB: All the variables in the Sample file are mandatory. The order of the variables is also very important. If any of the variables is missing in the submitted file, or the order of the variables differs from the required order (as shown in the table), the file will not be accepted by the GLASS IT platform.
A fragment of a RIS file opened in Microsoft Excel is shown on the screenshot below:
Variables COUNTRY, YEAR, SPECIMEN, GENDER, ORIGIN, AGEGROUP, and BATCHID in the Sample file have the same specifications as those in the RIS file and the same coded values (see Annex 1).
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Variable NUMSAMPLEDPATIENTS
NUMSAMPLEDPATIENTS is a mandatory Integer variable. It represents a number of patients with samples (BLOOD, URINE, STOOL, and GENITAL) collected for bacteriological testing and includes all positive samples (both isolates of the GLASS priority pathogens and other bacteria) as well as negative (no growth) samples.
For the BLOOD specimens, all blood samples taken for bacteriological testing are included. BLOOD specimens for fungi should be excluded. All URINE specimens should be counted, independently of the type of collection. For the STOOL specimens, all faecal sample from patients collected for bacteriological testing should be counted, excluding samples sent for detection of C. difficile and samples taken to detect viruses and parasites. For the GENITAL specimens, all samples taken from urethra (men) and cervical swabs (women) for identification of Neisseria gonorrhoeae should be counted, and specimens from other body sites should be excluded when reporting to GLASS.
Sample file organization
The Sample file is organized using the same aggregation and stratification approaches as for the RIS file. To create a stratified Sample file, the source database needs to contain “sample level” data, i.e.
include both positive and negative samples with all GLASS variables and with data for the GLASS stratifiers.
Removal of duplicate results
When several cultures are collected during patient management, duplicate findings for the same patient should be excluded from the source database before generating the aggregated data files.
Before starting the de-duplication process, it is advisable to review variables containing information about the patient and check, in particular, whether the database has patient identifiers or unique counters included. If they are missing, generate a variable with a unique patient identifier or counter.
When there are missing values, a unique identifier could be created e.g. from the patient’s personal information data for each missing value.
For each surveillance period (e.g. 12 months), only one result should be reported for each patient per surveyed specimen type and surveyed pathogen. For example, if two blood cultures from the same patient yield growth of E. coli, only the first should be included in the report; if growth of E. coli detected in one culture and of K. pneumoniae in the other, both results should be reported. If there is growth of E. coli in one blood culture and in one urinary culture from the same patient, both specimen types should be left in the database. If two records show similar results for the same specimen type and pathogen but the patient origin is different, both samples should be reported.
Repeated negative results for the same specimen type in the same patient should also be de- duplicated.
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In the example below4 three patients have several samples taken during the reporting year.
Duplicated records to be removed are marked red:
Sample ID Patient ID SPECIMEN PATHOGEN Origin
27 A BLOOD ESCCOL HO
244 A BLOOD ESCCOL HO
369 B BLOOD KLEPNE HO
394 B BLOOD NEGATIVE HO
438 B BLOOD NEGATIVE HO
626 A BLOOD ESCCOL CO
627 C BLOOD NEGATIVE HO
760 A BLOOD ESCCOL HO
792 B URINE NEGATIVE HO
801 A URINE KLEPNE HO
805 A URINE KLEPNE HO
900 C BLOOD NEGATIVE HO
How to generate GLASS data files
Export of AMR data to the GLASS format is implemented in the WHONET 2016 software: both RIS and SAMPLE files can be generated. The detailed information is available in the WHONET manual to support the WHO Global Antimicrobial Resistance Surveillance System (5).
If you are participating in international AMR surveillance networks, such as CAESAR, specific tools for data aggregation from CAESAR individual data format into GLASS aggregated data base will be available.
4 This is a simplified view of the database, not all variables that should be part of the source database are shown here
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There are also several generic data management tools which may be used for data transformation from national aggregated or individual data bases into GLASS aggregated format, using specifications provided in this document.
Validation of data
When the aggregated national data are uploaded in the GLASS IT platform, they are automatically validated and checked for inconsistencies. Nevertheless, it is important to validate the data throughout the data flow, starting from data entered at the surveillance sites and validating the data before starting the upload process.
The submitted files will be rejected or flagged as erroneous by the IT platform if, for example:
One or more variables are missing
The order of variables is different from what is required by the GLASS files specifications
There are missing values in the file
There are values in the file which are not included in the coded values lists
The specimen-pathogen or pathogen-antibiotic combinations in the file do not meet the inclusion criteria:
Specimen-Pathogen combinations to be included
Specimen
Pathogen:
STAAUR STRPNE KLEPNE ESCCOL ACISPP SALSPP SHISPP NEIGON
BLOOD
URINE
STOOL
GENITAL
15 Pathogen- Antibiotic combinations to be included
PATHOGEN ANTIBIOTIC combination Pathogen Code Antibiotic Code
Staphylococcus aureus - Oxacillin STAAUR OXA
Staphylococcus aureus - Cefoxitin STAAUR FOX
Staphylococcus aureus - Penicilinase-stable beta-lactams STAAUR J01DC
Streptococcus pneumoniae - Penicillin G STRPNE PEN
Streptococcus pneumoniae - Oxacillin STRPNE OXA
Streptococcus pneumoniae - Cefotaxime STRPNE CTX
Streptococcus pneumoniae - Ceftriaxone STRPNE CRO
Streptococcus pneumoniae - Co-trimoxazole STRPNE SXT
Streptococcus pneumoniae - Penicillins STRPNE J01C
Streptococcus pneumoniae - Third-generation cephalosporins STRPNE J01DD Streptococcus pneumoniae - Sulfonamides and trimethoprim STRPNE J01EE
Klebsiella pneumoniae - Cefotaxime KLEPNE CTX
Klebsiella pneumoniae - Ceftazidime KLEPNE CAZ
Klebsiella pneumoniae - Ceftriaxone KLEPNE CRO
Klebsiella pneumoniae - Cefepime KLEPNE FEP
Klebsiella pneumoniae - Doripenem KLEPNE DOR
Klebsiella pneumoniae - Ertapenem KLEPNE ETP
Klebsiella pneumoniae - Imipenem KLEPNE IPM
Klebsiella pneumoniae - Meropenem KLEPNE MEM
Klebsiella pneumoniae - Co-trimoxazole KLEPNE SXT
Klebsiella pneumoniae - Ciprofloxacin KLEPNE CIP
Klebsiella pneumoniae - Levofloxacin KLEPNE LVX
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Klebsiella pneumoniae - Colistin KLEPNE COL
Klebsiella pneumoniae - Third-generation cephalosporins KLEPNE J01DD Klebsiella pneumoniae - Fourth-generation cephalosporins KLEPNE J01DE
Klebsiella pneumoniae - Carbapenems KLEPNE J01DH
Klebsiella pneumoniae - Sulfonamides and trimethoprim KLEPNE J01EE
Klebsiella pneumoniae - Fluoroquinolones KLEPNE J01MA
Klebsiella pneumoniae - Polymyxins KLEPNE J01XB
Escherichia coli - Ampicillin ESCCOL AMP
Escherichia coli - Cefotaxime ESCCOL CTX
Escherichia coli - Ceftazidime ESCCOL CAZ
Escherichia coli - Ceftriaxone ESCCOL CRO
Escherichia coli - Cefepime ESCCOL FEP
Escherichia coli - Doripenem ESCCOL DOR
Escherichia coli - Ertapenem ESCCOL ETP
Escherichia coli - Imipenem ESCCOL IPM
Escherichia coli - Meropenem ESCCOL MEM
Escherichia coli - Co-trimoxazole ESCCOL SXT
Escherichia coli - Ciprofloxacin ESCCOL CIP
Escherichia coli - Levofloxacin ESCCOL LVX
Escherichia coli - Colistin ESCCOL COL
Escherichia coli - Penicillins ESCCOL J01C
Escherichia coli - Third-generation cephalosporins ESCCOL J01DD Escherichia coli - Fourth-generation cephalosporins ESCCOL J01DE
Escherichia coli - Carbapenems ESCCOL J01DH
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Escherichia coli - Sulfonamides and trimethoprim ESCCOL J01EE
Escherichia coli - Fluoroquinolones ESCCOL J01MA
Escherichia coli - Polymyxins ESCCOL J01XB
Acinetobacter spp. - Minocycline ACISPP MNO
Acinetobacter spp. - Tigecycline ACISPP TGC
Acinetobacter spp. - Doripenem ACISPP DOR
Acinetobacter spp. - Imipenem ACISPP IPM
Acinetobacter spp. - Meropenem ACISPP MEM
Acinetobacter spp. - Amikacin ACISPP AMK
Acinetobacter spp. - Gentamicin ACISPP GEN
Acinetobacter spp. - Colistin ACISPP COL
Acinetobacter spp. - Tetracyclines ACISPP J01AA
Acinetobacter spp. - Carbapenems ACISPP J01DH
Acinetobacter spp. - Aminoglycosides ACISPP J01GB
Acinetobacter spp. - Polymyxins ACISPP J01XB
Salmonella spp. - Cefotaxime SALSPP CTX
Salmonella spp. - Ceftazidime SALSPP CAZ
Salmonella spp. - Ceftriaxone SALSPP CRO
Salmonella spp. - Doripenem SALSPP DOR
Salmonella spp. - Ertapenem SALSPP ETP
Salmonella spp. - Imipenem SALSPP IPM
Salmonella spp. - Meropenem SALSPP MEM
Salmonella spp. - Ciprofloxacin SALSPP CIP
Salmonella spp. - Levofloxacin SALSPP LVX
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Salmonella spp. - Third-generation cephalosporins SALSPP J01DD
Salmonella spp. - Carbapenems SALSPP J01DH
Salmonella spp. - Fluoroquinolones SALSPP J01MA
Shigella spp. - Third-generation cephalosporins SHISPP J01DD
Shigella spp. - Macrolides SHISPP J01FA
Shigella spp. - Fluoroquinolones SHISPP J01MA
Shigella spp. - Cefotaxime SHISPP CTX
Shigella spp. - Ceftazidime SHISPP CAZ
Shigella spp. - Ceftriaxone SHISPP CRO
Shigella spp. - Azithromycin SHISPP AZM
Shigella spp. - Ciprofloxacin SHISPP CIP
Shigella spp. - Levofloxacin SHISPP LVX
Neisseria gonorrhoeae - Third-generation cephalosporins NEIGON J01DD
Neisseria gonorrhoeae - Macrolides NEIGON J01FA
Neisseria gonorrhoeae - Aminoglycosides NEIGON J01GB
Neisseria gonorrhoeae - Fluoroquinolones NEIGON J01MA
Neisseria gonorrhoeae - Aminocyclitols NEIGON J01XX
Neisseria gonorrhoeae - Ceftriaxone NEIGON CRO
Neisseria gonorrhoeae - Cefixime NEIGON CFM
Neisseria gonorrhoeae - Azithromycin NEIGON AZM
Neisseria gonorrhoeae - Gentamicin NEIGON GEN
Neisseria gonorrhoeae - Ciprofloxacin NEIGON CIP
Neisseria gonorrhoeae - Spectinomycin NEIGON SPT
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References
1. Global Antimicrobial Resistance Surveillance System: Manual for Early Implementation. Available at http://apps.who.int/iris/bitstream/10665/188783/1/9789241549400_eng.pdf?ua=1
2. Global Antimicrobial Resistance Surveillance System (GLASS): A guide to uploading aggregated AMR data. Available from the GLASS Secretariat.
3. Global Antimicrobial Resistance Surveillance System (GLASS): Enrolment guide for national focal points. Available from the GLASS Secretariat.
4. Global Antimicrobial Resistance Surveillance System (GLASS): A guide to completing the GLASS implementation questionnaire. Available from the GLASS Secretariat.
5. WHONET manual to support the WHO Global Antimicrobial Resistance Surveillance System.
Available from the GLASS Secretariat.
6. European Committee on Antimicrobial Susceptibility Testing – EUCAST (http://www.eucast.org/) 7. Performance standards for antimicrobial susceptibility testing: twenty-fifth informational supplement. Wayne, Pennsylvania: Clinical and Laboratory Standards Institute; 2015 (M100-S25)
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Annex 1. Coded values
1. COUNTRY
Code Label Country
AFG Afghanistan Islamic Republic of Afghanistan
ALB Albania Republic of Albania
DZA Algeria People’s Democratic Republic of Algeria
AND Andorra Principality of Andorra
AGO Angola Republic of Angola
ATG Antigua and Barbuda Antigua and Barbuda
ARG Argentina Argentine Republic
ARM Armenia Republic of Armenia
AUS Australia Australia
AUT Austria Republic of Austria
AZE Azerbaijan Republic of Azerbaijan
BHS Bahamas Commonwealth of the Bahamas
BHR Bahrain Kingdom of Bahrain
BGD Bangladesh People’s Republic of Bangladesh
BRB Barbados Barbados
BLR Belarus Republic of Belarus
BEL Belgium Kingdom of Belgium
BLZ Belize Belize
BEN Benin Republic of Benin
BTN Bhutan Kingdom of Bhutan
BOL Bolivia (Plurinational State of) Plurinational State of Bolivia
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BIH Bosnia and Herzegovina Bosnia and Herzegovina
BWA Botswana Republic of Botswana
BRA Brazil Federative Republic of Brazil
BRN Brunei Darussalam Brunei Darussalam
BGR Bulgaria Republic of Bulgaria
BFA Burkina Faso Burkina Faso
BDI Burundi Republic of Burundi
CPV Cabo Verde Republic of Cabo Verde
KHM Cambodia Kingdom of Cambodia
CMR Cameroon Republic of Cameroon
CAN Canada Canada
CAF Central African Republic Central African Republic
TCD Chad Republic of Chad
CHL Chile Republic of Chile
CHN China People’s Republic of China
COL Colombia Republic of Colombia
COM Comoros Union of the Comoros
COG Congo Republic of the Congo
COK Cook Islands Cook Islands
CRI Costa Rica Republic of Costa Rica
CIV Côte d’Ivoire Republic of Côte d’Ivoire
HRV Croatia Republic of Croatia
CUB Cuba Republic of Cuba
CYP Cyprus Republic of Cyprus
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CZE Czech Republic Czech Republic
PRK Democratic People’s Republic of Korea Democratic People’s Republic of Korea COD Democratic Republic of the Congo Democratic Republic of the Congo
DNK Denmark Kingdom of Denmark
DJI Djibouti Republic of Djibouti
DMA Dominica Commonwealth of Dominica
DOM Dominican Republic Dominican Republic
ECU Ecuador Republic of Ecuador
EGY Egypt Arab Republic of Egypt
SLV El Salvador Republic of El Salvador
GNQ Equatorial Guinea Republic of Equatorial Guinea
ERI Eritrea State of Eritrea
EST Estonia Republic of Estonia
ETH Ethiopia Federal Democratic Republic of Ethiopia
FJI Fiji Republic of Fiji
FIN Finland Republic of Finland
FRA France French Republic
GAB Gabon Gabonese Republic
GMB Gambia Islamic Republic of the Gambia
GEO Georgia Georgia
DEU Germany Federal Republic of Germany
GHA Ghana Republic of Ghana
GRC Greece Hellenic Republic
GRD Grenada Grenada
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GTM Guatemala Republic of Guatemala
GIN Guinea Republic of Guinea
GNB Guinea-Bissau Republic of Guinea-Bissau
GUY Guyana Republic of Guyana
HTI Haiti Republic of Haiti
HND Honduras Republic of Honduras
HUN Hungary Hungary
ISL Iceland Republic of Iceland
IND India Republic of India
IDN Indonesia Republic of Indonesia
IRN Iran (Islamic Republic) Islamic Republic of Iran
IRQ Iraq Republic of Iraq
IRL Ireland Ireland
ISR Israel State of Israel
ITA Italy Republic of Italy
JAM Jamaica Jamaica
JPN Japan Japan
JOR Jordan Hashemite Kingdom of Jordan
KAZ Kazakhstan Republic of Kazakhstan
KEN Kenya Republic of Kenya
KIR Kiribati Republic of Kiribati
KWT Kuwait State of Kuwait
KGZ Kyrgyzstan Kyrgyz Republic
LAO Lao People’s Democratic Republic Lao People’s Democratic Republic
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LVA Latvia Republic of Latvia
LBN Lebanon Lebanese Republic
LSO Lesotho Kingdom of Lesotho
LBR Liberia Republic of Liberia
LBY Libya Libya
LTU Lithuania Republic of Lithuania
LUX Luxembourg Grand Duchy of Luxembourg
MDG Madagascar Republic of Madagascar
MWI Malawi Republic of Malawi
MYS Malaysia Malaysia
MDV Maldives Republic of Maldives
MLI Mali Republic of Mali
MLT Malta Republic of Malta
MHL Marshall Islands Republic of the Marshall Islands
MRT Mauritania Islamic Republic of Mauritania
MUS Mauritius Republic of Mauritius
MEX Mexico United Mexican States
FSM Micronesia (Federated States of) Federated States of Micronesia
MCO Monaco Principality of Monaco
MNG Mongolia Mongolia
MNE Montenegro Montenegro
MAR Morocco Kingdom of Morocco
MOZ Mozambique Republic of Mozambique
MMR Myanmar Republic of the Union of Myanmar
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NAM Namibia Republic of Namibia
NRU Nauru Republic of Nauru
NPL Nepal Federal Democratic
ABC Netherlands Kingdom of the Netherlands
NZL New Zealand New Zealand
NIC Nicaragua Republic of Nicaragua
NER Niger Republic of the Niger
NGA Nigeria Federal Republic of Nigeria
NIU Niue Republic of Niue
NOR Norway Kingdom of Norway
OMN Oman Sultanate of Oman
PAK Pakistan Islamic Republic of Pakistan
PLW Palau Republic of Palau
PAN Panama Republic of Panama
PNG Papua New Guinea Independent State of Papua New Guinea
PRY Paraguay Republic of Paraguay
PER Peru Republic of Peru
PHL Philippines Republic of the Philippines
POL Poland Republic of Poland
PRT Portugal Portuguese Republic
QAT Qatar State of Qatar
KOR Republic of Korea Republic of Korea
MDA Republic of Moldova Republic of Moldova
ROU Romania Romania
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RUS Russian Federation Russian Federation
RWA Rwanda Republic of Rwanda
KNA Saint Kitts and Nevis Saint Kitts and Nevis
LCA Saint Lucia Saint Lucia
VCT Saint Vincent and the Grenadines Saint Vincent and the Grenadines
WSM Samoa Independent State of Samoa
SMR San Marino Republic of San Marino
STP Sao Tome and Principe Democratic Republic of Sao Tome and Principe
SAU Saudi Arabia Kingdom of Saudi Arabia
SEN Senegal Republic of Senegal
SRB Serbia Republic of Serbia
SYC Seychelles Republic of Seychelles
SLE Sierra Leone Republic of Sierra Leone
SGP Singapore Republic of Singapore
SVK Slovakia Slovak Republic
SVN Slovenia Republic of Slovenia
SLB Solomon Islands Solomon Islands
SOM Somalia Federal Republic of Somalia
ZAF South Africa Republic of South Africa
SSD South Sudan Republic of South Sudan
ESP Spain Kingdom of Spain
LKA Sri Lanka Democratic Socialist Republic of Sri Lanka
SDN Sudan Republic of the Sudan
SUR Suriname Republic of Suriname
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SWZ Swaziland Kingdom of Swaziland
SWE Sweden Kingdom of Sweden
CHE Switzerland Swiss Confederation
SYR Syrian Arab Republic Syrian Arab Republic
TJK Tajikistan Republic of Tajikistan
THA Thailand Kingdom of Thailand
MKD The former Yugoslav republic of Macedonia
The former Yugoslav republic of Macedonia
TLS Timor-Leste Democratic Republic of Timor-Leste
TGO Togo Togolese Republic
TON Tonga Kingdom of Tonga
TTO Trinidad and Tobago Republic of Trinidad and Tobago
TUN Tunisia Republic of Tunisia
TUR Turkey Republic of Turkey
TKM Turkmenistan Turkmenistan
TUV Tuvalu Tuvalu
UGA Uganda Republic of Uganda
UKR Ukraine Ukraine
ARE United Arab Emirates United Arab Emirates
GBR United Kingdom of Great Britain and Northern Ireland
United Kingdom of Great Britain and Northern Ireland
TZA United Republic of Tanzania United Republic of Tanzania USA United States of America United States of America
URY Uruguay Eastern Republic of Uruguay
UZB Uzbekistan Republic of Uzbekistan
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VUT Vanuatu Republic of Vanuatu
VEN Venezuela (Bolivarian Republic of) Bolivarian Republic of Venezuela
VNM Viet Nam Socialist Republic of Viet Nam
YEM Yemen Republic of Yemen
ZMB Zambia Republic of Zambia
ZWE Zimbabwe Republic of Zimbabwe
2. YEAR
Code 2009 2010 2011 2012 2013 2014 2015 2016
3. SPECIMEN
Code Specimen Label
BLOOD Blood BLOOD
URINE Urine URINE
STOOL Stool STOOL
GENITAL Urethral and cervical swabs GENITAL
29 4. PATHOGEN
Code Pathogen Label
ACISPP Acinetobacter spp. Acinetobacter spp.
ESCCOL Escherichia coli Escherichia coli
KLEPNE Klebsiella pneumoniae Klebsiella pneumoniae NEIGON Neisseria gonorrhoeae Neisseria gonorrhoeae
SALSPP Salmonella spp. Salmonella spp.
SHISPP Shigella spp. Shigella spp.
STAAUR Staphylococcus aureus Staphylococcus aureus STRPNE Streptococcus pneumoniae Streptococcus pneumoniae 5. GENDER
Code Gender Label
M Male Male
F Female Female
O Other Other
UNK Unknown Unknown
SUM SUM = M +F+O+UNK Not stratified 6. ORIGIN
Code Origin Label
HO Hospital origin Hospital origin
CO Community origin Community origin
UNK Unknown Unknown
SUM SUM=HO+CO+UNK Not stratified
30 7. AGE GROUP
Code Age groups Label
<1 <1 <1
01<04 1-4 1-4
05<14 5-14 5-14
15<24 15-24 15-24
25<34 25-34 25-34
35<44 35-44 35-44
45<54 45-54 45-54
55<64 55-64 55-64
65<74 65-74 65-74
75<84 75-84 75-84
85< 85+ 85+
UNK Unknown Unknown
SUM SUM= all age groups +UNK Not stratified 8. ANTIBIOTIC
AA Code Antimicrobial agent (antibiotic, AA) Label
MNO Minocycline Minocycline
TGC Tigecycline Tigecycline
AMP Ampicillin Ampicillin
PEN Penicillin G Penicillin G
OXA Oxacillin Oxacillin
FOX Cefoxitin Cefoxitin
CTX Cefotaxime Cefotaxime
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CAZ Ceftazidime Ceftazidime
CRO Ceftriaxone Ceftriaxone
CFM Cefixime Cefixime
FEP Cefepime Cefepime
DOR Doripenem Doripenem
ETP Ertapenem Ertapenem
IPM Imipenem Imipenem
MEM Meropenem Meropenem
SXT Co-trimoxazole Co-trimoxazole
AZM Azithromycin Azithromycin
AMK Amikacin Amikacin
GEN Gentamicin Gentamicin
CIP Ciprofloxacin Ciprofloxacin
LVX Levofloxacin Levofloxacin
COL Colistin Colistin
SPT Spectinomycin Spectinomycin
AC Code Antibiotic class (category, AC) Label
J01AA Tetracyclines Tetracyclines
J01C Penicillins Penicillins
J01DC Penicilinase-stable beta-lactams PSB-lactams
J01DD Third-generation cephalosporins 3 gen cephalosporins J01DE Fourth-generation cephalosporins 4 gen cephlosporins
J01DH Carbapenems Carbapenems
J01EE Sulfonamides and trimethoprim Sulfonamides-TMP
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J01FA Macrolides Macrolides
J01GB Aminoglycosides Aminoglycosides
J01MA Fluoroquinolones Quinolones
J01XB Polymyxins Polymyxins
J01XX Aminocyclitols Aminocyclitols
J01CA Penicillins with extended spectrum Penicillins with extended spectrum J01CE Beta-lactamase sensitive penicillins Beta-lactamase sensitive penicillins J01CF Beta-lactamase resistant penicillins Beta-lactamase resistant penicillins J01CR Penicillins combinations Penicillins combinations
9. BATCH ID
Code BatchID Label
DS1 Data Set 1 Data Set 1
DS2 Data Set 2 Data Set 2
DS3 Data Set 3 Data Set 3
DS4 Data Set 4 Data Set 4
DS5 Data Set 5 Data Set 5
WHONET WHONET WHONET
CAESAR CAESAR CAESAR
