The aim of this tracker is to provide an evidence-base for analysis of global and local trends, including comparisons across countries, locations, and human and animal sectors over time. Analysis of antimicrobial use is a key element for identifying areas at risk from AMR and can inform optimisation of antimicrobial consumption and use. The database therefore represents a powerful resource for analysis to highlight the latest antimicrobial use data and trends, as well as gaps in the existing evidence in order to influence surveillance, research and policy. The database can be filtered by author, title, journal, year of publication, location, and population.
Antimicrobial use (AMU) has been identified as one of the key drivers of antimicrobial resistance (AMR), a phenomenon which is causing increasing concern around the world, particularly in LMICs (Laxminarayan et al 2012). Antimicrobials are commonly used in human and animal health to treat infection, and in animal production to enhance growth (Van Boeckel et al 2015; Hao Van 2020). Extensive use of antimicrobials potentiates AMR by exerting a selection pressure on bacteria to select for resistant genes. Consequently, efforts to contain antimicrobial resistance have focussed on reducing antimicrobial consumption and ‘rationalising’ use. The surveillance of antimicrobial consumption and use is a key part of global plans to tackle AMR (WHO 2015), however limited synthesis of the existing data precludes systematic analysis, impeding efforts to develop targeted interventions in AMU.
Current estimates of antiicrobial use and resistance often utilise macro-level consumption data from national sales and imports and point prevalence surveys from hospitals. The European Surveillance of Antimicrobial Consumption (ESAC) is an example of a large-scale surveillance system using national sales or reimbursement data to track consumption of antimicrobials over time, enabling cross-country comparisons and informing public policy across Europe. This system complements the European Antimicrobial Resistance Surveillance System (EARSS). The majority of LMICs have not historically collected antimicrobial consumption data, but a system is now in place for this for animal antibiotic consumption data through the OIE, and the WHO is supporting capacity in IQVIA and is expanding their network of consumption data collection across Africa and Asia through the Fleming Fund (OIE 2016, WHO 2018).
Hospital-level point prevalence surveys are also useful for enabling benchmarking comparisons of antimicrobial use across healthcare facilities, quantifying the selection pressure on microbial populations, and assessing the impact AMR on morbidity and mortality. However, in many LMICs, antimicrobials can be purchased over the counter, and there is evidence of increasing consumption of antimicrobials in formal and informal community settings; including private and public drug shops, clinics, pharmacies, and long-term care facilities (Laxminarayan et al 2012; Kotwani et al 2011). In addition, in the context of a changing global food landscape, animal and plant production are key sites of interest in a One Health approach to the surveillance of AMU and control of AMR (Van Boeckel et al 2015).
Surveillance of antimicrobial use in the community beyond hospitals is more challenging due to the diversity of actors, facilities, and metrics involved (Chandy 2013). Nevertheless, an increasing number of studies have sought to investigate antimicrobial use at the household and community level, using a range of methods including simulated client visits, audits, outlet and community surveys (Holloway et al 2009). These studies offer a more granular analysis of the use of antimicrobials in different settings, helping to illuminate trends in availability, access, consumption and use of antimicrobials. Such data further enables critical consideration of characterisations of antimicrobial use as ‘rational’ or ‘irrational’, as well as the development of localised surveillance and intervention strategies.
Target Audience
The primary audience is researchers and programme implementers who require information on current and previous antibiotic use to inform analyses of how to understand and manage antibiotic use, relations with antibiotic resistance, and assessment of the impact of programmes around community-level antibiotic use.
Methods
Scope of database
The database includes all studies that describe empirical data that measures antibiotic use in terms of volume and/or types of antibiotics. Studies that describe reasons for antibiotic use will also be included as long as they also provide a measure of use. The database includes human and animal studies that specify a measure of use, from inception to present day. In order to provide different levels of data for analysis, the tracker will include national level studies, as well as end-user studies, for example, outlet and household surveys. The tracker does not include hospital-based point prevalence surveys. Users are able to select filters for the database using drop-down tabs at the top of the excel sheet.
Author(s)
Title
Journal
Year of Publication
Countries data collected in
Population e.g., Animal/human/aquaculture
Research question
This review is guided by the question: ‘What is the extent, range and nature of antimicrobial use in non-hospital settings around the world?’
Search strategy and eligibility criteria
The scoping review involved the systematic search, collection, extraction and analysis of studies relevant to antimicrobial use in non-hospital settings. The search terms were kept purposefully broad in order to encompass the diversity of studies involved. The searches were built using Boolean operators and truncation signs for each database. The following key terms will be used: (“antimicrobial use” OR “antibiotic use” OR “antimicrobial(s)” OR “antibiotic(s)) NOT (“hospital”). The ‘NOT hospital’ aspect of the search is defined broadly in the negative due to the diversity of community-level contexts and measures of ABU.
The search was refined using the eligibility criteria. Publications were eligible for inclusion if they address antimicrobial use in non-hospital settings and specify a measure of use (qualitative or quantitative). Measures might include percentage of customer encounters with an antibiotic prescribed or percentage of informants using antimicrobials within a defined period. Macro-level non-hospital-based studies (e.g., using pharmaceutical sales data) were included to enable scaled and comparative cross-country analysis. Studies which focus on antimicrobial resistance were included if there is a measure of ABU. No language or date restrictions were applied. The review will include publications in the English language, and those published from inception to present day.
Studies that do not address ABU in non-hospital settings, or do not include a measure of use, for example ‘knowledge, attitude and practice’ studies or discussion pieces, were excluded. Studies that focus on interventions without a defined measure of use were excluded. Assessment of the quality and risk of bias of studies is beyond the scope of this review. Study quality or risk of bias were not used as exclusion criteria.
Databases searched include Web of Science (WoS), including Web of Science Core Collection, BIOSIS Citation Index, KCI-Korean Journal Database, MEDLINE, Russian Science Citation Index, SciELO Citation Index. Future searches could include review of other databases such as PubMed and Scopus.
Data extraction and synthesis
The title and abstracts of the studies were reviewed, and relevant publications downloaded to EndNote X9 reference management software; any duplicates were removed. The full texts of the remaining articles were screened to identify those that meet the eligibility criteria; those that do not fit the eligibility criteria were removed. Data from the final set of studies were extracted and recorded in an excel database. The extraction sheet includes the following headings: (i) Author(s); (ii) Title; (iii) Journal; (iv) Year of Publication; (v) Countries data collected in; (vi) Population (Animal/Human/Aquaculture). Users are able to search and refine the final database using filters applied to each heading. The database will remain a live document, with regular updates possible through re-running searches. Version dates will be provided and the flow chart of included reports will be updated with each revised search.
Appendix 1: Search terms
Database
Set
Terms
Web of Science (1970-2020)Basic Title Search: All Databases
#1
“antimicrobial use*” OR “anti-microbial use*” OR “AMU” OR “antibiotic use*” OR “anti-biotic use*” OR “use of antimicrobial*” OR “use of anti-microbials*” OR “use of antibiotic*” OR “use of anti-biotic*” OR “antimicrobial drug*” OR “anti-microbial drug*” OR “antimicrobial drug use*” OR “anti-microbial drug use*” OR “antibiotic drug use*” OR “anti-biotic drug use*” OR “antibacterial drug use*” OR “anti-bacterial drug use*” OR “using antimicrobial*” OR “using anti-microbial*” OR “using antibiotic*” OR “using antibiotic*” OR “antimicrobial consumption” OR “anti-microbial consumption” OR “antibiotic consumption” OR “anti-biotic consumption” OR “antibacterial use*” OR “anti-bacterial use*” OR “antibacterial usage” OR “anti-bacterial usage” OR “use of antibacterial*” OR “use of anti-bacterial*” OR “antibacterial consumption” OR “anti-bacterial consumption” OR “antibiotic self-medication” OR “anti-biotic self-medication” OR “self-medication of antibiotic*” OR “self-medication of anti-biotic*” OR “antimicrobial self-medication” OR “anti-microbial self-medication” OR “self-medication of antimicrobial*” OR “self-medication of anti-microbial*” OR “self-medication of antibacterial*” OR “self-medication of anti-bacterial*” OR “antibacterial self-medication” OR “anti-bacterial self-medication”
Basic Title Search: All Databases
#2
NOT hospital*
Advanced Search: Combine
#3
COMBINE (#1 NOT #2)
Appendix 2: Screening tool
Title/abstract screening:
Does the title/abstract refer to antimicrobial use in a non-hospital setting?
Yes
No
Not sure
2. Does the title/abstract/full text refer to measure of use of antimicrobials?
Yes
No
Not sure
Inclusion/Exclusion criteria:
Inclusion:
Original research study
Non hospital-based study
Measure of antimicrobial use (Qualitative or Quantitative)
Exclusion:
Hospital-based study e.g. point prevalence studies
Antimicrobial Use Tracker
The aim of this tracker is to provide an evidence-base for analysis of global and local trends, including comparisons across countries, locations, and human and animal sectors over time. Analysis of antimicrobial use is a key element for identifying areas at risk from AMR and can inform optimisation of antimicrobial consumption and use. The database therefore represents a powerful resource for analysis to highlight the latest antimicrobial use data and trends, as well as gaps in the existing evidence in order to influence surveillance, research and policy. The database can be filtered by author, title, journal, year of publication, location, and population.
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Rationale
Antimicrobial use (AMU) has been identified as one of the key drivers of antimicrobial resistance (AMR), a phenomenon which is causing increasing concern around the world, particularly in LMICs (Laxminarayan et al 2012). Antimicrobials are commonly used in human and animal health to treat infection, and in animal production to enhance growth (Van Boeckel et al 2015; Hao Van 2020). Extensive use of antimicrobials potentiates AMR by exerting a selection pressure on bacteria to select for resistant genes. Consequently, efforts to contain antimicrobial resistance have focussed on reducing antimicrobial consumption and ‘rationalising’ use. The surveillance of antimicrobial consumption and use is a key part of global plans to tackle AMR (WHO 2015), however limited synthesis of the existing data precludes systematic analysis, impeding efforts to develop targeted interventions in AMU.
Current estimates of antiicrobial use and resistance often utilise macro-level consumption data from national sales and imports and point prevalence surveys from hospitals. The European Surveillance of Antimicrobial Consumption (ESAC) is an example of a large-scale surveillance system using national sales or reimbursement data to track consumption of antimicrobials over time, enabling cross-country comparisons and informing public policy across Europe. This system complements the European Antimicrobial Resistance Surveillance System (EARSS). The majority of LMICs have not historically collected antimicrobial consumption data, but a system is now in place for this for animal antibiotic consumption data through the OIE, and the WHO is supporting capacity in IQVIA and is expanding their network of consumption data collection across Africa and Asia through the Fleming Fund (OIE 2016, WHO 2018).
Hospital-level point prevalence surveys are also useful for enabling benchmarking comparisons of antimicrobial use across healthcare facilities, quantifying the selection pressure on microbial populations, and assessing the impact AMR on morbidity and mortality. However, in many LMICs, antimicrobials can be purchased over the counter, and there is evidence of increasing consumption of antimicrobials in formal and informal community settings; including private and public drug shops, clinics, pharmacies, and long-term care facilities (Laxminarayan et al 2012; Kotwani et al 2011). In addition, in the context of a changing global food landscape, animal and plant production are key sites of interest in a One Health approach to the surveillance of AMU and control of AMR (Van Boeckel et al 2015).
Surveillance of antimicrobial use in the community beyond hospitals is more challenging due to the diversity of actors, facilities, and metrics involved (Chandy 2013). Nevertheless, an increasing number of studies have sought to investigate antimicrobial use at the household and community level, using a range of methods including simulated client visits, audits, outlet and community surveys (Holloway et al 2009). These studies offer a more granular analysis of the use of antimicrobials in different settings, helping to illuminate trends in availability, access, consumption and use of antimicrobials. Such data further enables critical consideration of characterisations of antimicrobial use as ‘rational’ or ‘irrational’, as well as the development of localised surveillance and intervention strategies.
Target Audience
The primary audience is researchers and programme implementers who require information on current and previous antibiotic use to inform analyses of how to understand and manage antibiotic use, relations with antibiotic resistance, and assessment of the impact of programmes around community-level antibiotic use.
Methods
Scope of database
The database includes all studies that describe empirical data that measures antibiotic use in terms of volume and/or types of antibiotics. Studies that describe reasons for antibiotic use will also be included as long as they also provide a measure of use. The database includes human and animal studies that specify a measure of use, from inception to present day. In order to provide different levels of data for analysis, the tracker will include national level studies, as well as end-user studies, for example, outlet and household surveys. The tracker does not include hospital-based point prevalence surveys. Users are able to select filters for the database using drop-down tabs at the top of the excel sheet.
Research question
This review is guided by the question: ‘What is the extent, range and nature of antimicrobial use in non-hospital settings around the world?’
Search strategy and eligibility criteria
The scoping review involved the systematic search, collection, extraction and analysis of studies relevant to antimicrobial use in non-hospital settings. The search terms were kept purposefully broad in order to encompass the diversity of studies involved. The searches were built using Boolean operators and truncation signs for each database. The following key terms will be used: (“antimicrobial use” OR “antibiotic use” OR “antimicrobial(s)” OR “antibiotic(s)) NOT (“hospital”). The ‘NOT hospital’ aspect of the search is defined broadly in the negative due to the diversity of community-level contexts and measures of ABU.
The search was refined using the eligibility criteria. Publications were eligible for inclusion if they address antimicrobial use in non-hospital settings and specify a measure of use (qualitative or quantitative). Measures might include percentage of customer encounters with an antibiotic prescribed or percentage of informants using antimicrobials within a defined period. Macro-level non-hospital-based studies (e.g., using pharmaceutical sales data) were included to enable scaled and comparative cross-country analysis. Studies which focus on antimicrobial resistance were included if there is a measure of ABU. No language or date restrictions were applied. The review will include publications in the English language, and those published from inception to present day.
Studies that do not address ABU in non-hospital settings, or do not include a measure of use, for example ‘knowledge, attitude and practice’ studies or discussion pieces, were excluded. Studies that focus on interventions without a defined measure of use were excluded. Assessment of the quality and risk of bias of studies is beyond the scope of this review. Study quality or risk of bias were not used as exclusion criteria.
Databases searched include Web of Science (WoS), including Web of Science Core Collection, BIOSIS Citation Index, KCI-Korean Journal Database, MEDLINE, Russian Science Citation Index, SciELO Citation Index. Future searches could include review of other databases such as PubMed and Scopus.
Data extraction and synthesis
The title and abstracts of the studies were reviewed, and relevant publications downloaded to EndNote X9 reference management software; any duplicates were removed. The full texts of the remaining articles were screened to identify those that meet the eligibility criteria; those that do not fit the eligibility criteria were removed. Data from the final set of studies were extracted and recorded in an excel database. The extraction sheet includes the following headings: (i) Author(s); (ii) Title; (iii) Journal; (iv) Year of Publication; (v) Countries data collected in; (vi) Population (Animal/Human/Aquaculture). Users are able to search and refine the final database using filters applied to each heading. The database will remain a live document, with regular updates possible through re-running searches. Version dates will be provided and the flow chart of included reports will be updated with each revised search.
Appendix 1: Search terms
Appendix 2: Screening tool
Title/abstract screening:
2. Does the title/abstract/full text refer to measure of use of antimicrobials?
Inclusion/Exclusion criteria:
Inclusion:
Exclusion: