Using Priorities between Human and Livestock Bacterial Antimicrobial Resistance (AMR) to Identify Data Gaps in Livestock AMR Surveillance

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Swetschinski, Christina Fastl, and 15 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4253597/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Sep, 2024 Read the published version in BMC Infectious Diseases → Version 1 posted 11 You are reading this latest preprint version Abstract Background Bacterial antimicrobial resistance (AMR) is a global threat to both humans and livestock. Despite this, there is limited global consensus on data-informed, priority areas for intervention in both sectors. We compare current livestock AMR data collection efforts with other variables pertinent to human and livestock AMR to identify critical data gaps and mutual priorities. Methods We globally synthesized livestock AMR data from open-source surveillance reports and point prevalence surveys stratified for six pathogens ( Escherichia coli , Staphylococcus aureus , non-typhoidal Salmonella , Campylobacter spp., Enterococcus faecalis , Enterococcus faecium ) and eleven antimicrobial classes important in human and veterinary use, published between 2000 and 2020. We also included all livestock species represented in the data: cattle, chickens, pigs, sheep, turkeys, ducks, horses, buffaloes, and goats. We compared this data with intended priorities calculated from: disability-adjusted life years (DALYs), livestock antimicrobial usage (AMU), livestock biomass, and a global correlation exercise between livestock and human proportion of resistant isolates. Results Resistance to fluoroquinolones and macrolides in Staphylococcus aureus were identified as priorities in many countries but, less than 10% of these reported livestock AMR data. Resistance data for Escherichia coli specific to cattle, chickens, and pigs, which we prioritized, were also well collected. AMR data collection on non-typhoidal Salmonella and other livestock species were often not prioritized. Of 232 categories prioritized by at least one country, data were only collected for 48% (n = 112). Conclusions The lack of livestock AMR data globally for broad resistance in Staphylococcus aureus could underplay their zoonotic threat. Countries can bolster livestock AMR data collection, reporting, and intervention setting for Staphylococcus aureus as done for Escherichia coli . This framework can provide guidance on areas to strengthen AMR surveillance and decision-making for humans and livestock, and if done routinely, can adapt to resistance trends and priorities. Antimicrobial resistance data gaps surveillance livestock Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 BACKGROUND In recent decades, bacterial antimicrobial resistance (AMR) has been identified as a global health threat; estimates indicate it caused 1.27 million human deaths worldwide in 2019 ( 1 ). There are multiple mechanisms by which AMR emerges, such as the interaction between the human sector and the wider environment ( 2 ). Livestock are often treated with the same antimicrobials as humans, and the use of antimicrobials for growth promotion in some countries and disease prevention instead of other hygiene practices creates more avenues for resistance to evolve ( 2 , 3 ). This leads to a complex interplay between antimicrobial usage (AMU) and resistance in humans, animals, and the environment ( 3 ). Several exposure pathways have been proposed linking AMR in humans to the wider environment. These include active ingredients leaching into the environment, introduction into the food chain through animal source foods, or through direct transmission from infectious livestock ( 4 – 6 ). Livestock AMR commonly arises from interactions with AMU; projections estimate global livestock AMU will increase by 8% between 2020 and 2030 given current trends of consumption ( 7 ). Additionally, there is growing evidence showing significant associations between AMU and AMR in both food-producing animals and humans ( 8 ). Due to global AMR and AMU data for livestock and humans being fragmented in availability and quality, there is limited understanding of this relationship in low- and middle- income countries (LMICs) ( 7 , 9 , 10 ). Given the transmission dynamics of AMR between livestock and humans, when analyzing the trends in AMR in humans globally, it is important to understand the global data landscape of animal AMR, particularly when there is increasing uncertainty and concern about the degree to which the agricultural sector contributes to human AMR ( 3 ). Although many human and animal health organizations have outlined AMR as a shared concern, and the need for coordinated monitoring and intervention efforts, there is limited understanding of the mutual antimicrobial class and pathogen combinations of concern on a global scale ( 11 ). Recent research has assessed the global burden and geographic variation of AMR in humans and animals separately for well-represented antimicrobial class and pathogen combinations ( 1 , 7 , 12 ). We aim to build upon these global analyses to highlight the global data gaps in livestock AMR against an evaluation of priorities among shared human and veterinary actors. Through this global analysis and identification of gaps, we present suggestions for strengthening current surveillance of AMR specific to pathogen, antimicrobial class, and livestock species. METHODS To identify antimicrobial classes of shared importance in humans and livestock, we utilized the main global antimicrobial prioritization frameworks pertaining to humans by World Health Organization (WHO) and to animals by World Organisation for Animal Health (WOAH) ( 13 , 14 ). We then evaluated the global coverage of livestock bacterial AMR data by pathogen-antimicrobial-class-livestock (referred to as a category) considering all pathogens available in this data. We created a composite indicator from globally available factors important in human and livestock bacterial AMR to determine category ranks: human disability adjusted life-years (DALYs) attributable to AMR, livestock AMU, livestock biomass, and a global correlation assessment between livestock and human AMR. We ordered categories within each country and designated the top ten percent of values for our composite indicator in a country as priorities and compared the alignment of these to the availability of livestock AMR data on a national and global scale. Compilation of livestock AMR data sources We used WHO and WOAH reports to identify antimicrobials of shared human and veterinary importance ( 13 , 14 ). The top two tiers were considered in both (WHO: “Highest priority of critical importance” and “High priority of critical importance”. WOAH: “Critically important” and “Highly important”). Only antimicrobial classes that fell into these tiers in both WHO and WOAH reports were included in subsequent analyses. Livestock AMR data, specifically the proportion of resistant isolates, from 2000 to 2020 were extracted for each category of pathogen-antimicrobial-class-livestock. All pathogens which impacted both humans and livestock were considered. We used two different types of sources: routine surveillance systems and a published database of individual studies. Resistance data for high-income countries (HICs) were retrieved from various surveillance systems worldwide via open-access sites and reports. Resistance data for LMICs were sourced from resistancebank.org, a repository of point prevalence surveys collected from peer-review literature used as surrogates for systematic surveillance data in LMICs ( 15 , 16 ). For a detailed breakdown of countries covered, further information on the data sources, and additional methods, refer to Supplementary Material file 1 (SM1). Creation of composite indicator We created a composite indicator utilizing globally complete factors in human and animal AMR burden to evaluate the relative importance of each category and evaluate the magnitude of subsequent gaps (Fig. 1 ). The four factors we considered were: human DALYs attributable to AMR, livestock AMU (mg/kg), livestock biomass (population correction units), and a global correlation assessment between livestock and human proportion of resistant isolates. We chose these factors to represent the various transmission and impact points of AMR from livestock to humans in addition to having global coverage. Livestock AMU has been linked to the proliferation of livestock and human AMR, and biomass captures the size of the livestock population for which antimicrobials could be used and therefore propagate AMR ( 8 , 17 ). The correlation assessment aims to capture the pathway connecting livestock and human AMR, and human burden of disease due to AMR is captured through the inclusion of DALYs. Human DALYs, sourced from the Global Research on Antimicrobial Resistance (GRAM) project, were specified by pathogen and antimicrobial class, and ranked per country ( 1 ). We assumed all livestock species would receive the same rank per specification. No estimates for Campylobacter spp. were present. Livestock AMU, sourced from Mulchandani et al., was specified by antimicrobial class and livestock species, and were ranked per country ( 7 ). We assumed all pathogens would receive the same rank per specification. Livestock biomass, sourced from Mulchandani et al., was specified by livestock species, and were ranked per country ( 7 ). We assumed all pathogens and antimicrobial classes would receive the same rank per specification. Biomass estimates were present for cattle, chicken, pigs, and sheep. The global correlation exercise assessed the correlation between the proportion of resistant isolates in livestock (sourced from the livestock AMR data considered) and humans (sourced from the GRAM project) ( 1 ). Spearman correlation coefficients and p-values were calculated by each unique pathogen-antimicrobial-class-livestock category. Statistical significance levels were denoted as follows: p < 0.0001 (****), p < 0.001 (***), p < 0.01 (**), and p < 0.05 (*). Categories with positive correlations were ranked first by ascending order of significance (i.e. lower p-value), with negative correlations receiving the lowest rank. Ranks were determined at a global level, then all countries were assumed to receive the same rank for a particular category. For more details about ranking factors and assumptions, refer to SM1. Categories for which there were no values were assigned the lowest rank, and ranks were standardized using min-max standardization: $${X}_{stand}=\frac{X-{X}_{min}}{{{X}_{max}-X}_{min}}$$ We aggregated the standardized ranks in each indicator to create a composite indicator per pathogen- antimicrobial class-livestock species for each country. Designation of ‘priorities’ The top ten percent of ranked categories per country were designated as priorities and were compared to the livestock AMR data. Three classifications detailing this alignment were identified: prioritized categories without data, prioritized categories with data, and non-prioritized categories with data. This comparison was also propagated to a global level. Please refer to SM1 for more details on the creation of the composite indicator and priorities. RESULTS Livestock AMR data gaps Eleven antimicrobials were identified of shared importance in both human and veterinary use: third generation cephalosporins, fourth generation cephalosporins, fluoroquinolones, macrolides, quinolones, polymyxins, aminoglycosides, aminopenicillins, aminopenicillins with beta-lactamase inhibitors, ansamycins, and phosphonic acid derivatives. The pathogens for which livestock AMR data was available for were: Campylobacter spp., Escherichia coli , Enterococcus faecalis , Enterococcus faecium , non-typhoidal Salmonella , and Staphylococcus aureus . With 109 countries (61% LMICs, 39% HICs) reporting livestock AMR data, the majority of data were specific to cattle, chickens, and pigs, with an emphasis on E. coli and non-typhoidal Salmonella resistance (Fig. 2 ). Aminoglycoside-resistant E. coli and non-typhoidal Salmonella , as well as aminopenicillin-resistant non-typhoidal Salmonella in chickens were the most frequent categories (76 countries), followed by fluoroquinolone-resistant non-typhoidal Salmonella (n = 73), fluoroquinolone- (n = 72) and third-generation-cephalosporin- (n = 70) resistant E. coli in chickens. This was largely driven by high-income countries that primarily only tracked these three species and pathogens; for example, 41% of countries which reported for aminoglycoside-resistant E. coli in chickens were high-income (Fig. 2 ). Reporting on Enterococcus spp. and other livestock species was limited – for example, only ten countries reported data on macrolide-resistant Enterococcus spp. in chickens, and three countries reported data on fluoroquinolone-resistant E. coli in ducks. Most high-income countries did not report on resistant S.aureus , or only reported methicillin-resistant S. aureus . Reported data from LMICs were more broadly spread across categories, and some livestock species were more likely to be or were only reported through these countries (Fig. 2 ). Resistance data for buffaloes were only reported in China, and resistance to classes like ansamycins were reported by 22 LMICs. The scope of resistancebank.org did not include Enterococcus spp, hence no livestock AMR data from LMICs for this pathogen was available. Composite indicator patterns When assessing human DALYs, E. coli resistance typically ranked higher than other pathogens; 118/194 countries ranked third-generation-cephalosporin-resistant- E. coli highest. There were no DALY estimates for resistant Campylobacter spp. With livestock AMU by country, aminopenicillin usage in sheep, and macrolide, aminopenicillin, and phosphonic acid derivatives’ usage in pigs were ranked higher compared to other categories. Usage of third and fourth generation cephalosporins were generally lower ranked than other categories. When considering livestock biomass, cattle were ranked the highest compared to chickens, pigs, and sheep in 110/194 countries. Looking at the global correlation assessment, the highest ranked categories were macrolide-resistant S. aureus in pigs; aminopenicillin-resistant E. coli in cattle and chickens; aminopenicillin-and-beta-lactamase-inhibitor-resistant E. coli in cattle; fluoroquinolone-resistant E. coli in cattle, chickens, and pigs; and third-generation-cephalosporin-resistant E. coli in cattle, chickens, and pigs. Categories using sheep AMR data like third-generation-cephalosporin-resistant E. coli in sheep were not significant and therefore ranked low. In aggregating these globally, fluoroquinolone-resistant E. coli in cattle was the highest ranked category, followed by aminopenicillin-and-beta-lactamase-inhibitor-resistant E. coli in cattle, and third-generation-cephalosporin-resistant E. coli in cattle (Fig. 3 ). Many patterns prevailed in the top ten ranked categories, where fluoroquinolone-, third-generation-cephalosporin-, and aminopenicillin-resistant E. coli in cattle, chickens, and pigs, as well as macrolide-resistant S. aureus in pigs were highest ranked in comparison to other categories in several countries. Resistant Campylobacter spp. and Enterococcus spp. in buffaloes, ducks, goats, horses, and turkeys across all considered antimicrobial classes were lower ranked compared to their other counterparts. Alignment of priorities and livestock AMR data gaps In assessing our priorities generated from the top 10% of values from the composite indicator per country, we prioritized aminopenicillin-resistant E. coli in cattle, chickens, and sheep; aminopenicillin-and-beta-lactamase-inhibitor-resistant E. coli in cattle; fluoroquinolone-resistant E. coli in cattle, chickens, and pigs; third-generation-cephalosporin-resistant E. coli in cattle, chickens, and pigs; and macrolide-resistant S. aureus in pigs for all countries. Of 232 categories prioritized in at least one country, data were only collected for 48% (n = 112) (Fig. 4 ). Macrolide-resistant S. aureus in chicken and pigs particularly had notable mismatches between prioritization and data collection; only 7% and 4% of countries collected data in these categories respectively despite being globally prioritized. Additionally, less than 15% of countries collected data for fluoroquinolone-resistant S. aureus in cattle (13%), chickens (8%), and pigs (7%). Fluoroquinolone-resistant non-typhoidal Salmonella in sheep was also similarly misaligned, where only 6% of the 178 countries that prioritized the category collected data for it. On the other hand, aminopenicillin- and fluoroquinolone-resistant E. coli data in chickens were reported the most by countries when prioritized at 39% and 37% respectively (at least 72/194 countries). No country had livestock AMR data for all prioritized categories determined; China and the Republic of Korea had the highest percentage of prioritized categories with data at 66% (40/61) and 65% (39/60) respectively (Fig. 5 ). Approximately half or 95 of 194 countries (49%) did not have data for any of their indicated national priorities, whilst three quarters of countries had less than 20% of identified priorities with data. DISCUSSION Our study aimed to identify key gaps where strengthening surveillance in livestock on AMR could provide potential benefits to both livestock and human AMR. There has been growing evidence of significant, bi-directional interactions between livestock and human AMR and AMU, therefore we wanted to account for these elements with specificity by pathogen-antimicrobial-class-livestock ( 8 , 18 ). We can then focus our attention onto data gaps within categories that we prioritized to engage data collection efforts in globally relevant areas in the human-livestock space, and better direct and utilize funds for global livestock AMR surveillance. The most notable data gaps we found for prioritized categories were for S. aureus , where especially macrolide- and fluoroquinolone- resistant S. aureus in chickens and pigs had limited data in relation to their prioritization. Macrolides and fluoroquinolones have been identified as antimicrobials of high concern in both human and veterinary medicine ( 13 , 14 ). Macrolides are important in treating several common infections in pigs such as pneumonia, and the genetic mechanism encoding macrolide resistance in S. aureus has been identified as a global concern in due to the potential for horizontal gene transfer between bacterial species and genera ( 19 ). Resistance to fluoroquinolones is also concerning because of the class’s use in treating a wide range of pathogens and infections in both human and veterinary medicine ( 20 ). While currently most HICs only report data for methicillin-resistant S. aureus (MRSA) strains, collection efforts in these systems can be expanded to include S. aureus resistant to other antimicrobials in addition to methicillin. With S. aureus being a leading cause of human deaths due to AMR, expanding data monitoring to other antimicrobials can allow for better assessment of livestock resistance trends and potential impacts on human AMR ( 1 ). Furthermore, we identified fluoroquinolone-resistant non-typhoidal Salmonella in sheep as an area for several countries to prioritize AMR data collection in. Fluoroquinolone-resistant Salmonella has been identified as a high priority pathogen by WHO, and could pose a zoonotic threat through human ingestion of contaminated animal products ( 21 , 22 ). Although limited countries have economically significant and sizeable sheep populations which has impacted the creation of livestock-based estimates like AMU, expanding monitoring efforts to include sheep samples in these countries could appropriately assess concern for this category ( 7 ). Our prioritization framework provides a guide for countries to improve and target surveillance efforts to areas of most need. In the majority of countries that did not have livestock AMR data for prioritized categories, we found no reports or monitoring systems tracking livestock AMR. The majority of these were also LMICs, which often face additional technological, financial and resource limitations that impact the setup and operation of these monitoring systems ( 23 ). With LMICs often having high burden due to AMR, expanding surveillance and monitoring efforts is critical to assessing and tackling the threat of AMR in these countries ( 1 , 24 ). Choosing pathogens, antimicrobial classes, and livestock species of greatest concern through prioritization can factor into the allocation of limited funds and resources in these countries. For other countries where livestock AMR data is available for prioritized categories but missing data for others, existing systems can be broadened to track data that reflects all local concerns. No country had 100% alignment in livestock AMR data for prioritized categories. An area where countries are extensively monitoring livestock AMR that matches up with our global priorities is aminopenicillin- and fluoroquinolone-resistant E. coli monitoring in cattle, chickens, and pigs. This is supported by other reports where WHO has identified E. coli as a critical pathogen; significant links have been found between humans and livestock for fluoroquinolone- and aminopenicillin-resistant E. coli , and cattle, chickens, and pigs have been shown to contribute to over 90% of global animal biomass ( 7 , 8 , 21 ). If similar efforts can be expanded to other areas of priority with limited data, we can better parse livestock resistance trends and corroborate areas of concern to implement behavioral interventions. Many countries have the capabilities and infrastructure to monitor AMR, as seen in examples where livestock AMR data was available for rarely prioritized categories such as fluoroquinolone-resistant non-typhoidal Salmonella in chickens; they can utilize these to strengthen categories with currently limited data. In order to assign relevant priorities to and build dynamic surveillance systems, the framework outlined here should be conducted periodically to reflect changes in livestock usage and biomass patterns, as well as resulting human AMR burden. Other antimicrobial classes and pathogens may become a greater concern in the future and may therefore warrant identification as a new priority; performing this exercise iteratively can help adjust national and global priorities so that appropriate infrastructure can be expanded to include new surveillance categories. Limitations The individual factors used in our study had limitations based on availability of data used to estimate them, which impacted prioritization. For example, DALYs attributed to resistant Campylobacter spp. were not available due to limited data and thus received the lowest rank, decreasing its likelihood for prioritization despite the pathogen being a shared concern in human and livestock sectors ( 1 ). Facilitating data collection and reporting for these factors can better ensure priorities reflect current trends. Furthermore, with Enterococcus spp. data in LMICs not reflected in resistancebank.org, we were unable to assess if data gaps in categories pertaining to the pathogen were reflective of global monitoring efforts, or an artifact of the data sources we considered. With categories like fluoroquinolone-resistant E. faecalis in chickens being globally prioritized, including additional data sources detailing Enterococcus spp. resistance could better highlight true mismatches in livestock AMR data and our intended priorities. We also propagated assumptions for each indicator; all animals assumed to result in the same number of human DALYs for a particular antimicrobial class-pathogen combination, and correlations between livestock and human AMR were not detailed by transmission pathways for example. Contact rates between livestock and humans can vary with different production systems that can impact comparability between countries, and AMR transmission rates could differ between direct contact with livestock versus consumption of animal-sourced foods. Source attribution studies can help elucidate the differences between these modes of transmission, and annotated risks obtained from these varying by country and sector for example could replace the current correlation indicator to create more tailored priorities ( 25 , 26 ). Many organizations have outlined the need for a One Health approach to align human, animal, and environmental sectors together in defining national capacities and performance of systems related to AMR ( 27 , 28 ). We only focused on the human-livestock space, and thus did not include aquaculture, environmental samples, wild animals or pets. Considering these sectors in a similar indicator approach could give a more comprehensive perspective on how incomplete our current understanding of the risk of environmental transmission in propagating human AMR. Additionally, in our consolidation of non-LMIC sources of livestock AMR data, we recognize that there may have been sources that were not captured in our exercise. For European countries, most livestock AMR data were extracted from the European Food Safety Authority (EFSA) reporting system. Data were limited by requirements at time of submission by countries and may not entirely reflect what is collected at country level. Though we supplemented wherever possible with national reports for combinations of pathogen, antimicrobial class, and livestock species not reported in EFSA, further validation of livestock AMR data with national surveillance efforts in European countries can better identify data gaps. And though we were still able to identify meaningful global priorities, countries can repeat this methods framework as necessary to include both newer livestock AMR data and updated estimates for variables utilized in the prioritization framework and re-define national priorities. CONCLUSIONS Our study provides a framework for countries to review and strengthen the collection of livestock AMR data aligned with local priorities relevant in the possible transmission of AMR from livestock to humans. This could allow for more effective, targeted funding and resource allocation particularly in resource-limited settings, prioritizing areas of most concern. With changing trends in resistance and constituent factors, regular evaluation of priorities can help countries dynamically adapt and address emerging threats as AMR continues to evolve. Abbreviations AMR Antimicrobial resistance AMU Antimicrobial usage LMICs Low- and middle- income countries WHO World Health Organization WOAH World Organisation for Animal Health DALYs Disability adjusted life-years HICs High-income countries SM1 Supplementary Material file 1 GRAM Global Research on Antimicrobial Resistance EFSA European Food Safety Authority Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials All livestock AMR data evaluated were obtained from open-access surveillance reports and data repositories that are listed and cited in full in SM1. The final dataset used in analyses can be obtained upon reasonable request from the corresponding author. Competing interests The authors declare that they have no competing interests. Funding This research was funded by the Bill & Melinda Gates Foundation and the UK Foreign, Commonwealth and Development Office (ID INV-005366). Author contributions NV: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing- original draft preparation, Writing – review and editing LRS: Conceptualization, Data curation, Validation, CF: Conceptualization, Data curation, Validation, Writing – review and editing CDB: Conceptualization, Validation, Writing – review and editing NGC: Data curation, Validation, Writing – review and editing RM: Conceptualization, Data curation, Validation, Writing- review and editing CZ: Validation, Data curation, Writing – review and editing TM: Writing – review and editing KSI: Writing – review and editing SBM: Conceptualization, Validation, Writing – review and editing LAC: Writing – review and editing JSA: Conceptualization, Validation, Writing – review and editing BH: Funding acquisition, Writing – review and editing JR: Funding acquisition, Writing – review and editing BD: Conceptualization, Funding acquisition, Project administration, Writing – review and editing BS: Writing – review and editing TVB: Conceptualization, Validation, Writing – review and editing DMP: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing – review and editing Acknowledgements This research was conducted on behalf of the Global Burden of Animal Diseases (GBADs) programme which is led by the University of Liverpool and the World Organisation for Animal Health (WOAH). 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Supplementary Files AMRrankinggapsSM1BMC.docx AMRrankinggapsSM2.pdf Cite Share Download PDF Status: Published Journal Publication published 26 Sep, 2024 Read the published version in BMC Infectious Diseases → Version 1 posted Editorial decision: Revision requested 24 Jun, 2024 Reviews received at journal 20 Jun, 2024 Reviewers agreed at journal 21 May, 2024 Reviews received at journal 20 May, 2024 Reviewers agreed at journal 20 May, 2024 Reviewers agreed at journal 17 Apr, 2024 Reviewers invited by journal 15 Apr, 2024 Editor invited by journal 14 Apr, 2024 Submission checks completed at journal 14 Apr, 2024 Editor assigned by journal 14 Apr, 2024 First submitted to journal 11 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Swetschinski","email":"","orcid":"","institution":"Institute for Health Metrics and Evaluation","correspondingAuthor":false,"prefix":"","firstName":"Lucien","middleName":"R.","lastName":"Swetschinski","suffix":""},{"id":292226223,"identity":"695167c8-3d31-4e2d-bced-7e9a9177b3f9","order_by":2,"name":"Christina Fastl","email":"","orcid":"","institution":"Global Burden of Animal Diseases Programme","correspondingAuthor":false,"prefix":"","firstName":"Christina","middleName":"","lastName":"Fastl","suffix":""},{"id":292226224,"identity":"439fca73-fb86-4943-b4b4-5d39250b80ce","order_by":3,"name":"Carlotta Di Bari","email":"","orcid":"","institution":"Global Burden of Animal Diseases Programme","correspondingAuthor":false,"prefix":"","firstName":"Carlotta","middleName":"Di","lastName":"Bari","suffix":""},{"id":292226225,"identity":"56d35a28-cb41-4475-918c-2941dec80932","order_by":4,"name":"Nicola G. Criscuolo","email":"","orcid":"","institution":"ETH Zürich","correspondingAuthor":false,"prefix":"","firstName":"Nicola","middleName":"G.","lastName":"Criscuolo","suffix":""},{"id":292226226,"identity":"020fb9a6-44c7-4329-875e-fddeedbe9161","order_by":5,"name":"Ranya Mulchandani","email":"","orcid":"","institution":"ETH Zürich","correspondingAuthor":false,"prefix":"","firstName":"Ranya","middleName":"","lastName":"Mulchandani","suffix":""},{"id":292226227,"identity":"9c5525e1-77c2-47c3-90c0-b7dcfd166174","order_by":6,"name":"Cheng Zhao","email":"","orcid":"","institution":"ETH Zürich","correspondingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Zhao","suffix":""},{"id":292226228,"identity":"47146120-2152-4a69-ac36-57c056b90030","order_by":7,"name":"Tomislav Meštrović","email":"","orcid":"","institution":"University North","correspondingAuthor":false,"prefix":"","firstName":"Tomislav","middleName":"","lastName":"Meštrović","suffix":""},{"id":292226229,"identity":"5e5b6a37-2ebc-46a3-bbaf-83f10f0bda8f","order_by":8,"name":"Kevin S. Ikuta","email":"","orcid":"","institution":"University of California Los Angeles","correspondingAuthor":false,"prefix":"","firstName":"Kevin","middleName":"S.","lastName":"Ikuta","suffix":""},{"id":292226230,"identity":"974a755a-77c0-485a-9e8c-7f56f7cef05f","order_by":9,"name":"Sara Babo Martins","email":"","orcid":"","institution":"Global Burden of Animal Diseases Programme","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"Babo","lastName":"Martins","suffix":""},{"id":292226231,"identity":"b77d9f86-64f4-400c-9626-fd0f543aa66e","order_by":10,"name":"Lucy A. Coyne","email":"","orcid":"","institution":"University of Liverpool","correspondingAuthor":false,"prefix":"","firstName":"Lucy","middleName":"A.","lastName":"Coyne","suffix":""},{"id":292226232,"identity":"44b88782-9781-472a-a755-afdfcad9feb1","order_by":11,"name":"João Sucena Afonso","email":"","orcid":"","institution":"Global Burden of Animal Diseases Programme","correspondingAuthor":false,"prefix":"","firstName":"João","middleName":"Sucena","lastName":"Afonso","suffix":""},{"id":292226233,"identity":"3f3860ff-ce68-4872-8d3a-24787d8822c6","order_by":12,"name":"Ben Huntington","email":"","orcid":"","institution":"Global Burden of Animal Diseases Programme","correspondingAuthor":false,"prefix":"","firstName":"Ben","middleName":"","lastName":"Huntington","suffix":""},{"id":292226234,"identity":"d0caa58a-ab99-4c1f-9f93-9c1d8b0726ac","order_by":13,"name":"Jonathan Rushton","email":"","orcid":"","institution":"Global Burden of Animal Diseases Programme","correspondingAuthor":false,"prefix":"","firstName":"Jonathan","middleName":"","lastName":"Rushton","suffix":""},{"id":292226235,"identity":"da5f4a25-4547-4c47-a554-1c45e0167134","order_by":14,"name":"Brecht Devleesschauwer","email":"","orcid":"","institution":"Global Burden of Animal Diseases Programme","correspondingAuthor":false,"prefix":"","firstName":"Brecht","middleName":"","lastName":"Devleesschauwer","suffix":""},{"id":292226236,"identity":"8ad5f4d9-c07f-4f1a-ad58-0fbc72fd020d","order_by":15,"name":"Benn Sartorius","email":"","orcid":"","institution":"UQ Centre for Clinical Research (UQCCR), University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Benn","middleName":"","lastName":"Sartorius","suffix":""},{"id":292226237,"identity":"d6215651-f3d7-4199-96fb-f5837ac8b221","order_by":16,"name":"Thomas P. Van Boeckel","email":"","orcid":"","institution":"ETH Zürich","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"P. Van","lastName":"Boeckel","suffix":""},{"id":292226238,"identity":"d0c84b08-7780-4ab2-92ed-7cd79add9451","order_by":17,"name":"David M. Pigott","email":"","orcid":"","institution":"Global Burden of Animal Diseases Programme","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"M.","lastName":"Pigott","suffix":""}],"badges":[],"createdAt":"2024-04-11 16:28:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4253597/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4253597/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12879-024-09847-3","type":"published","date":"2024-09-26T15:57:32+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54902314,"identity":"f7b4481b-06ac-4cd3-a3e0-4a84f99ceadb","added_by":"auto","created_at":"2024-04-18 10:40:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":180987,"visible":true,"origin":"","legend":"\u003cp\u003eDiagram displaying the creation of the composite indicator (referenced here as metaranks) based on human disability adjusted life-years (DALYs) attributable to AMR, livestock AMU (mg/kg), livestock biomass (population correction units), and a global correlation assessment between livestock and human proportion of resistant isolates (represented as significance levels of Spearman correlation estimates) by antimicrobial class, pathogen, and livestock species (also represented here as a cell). Values displayed, from initial input values, to ranks, and final composite indicators, are taken from the example of fluoroquinolones, \u003cem\u003eE. coli \u003c/em\u003eand pigs, with country-specific examples referencing the Philippines.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4253597/v1/5de02b45b5f3ab7145278423.png"},{"id":54902313,"identity":"3b30736c-3702-4978-a6ed-dff3caf663e7","added_by":"auto","created_at":"2024-04-18 10:40:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":413643,"visible":true,"origin":"","legend":"\u003cp\u003eLivestock AMR data coverage for all countries extracted (n=109) aggregated by livestock species, antimicrobial classes and pathogen species. Antimicrobials are ordered top to bottom by shared human and animal relevance. Dark brown shows the most number of countries that have AMR data for the specific antimicrobial class, pathogen, and livestock species. Lighter brown and peach indicate less number of countries with AMR data for the specific antimicrobial class, pathogen, and livestock species, and white shows there are no countries from data extracted that have AMR data for the specific antimicrobial class, pathogen, and livestock species.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4253597/v1/461707e246956f02339550f0.png"},{"id":54902315,"identity":"00b603c9-7a17-4a28-a5cb-4b678f4d34fc","added_by":"auto","created_at":"2024-04-18 10:40:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":431846,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal metaranks (n=194) accounting for livestock antimicrobial usage (AMU), livestock biomass (population correction units), human DALYs attributable to AMR, and significance levels of correlations between human and livestock proportion of resistance. Global metaranks were calculated for a particular livestock species, antimicrobial classes and pathogen species combination. Antimicrobials are ordered top to bottom by shared human and animal relevance. Dark purple shows the highest metaranks calculated for the specific antimicrobial class, pathogen, and livestock species. Lighter purple and blue indicate a lower metarank for the specific antimicrobial class, pathogen, and livestock species.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4253597/v1/4d8be908757945dbe87ca6f4.png"},{"id":54902958,"identity":"7a401f3a-5e74-4462-b2bc-b553981ef289","added_by":"auto","created_at":"2024-04-18 10:48:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":525937,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal percentage of countries (n=194) with data for a particular livestock species, antimicrobial classes and pathogen species combination if it was prioritized. Antimicrobials are ordered top to bottom by shared human and animal relevance. White and lighter colors relay a low percentage of countries with data, and darker colors indicate a higher percentage of countries with data. Cells for which no countries have prioritized the category are grey. Numbers in each cell correspond to the number of countries that have prioritized that particular category.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4253597/v1/3d7736a4f9f8c7e0136893a1.png"},{"id":54902959,"identity":"5b62b2c5-008c-4c38-b12a-c0a68f7dcf79","added_by":"auto","created_at":"2024-04-18 10:48:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":990567,"visible":true,"origin":"","legend":"\u003cp\u003eMap showing the percentage of priorities that have recorded livestock AMR data for a particular category of antimicrobial class, pathogen, and livestock species per country. Darker blue shows a higher percentage of countries with livestock AMR data for prioritized categories, lighter blue shows a lower percentage. White represents countries without livestock AMR data for prioritized categories, and locations without any prioritized categories are colored grey.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4253597/v1/462531c69f01b72f15a48c33.png"},{"id":65628001,"identity":"5e40674b-e392-43fc-8e52-b8f94fd3ea2e","added_by":"auto","created_at":"2024-09-30 16:17:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2777649,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4253597/v1/b87742f9-ab87-4057-8445-802b4a5ff2f7.pdf"},{"id":54902319,"identity":"e10d7c11-9131-49fa-a87d-1ca3139a7b91","added_by":"auto","created_at":"2024-04-18 10:40:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":8007861,"visible":true,"origin":"","legend":"","description":"","filename":"AMRrankinggapsSM1BMC.docx","url":"https://assets-eu.researchsquare.com/files/rs-4253597/v1/47adaf293b0449baa7f7f447.docx"},{"id":54902317,"identity":"322069cf-93d5-4234-a8bb-bdddffa14e62","added_by":"auto","created_at":"2024-04-18 10:40:00","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":7869853,"visible":true,"origin":"","legend":"","description":"","filename":"AMRrankinggapsSM2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4253597/v1/4fb6d88fa95582983ab7bfb6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Using Priorities between Human and Livestock Bacterial Antimicrobial Resistance (AMR) to Identify Data Gaps in Livestock AMR Surveillance","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eIn recent decades, bacterial antimicrobial resistance (AMR) has been identified as a global health threat; estimates indicate it caused 1.27\u0026nbsp;million human deaths worldwide in 2019 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). There are multiple mechanisms by which AMR emerges, such as the interaction between the human sector and the wider environment (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Livestock are often treated with the same antimicrobials as humans, and the use of antimicrobials for growth promotion in some countries and disease prevention instead of other hygiene practices creates more avenues for resistance to evolve (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). This leads to a complex interplay between antimicrobial usage (AMU) and resistance in humans, animals, and the environment (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral exposure pathways have been proposed linking AMR in humans to the wider environment. These include active ingredients leaching into the environment, introduction into the food chain through animal source foods, or through direct transmission from infectious livestock (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Livestock AMR commonly arises from interactions with AMU; projections estimate global livestock AMU will increase by 8% between 2020 and 2030 given current trends of consumption (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Additionally, there is growing evidence showing significant associations between AMU and AMR in both food-producing animals and humans (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Due to global AMR and AMU data for livestock and humans being fragmented in availability and quality, there is limited understanding of this relationship in low- and middle- income countries (LMICs) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Given the transmission dynamics of AMR between livestock and humans, when analyzing the trends in AMR in humans globally, it is important to understand the global data landscape of animal AMR, particularly when there is increasing uncertainty and concern about the degree to which the agricultural sector contributes to human AMR (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough many human and animal health organizations have outlined AMR as a shared concern, and the need for coordinated monitoring and intervention efforts, there is limited understanding of the mutual antimicrobial class and pathogen combinations of concern on a global scale (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Recent research has assessed the global burden and geographic variation of AMR in humans and animals separately for well-represented antimicrobial class and pathogen combinations (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). We aim to build upon these global analyses to highlight the global data gaps in livestock AMR against an evaluation of priorities among shared human and veterinary actors. Through this global analysis and identification of gaps, we present suggestions for strengthening current surveillance of AMR specific to pathogen, antimicrobial class, and livestock species.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eTo identify antimicrobial classes of shared importance in humans and livestock, we utilized the main global antimicrobial prioritization frameworks pertaining to humans by World Health Organization (WHO) and to animals by World Organisation for Animal Health (WOAH) (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). We then evaluated the global coverage of livestock bacterial AMR data by pathogen-antimicrobial-class-livestock (referred to as a category) considering all pathogens available in this data. We created a composite indicator from globally available factors important in human and livestock bacterial AMR to determine category ranks: human disability adjusted life-years (DALYs) attributable to AMR, livestock AMU, livestock biomass, and a global correlation assessment between livestock and human AMR. We ordered categories within each country and designated the top ten percent of values for our composite indicator in a country as priorities and compared the alignment of these to the availability of livestock AMR data on a national and global scale.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCompilation of livestock AMR data sources\u003c/h2\u003e \u003cp\u003eWe used WHO and WOAH reports to identify antimicrobials of shared human and veterinary importance (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The top two tiers were considered in both (WHO: \u0026ldquo;Highest priority of critical importance\u0026rdquo; and \u0026ldquo;High priority of critical importance\u0026rdquo;. WOAH: \u0026ldquo;Critically important\u0026rdquo; and \u0026ldquo;Highly important\u0026rdquo;). Only antimicrobial classes that fell into these tiers in both WHO and WOAH reports were included in subsequent analyses.\u003c/p\u003e \u003cp\u003eLivestock AMR data, specifically the proportion of resistant isolates, from 2000 to 2020 were extracted for each category of pathogen-antimicrobial-class-livestock. All pathogens which impacted both humans and livestock were considered. We used two different types of sources: routine surveillance systems and a published database of individual studies. Resistance data for high-income countries (HICs) were retrieved from various surveillance systems worldwide via open-access sites and reports. Resistance data for LMICs were sourced from resistancebank.org, a repository of point prevalence surveys collected from peer-review literature used as surrogates for systematic surveillance data in LMICs (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). For a detailed breakdown of countries covered, further information on the data sources, and additional methods, refer to Supplementary Material file 1 (SM1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCreation of composite indicator\u003c/h2\u003e \u003cp\u003eWe created a composite indicator utilizing globally complete factors in human and animal AMR burden to evaluate the relative importance of each category and evaluate the magnitude of subsequent gaps (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The four factors we considered were: human DALYs attributable to AMR, livestock AMU (mg/kg), livestock biomass (population correction units), and a global correlation assessment between livestock and human proportion of resistant isolates. We chose these factors to represent the various transmission and impact points of AMR from livestock to humans in addition to having global coverage. Livestock AMU has been linked to the proliferation of livestock and human AMR, and biomass captures the size of the livestock population for which antimicrobials could be used and therefore propagate AMR (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The correlation assessment aims to capture the pathway connecting livestock and human AMR, and human burden of disease due to AMR is captured through the inclusion of DALYs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHuman DALYs, sourced from the Global Research on Antimicrobial Resistance (GRAM) project, were specified by pathogen and antimicrobial class, and ranked per country (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). We assumed all livestock species would receive the same rank per specification. No estimates for \u003cem\u003eCampylobacter\u003c/em\u003e spp. were present. Livestock AMU, sourced from Mulchandani et al., was specified by antimicrobial class and livestock species, and were ranked per country (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). We assumed all pathogens would receive the same rank per specification. Livestock biomass, sourced from Mulchandani et al., was specified by livestock species, and were ranked per country (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). We assumed all pathogens and antimicrobial classes would receive the same rank per specification. Biomass estimates were present for cattle, chicken, pigs, and sheep. The global correlation exercise assessed the correlation between the proportion of resistant isolates in livestock (sourced from the livestock AMR data considered) and humans (sourced from the GRAM project) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Spearman correlation coefficients and p-values were calculated by each unique pathogen-antimicrobial-class-livestock category. Statistical significance levels were denoted as follows: p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 (****), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (***), p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 (**), and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (*). Categories with positive correlations were ranked first by ascending order of significance (i.e. lower p-value), with negative correlations receiving the lowest rank. Ranks were determined at a global level, then all countries were assumed to receive the same rank for a particular category. For more details about ranking factors and assumptions, refer to SM1. Categories for which there were no values were assigned the lowest rank, and ranks were standardized using min-max standardization:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${X}_{stand}=\\frac{X-{X}_{min}}{{{X}_{max}-X}_{min}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWe aggregated the standardized ranks in each indicator to create a composite indicator per pathogen- antimicrobial class-livestock species for each country.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDesignation of \u0026lsquo;priorities\u0026rsquo;\u003c/h2\u003e \u003cp\u003eThe top ten percent of ranked categories per country were designated as priorities and were compared to the livestock AMR data. Three classifications detailing this alignment were identified: prioritized categories without data, prioritized categories with data, and non-prioritized categories with data. This comparison was also propagated to a global level. Please refer to SM1 for more details on the creation of the composite indicator and priorities.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eLivestock AMR data gaps\u003c/h2\u003e \u003cp\u003eEleven antimicrobials were identified of shared importance in both human and veterinary use: third generation cephalosporins, fourth generation cephalosporins, fluoroquinolones, macrolides, quinolones, polymyxins, aminoglycosides, aminopenicillins, aminopenicillins with beta-lactamase inhibitors, ansamycins, and phosphonic acid derivatives. The pathogens for which livestock AMR data was available for were: \u003cem\u003eCampylobacter\u003c/em\u003e spp., \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eEnterococcus faecalis\u003c/em\u003e, \u003cem\u003eEnterococcus faecium\u003c/em\u003e, non-typhoidal \u003cem\u003eSalmonella\u003c/em\u003e, and \u003cem\u003eStaphylococcus aureus\u003c/em\u003e. With 109 countries (61% LMICs, 39% HICs) reporting livestock AMR data, the majority of data were specific to cattle, chickens, and pigs, with an emphasis on \u003cem\u003eE. coli\u003c/em\u003e and non-typhoidal \u003cem\u003eSalmonella\u003c/em\u003e resistance (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAminoglycoside-resistant \u003cem\u003eE. coli\u003c/em\u003e and non-typhoidal \u003cem\u003eSalmonella\u003c/em\u003e, as well as aminopenicillin-resistant non-typhoidal \u003cem\u003eSalmonella\u003c/em\u003e in chickens were the most frequent categories (76 countries), followed by fluoroquinolone-resistant non-typhoidal \u003cem\u003eSalmonella\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;73), fluoroquinolone- (n\u0026thinsp;=\u0026thinsp;72) and third-generation-cephalosporin- (n\u0026thinsp;=\u0026thinsp;70) resistant \u003cem\u003eE. coli\u003c/em\u003e in chickens. This was largely driven by high-income countries that primarily only tracked these three species and pathogens; for example, 41% of countries which reported for aminoglycoside-resistant \u003cem\u003eE. coli\u003c/em\u003e in chickens were high-income (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Reporting on \u003cem\u003eEnterococcus\u003c/em\u003e spp. and other livestock species was limited \u0026ndash; for example, only ten countries reported data on macrolide-resistant \u003cem\u003eEnterococcus\u003c/em\u003e spp. in chickens, and three countries reported data on fluoroquinolone-resistant \u003cem\u003eE. coli\u003c/em\u003e in ducks. Most high-income countries did not report on resistant \u003cem\u003eS.aureus\u003c/em\u003e, or only reported methicillin-resistant \u003cem\u003eS. aureus\u003c/em\u003e. Reported data from LMICs were more broadly spread across categories, and some livestock species were more likely to be or were only reported through these countries (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Resistance data for buffaloes were only reported in China, and resistance to classes like ansamycins were reported by 22 LMICs. The scope of resistancebank.org did not include \u003cem\u003eEnterococcus\u003c/em\u003e spp, hence no livestock AMR data from LMICs for this pathogen was available.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eComposite indicator patterns\u003c/h2\u003e \u003cp\u003eWhen assessing human DALYs, \u003cem\u003eE. coli\u003c/em\u003e resistance typically ranked higher than other pathogens; 118/194 countries ranked third-generation-cephalosporin-resistant-\u003cem\u003eE. coli\u003c/em\u003e highest. There were no DALY estimates for resistant \u003cem\u003eCampylobacter\u003c/em\u003e spp. With livestock AMU by country, aminopenicillin usage in sheep, and macrolide, aminopenicillin, and phosphonic acid derivatives\u0026rsquo; usage in pigs were ranked higher compared to other categories. Usage of third and fourth generation cephalosporins were generally lower ranked than other categories. When considering livestock biomass, cattle were ranked the highest compared to chickens, pigs, and sheep in 110/194 countries. Looking at the global correlation assessment, the highest ranked categories were macrolide-resistant \u003cem\u003eS. aureus\u003c/em\u003e in pigs; aminopenicillin-resistant \u003cem\u003eE. coli\u003c/em\u003e in cattle and chickens; aminopenicillin-and-beta-lactamase-inhibitor-resistant \u003cem\u003eE. coli\u003c/em\u003e in cattle; fluoroquinolone-resistant \u003cem\u003eE. coli\u003c/em\u003e in cattle, chickens, and pigs; and third-generation-cephalosporin-resistant \u003cem\u003eE. coli\u003c/em\u003e in cattle, chickens, and pigs. Categories using sheep AMR data like third-generation-cephalosporin-resistant \u003cem\u003eE. coli\u003c/em\u003e in sheep were not significant and therefore ranked low.\u003c/p\u003e \u003cp\u003eIn aggregating these globally, fluoroquinolone-resistant \u003cem\u003eE. coli\u003c/em\u003e in cattle was the highest ranked category, followed by aminopenicillin-and-beta-lactamase-inhibitor-resistant \u003cem\u003eE. coli\u003c/em\u003e in cattle, and third-generation-cephalosporin-resistant \u003cem\u003eE. coli\u003c/em\u003e in cattle (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Many patterns prevailed in the top ten ranked categories, where fluoroquinolone-, third-generation-cephalosporin-, and aminopenicillin-resistant \u003cem\u003eE. coli\u003c/em\u003e in cattle, chickens, and pigs, as well as macrolide-resistant \u003cem\u003eS. aureus\u003c/em\u003e in pigs were highest ranked in comparison to other categories in several countries. Resistant \u003cem\u003eCampylobacter\u003c/em\u003e spp. and \u003cem\u003eEnterococcus spp.\u003c/em\u003e in buffaloes, ducks, goats, horses, and turkeys across all considered antimicrobial classes were lower ranked compared to their other counterparts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAlignment of priorities and livestock AMR data gaps\u003c/h2\u003e \u003cp\u003eIn assessing our priorities generated from the top 10% of values from the composite indicator per country, we prioritized aminopenicillin-resistant \u003cem\u003eE. coli\u003c/em\u003e in cattle, chickens, and sheep; aminopenicillin-and-beta-lactamase-inhibitor-resistant \u003cem\u003eE. coli\u003c/em\u003e in cattle; fluoroquinolone-resistant \u003cem\u003eE. coli\u003c/em\u003e in cattle, chickens, and pigs; third-generation-cephalosporin-resistant \u003cem\u003eE. coli\u003c/em\u003e in cattle, chickens, and pigs; and macrolide-resistant \u003cem\u003eS. aureus\u003c/em\u003e in pigs for all countries.\u003c/p\u003e \u003cp\u003eOf 232 categories prioritized in at least one country, data were only collected for 48% (n\u0026thinsp;=\u0026thinsp;112) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Macrolide-resistant \u003cem\u003eS. aureus\u003c/em\u003e in chicken and pigs particularly had notable mismatches between prioritization and data collection; only 7% and 4% of countries collected data in these categories respectively despite being globally prioritized. Additionally, less than 15% of countries collected data for fluoroquinolone-resistant \u003cem\u003eS. aureus\u003c/em\u003e in cattle (13%), chickens (8%), and pigs (7%). Fluoroquinolone-resistant non-typhoidal \u003cem\u003eSalmonella\u003c/em\u003e in sheep was also similarly misaligned, where only 6% of the 178 countries that prioritized the category collected data for it. On the other hand, aminopenicillin- and fluoroquinolone-resistant \u003cem\u003eE. coli\u003c/em\u003e data in chickens were reported the most by countries when prioritized at 39% and 37% respectively (at least 72/194 countries).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNo country had livestock AMR data for all prioritized categories determined; China and the Republic of Korea had the highest percentage of prioritized categories with data at 66% (40/61) and 65% (39/60) respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Approximately half or 95 of 194 countries (49%) did not have data for any of their indicated national priorities, whilst three quarters of countries had less than 20% of identified priorities with data.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur study aimed to identify key gaps where strengthening surveillance in livestock on AMR could provide potential benefits to both livestock and human AMR. There has been growing evidence of significant, bi-directional interactions between livestock and human AMR and AMU, therefore we wanted to account for these elements with specificity by pathogen-antimicrobial-class-livestock (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). We can then focus our attention onto data gaps within categories that we prioritized to engage data collection efforts in globally relevant areas in the human-livestock space, and better direct and utilize funds for global livestock AMR surveillance.\u003c/p\u003e \u003cp\u003eThe most notable data gaps we found for prioritized categories were for \u003cem\u003eS. aureus\u003c/em\u003e, where especially macrolide- and fluoroquinolone- resistant \u003cem\u003eS. aureus\u003c/em\u003e in chickens and pigs had limited data in relation to their prioritization. Macrolides and fluoroquinolones have been identified as antimicrobials of high concern in both human and veterinary medicine (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Macrolides are important in treating several common infections in pigs such as pneumonia, and the genetic mechanism encoding macrolide resistance in \u003cem\u003eS. aureus\u003c/em\u003e has been identified as a global concern in due to the potential for horizontal gene transfer between bacterial species and genera (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Resistance to fluoroquinolones is also concerning because of the class\u0026rsquo;s use in treating a wide range of pathogens and infections in both human and veterinary medicine (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). While currently most HICs only report data for methicillin-resistant \u003cem\u003eS. aureus\u003c/em\u003e (MRSA) strains, collection efforts in these systems can be expanded to include \u003cem\u003eS. aureus\u003c/em\u003e resistant to other antimicrobials in addition to methicillin. With \u003cem\u003eS. aureus\u003c/em\u003e being a leading cause of human deaths due to AMR, expanding data monitoring to other antimicrobials can allow for better assessment of livestock resistance trends and potential impacts on human AMR (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Furthermore, we identified fluoroquinolone-resistant non-typhoidal \u003cem\u003eSalmonella\u003c/em\u003e in sheep as an area for several countries to prioritize AMR data collection in. Fluoroquinolone-resistant \u003cem\u003eSalmonella\u003c/em\u003e has been identified as a high priority pathogen by WHO, and could pose a zoonotic threat through human ingestion of contaminated animal products (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Although limited countries have economically significant and sizeable sheep populations which has impacted the creation of livestock-based estimates like AMU, expanding monitoring efforts to include sheep samples in these countries could appropriately assess concern for this category (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur prioritization framework provides a guide for countries to improve and target surveillance efforts to areas of most need. In the majority of countries that did not have livestock AMR data for prioritized categories, we found no reports or monitoring systems tracking livestock AMR. The majority of these were also LMICs, which often face additional technological, financial and resource limitations that impact the setup and operation of these monitoring systems (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). With LMICs often having high burden due to AMR, expanding surveillance and monitoring efforts is critical to assessing and tackling the threat of AMR in these countries (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Choosing pathogens, antimicrobial classes, and livestock species of greatest concern through prioritization can factor into the allocation of limited funds and resources in these countries.\u003c/p\u003e \u003cp\u003eFor other countries where livestock AMR data is available for prioritized categories but missing data for others, existing systems can be broadened to track data that reflects all local concerns. No country had 100% alignment in livestock AMR data for prioritized categories. An area where countries are extensively monitoring livestock AMR that matches up with our global priorities is aminopenicillin- and fluoroquinolone-resistant \u003cem\u003eE. coli\u003c/em\u003e monitoring in cattle, chickens, and pigs. This is supported by other reports where WHO has identified \u003cem\u003eE. coli\u003c/em\u003e as a critical pathogen; significant links have been found between humans and livestock for fluoroquinolone- and aminopenicillin-resistant \u003cem\u003eE. coli\u003c/em\u003e, and cattle, chickens, and pigs have been shown to contribute to over 90% of global animal biomass (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). If similar efforts can be expanded to other areas of priority with limited data, we can better parse livestock resistance trends and corroborate areas of concern to implement behavioral interventions. Many countries have the capabilities and infrastructure to monitor AMR, as seen in examples where livestock AMR data was available for rarely prioritized categories such as fluoroquinolone-resistant non-typhoidal \u003cem\u003eSalmonella\u003c/em\u003e in chickens; they can utilize these to strengthen categories with currently limited data.\u003c/p\u003e \u003cp\u003eIn order to assign relevant priorities to and build dynamic surveillance systems, the framework outlined here should be conducted periodically to reflect changes in livestock usage and biomass patterns, as well as resulting human AMR burden. Other antimicrobial classes and pathogens may become a greater concern in the future and may therefore warrant identification as a new priority; performing this exercise iteratively can help adjust national and global priorities so that appropriate infrastructure can be expanded to include new surveillance categories.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe individual factors used in our study had limitations based on availability of data used to estimate them, which impacted prioritization. For example, DALYs attributed to resistant \u003cem\u003eCampylobacter\u003c/em\u003e spp. were not available due to limited data and thus received the lowest rank, decreasing its likelihood for prioritization despite the pathogen being a shared concern in human and livestock sectors (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Facilitating data collection and reporting for these factors can better ensure priorities reflect current trends. Furthermore, with \u003cem\u003eEnterococcus\u003c/em\u003e spp. data in LMICs not reflected in resistancebank.org, we were unable to assess if data gaps in categories pertaining to the pathogen were reflective of global monitoring efforts, or an artifact of the data sources we considered. With categories like fluoroquinolone-resistant \u003cem\u003eE. faecalis\u003c/em\u003e in chickens being globally prioritized, including additional data sources detailing \u003cem\u003eEnterococcus\u003c/em\u003e spp. resistance could better highlight true mismatches in livestock AMR data and our intended priorities.\u003c/p\u003e \u003cp\u003eWe also propagated assumptions for each indicator; all animals assumed to result in the same number of human DALYs for a particular antimicrobial class-pathogen combination, and correlations between livestock and human AMR were not detailed by transmission pathways for example. Contact rates between livestock and humans can vary with different production systems that can impact comparability between countries, and AMR transmission rates could differ between direct contact with livestock versus consumption of animal-sourced foods. Source attribution studies can help elucidate the differences between these modes of transmission, and annotated risks obtained from these varying by country and sector for example could replace the current correlation indicator to create more tailored priorities (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMany organizations have outlined the need for a One Health approach to align human, animal, and environmental sectors together in defining national capacities and performance of systems related to AMR (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). We only focused on the human-livestock space, and thus did not include aquaculture, environmental samples, wild animals or pets. Considering these sectors in a similar indicator approach could give a more comprehensive perspective on how incomplete our current understanding of the risk of environmental transmission in propagating human AMR. Additionally, in our consolidation of non-LMIC sources of livestock AMR data, we recognize that there may have been sources that were not captured in our exercise. For European countries, most livestock AMR data were extracted from the European Food Safety Authority (EFSA) reporting system. Data were limited by requirements at time of submission by countries and may not entirely reflect what is collected at country level. Though we supplemented wherever possible with national reports for combinations of pathogen, antimicrobial class, and livestock species not reported in EFSA, further validation of livestock AMR data with national surveillance efforts in European countries can better identify data gaps. And though we were still able to identify meaningful global priorities, countries can repeat this methods framework as necessary to include both newer livestock AMR data and updated estimates for variables utilized in the prioritization framework and re-define national priorities.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eOur study provides a framework for countries to review and strengthen the collection of livestock AMR data aligned with local priorities relevant in the possible transmission of AMR from livestock to humans. This could allow for more effective, targeted funding and resource allocation particularly in resource-limited settings, prioritizing areas of most concern. With changing trends in resistance and constituent factors, regular evaluation of priorities can help countries dynamically adapt and address emerging threats as AMR continues to evolve.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eAMR\u0026nbsp;\u003c/strong\u003eAntimicrobial resistance\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAMU\u003c/strong\u003e Antimicrobial usage\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLMICs\u003c/strong\u003e Low- and middle- income countries\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWHO\u003c/strong\u003e World Health Organization\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWOAH\u003c/strong\u003e World Organisation for Animal Health\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDALYs\u003c/strong\u003e Disability adjusted life-years\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHICs\u003c/strong\u003e High-income countries\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSM1\u003c/strong\u003e Supplementary Material file 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGRAM\u003c/strong\u003e Global Research on Antimicrobial Resistance\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEFSA\u003c/strong\u003e European Food Safety Authority\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll livestock AMR data evaluated were obtained from open-access surveillance reports and data repositories that are listed and cited in full in SM1. The final dataset used in analyses can be obtained upon reasonable request from the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Bill \u0026amp; Melinda Gates Foundation and the UK Foreign, Commonwealth and Development Office (ID INV-005366).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNV: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing- original draft preparation, Writing \u0026ndash; review and editing\u003c/p\u003e\n\u003cp\u003eLRS: Conceptualization, Data curation, Validation,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCF: Conceptualization, Data curation, Validation, Writing \u0026ndash; review and editing\u003c/p\u003e\n\u003cp\u003eCDB: Conceptualization, Validation, Writing \u0026ndash; review and editing\u003c/p\u003e\n\u003cp\u003eNGC: Data curation, Validation, Writing \u0026ndash; review and editing\u003c/p\u003e\n\u003cp\u003eRM: Conceptualization, Data curation, Validation, Writing- review and editing\u003c/p\u003e\n\u003cp\u003eCZ: Validation, Data curation, Writing \u0026ndash; review and editing\u003c/p\u003e\n\u003cp\u003eTM: Writing \u0026ndash; review and editing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKSI: Writing \u0026ndash; review and editing\u003c/p\u003e\n\u003cp\u003eSBM: Conceptualization, Validation, Writing \u0026ndash; review and editing\u003c/p\u003e\n\u003cp\u003eLAC: Writing \u0026ndash; review and editing\u003c/p\u003e\n\u003cp\u003eJSA: Conceptualization, Validation, Writing \u0026ndash; review and editing\u003c/p\u003e\n\u003cp\u003eBH: Funding acquisition, Writing \u0026ndash; review and editing\u003c/p\u003e\n\u003cp\u003eJR: Funding acquisition, Writing \u0026ndash; review and editing\u003c/p\u003e\n\u003cp\u003eBD: Conceptualization, Funding acquisition, Project administration, Writing \u0026ndash; review and editing\u003c/p\u003e\n\u003cp\u003eBS: Writing \u0026ndash; review and editing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTVB: Conceptualization, Validation, Writing \u0026ndash; review and editing\u003c/p\u003e\n\u003cp\u003eDMP: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing \u0026ndash; review and editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was conducted on behalf of the Global Burden of Animal Diseases (GBADs) programme which is led by the University of Liverpool and the World Organisation for Animal Health (WOAH). A full list of the GBADs collaborators can be accessed here: https://animalhealthmetrics.org/acknowledgments/.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAntimicrobial Resistance Collaborators. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet. 2022;399(10325):629\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaxminarayan R, Matsoso P, Pant S, Brower C, R\u0026oslash;ttingen JA, Klugman K, et al. Access to effective antimicrobials: A worldwide challenge. Lancet. 2016;387(10014):168\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoolhouse M, Ward M, Van Bunnik B, Farrar J. Antimicrobial resistance in humans, livestock and the wider environment. Philos Trans R Soc Lond B Biol Sci. 2015;370(1670):20140083.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinger AC, Shaw H, Rhodes V, Hart A. Review of Antimicrobial Resistance in the Environment and Its Relevance to Environmental Regulators. Front Microbiol. 2016;7:1728.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilbergeld EK, Graham J, Price LB. Industrial food animal production, antimicrobial resistance, and human health. Annu Rev Public Health. 2008;29:151\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlous G, Huss A, Heederik DJJ, Coutinho RA. Human-livestock contacts and their relationship to transmission of zoonotic pathogens, a systematic review of literature. One Health. 2016;2:65\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMulchandani R, Wang Y, Gilbert M, Van Boeckel TP. Global trends in antimicrobial use in food-producing animals: 2020 to 2030. PLOS Glob Public Health. 2023;3(2):e0001305.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEuropean Centre for Disease Prevention and Control, European Food Safety Authority, European Medicines Agency. Third joint inter-agency report on integrated analysis of consumption of antimicrobial agents and occurrence of antimicrobial resistance in bacteria from humans and food-producing animals in the EU/EEA: JIACRA. III 2016\u0026ndash;2018. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIskandar K, Molinier L, Hallit S, Sartelli M, Hardcastle TC, Haque M, et al. Surveillance of antimicrobial resistance in low- and middle-income countries: a scattered picture. Antimicrob Resist Infect Control. 2021;10(1):63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBertagnolio S, Suthar AB, Tosas O, Van Weezenbeek K. Antimicrobial resistance: Strengthening surveillance for public health action. PLoS Med. 2023;20(7):e1004265.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePokharel S, Raut S, Adhikari B. Tackling antimicrobial resistance in low-income and middle-income countries. BMJ Glob Health. 2019;4(6):e002104.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Boeckel TP, Pires J, Silvester R, et al. Global trends in antimicrobial resistance in animals in low- and middle-income countries. Science. 2019;365:6459.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Critically Important Antimicrobials For Human Medicine, 6th Revision. Geneva; 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Organisation for Animal Health. OIE List of Antimicrobial Agents of Veterinary Importance. Paris; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eresistancebank.org. resistancebank.org. Accessed 6 May 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCriscuolo NG, Pires J, Zhao C, Van Boeckel TP. resistancebank.org, an open-access repository for surveys of antimicrobial resistance in animals. Sci Data. 2021;8(1):189.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFDA Center for Veterinary Medicine. FDA\u0026rsquo;s Proposed Method for Adjusting Data on Antimicrobials Sold or Distributed for Use in Food-Producing Animals, Using a Biomass Denominator Objective. 2017. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fda.gov/files/animal%20\u0026amp;%20veterinary/published/FDA%E2%80%99s-Proposed-Method-for-Adjusting-Data-on-Antimicrobials-Sold-or-Distributed-for-Use-in-Food-Producing-Animals-Using-a-Biomass-Denominator--Technical-Paper.pdf\u003c/span\u003e\u003cspan address=\"https://www.fda.gov/files/animal%20\u0026amp;%20veterinary/published/FDA%E2%80%99s-Proposed-Method-for-Adjusting-Data-on-Antimicrobials-Sold-or-Distributed-for-Use-in-Food-Producing-Animals-Using-a-Biomass-Denominator--Technical-Paper.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 21 Feb 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuloi D, Ward MJ, Pedersen AB, F\u0026egrave; EM, Woolhouse MEJ, Van Bunnik BAD. Are Food Animals Responsible for Transfer of Antimicrobial-Resistant Escherichia coli or Their Resistance Determinants to Human Populations? A Systematic Review. Foodborne Pathog Dis. 2018;15(8):467\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePy\u0026ouml;r\u0026auml;l\u0026auml; S, Baptiste KE, Catry B, van Duijkeren E, Greko C, Moreno MA, et al. Macrolides and lincosamides in cattle and pigs: Use and development of antimicrobial resistance. Vet J. 2014;200(2):230\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRedgrave LS, Sutton SB, Webber MA, Piddock LJV. Fluoroquinolone resistance: mechanisms, impact on bacteria, and role in evolutionary success. Trends Microbiol. 2014;22(8):438\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. WHO publishes list of bacteria for which new antibiotics are urgently needed. 2017. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news/item/27-02-2017-who-publishes-list-of-bacteria-for-which-new-antibiotics-are-urgently-needed\u003c/span\u003e\u003cspan address=\"https://www.who.int/news/item/27-02-2017-who-publishes-list-of-bacteria-for-which-new-antibiotics-are-urgently-needed\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 10 Oct 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMichael GB, Schwarz S. Antimicrobial resistance in zoonotic nontyphoidal Salmonella: an alarming trend? Clin Microbiol Infect. 2016;22(12):968\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOdekunle FF, Odekunle RO, Shankar S. Why sub-Saharan Africa lags in electronic health record adoption and possible strategies to increase its adoption in this region. Int J Health Sci (Qassim). 2017;11(4):59\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFuruya-Kanamori L, Yakob L. Filling the gaps in global antimicrobial resistance research/surveillance. BMC Infect Dis. 2020;20(1):39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePires SM, Duarte AS, Hald T. Source Attribution and Risk Assessment of Antimicrobial Resistance. Microbiol Spectr. 2018;6(3).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang KL, Caffrey NP, N\u0026oacute;brega DB, Cork SC, Ronksley PE, Barkema HW, et al. Restricting the use of antibiotics in food-producing animals and its associations with antibiotic resistance in food-producing animals and human beings: a systematic review and meta-analysis. Lancet Planet Health. 2017;1(8):e316\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Global Action Plan on Antimicrobial Resistance. Geneva; 2015. Accessed 16 Sept 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnited Nations Environment Programme, Food and, Organization A, World Organisation for Animal Health, World Health Organization. One Health Joint Plan of Action, 2022\u0026ndash;2026. One Health Joint Plan of Action, 2022\u0026ndash;2026. FAO; UNEP; WHO; World Organisation for Animal Health (WOAH) (founded as OIE); 2022.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Antimicrobial resistance, data gaps, surveillance, livestock","lastPublishedDoi":"10.21203/rs.3.rs-4253597/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4253597/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBacterial antimicrobial resistance (AMR) is a global threat to both humans and livestock. Despite this, there is limited global consensus on data-informed, priority areas for intervention in both sectors. We compare current livestock AMR data collection efforts with other variables pertinent to human and livestock AMR to identify critical data gaps and mutual priorities.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe globally synthesized livestock AMR data from open-source surveillance reports and point prevalence surveys stratified for six pathogens (\u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, non-typhoidal \u003cem\u003eSalmonella\u003c/em\u003e, \u003cem\u003eCampylobacter\u003c/em\u003e spp., \u003cem\u003eEnterococcus faecalis\u003c/em\u003e, \u003cem\u003eEnterococcus faecium\u003c/em\u003e) and eleven antimicrobial classes important in human and veterinary use, published between 2000 and 2020. We also included all livestock species represented in the data: cattle, chickens, pigs, sheep, turkeys, ducks, horses, buffaloes, and goats. We compared this data with intended priorities calculated from: disability-adjusted life years (DALYs), livestock antimicrobial usage (AMU), livestock biomass, and a global correlation exercise between livestock and human proportion of resistant isolates.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eResistance to fluoroquinolones and macrolides in \u003cem\u003eStaphylococcus aureus\u003c/em\u003e were identified as priorities in many countries but, less than 10% of these reported livestock AMR data. Resistance data for \u003cem\u003eEscherichia coli\u003c/em\u003e specific to cattle, chickens, and pigs, which we prioritized, were also well collected. AMR data collection on non-typhoidal \u003cem\u003eSalmonella\u003c/em\u003e and other livestock species were often not prioritized. Of 232 categories prioritized by at least one country, data were only collected for 48% (n\u0026thinsp;=\u0026thinsp;112).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe lack of livestock AMR data globally for broad resistance in \u003cem\u003eStaphylococcus aureus\u003c/em\u003e could underplay their zoonotic threat. Countries can bolster livestock AMR data collection, reporting, and intervention setting for \u003cem\u003eStaphylococcus aureus\u003c/em\u003e as done for \u003cem\u003eEscherichia coli\u003c/em\u003e. This framework can provide guidance on areas to strengthen AMR surveillance and decision-making for humans and livestock, and if done routinely, can adapt to resistance trends and priorities.\u003c/p\u003e","manuscriptTitle":"Using Priorities between Human and Livestock Bacterial Antimicrobial Resistance (AMR) to Identify Data Gaps in Livestock AMR Surveillance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-18 10:39:55","doi":"10.21203/rs.3.rs-4253597/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-24T04:27:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-20T14:53:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62853464797565583832605583305065020921","date":"2024-05-21T09:57:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-20T14:46:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"218901953001727143402062403372827129067","date":"2024-05-20T13:39:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6665ad93-f444-4aed-a844-c53aa5186b51","date":"2024-04-17T08:09:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-15T07:40:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-15T02:27:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-15T01:09:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-15T01:09:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2024-04-11T16:26:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ec88a09f-f237-4f18-886e-e887f4440424","owner":[],"postedDate":"April 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-30T16:09:56+00:00","versionOfRecord":{"articleIdentity":"rs-4253597","link":"https://doi.org/10.1186/s12879-024-09847-3","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2024-09-26 15:57:32","publishedOnDateReadable":"September 26th, 2024"},"versionCreatedAt":"2024-04-18 10:39:55","video":"","vorDoi":"10.1186/s12879-024-09847-3","vorDoiUrl":"https://doi.org/10.1186/s12879-024-09847-3","workflowStages":[]},"version":"v1","identity":"rs-4253597","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4253597","identity":"rs-4253597","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}