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As part of an innovative Tripartite-EU collaboration Project that supports seven South American countries, a Landscape Analysis Tool (LAT) was developed and implemented to collect data to complement the Tripartite AMR Country Self-Assessment Survey (TrACSS) process. The LAT enables collection of broader and deeper information to guide development of priority One Health activities, and strengthen national action plans to combat antimicrobial resistance. The Project developed the tool, trained a consultant pool in its use, and implemented it in conjunction with multi-sectoral country teams. The main results were seven priority-informed country workplans that proposed specific activities in line with the Strategic Objectives of each country's national action plan. LAT implementation clearly showed that the tool is a strong complement to the TrACSS process and that there can be considerable benefit to the process of collecting additional data layers, especially to strengthen country ownership of AMR-related information and solidifying multisectoral engagement. Countries elsewhere might consider implementing this complementary tool – either once to establish a baseline – or periodically to gain a better ongoing understanding of the situation on the ground.

In response to the need to manage Antimicrobial Resistance (AMR), countries have produced National Action Plans (NAPs), which require detailed information of the AMR situation in all sectors. Considering the limited information that is publicly available for an analysis of the AMR situation in animal production, the FAO Regional Office for Latin America and the Caribbean (FAO RLC) developed the “FAO tool for a situation analysis of AMR risks in the food and agriculture sectors.” The objective of this paper is to present the methodology developed for a qualitative evaluation of the risk factors of AMR toward animal and human health, based on terrestrial and aquatic production systems and their associated national public and private mitigation measures. The tool was developed reflecting the AMR epidemiological model and the guidelines to conduct a risk analysis of AMR from the Codex Alimentarius and WOAH. Applied in four stages of progressive development, the objective of the tool is to provide a qualitative and systematic assessment of the risks of AMR from animal production systems, to animal and human health, and to identify gaps in cross cutting factors in AMR management. The tool consists of three instruments: (i) a survey to collect data for a situation analysis of AMR risks; (ii) a methodological procedure for the analysis of the information obtained; (iii) instructions for the preparation of a national roadmap for the containment of AMR at a national level. Based on the results from the information analysis, a roadmap is prepared by guiding and prioritizing the needs and sectoral actions for the containment of AMR under an intersectoral, multidisciplinary and collaborative approach, and according to country priorities and resources. The tool helps to determine, visualize and prioritize the risk factors and challenges that contribute to AMR from the animal production sector and that need to be addressed to manage AMR.

BackgroundFoodborne diseases are important worldwide, resulting in considerable morbidity and mortality. To our knowledge, we present the first global and regional estimates of the disease burden of the most important foodborne bacterial, protozoal, and viral diseases.Methods and findingsWe synthesized data on the number of foodborne illnesses, sequelae, deaths, and Disability Adjusted Life Years (DALYs), for all diseases with sufficient data to support global and regional estimates, by age and region. The data sources included varied by pathogen and included systematic reviews, cohort studies, surveillance studies and other burden of disease assessments. We sought relevant data circa 2010, and included sources from 1990-2012. The number of studies per pathogen ranged from as few as 5 studies for bacterial intoxications through to 494 studies for diarrheal pathogens. To estimate mortality for Mycobacterium bovis infections and morbidity and mortality for invasive non-typhoidal Salmonella enterica infections, we excluded cases attributed to HIV infection. We excluded stillbirths in our estimates. We estimate that the 22 diseases included in our study resulted in two billion (95% uncertainty interval [UI] 1.5-2.9 billion) cases, over one million (95% UI 0.89-1.4 million) deaths, and 78.7 million (95% UI 65.0-97.7 million) DALYs in 2010. To estimate the burden due to contaminated food, we then applied proportions of infections that were estimated to be foodborne from a global expert elicitation. Waterborne transmission of disease was not included. We estimate that 29% (95% UI 23-36%) of cases caused by diseases in our study, or 582 million (95% UI 401-922 million), were transmitted by contaminated food, resulting in 25.2 million (95% UI 17.5-37.0 million) DALYs. Norovirus was the leading cause of foodborne illness causing 125 million (95% UI 70-251 million) cases, while Campylobacter spp. caused 96 million (95% UI 52-177 million) foodborne illnesses. Of all foodborne diseases, diarrheal and invasive infections due to non-typhoidal S. enterica infections resulted in the highest burden, causing 4.07 million (95% UI 2.49-6.27 million) DALYs. Regionally, DALYs per 100,000 population were highest in the African region followed by the South East Asian region. Considerable burden of foodborne disease is borne by children less than five years of age. Major limitations of our study include data gaps, particularly in middle- and high-mortality countries, and uncertainty around the proportion of diseases that were foodborne.ConclusionsFoodborne diseases result in a large disease burden, particularly in children. Although it is known that diarrheal diseases are a major burden in children, we have demonstrated for the first time the importance of contaminated food as a cause. There is a need to focus food safety interventions on preventing foodborne diseases, particularly in low- and middle-income settings.