Timothy P. Robinson

Global trends in antimicrobial use in food animals

Significance Antimicrobials are used in livestock production to maintain health and productivity. These practices contribute to the spread of drug-resistant pathogens in both livestock and humans, posing a significant public health threat. We present the first global map (228 countries) of antibiotic consumption in livestock and conservatively estimate the total consumption in 2010 at 63,151 tons. We project that antimicrobial consumption will rise by 67% by 2030, and nearly double in Brazil, Russia, India, China, and South Africa. This rise is likely to be driven by the growth in consumer demand for livestock products in middle-income countries and a shift to large-scale farms where antimicrobials are used routinely. Our findings call for initiatives to preserve antibiotic effectiveness while simultaneously ensuring food security in low- and lower-middle-income countries. Demand for animal protein for human consumption is rising globally at an unprecedented rate. Modern animal production practices are associated with regular use of antimicrobials, potentially increasing selection pressure on bacteria to become resistant. Despite the significant potential consequences for antimicrobial resistance, there has been no quantitative measurement of global antimicrobial consumption by livestock. We address this gap by using Bayesian statistical models combining maps of livestock densities, economic projections of demand for meat products, and current estimates of antimicrobial consumption in high-income countries to map antimicrobial use in food animals for 2010 and 2030. We estimate that the global average annual consumption of antimicrobials per kilogram of animal produced was 45 mg⋅kg−1, 148 mg⋅kg−1, and 172 mg⋅kg−1 for cattle, chicken, and pigs, respectively. Starting from this baseline, we estimate that between 2010 and 2030, the global consumption of antimicrobials will increase by 67%, from 63,151 ± 1,560 tons to 105,596 ± 3,605 tons. Up to a third of the increase in consumption in livestock between 2010 and 2030 is imputable to shifting production practices in middle-income countries where extensive farming systems will be replaced by large-scale intensive farming operations that routinely use antimicrobials in subtherapeutic doses. For Brazil, Russia, India, China, and South Africa, the increase in antimicrobial consumption will be 99%, up to seven times the projected population growth in this group of countries. Better understanding of the consequences of the uninhibited growth in veterinary antimicrobial consumption is needed to assess its potential effects on animal and human health.

Mapping the global distribution of livestock.

Livestock contributes directly to the livelihoods and food security of almost a billion people and affects the diet and health of many more. With estimated standing populations of 1.43 billion cattle, 1.87 billion sheep and goats, 0.98 billion pigs, and 19.60 billion chickens, reliable and accessible information on the distribution and abundance of livestock is needed for a many reasons. These include analyses of the social and economic aspects of the livestock sector; the environmental impacts of livestock such as the production and management of waste, greenhouse gas emissions and livestock-related land-use change; and large-scale public health and epidemiological investigations. The Gridded Livestock of the World (GLW) database, produced in 2007, provided modelled livestock densities of the world, adjusted to match official (FAOSTAT) national estimates for the reference year 2005, at a spatial resolution of 3 minutes of arc (about 5×5 km at the equator). Recent methodological improvements have significantly enhanced these distributions: more up-to date and detailed sub-national livestock statistics have been collected; a new, higher resolution set of predictor variables is used; and the analytical procedure has been revised and extended to include a more systematic assessment of model accuracy and the representation of uncertainties associated with the predictions. This paper describes the current approach in detail and presents new global distribution maps at 1 km resolution for cattle, pigs and chickens, and a partial distribution map for ducks. These digital layers are made publically available via the Livestock Geo-Wiki (http://www.livestock.geo-wiki.org), as will be the maps of other livestock types as they are produced.

Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia

Two epidemic waves of an avian influenza A (H7N9) virus have so far affected China. Most human cases have been attributable to poultry exposure at live-poultry markets, where most positive isolates were sampled. The potential geographic extent of potential re-emerging epidemics is unknown, as are the factors associated with it. Using newly assembled data sets of the locations of 8,943 live-poultry markets in China and maps of environmental correlates, we develop a statistical model that accurately predicts the risk of H7N9 market infection across Asia. Local density of live-poultry markets is the most important predictor of H7N9 infection risk in markets, underscoring their key role in the spatial epidemiology of H7N9, alongside other poultry, land cover and anthropogenic predictor variables. Identification of areas in Asia with high suitability for H7N9 infection enhances our capacity to target biosurveillance and control, helping to restrict the spread of this important disease.

Role for migratory wild birds in the global spread of avian influenza H5N8

Migration of influenza in wild birds Virus surveillance in wild birds could offer an early warning system that, combined with adequate farm hygiene, would lead to effective influenza control in poultry units. The Global Consortium for H5N8 and Related Influenza Viruses found that the H5 segment common to the highly pathogenic avian influenza viruses readily reassorts with other influenza viruses (see the Perspective by Russell). H5 is thus a continual source of new pathogenic variants. These data also show that the H5N8 virus that recently caused serious outbreaks in European and North American poultry farms came from migrant ducks, swans, and geese that meet at their Arctic breeding grounds. Because the virus is so infectious, culling wild birds is not an effective control measure. Science, this issue p. 213; see also p. 174 High pathogenicity avian H5 influenza disperses around the Northern Hemisphere in long-distant migrant geese and ducks. Avian influenza viruses affect both poultry production and public health. A subtype H5N8 (clade 2.3.4.4) virus, following an outbreak in poultry in South Korea in January 2014, rapidly spread worldwide in 2014–2015. Our analysis of H5N8 viral sequences, epidemiological investigations, waterfowl migration, and poultry trade showed that long-distance migratory birds can play a major role in the global spread of avian influenza viruses. Further, we found that the hemagglutinin of clade 2.3.4.4 virus was remarkably promiscuous, creating reassortants with multiple neuraminidase subtypes. Improving our understanding of the circumpolar circulation of avian influenza viruses in migratory waterfowl will help to provide early warning of threats from avian influenza to poultry, and potentially human, health.

Malaria prevention in highland Kenya: indoor residual house-spraying vs. insecticide-treated bednets

This study compares the effectiveness and cost‐effectiveness of indoor residual house‐spraying (IRS) and insecticide‐treated bednets (ITNs) against infection with Plasmodium falciparum as part of malaria control in the highlands of western Kenya. Homesteads operationally targeted for IRS and ITNs during a district‐based emergency response undertaken by an international relief agency were selected at random for evaluation. Five hundred and ninety homesteads were selected (200 with no vector control, 200 with IRS and 190 with ITNs). In July 2000, residents in these homesteads were randomly sampled according to three age‐groups: 6 months–4 years, 5–15 years, and > 15 years for the presence of P. falciparum antigen (Pf HRP‐2) using the rapid whole blood immunochromatographic test (ICT). The prevalence of P. falciparum infection amongst household members not protected by either IRS or ITN was 13%. Sleeping under a treated bednet reduced the risk of infection by 63% (58–68%) and sleeping in a room sprayed with insecticide reduced the risk by 75% (73–76%). The economic cost per infection case prevented by IRS was US

Spatial statistics and geographical information systems in epidemiology and public health

9 compared to US

Antibiotic resistance is the quintessential One Health issue

29 for ITNs. This study suggests that IRS may be both more effective and cheaper than ITNs in communities subjected to low, seasonal risks of infection and as such should be considered as part of the control armamentarium for malaria prevention.

Estimating the costs of tsetse control options: an example for Uganda.

This chapter surveys the principles behind spatial statistics and geographic information systems (GIS), and their application to epidemiology and public health. Like the other introductory chapters, it is aimed mainly to facilitate understanding in the chapters specific to certain diseases that follow, and to provide a short introduction to the field. A brief overview of spatial statistics and GIS is provided in the introduction. The sections that follow explore the ways in which we can map the distribution of disease, ways in which we can look for spatial patterns in the distribution of disease, and ways in which we can apply spatial statistics and GIS to the problem of identifying the causal factors of observed patterns. In the last section I discuss some of the ways in which these techniques have been applied to assist decision making for disease intervention, and conclude by discussing future developments in the field, and some of the issues surrounding the integration of spatial statistics and GIS.

The global distribution of Crimean-Congo hemorrhagic fever

International Livestock Research Institute, Nairobi, Kenya; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China; Oxford University Clinical Research Unit, Wellcome Trust Major Overseas Programme, Ho Chi Minh City, Vietnam; Institute of Infection and Global Health, University of Liverpool, Liverpool, UK; Université Libre de Bruxelles, Brussels, Belgium; Institute for Health Metrics and Evaluation, University of Washington, Seattle, USA; Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, UK; Research Group for Preventive Technology in Livestock, Faculty of Veterinary Medicine, Khon Kaen University, Khon Kaen, Thailand; Public Health Foundation of India, Delhi, India; Kenya Medical Research Institute, Nairobi, Kenya; Center for Disease Dynamics, Economics and Policy, Washington DC, USA; Food and Agriculture Organization of the United Nations, Rome, Italy; Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden; National Institute of Veterinary Research, Hanoi, Vietnam; Institute of Integrative Biology and Center for Adaptation to a Changing Environment, Swiss Federal Institute of Technology, Zurich, Switzerland; Centre for Immunity, Infection & Evolution, University of Edinburgh, Edinburgh, UK

Mapping bovine tuberculosis in Great Britain using environmental data

Decision-making and financial planning for tsetse control is complex, with a particularly wide range of choices to be made on location, timing, strategy and methods. This paper presents full cost estimates for eliminating or continuously controlling tsetse in a hypothetical area of 10,000km(2) located in south-eastern Uganda. Four tsetse control techniques were analysed: (i) artificial baits (insecticide-treated traps/targets), (ii) insecticide-treated cattle (ITC), (iii) aerial spraying using the sequential aerosol technique (SAT) and (iv) the addition of the sterile insect technique (SIT) to the insecticide-based methods (i-iii). For the creation of fly-free zones and using a 10% discount rate, the field costs per km(2) came to US