Global trends in antimicrobial use in food animalsThomas P. Van Boeckela,1, Charles Browerb, Marius Gilbertc,d, Bryan T. Grenfella,e,f, Simon A. Levina,g,h,1,Timothy P. Robinsoni, Aude Teillanta,e, and Ramanan Laxminarayanb,e,j,1

aDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544; bCenter for Disease Dynamics, Economics & Policy,Washington, DC 20036; cUniversite Libre de Bruxelles, B1050 Brussels, Belgium; dFonds National de la Recherche Scientifique, B1000 Brussels, Belgium;ePrinceton Environmental Institute, Princeton, NJ 08544; fFogarty International Center, National Institutes of Health, Bethesda, MD 20892; gBeijer Instituteof Ecological Economics, 10405 Stockholm, Sweden; hResources for the Future, Washington, DC 20036; iInternational Livestock Research Institute, 00100Nairobi, Kenya; and jPublic Health Foundation of India, New Delhi 110070, India

Contributed by Simon A. Levin, February 18, 2015 (sent for review November 21, 2014; reviewed by Delia Grace and Lance B. Price)

Demand for animal protein for human consumption is risingglobally at an unprecedented rate. Modern animal productionpractices are associated with regular use of antimicrobials, poten-tially increasing selection pressure on bacteria to become resistant.Despite the significant potential consequences for antimicrobialresistance, there has been no quantitative measurement of globalantimicrobial consumption by livestock. We address this gap byusing Bayesian statistical models combining maps of livestockdensities, economic projections of demand for meat products, andcurrent estimates of antimicrobial consumption in high-incomecountries to map antimicrobial use in food animals for 2010 and2030. We estimate that the global average annual consumption ofantimicrobials per kilogram of animal produced was 45 mg·kg−1,148 mg·kg−1, and 172 mg·kg−1 for cattle, chicken, and pigs, respec-tively. Starting from this baseline, we estimate that between 2010and 2030, the global consumption of antimicrobials will increaseby 67%, from 63,151 ± 1,560 tons to 105,596 ± 3,605 tons. Up toa third of the increase in consumption in livestock between 2010and 2030 is imputable to shifting production practices in middle-income countries where extensive farming systems will be re-placed by large-scale intensive farming operations that routinelyuse antimicrobials in subtherapeutic doses. For Brazil, Russia,India, China, and South Africa, the increase in antimicrobial con-sumption will be 99%, up to seven times the projected populationgrowth in this group of countries. Better understanding of the con-sequences of the uninhibited growth in veterinary antimicrobialconsumption is needed to assess its potential effects on animaland human health.

antimicrobials | livestock | mapping | drug resistance | linear regression

Antimicrobials are widely used for disease prevention andgrowth promotion in food animals. In the United States,

antimicrobial use in food animals is estimated to account for∼80% of the nation’s annual antimicrobial consumption (1), asignificant fraction of which involves antimicrobials that areimportant in human medicine in the treatment of common infec-tions and also necessary to perform medical procedures such asmajor surgeries, organ transplantation, and chemotherapy (2).This widespread use of antimicrobials in livestock contrib-

utes––by means of natural selection––to the emergence of anti-microbial-resistant bacteria (ARBs) and has significant publichealth implications: ARBs of animal origin can be transmitted tohumans through the environment (3) and food products (4) andto agricultural workers by direct contact (5). Although direct cau-sality is difficult to establish because of the ecological nature ofantibiotic selection pressure, studies have shown a close associationbetween the prevalence of livestock-associated ARBs in animalsand in humans (6), as well as between the levels of antimicrobialuse in animals at a population level, and the prevalence of ARBs inanimals (7) and in humans (8). A recent study from seven Euro-pean countries (Norway, Sweden, Denmark, Austria, Switzerland,The Netherlands, and Belgium) showed a strong correlation be-tween consumption levels for eight classes of antimicrobials (9) andthe prevalence of antimicrobial-resistant commensal Escherichia

coli in pigs, poultry, and cattle. Several works additionally sug-gested that repeated exposure to low doses of antimicrobialagents––the context in which growth-promoting antimicrobialsand prophylactic are administered––creates ideal conditions forthe emergence and spread of ARBs in animals (10).In low- and middle-income countries, rising incomes have

driven an unprecedented growth in demand for animal protein(11) and, as a result, the global biomass of animals raised forfood now exceeds the global biomass of humans (12). In Asia,daily animal protein intake grew from 7 grams per capita per dayto 25 grams per capita per day (12) between 1960 and 2013 whilethe proportion of the diet coming from rice and wheat pro-gressively decreased, primarily among higher-income adults (13).To meet this demand, countries such as Brazil, Russia, India,China, and South Africa (BRICS) have shifted toward highlycost-efficient and vertically integrated intensive livestock pro-duction systems. Because these production systems necessitateantimicrobials to keep animals healthy and maintain productivity,rising incomes in transitioning countries are effectively driving anincrease in antimicrobial consumption and thereby antimicrobialresistance. Meanwhile, multiresistant ARBs have been isolated infood animals in BRICS countries (14, 15) and throughout the de-veloping world where the use of antimicrobials for growth pro-motion remains largely unregulated (16).The challenges of the nutritional transition to animal protein-

based diets and the rise of antimicrobial resistance are thusclosely linked: The use of antimicrobials as growth promoters

Significance

Antimicrobials are used in livestock production to maintainhealth and productivity. These practices contribute to the spreadof drug-resistant pathogens in both livestock and humans, pos-ing a significant public health threat. We present the first globalmap (228 countries) of antibiotic consumption in livestock andconservatively estimate the total consumption in 2010 at 63,151tons. We project that antimicrobial consumption will rise by67% by 2030, and nearly double in Brazil, Russia, India, China,and South Africa. This rise is likely to be driven by the growthin consumer demand for livestock products in middle-incomecountries and a shift to large-scale farms where antimicrobialsare used routinely. Our findings call for initiatives to preserveantibiotic effectiveness while simultaneously ensuring foodsecurity in low- and lower-middle-income countries.

Author contributions: T.P.V.B. and R.L. designed research; T.P.V.B., C.B., and A.T. per-formed research; C.B. and M.G. contributed new reagents/analytic tools; T.P.V.B. andA.T. analyzed data; and T.P.V.B., C.B., M.G., B.T.G., S.A.L., T.P.R., A.T., and R.L. wrotethe paper.

Reviewers: D.G., International Livestock Research Institute; and L.B.P., Translational Ge-nomics Research Institute.

The authors declare no conflict of interest.1To whom correspondence may be addressed. Email: thomas.van.boeckel@gmail.com,slevin@princeton.edu, or ramanan@cddep.org.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1503141112/-/DCSupplemental.

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and therapeutics to support the growing demand for meat isplacing ever greater selection pressure for resistant strains ofbacteria to evolve. Whereas trends in antibiotic consumption inhumans are now being tracked in most high-income and somemiddle-income countries through databases on antibiotic sales(17, 18), antimicrobial consumption in livestock has receivedcomparatively little attention. Expert opinion suggests that globalconsumption of antimicrobials in animals is twice that of humans(19). However, the underlying data from the veterinary sectorsupporting these claims are weak and lack standardization.Without reliable evidence to estimate global antimicrobial con-sumption in livestock, the links between antimicrobial consump-tion and resistance patterns are poorly quantified, and efforts andpolicies to optimize antibiotic use in animals are poorly targeted.This study addresses these gaps in our understanding of anti-

microbial use in animals. We use statistical models combiningmaps of livestock densities and reports of antimicrobial consump-tion in high-income countries to estimate and map the globalconsumption of antimicrobials in food animals for 2010. We thenproject antimicrobial consumption trends for the year 2030 basedon growth projections of the consumption of livestock products.

ResultsOverall Antimicrobial Consumption Trends. Global consumption ofantimicrobials in food animal production was estimated at 63,151(±1,560) tons in 2010 and is projected to rise by 67%, to 105,596(±3,605) tons, by 2030. Two thirds (66%) of the global increase(67%) in antimicrobial consumption is due to the growing numberof animals raised for food production. The remaining third (34%)is imputable to a shift in farming practices, with a larger pro-portion of animals projected to be raised in intensive farmingsystems by 2030. In Asia alone, as much as 46% of the increasein antimicrobial consumption by 2030 is likely due to shifts inproduction systems. By 2030, antimicrobial consumption in Asiais projected to be 51,851 tons, representing 82% of the currentglobal antimicrobial consumption in food animals in 2010.In 2010, the five countries with the largest shares of global

antimicrobial consumption in food animal production wereChina (23%), the United States (13%), Brazil (9%), India (3%),and Germany (3%) (Fig. 1). By 2030, this ranking is projected tobe China (30%), the United States (10%), Brazil (8%), India(4%), and Mexico (2%). Among the 50 countries with the largestamounts of antimicrobials used in livestock in 2010, the fivecountries with the greatest projected percentage increases inantimicrobial consumption by 2030 are likely to be Myanmar(205%), Indonesia (202%), Nigeria (163%), Peru (160%), andVietnam (157%). China and Brazil are among the largest con-sumers of antimicrobials currently but are not the countries with

the most rapid projected increases in antimicrobial consumption.This indicates that these two countries have already initiated ashift toward more intensified livestock production systems usingantimicrobials to maintain animal health and increase productivity.Antimicrobial consumption for animals in the BRICS coun-tries is expected to grow by 99% by 2030, whereas their humanpopulations are only expected to grow by 13% over the sameperiod (20).

Consumption by Type of Livestock. The global estimates of anti-microbial consumption presented in this study are based onspecies-specific coefficients of antimicrobial consumption perpopulation correction unit (PCU). Using a Bayesian regressionframework, we estimated the posteriors distributions for thesecoefficients for intensively farmed animals (Fig. 2). The mean ofthe posterior for antimicrobial consumption in cattle was gen-erally lower (45 mg/PCU) than for chickens (148 mg/PCU) andpigs (172 mg/PCU). The difference in Bayes’ factors between acomplete regression model including cattle, chickens, and pigsand regression models including two types of animals were, re-spectively, 1.32 for a model including just chicken and pigs, 78for a model including just cattle and pigs, and 1.72 × 109 for amodel including just cattle and chickens. This indicates thatdropping chicken and pigs from the regression models resultedin a significant loss of predictive power to estimate the overallantimicrobial consumption, and that the number of pig PCUsbest explained the differences in overall antimicrobial consump-tion between countries. The higher dispersion of the posteriordistribution of chicken production compared with that of pig pro-duction suggests that intensive chicken production showed a widerrange of intensity of antimicrobial use across countries than didpork production.

Geographical Patterns. Antimicrobial consumption displayed im-portant geographic heterogeneity across continents. In Southand Southeast Asia, antimicrobial consumption hotspots includethe southeast coast of China, Guangdong and Sichuan provinces,(Fig. 3, Top), the Red River delta in Vietnam, the northernsuburbs of Bangkok, and the south coast of India and the cities ofMumbai and Delhi. In the Americas, the highest consumption ofantimicrobials was observed in the south of Brazil, the suburbsof Mexico City, and midwestern and southern United States. Theonly notable hotspots of antimicrobial consumption in Africawere the Nile delta and the city of Johannesburg and its sur-rounding townships. The uncertainty bounds associated with thespatial predictions of antimicrobial consumption are presentedin Fig. 3 (Bottom). In general, the SDs of the coefficients ofantimicrobial consumption per PCU were moderate in regionswhere intensive farming practices are common and food animalsare densely populated. Higher uncertainty in the model predictionwas observed in Central Asia, Ethiopia, Canada, and eastern In-dia, for example.When disaggregated by food animal species, the geographical

distributions of antimicrobial consumption display distinct spa-tial patterns according to regional production patterns. Fig. S1shows these patterns within the European Union. Most of theantimicrobial consumption associated with chicken productionis found in Flanders (Belgium), The Netherlands, the BritishMidlands, Brittany (France), and the Po Valley (Italy). Con-sumption in pork production is largely concentrated in northernGermany, Denmark, The Netherlands, northern France, north-ern Belgium, Madrid and the autonomous region of Cataluña inSpain, and the Po Valley. Comparatively, the geographic inten-sity of antimicrobial consumption in cattle production was lowacross Europe because of the lower use of antimicrobials per PCUand the lower animal densities characteristic of cattle, comparedwith chicken and pig.

Fig. 1. (A) Largest five consumers of antimicrobials in livestock in 2010.(B) Largest five consumers of antimicrobials in livestock in 2030 (projected).(C) Largest Increase in antimicrobial consumption between 2010 and 2030.(D) Largest relative increase in antimicrobial consumption between 2010 and2030. CHN, China; USA, United States; BRA, Brazil; DEU, Germany; IND, India;MEX, Mexico; IDN, Indonesia; MMR, Myanmar; NGA, Nigeria; PER, Peru;PHL, Philippines.

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In Asia, antimicrobial consumption in chicken and pigs is ex-pected to grow by 129% and 124%, respectively, by 2030 (Fig. 4).However, the total acreage of areas where antimicrobial con-sumption is currently greater than 30 kg·km−2 will grow by 4%for pork and 143% for chicken. This has potentially importantlogistical implications for surveillance programs to track the emer-gence of ARBs over larger portions of land. The extreme growthin consumption for chickens is primarily the result of the expan-sion of this sector in India alone, where areas of high consumption(30 kg·km−2) are expected to grow 312% by 2030.

DiscussionIn this study, we use statistical models to map the global con-sumption of antimicrobials in food animals for 2010 and 2030.This is the first study that we are aware of that attempts to quantifyantimicrobial consumption in food animals at a global scale.As with any model-based study, our analysis is subject to

assumptions and limitations. Data on antimicrobial use in live-stock are scarce, stemming from both the lack of publicly fundedsurveillance systems and the reluctance of food animal pro-ducers, animal feed producers, and veterinary pharmaceuticalcompanies to provide comprehensive reports of antimicrobialconsumption or sales. For this study, estimates of antimicrobialconsumption could be obtained for only 32 countries, all of whichwere high income. These data were first interpolated amongother high-income countries and subsequently extrapolated toestimate antimicrobial consumption in intensive production sys-tems of low- and middle-income countries. This modeling strat-egy was necessarily chosen as a result of the lack of systematic andreliable reports on antimicrobial sales in middle- and low-incomecountries. The underlying assumption implicit to this modelingstrategy is that because they are highly standardized, intensivefarming operations use similar quantities of antimicrobials acrosshigh-, middle-, and low-income countries. Additionally, severalEuropean countries used to train the statistical model have ex-perienced declining sales of antimicrobials for animal consump-tion, ranging from 0.4% to 28% between 2010 and 2011 (21), andseveral Organization for Economic Co-operation and Devel-opment (OECD) countries are currently engaged in initiativesaiming at reducing antibiotic use in livestock production (22).Additionally, patterns of antimicrobial consumption in middle-income and high-income countries differ in many respects. Inmost instances, the absence of clear legislative framework on theuse of antimicrobials in livestock production in most middle- andlow-income countries may result in increased irrational con-sumption. Our estimates of antibiotic consumption in 2010 maythus represent an overestimation of the current consumption levels.However, several of our assumptions may also result in under-estimating the global antimicrobial consumption. First, among thecountries used to train the statistical models, 25 are subject to

a ban on antimicrobial use for growth promotion, and two aresubject to a partial ban (Australia and New Zealand) (16). Second,the United States, where withdrawal of antimicrobials for growthpromotion is voluntary, was excluded from the model-fitting pro-cedure to prevent its comparatively larger statistical weight fromartificially increasing the significance of the linear regression. In-dividual US state estimates could have helped overcome theseissues, but these were not available. As a result, we likely under-estimate consumption of antimicrobials in livestock in the UnitedStates. Additionally, because this information was missing in themajority of countries used to train the statistical models, this studydoes not evaluate antimicrobial consumption on a compound-specific basis. Finally, the total volume figures do not account forchoices of drugs, potential differences in drug potencies, resistanceselection pressures, or use for treatment in human medicine.A potential caveat of this study is that ionophores—compoundsthat are used only in animals—are reflected in our estimates of theglobal antimicrobial consumption because these are generallypooled with medically important antimicrobials in national reportsof total antimicrobial sales. As a result, our estimates of antimi-crobial consumption may not always reflect differences in expo-sures to antimicrobials among countries.The introduction of a binary distinction between extensively

and intensively raised livestock masks a variety of productionprocesses prevalent globally but is relatively well documented forpoultry production systems in Asia (Fig. S2) (23) and was re-cently validated for Thailand (24). For pigs, production systemsof intermediate size (semiintensive) may represent a nonnegligibleshare of production, but these were not treated as an individualcategory for this study. Our simplifications were chosen over morespeculative and arbitrary modeling assumptions that could haveintroduced additional uncertainty and potentially affected theprojected trends for growth in antimicrobial consumption. Theestimates presented in this study should therefore be seen as con-servative estimates of antimicrobial consumption in food pro-duction, barring major changes in the global regulatory frameworkof these substances over the next 2 decades.Globally, intensive livestock farming has increased food pro-

duction at a low cost per unit produced, but perhaps at an un-recognized price paid in increased antimicrobial resistance.Linking antimicrobial consumption in animals to drug-resistantinfections of humans is inherently complex owing to the ecologicalnature of the selection pressure for drug-resistant pathogens aswell as the existence of indirect routes of transmission throughthe environment. However, in recent years, a growing body of

Antimicrobial (mg) / PCU

Pro

b. d

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0 100 200 300

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Fig. 2. Posterior distributions for estimates of antimicrobial consumption incattle, chickens, and pigs in OECD countries.

Log10 [(mg/pixel)+1]004567891

Std. Dev. mg/PCUN a

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75 015 00

20 00

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Fig. 3. Global antimicrobial consumption in livestock in milligrams per 10 km2

pixels (Top) and average SD of estimates of milligrams per PCU (Bottom).

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evidence has accumulated that strengthens the hypothesis that theroutine (25) use of antimicrobials in intensive animal productionsystems constitutes a waste of natural resources (26)—antimicro-bial effectiveness—that is of crucial importance in human medi-cine. Intensive farming practices have not only been associatedwith antimicrobial resistance in animals, humans, and meat butalso with numerous other livestock diseases such as highly path-ogenic avian influenza H5N1 (27) and porcine reproductive andrespiratory syndrome (28). Beyond animal health and antimicro-bial resistance, other negative externalities associated with poorlyregulated intensive farming include water and soil pollution (29),loss of biodiversity (30), and decline of meat nutritional quality(31). All of these have severe consequences that potentially out-weigh long-term benefits of increased productivity.Mapping the antimicrobial consumption in livestock provides

a baseline estimate of its global importance. Similar mappingexercises have been conducted for other major public healthissues, such as malaria (32) or tuberculosis (33). Leading acomparable initiative for antimicrobial consumption providesseveral insights. First, it provides an objective data-driven esti-mate of the potential magnitude of antimicrobial consumption atthe global scale, whereas previous estimates were based on ex-pert opinions (34). Second, it identifies regions at higher risk ofemergence of drug-resistant pathogens—places where surveil-lance and intervention efforts should be targeted. Third, thisbaseline estimate can be used to evaluate the progress (35) offuture antimicrobial stewardship efforts. Finally, this approachcan be adapted to predict antimicrobial consumption in the fu-ture using updated maps of livestock, and thus continuouslyupdate projections for the evolution of antimicrobial resistancein livestock and humans.If regulatory action is not taken, our projections suggest that

global antimicrobial consumption in food animals will grow byat least 67% by 2030. This corresponds to a compound annual

growth rate of 2.60%, a rate comparable to the compound an-nual growth rate for consumption of antibiotics in humans forthe period 2000–2010 (2.84%) (18) but almost threefold theprojected annual growth rate of the human population (0.98%)from 2010 to 2030. In 2001, Wise estimated the annual antimi-crobial market at 100,000–200,000 tons (34), but Wise’s meth-odology is unknown to us. Under the plausible hypothesis thatglobal human antimicrobial consumption is likely smaller or atbest equivalent to animal consumption, and that human con-sumption grew by 36% between 2000 and 2010 (17), we find thatour estimate for antimicrobial consumption in 2010 (63,151 tons)is surprisingly consistent with the expert opinion estimate (34).Finally, up to one third of the increase in antimicrobial consump-tion in animals between 2010 and 2030 will be imputable to a shifttoward intensive production systems where antimicrobials areused routinely in subtherapeutic doses for disease prevention andgrowth promotion, rather than for disease treatment (16, 36, 37).In 2010, China was the largest antimicrobial consumer for

livestock, and we estimate that its livestock industry will use up to30% of the global antimicrobial production by 2030. Anothercountry contributing to a large share of the overall growth inantimicrobial consumption in food animals, if current trendscontinue, is India––a country already confronted with antibioticoveruse in human medicine and an extremely high (and increasing)prevalence of ARBs (e.g., ∼95% of adults in India carry bacteriaresistant to β-lactam antimicrobials) (38). Widespread resistancemay be more consequential for India than for other countries be-cause India’s bacterial disease burden is among the highest in theworld, and therefore antimicrobials play a critical role in limitingmorbidity and mortality (39). Currently, India has no regulatoryprovisions for the use of antimicrobials in cattle, chicken, and pigsraised for domestic consumption, nor do the majority of middle-income countries for which substantial growth in antimicrobialconsumption over the next 15 y is predicted (16). Recent studiesin various regions of India have discovered antimicrobial resi-dues in food animal products (such as chicken meat and milk)(40), indicating that antibiotic use in food animal production iswidespread and current regulation is nonexistent for domesticproduction. Limiting antimicrobial consumption in both humansand livestock may present a formidable challenge for Indianpublic health authorities, but it might also be an opportunity forthe country to take a regional lead in tackling this problem. Forinstance, neighbors such as Pakistan, Bangladesh, Nepal, and SriLanka are likely to be guided and influenced by regulatory actionin India, given the interconnectedness of the region’s pharma-ceutical commerce (41).The role of aquaculture has not been investigated in the

present study. However, this industry may represent a large shareof the antimicrobial consumption for an increasing number ofcountries in Southeast Asia. For instance, studies of antimicro-bial consumption in fish farming in Chile (42) and shrimp farmingin Vietnam (43) demonstrate that aquaculture is associated withextremely high rates of antimicrobial consumption per PCU [upto 1,400 mg/PCU reported for salmon farming in Chile (42)]. Asthe aquaculture industry grows (44) and shifts toward more-efficient production systems, it could constitute a major source ofantimicrobial contamination of the aquatic environment over thecoming decades.The analysis presented here is based on the very limited

available evidence on antimicrobial consumption in livestockproduction. We provide somewhat crude estimates of presentand projected antimicrobial consumption in food animals in2030. Our estimates of absolute values for the global antimi-crobial consumption should therefore be viewed with caution.Antimicrobial consumption levels in middle- and low-incomecountries were extrapolated from consumption levels in intensiveproduction systems in high-income countries. This methodologymay result in significant uncertainties when evaluating the global

A

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Antimicrobial (Kg)No Data

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Fig. 4. Antimicrobial consumption in chickens (A) and pigs (B) in 2010.Purple indicates new areas where antimicrobial consumption will exceed30 kg per 10 km2 by 2030.

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antimicrobial consumption level in livestock production. How-ever, working under the hypothesis that our methodology issubject to a systematic error over the period 2010–2030, weproject that, in relative terms, the consumption of antimicrobialsin food animals will grow significantly by 2030 owing to an im-portant increase in demand for meat in middle- and low-incomecountries. This indicates a potentially growing contribution ofthese countries to the global burden of antimicrobial resistance.In fast-growing Asian countries, this will constitute a seriouschallenge because these countries are currently experiencing themost rapid increase in demand for meat products (45), but re-gulations on antimicrobial use (for the domestic market) are stilllacking and surveillance information on antimicrobial consump-tion is either nonexistent or not publicly available. Despite itsregional nature, this process will unavoidably drive a global in-crease in the prevalence of ARBs through trade and transportnetworks, with potentially important consequences for humanhealth. This rise of ARBs seems potentially reversible for certaincompounds: Withdrawal of antimicrobials for growth promotionin several European countries led to a decrease in the prevalenceof ARBs, but the duration needed for reversal is unclear and thesubject of ongoing investigation (7, 46).Given the potential costs of inaction, this study, among others,

calls for urgent and concerted action in all countries, which isneeded to limit the overuse and abuse of antimicrobials in foodanimal production (2, 18, 47). These actions should include(i) implementation of a publicly funded international surveil-lance network of antimicrobial consumption in food animals incountries undergoing rapid intensification in the livestock sector,(ii) collaboration with veterinary drug manufacturers and animalfeed producers to cross-validate estimates of consumption withsales data, (iii) implementation of an international agenda toharmonize regulatory frameworks among countries, and (iv) theultimate phasing out of antimicrobial use for growth promotion,based on the successful experience in the European Union andthe new biological (48, 49) and economic (50, 51) evidencechallenging the purported benefits of antimicrobial use in foodanimal production.

MethodsIn the absence of systematic and harmonized data on antimicrobial con-sumption in livestock, we use indirect means to estimate antimicrobialconsumption (in milligrams of active ingredient per kilogram of animal) forcattle, pigs, and chickens raised in both extensive and intensive farmingsystems in 228 countries. In our study, intensive production refers to highinput–high output systems that, compared with extensive systems (backyardproduction), achieve greater economies of scale and efficiency while alsopossibly using mechanized labor, operating with high animal densities, andusing specialized breeds with rapid weight gain and high feed conversionratios. The coefficients calculated for each type of livestock and for eachsystem were subsequently applied to high-resolution maps of livestock pop-ulation densities to predict the geographic distribution of antimicrobial con-sumption in food producing animals for the years 2010 and 2030.

Antimicrobials. Data on antimicrobial consumption in food animals wereobtained from government veterinary agencies, agriculture ministries, sci-entific reports and publications, and personal communications with academicresearchers; all data are included in Table S1. Data were collected by con-tacting relevant government ministries or agencies and through a systematicsearch of studies on PubMed. The search terms and a detailed description ofthe data collection process are given in Table S1. The majority of countrieseither do not collect or do not release data on veterinary antimicrobialconsumption. Across countries, data on antimicrobial consumption werefound in three formats: (i) estimates of overall antimicrobial consumption,(ii) estimates of consumption by livestock species, and (iii) estimates ofconsumption per PCU disaggregated by species type (e.g., chickens, cattle,and pigs). PCUs are used to compare population and production of differenttypes of livestock across countries and correspond to 1 kg of living orslaughtered animal (21). For example, a herd of 10 pigs each weighting 100 kgcorresponds to 1,000 PCUs. Assuming that antimicrobial consumption inchickens, cattle, and pigs represents the majority of antimicrobial consumption

in food-producing animals, the total consumption of antimicrobials was cal-culated for each country by pooling the estimates collected in case ii or bymultiplying the per PCU figure by the total national PCU for each type oflivestock in case iii. When data could not be obtained for the reference year2010, the antimicrobial estimates obtained for another year were adjustedusing the ratio of overall antimicrobial consumption between 2010 and thecorresponding year. Estimates of total antimicrobial consumption could beobtained for 32 countries, including 28 member states of the OECD and fourcandidate-OECD countries (Cyprus, Latvia, Lithuania, and Bulgaria).

Animal Census. To calculate estimates of antimicrobial consumption per PCUthat could be applied at the pixel level to generate total antimicrobialconsumption maps, we estimated national PCUs as a function of the numberof living animals. Thus, total PCUs in a country or a pixel for livestock type k inthe production system s were defined as follows:

PCUk,s = Ank,s ·�1+nk,s

�·�

Yk

RðCW=LWÞ,k

�

where An  k   is the number of living animals,  nk,s is the number of productioncycles in each production system (extensive or intensive), Y is the quantityof meat per animal (carcass weight) obtained for each country fromFAOSTAT, and RðCW=LWÞ,k is the killing-out percentage (or dressing per-centage)—that is, the ratio of carcass weight to live weight of an animal—obtained from literature estimates (52). The last term of this equation canbe interpreted as the animal weight reconstructed from country-specificproductivity figures.

To reflect differences in productivity, distinct values were used for thenumber of production cycles in extensive (nc,Ext ) and intensive (  nc,Int ) pro-duction systems. Working under the assumption that extensive farmingrepresents the bulk of livestock production in low-income countries, nExt wasestimated from the median number of production cycles in the quartile ofcountries characterized by the lowest gross domestic product (GDP) percapita (World Bank estimates). This value was considered identical in allcountries on the basis that backyard productivity displays little variabilityacross low-income countries (the ratio of SD to the mean in the lower GDPper capita quartile was 0.65 for cattle, 0.44 for chickens, and 0.91 for pigs).The number of production cycles in intensive systems was calculated byimputation as nc,Int = ðS−   nExt ·AnExtÞ=AnInt , where S is the total number ofanimals slaughtered in 2010.

Statistical Models. Antimicrobial consumption per PCU for each type oflivestock in extensive and intensive systems was estimated using Bayesiansregressions through a three-step procedure described in SI Text and Fig. S3.

Mapping Predictions. To generate spatially explicit estimates of antimicrobialuse, the estimates of consumption per PCU in each country weremultiplied bythe corresponding PCU values in each pixel. The uncertainty of the mappedprediction was quantified by weighting the respective SD for each livestockcoefficient by the relative PCU for each pixel. Maps of chickens and pigs,disaggregated by extensive and intensive production systems for the year2010, were obtained from Gilbert et al. (24). For cattle, maps of populationdensities disaggregated between extensive and intensive were generatedfrom the total population densities obtained from Robinson et al. (53). Weused a threshold of five cattle head per kilometer to allocate animals to amap of intensively raised cattle. The map of extensively raised cattle wasgenerated by subtracting the intensively raised from the total number ofhead in each pixel. Livestock densities in 2030 were estimated based on aprojection of meat consumption in 2030 (45). Assuming a constant value forthe compound annual growth rate from 2000 to 2030, this value was used tocalculate the ratio of meat consumption R30/10 between 2010 and 2030,

R30=10 =  An2030

An2010=�An2030

An2000

�23

where An is the number of animals in each pixel. The projected livestockdensities for 2030 were estimated by multiplying animal densities per pixelin 2010 by R30/10. For pigs and chickens, we used the model developed byGilbert et al. (24) that quantifies the country-level proportion of extensivelyraised chicken and pig stock as a function of GDP per capita in purchasingpower parity. On the national level, the proportion of chickens or pigs raisedintensively showed a good correlation with this metric because the de-velopment of cost-efficient, large-scale farms typically requires substantialinvestments and influx of capital.

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The proportion of animals raised in extensive production systems wascalculated by replacing the current GDP values in the model by those of theyear 2030, obtained from linear projection of GDP estimates from the In-ternational Monetary Fund for the period 1980–2018. For cattle, in the ab-sence of reliable global estimates for shift in production structure, theproportion of animals raised extensively and intensively was assumed to beidentical for 2010 and 2030. All maps were resampled at 0.08333 decimaldegree resolution (∼10 kilometers at the equator).

ACKNOWLEDGMENTS. Data collection was supported by the Organization forEconomic Co-operation and Development (SRM 500026083). B.T.G. and T.P.V.B.were supported by the Science and Technology Directorate, Department ofHomeland Security, Contract HSHQDC-12-C-00058. B.T.G. and S.A.L. weresupported by the Research and Policy for Infectious Disease Dynamics programof the Science and Technology Directorate, Department of Homeland Security.B.T.G. was supported by the Bill & Melinda Gates Foundation and by the FogartyInternational Center, National Institutes of Health. S.A.L., A.T., and R.L. weresupported by Princeton University through its Grand Challenges Program.

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6 of 6 | www.pnas.org/cgi/doi/10.1073/pnas.1503141112 Van Boeckel et al.

Supporting InformationVan Boeckel et al. 10.1073/pnas.1503141112SI Text

Protocol S1: Three-Step Procedure to Calculate Coefficientsof Antimicrobial Consumption per Population CorrectionUnitsStep 1. OECD countries with published estimates of overall an-timicrobial consumption were used to train a linear regressionmodel to predict the overall consumption of antimicrobials inmilligrams in other OECD countries (AmOECD) as a function ofthe log-transformed stock of animals (Fig. S3). The model wasweighted by the total size of the livestock sector (PCUs) in eachcountry. To avoid overestimating antimicrobial consumption, weexcluded the United States from the training set, since it is knownto have uncharacteristically high consumption of antimicrobialscompared with other OECD countries.

Step 2. A Bayesian linear regression model was fitted to the totalconsumption of antimicrobials AmOECD, to estimate con-sumption per PCU for each type of livestock in intensive pro-duction systems in 37 countries (all OECD countries, as well asfour candidate-OECD countries, except the United States). Theprior distributions used for the regression coefficients were as-

sumed to be Gaussian, with means and SDs corresponding to theanimal-specific antimicrobial consumption per PCU that could beobtained for 10 countries (Australia, Belgium, Denmark, France,Japan, Korea, The Netherlands, New Zealand, Sweden, and theUnited Kingdom). The relative importance of cattle, chickens, orpigs in explaining the variability in total antimicrobial con-sumption was quantified using the difference in the Bayes factorvalue between a full model including all animal types and a modelexcluding the animal type of interest.

Step 3. For each country, a value for antimicrobial consumptionper PCU in intensive production systems was sampled from theestimated posteriors distribution. The corresponding coefficientsfor extensive systems were calculated using a randomly sampledscaling ratio ranging from 0.05 to 0.5 times the value sampled forintensive systems. The procedure was repeated 50 times for eachcountry to compute an SD and average coefficient value for eachtype of livestock. An overall confidence interval for global an-timicrobial consumption in food animals was calculated bycombining the SD of the antimicrobial consumption predicted ineach country in relation to the share of each country in the globalantimicrobial consumption.

Antimicrobial (Kg)

No anim

al

0 - 1010

- 30

30- 25

0

> 250

A B C

Fig. S1. Antimicrobial consumption in livestock in the European Union in 2010 for cattle (A), chickens (B), and pigs (C).

Fig. S2. Distribution of farm sizes in Thailand. The bimodal distribution shows that farms are distributed in two distinct groups corresponding to differentproduction systems: A large number of farms have a median size of 10 birds, whereas another large group of farms host between 5,000 and 10,000 birds.

Van Boeckel et al. www.pnas.org/cgi/content/short/1503141112 1 of 5

Fig. S3. Association between national antimicrobial consumption obtained from national reports (Table S1) and population correction units (kilograms ofmeat) in OECD countries in 2010 for cattle, chicken, and pigs. Gray dots represent the total production, and red dots represent intensive production alone.

Van Boeckel et al. www.pnas.org/cgi/content/short/1503141112 2 of 5

Table

S1.

Datausedto

inform

themodel

presentedin

this

study

Country

Cattle,

mg/PCU

Chicke

ns,

mg/PCU

Pigs,

mg/PCU

Annual

sales

cattle,kg

Annual

sales

chicke

ns,kg

Annual

sales

pigs,kg

Totalan

nual

sales,kg

Yea

rDatasource

URL

Australia

––

–13

3,30

0*40

6,40

010

4,20

064

4,00

020

09–20

10APV

MA

arch

ive.ap

vma.gov.au

/publications/reports/docs/

antimicrobial_sales_report_m

arch

-201

4.pdf

Austria

––

––

–63

,000

2010

EMA

www.ema.eu

ropa.eu

/docs/en_G

B/docu

men

t_lib

rary/Rep

ort/201

3/10

/WC50

0152

311.pdf

––

26.1

†–

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2010

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rly/20

14/07/

22/vr.10

2520

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htm

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26,079

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Den

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Finland

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2010

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www.ema.eu

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/docs/en_G

B/docu

men

t_lib

rary/Rep

ort/201

3/10

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cewithDr.Han

nele

Nau

holz

(ETT

)

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ce–

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https://w

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Van Boeckel et al. www.pnas.org/cgi/content/short/1503141112 3 of 5

Table

S1.

Cont.

Country

Cattle,

mg/PCU

Chicke

ns,

mg/PCU

Pigs,

mg/PCU

Annual

sales

cattle,kg

Annual

sales

chicke

ns,kg

Annual

sales

pigs,kg

Totalan

nual

sales,kg

Yea

rDatasource

URL

Latvia

––

––

––

6,60

020

10EM

Awww.ema.eu

ropa.eu

/docs/en_G

B/docu

men

t_lib

rary/Rep

ort/201

3/10

/WC50

0152

311.pdf

Lithuan

ia–

––

––

–16

,000

2010

EMA

www.ema.eu

ropa.eu

/docs/en_G

B/docu

men

t_lib

rary/Rep

ort/201

3/10

/WC50

0152

311.pdf

Japan

––

–59

,490

89,782

506,54

965

5,82

0.50

2010

MAFF

††

www.m

aff.go.jp

/nva

l/iya

kutou/han

baidak

a/pdf/h22

han

baidak

abessatu.pdf

Korea

––

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,443

204,47

258

1,50

784

3,42

220

10QIA

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lib.qia.go.kr/Se

arch

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0953

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EC%

B6%

95%

EC%

82%

B0%

20%

ED%

95%

AD%

EC%

83%

9D%

EC%

A0%

9C%

20%

EB%

82%

B4%

EC%

84%

B1%

EA%

B7%

A0%

20%

EA%

B0%

90%

EC%

8B%

9C%

EC%

B2%

B4%

EA%

B3%

84%

20%

EA%

B5%

AC%

EC%

B6%

95Th

eNetherlands

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1,00

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174,70

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1675

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2002

346

New

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24,000

57,043

2010

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Norw

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Poland

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Portugal

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Spain

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Van Boeckel et al. www.pnas.org/cgi/content/short/1503141112 4 of 5

Antibioticsalesforuse

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hedataco

llectionsearch

procedure

invo

lved

both

contactingreleva

ntgove

rnmen

tministriesan

dag

encies

regardingav

ailable

datasources

andasystem

atic

search

ofex

istingpublished

studiesonPu

bMed

.In

theinitialstag

esofoursearch

forspecies-specific

sales/co

nsumptiondata,

weco

ntacted

theag

encies

listedas

datasources

forthe25

Europea

nco

untriespresentedin

theEu

ropea

nSu

rveilla

nce

ofVeterinaryAntimicrobialConsumptionreport

(4)as

wellas

allve

terinarymed

icines

authorities

listedbytheHea

dsofMed

icines

Agen

cies

web

site

(forfurther

inform

ation,seewww.hma.eu

/vdirectory).Allnational

reportslistedin

Annex

6oftheES

VACreport

wereaccessed

forspecies-specific

data(4).Fo

rtheremainingOEC

Dco

untriesnotlistedin

theES

VACreport,

asystem

atic

search

forthereleva

ntgove

rnmen

tministryorag

ency

was

perform

edonGoogle

usingthefollo

wingterm

sin

conjunctionwithaco

untrynam

e:“(M

inistryORAgen

cy)AND

(Agricu

lture

ORHea

lth),”

“VeterinaryMed

icines

(Directorate

OR

Authority),”

“Med

icines

Directorate,”

“Drug

Controlle

r,”“Fo

od

and

Drug

Administration,”

“VeterinaryAssociation,”

“Fo

od

Safety,”

and

“(A

ntimicrobialOR

Antibiotic)

(ConsumptionORSa

les)Su

rveilla

nce.”

Wethen

contacted

ministries/ag

encies

from

search

resultsan

dwould

either

receivedatadirectlyfrom

theag

ency/m

inistryco

ntacted

,beinform

edthat

dataarenotav

ailable,

orbedirectedto

another

ministry/ag

ency.A

limitationofthissearch

procedure

was

theuse

ofEn

glishsearch

term

swhen

man

yco

untries’ministries/ag

encies

listtheirnam

esin

nativelanguag

es.H

owev

er,thereleva

nt

agen

cies

forallOEC

Dco

untrieswereco

ntacted

regardingtheav

ailability

ofdata.

Thesearch

forex

istingpublished

studiesonPu

bMed

usedthefollo

wingterm

sin

conjunctionwithaco

untrynam

e:“Veterinary

(AntibioticORAntimicrobial)(ConsumptionORSa

les)”an

d“(A

ntibioticORAntimicrobial)(ConsumptionORSa

les)AND(FoodAnim

alsORLive

stock).”

ANSE

S,Agen

ceNational

deSé

curité

Sanitaire

(France);APV

MA,

Australia

nPe

sticides

andVeterinaryMed

icines

Authority

(Australia

);BelVet-SAC,B

elgianVeterinarySu

rveilla

nce

ofAntibacterial

Consumption(Belgium);CAHI,Can

adianAnim

alHea

lthInstitute

(Can

ada);D

ANMAP,

Dan

ishProgram

forsurveilla

nce

ofan

timicrobialco

nsumptionan

dresistan

cein

bacteriafrom

anim

als,

foodan

dhuman

s(D

enmark);EM

A(orES

VAC),Eu

ropea

nMed

icines

Agen

cy(orEu

ropea

nSu

rveilla

nce

of

VeterinaryAntimicrobialC

onsumption)(Eu

rope);E

TT,A

ssociationforAnim

alDisea

sePrev

ention(Finland);FD

A,U

SFo

odan

dDrugAdministration(U

nited

States

ofAmerica);M

AFF,M

inistryofAgricu

lture,Forestry

and

Fisheries(Jap

an);MARAN,M

onitoringofAntimicrobialR

esistance

andAntibioticUse

inAnim

alsin

theNetherlands(N

etherlands);M

PI,M

inistryforPrim

aryIndustries

(New

Zealan

d);QIA,A

nim

alan

dPlan

tQuaran

tine

Agen

cy(Korea);SVA(orSV

ARM),National

VeterinaryInstitute

(orSw

edishVeterinaryAntimicrobialR

esistance

Monitoring)(Sw

eden

);Sw

issM

edic,SwissAgen

cyforTh

erap

euticProducts(Switze

rlan

d);VMD,V

eterinary

Med

icines

Directorate

(United

Kingdom).

*Sales

dataforcattle

andshee

pwerepooledtogether––therefore,thisfigure

likelyrepresents

anove

restim

ationofuse

incattle.T

otala

nnual

salesdataincludes

salesforfood-producingan

imalsonly

(sales

for

use

inco

mpan

ionan

imalswereex

cluded

).†Use

dataweregiven

inmilligramsan

tibioticper

biomass(kilo

grams)

produced

per

year.In

thestudyfrom

which

this

datapointwas

extracted,electronic

drug

application

reco

rdsfrom

farm

ersfrom

75co

nve

ntional

pig

farm

swererevisedan

dch

ecke

dfortheirplausibility.Th

eregistereddrugam

ounts

wereve

rified

byco

mparingthefarm

ers’reco

rdswithve

terinarians’dispen

sary

reco

rds.

‡Sp

ecies-specific

annual

salesdatawereex

trap

olatedusingamodel

based

ondatafrom

Persoonset

al.(5),Callenset

al.(6),an

dPa

rdonet

al.(7).

§To

talan

nual

sales(inkilogramsactive

ingredient)

for20

10was

back-calculatedfrom

2011

ESVAC(Europea

nMed

icines

Agen

cy)databecau

se20

10datawerenotav

ailable.Th

iscalculationwas

perform

edby

dividingthe20

11ES

VACdatabythemed

ianoftheratioof20

11ES

VACdatato

2010

ESVACdataforallco

untrieswithdataav

ailable

for20

11[i.e.,1/med

ian(ESV

AC20

11/ESV

AC20

10)].

{ Totalan

nual

salesdataincludes

salesforfood-producingan

imalsonly

(sales

foruse

inco

mpan

ionan

imalswereex

cluded

).#Annual

salesdataforch

icke

nsincludes

only

broile

rs,breed

ingan

drearingbroile

rs,an

dlaye

rs––thisfigure

does

notincludean

yother

poultry

birds.

k Annual

salesdataforch

icke

nsincludes

only

broile

rs––thisfigure

does

notincludean

yother

poultry

birds.

**Datagiven

inmilligramsofan

timicrobialper

kilogram

liveweightoffoodan

imal

trea

tedan

dtherefore

iseq

uivalen

tto

milligramsper

PCU.

††Data

weredisag

gregated

into

thean

nual

salesvo

lume-specific

antibiotics.Th

erefore,totalsalesvo

lumeforallan

tibiotics

was

calculated

bysumming

across

allan

tibiotics.Dataon

antihelmintics

and

antiprotozo

alswas

also

colle

cted

butnotincluded

inthesefigures.

‡‡Dataweretran

slated

byChan

g-YongChoioftheResea

rchInstitute

forAgricu

lture

andLife

Science

atSe

oulNational

University.

§§Sp

ecies-specifican

nual

salesdatawerederived

from

use

dataco

llected

inaMARANsample

survey

.Totala

nnual

salesrefers

only

tocattle,p

oultry,a

ndpigs.Plea

senote

that

thedataforcattle

isthesum

ofuse

for“Cattle”

(29,93

0kg

)an

d“Vea

lCalve

san

dYoungBee

f”(163

,990

kg).

{{MPI

presentedsalesdataforpoultry

andpigspooledtogether.Th

esedatapoints

weredisag

gregated

into

species-specificco

nstan

tsbyassumingaco

nstan

tco

nsumptionper

PCU

togen

eratespecies-specific

coefficien

tsusedin

thepriors.

##Dataarepresentedas

milligramsper

kilogram

slau

ghteredpig.T

oco

nve

rtunitsto

mg/PCUliv

ean

imal,thisdatapointwas

multiplie

dby1/0.7where0.7represents

theratioofliv

eweightto

carcassweightfor

pigs.In

other

words,1kg

ofliv

epig

was

raised

forev

ery0.7kg

ofpig

mea

tproduced.

kkAnnual

salesdataforch

icke

nsincludes

only

broile

rs––this

figure

does

notincludean

yother

poultry

birds.“In

2010

,thetotalsaleswhereGallusgalluswas

given

asspecieswas

44.5

kgofwhich78

%were

aminopen

icillins”

(SVARM

2010

).**

*Totalan

nual

salesdataincludesalesforfood-producingan

imalsonly

(sales

foruse

inco

mpan

ionan

imalswereex

cluded

).†††VMD

presentedmilligramsper

PCU

dataforpigsan

dpoultry

pooledtogether.Annual

salesdatabyspecieswas

presentedin

thefollo

wingcategories:“CattleOnly

Products,”“PigOnly

Products,”“Po

ultry

Only

Products,”“Pigan

dPo

ultry

Combined

Products,”an

d“MultiS

peciesProductsin

FoodAnim

alsOnly.”

“Po

ultry

Only”an

d“Pigan

dPo

ultry

Combined

”product

salesweresummed

toyieldoursalesestimate

forpoultry.W

eperform

edthean

alogousprocedure

toderiveoursalesestimateforpigs.W

eex

cluded

salesof“MultiS

peciesProductsin

FoodAnim

als”

inan

attemptto

preve

ntove

restim

ationofan

tibioticuse.

Species-specific

consumptionwas

derived

from

thisdatabyassumingtheco

efficien

tsforco

nsumptionper

PCU

wereiden

tical.

‡‡‡To

talan

nual

salesdataincludes

salesforfood-producingan

imalsonly

(sales

foruse

inco

mpan

ionan

imalswereex

cluded

).Th

eFD

Apresented20

11salesdatafordomesticco

nsumptiondisag

gregated

by

antimicrobialclass.Th

erefore,ourestimateisthesum

ofsalesofallan

timicrobialclassesfordomesticco

nsumptiononly.

§§§Apleyet

al.(3)

presenteddataas

“National

Estimates

ofTo

talK

ilogramsofSw

ineIn-Fee

dAntimicrobialsforAllProductionCyclesin

aYea

rbyAntimicrobialan

dRea

son.”

Therefore,ourestimateisthesum

across

allan

timicrobials

andreasons.

1.Trau

fflerM,Griesbacher

A,Fu

chsK,KöferJ(201

4)Antimicrobialdruguse

inAustrian

pig

farm

s:Plau

sibility

checkofelectronic

on-farm

reco

rdsan

destimationofco

nsumption.VeterinaryRecord

175(16

):40

2.2.

BondtN,JensenVF,

Puister-JansenLF

,va

nGeijlswijk

IM(201

3)Comparingan

timicrobialex

posure

based

onsalesdata.

Prev

Vet

Med

108(1):10–

20.

3.ApleyMD,Bush

EJ,MorrisonRB,Singer

RS,

SnelsonH

(201

2)Use

estimates

ofin-fee

dan

timicrobialsin

swineproductionin

theUnited

States.Fo

odbornePa

thogDis9(3):272

–27

9.4.

Europea

nMed

icines

Agen

cy(201

3)Sa

lesofve

terinaryan

timicrobialag

ents

in25

EU/EEA

countriesin

2011

-Th

irdES

VACRep

ort.Ava

ilable

at:www.ema.eu

ropa.eu

/ema/index

.jsp?curl=pag

es/reg

ulation/docu

men

t_listing/docu

men

t_listing_

0003

02.jsp.AccessedMarch

10,20

15.

5.Pe

rsoonsD,et

al.(201

2)Antimicrobialuse

inBelgianbroile

rproduction.Prev

Vet

Med

105(4):320

–32

5.6.

CallensB,et

al.(201

2)Prophylactican

dmetap

hylactican

timicrobialuse

inBelgianfatten

ingpig

herds.Prev

Vet

Med

106(1):53–

62.

7.Pa

rdonB,et

al.(201

2)Prospective

studyonquan

titative

andqualitativean

timicrobialan

dan

ti-inflam

matory

druguse

inwhiteve

alcalves.JAntimicrobChem

other

67(4):10

27–10

38.

Van Boeckel et al. www.pnas.org/cgi/content/short/1503141112 5 of 5