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Institute of Medicine (US). Improving Food Safety Through a One Health Approach: Workshop Summary. Washington (DC): National Academies Press (US); 2012.



and 4.

During the past decade, fruits and vegetables have become leading vehicles of food-borne illnesses. Furthermore, many plant-based foods and ingredients, not previously considered a risk, have been associated with food-borne disease outbreaks. Most of the pathogens that have been identified as causative agents in these illnesses or outbreaks are enteric zoonotic pathogens that are typically associated with animal hosts. Transmission of zoonotic pathogens from animals to plant systems occurs by a variety of routes, but the initial contributing factor is the discharge of animal manure into the environment. Using a “One Health” approach that focuses on animal, human, and environmental health concurrently can provide practical and effective interventions for reducing the incidence of such outbreaks. This paper addresses this concept by providing recent food-borne disease outbreak data related to fruits and vegetables, delineating findings regarding the prevalence of pathogens in animal manures and describing the vehicles that transmit pathogens from manure to produce fields, and discussing the merits of reducing pathogen transmission through interventions that would not adversely affect the health of the environment or animals.

Outbreaks and Illnesses Associated with Fresh Fruits and Vegetables

Food-borne illnesses have been a persistent challenge to public health and are now being detected with greater frequency largely because of enhanced surveillance systems that have been implemented in many countries. These enhanced surveillance systems have during the past decade revealed that the proportion of total outbreaks attributed to produce is significant (Lynch et al., 2009) but varies with the country. For example, only 4 percent of all food-borne outbreaks reported in Australia from 2001 to 2005 were attributed to fresh produce (Kirk et al., 2008); similarly, in Canada, between 1976 and 2005, 3.7 percent of 5,745 outbreaks with a known vehicle of transmission were attributed to produce (Ravel et al., 2009). However, in contrast, data from the Centers for Disease Control and Prevention (CDC) identified produce as either the first or second leading vehicle in food-borne disease outbreaks attributed to a single commodity within the United States for the period 2006-2008 (Table A3-1). Furthermore, outbreak surveillance data of produce items compiled by the CDC during the period 2000-2009 revealed that leafy greens were the most common item associated with food-borne disease, followed by tomatoes and cantaloupes (Table A3-2). Moreover, attribution risk rankings of fresh produce–associated outbreaks in the United States identified enterohemorrhagic Escherichia coli in leafy greens as the leading pathogen-produce vehicle combination, followed by Salmonella spp. in tomatoes, and Salmonella spp. in leafy greens (Anderson et al., 2011). Further differentiation of vehicles of produce-associated outbreaks that occurred in the United States during the period of 1998-2008 revealed that fresh-cut produce accounted for 56 percent, 36 percent, and 17 percent of the outbreaks attributed to leafy greens, tomatoes, and melons, respectively (Sneed, 2010).

An evaluation of selected produce-associated outbreaks that occurred during the past 5 years revealed several common features (Table A3-3). These outbreaks often were multistate or multinational in nature and reflect the large areas to which the foods are distributed. With imports accounting for nearly 39 percent of fresh fruits and 14 percent of fresh vegetables in 2005 (Johnson, 2012), improved sampling and pathogen testing of produce at the borders of the United States offers one barrier for reducing the likelihood of contaminated produce from entering the retail sector. However, better implementation both domestically and abroad of best food safety practices for producing and processing fruits and vegetables would have even more impact on reducing pathogen contamination and the likelihood of produce-borne illnesses. This approach would address a significant contributing factor associated with several recent produce outbreaks, which is that contamination occurs on the farm where production and processing can occur. For example, in a multistate outbreak of listeriosis in 2011 that resulted in 34 deaths and was the most deadly food-borne outbreak in the United States since 1924, four outbreak-associated strains of Listeria monocytogenes were traced back to whole cantaloupes and packing equipment on Jensen Farms in Colorado (CDC, 2011c). In another 2011 outbreak, fenugreek seeds that were likely contaminated with fecal matter led to the largest outbreak in the number of cases of hemolytic uremic syndrome (22.3 percent of 4,075 total cases) ever reported in the world (WHO, 2011).

Surveillance of Pathogens in Retail Produce

A number of studies have been conducted to determine the prevalence of enteric pathogens on fruits and vegetables, and the results varied with respect to the country of origin and the target pathogen. For Salmonella, there was for most developed countries a very low prevalence in cabbage, lettuce, and mixed salads, whereas higher prevalences were observed for developing countries where agricultural production and hygienic practices were of a lower level of sanitation (Table A3-4). The presence of helminth and protozoan parasites in leafy greens (Table A3-5), however, likely reflects the ability of these pathogens to resist standard chlorine-based wastewater treatments (Erickson and Ortega, 2006; Graczyk et al., 2007). The relatively low occurrence of pathogen contamination on produce makes it inherently difficult to rank the degree of risk associated with the various sources of contamination by which enteric pathogens are transmitted from animals to plant production environments.

Pathogens in Manures from Domesticated Animal

A large number of zoonotic pathogens reside and grow in the gastrointestinal tract of domesticated animals (poultry, cattle, swine, sheep, and goats) and are shed in their feces asymptomatically, often in very large numbers. Those enteric pathogens associated with the largest number of food-borne disease outbreaks and illnesses include Campylobacter jejuni, Salmonella spp., Shiga toxin–producing enterohemorrhagic Escherichia coli (STEC), and Cryptosporidium parvum. Many studies have been conducted to determine the prevalence of these pathogens in the feces of domesticated animals. A selection of results of recent studies are shown in Tables A3-6 to A3-9 to illustrate the range of pathogen prevalences and cell numbers that may occur within animal wastes and between and within different groups of animals. For Cryptosporidium, not all species are pathogenic for humans. For example, currently there are at least 16 recognized species of Cryptosporidium, of which two most affect humans, C. hominis and C. parvum (Jagai et al., 2010). Therefore, when results do not differentiate species of Cryptosporidium, the potential risk of those manures to human health may be overestimated.

Management of Wastes from Domesticated Animals

Globally, food animal production has increased more than fivefold in the past 50 years due in large part to the adoption of the industrialized concentrated animal production model. With multinational companies expanding their operations overseas, estimates indicate that concentrated animal feeding operations (CAFOs) provide 74 percent of poultry, 50 percent of pork, and 43 percent of beef produced worldwide (Halweil and Nierenberg, 2004). Accompanying this expansion in production has been the challenge of managing the massive quantities of animal wastes that are generated in one location. For example, in China, animal waste was estimated to be 3.2 billion tons, which was three times the amount of industrial solid waste produced in that same year (Wang et al., 2005). Within the United States, it has also been reported that confined food animals produce approximately 335 million dry tons of waste per year, which is more than 40 times the amount of human biosolids waste generated from wastewater treatment plants (Graham and Nachman, 2010). The vast majority of this animal waste is applied to land without any required treatment for reduction of pathogens as is required for human biosolids (EPA, 2004).

There are two primary forms of animal wastes generated at CAFOs. In the case of broiler units, solid waste is generated either as single-use, partial reuse, or multiuse litter (Bolan et al., 2010). In confined swine and cattle operations, water is used to flush waste from the floors where the animals are housed, and the liquid slurry is channeled into large ponds for storage (Graham and Nachman, 2010). The application of animal wastes to land is largely based on agronomic requirements, geography, and commodity choices. For example, corn receives more than half of the land-applied manure, of which most of the manure is from dairy and hog stock because of the use of corn as a major feed crop for dairy and hog operations and the high growth nutrient requirement of corn for nitrogen-rich manure. Hay and grasses are the second largest of the crops fertilized by manure, which is mostly from hog, broiler, and dairy producers (MacDonald et al., 2009). Poultry litter, on the other hand, is frequently used as a fertilizer for cotton, peanuts, and fresh produce (Boyhan and Hill, 2008).

Direct Transmission of Enteric Pathogens from Animal Wastes to Produce Fields

Animal manures applied to fields to be used for fruit and vegetable production have the potential to be a direct source of enteric pathogens if there has not been sufficient holding time between planting and harvest. The U.S. Department of Agriculture (USDA) National Organic Program permits the incorporation of raw manure into soil 120 days before harvest if the food crop has direct contact with the soil; however, only 90 days prior to harvest is required if crops have no contact with the soil (7 Code of Federal Regulations [CFR] 205.203). In contrast, more stringent requirements have been set by the Leafy Greens Marketing Agreement in which 1 year between application of raw manure and harvest of the crop is advocated (LGMA, 2012). As part of the Food Safety Modernization Act, it is anticipated that the Food and Drug Administration will include in its produce rule a required time interval between manure application to fields and either the planting or harvest of crops that would be consumed raw.

Transmission via Runoff of Enteric Pathogens from Animal Waste–Applied Lands to Produce Fields

One of the routes by which enteric pathogens may be indirectly transferred to produce fields from domesticated animal waste deposited or stored on land adjacent to produce fields is via storm runoff. Many studies have revealed that enteric pathogens can move both horizontally and vertically to contaminate land, surface waters, and ground waters adjacent to produce fields (Cooley et al., 2007; Forslund et al., 2011). In these situations, the risk of pathogen contamination of produce will be dependent on a number of factors, including the attachment strength of the pathogen to soil particles, the interval between the manure application and the precipitation events, the kinetic energy of the rainfall, the topographical slope that affects the direction and velocity of water flow, and the density of vegetation between the waste source and the destination site (Ferguson et al., 2007; Hodgon et al., 2009; Jamieson et al., 2002; Lewis et al., 2010; Mishra et al., 2008; Saini et al., 2003; Tyrrel and Quinton, 2003). In addition, the physical state of the waste will also affect the direction of movement of the pathogens with greater percolation occurring by a liquid slurry source and greater overland transport for a solid manure source (Forslund et al., 2011; Semenov et al., 2009).

Transmission of Enteric Pathogens from Waste-Contaminated Water Sources to Produce Fields

Storm runoff carrying pathogens from animal wastes does not necessarily have to pass through agricultural produce fields to be a source of contamination. Collection in surface waters and subsequent use of that water to irrigate produce crops is another means to disseminate the pathogens. Surveys of environmental water sources for pathogen contamination have revealed significant contamination with Salmonella spp., STEC, and protozoan parasites (Table A3-10); however, contamination appears to be sporadic and is often associated with recent rain events and seasonality (Gaertner et al., 2009; Haley et al., 2009). Enhanced survival of pathogens in the sediment (Chandran et al., 2011; Garzio-Hadzick et al., 2010) and resuspension of the organisms into the water column may also perpetuate the risk. Contamination of surface waters, moreover, has been associated with the concentration of food animals raised in the area (Cooley et al., 2007; Johnson et al., 2003; Tserendorj et al., 2011; Wilkes et al., 2011). Salmonella and Cryptosporidium contamination of watersheds not impacted by human or domesticated animal production has been observed (Edge et al., 2012; Patchanee et al., 2010), which suggests that there is a level of natural occurrence of these pathogens from wildlife sources.

Several epidemiological studies lend support to the role that contaminated irrigation water serves as a transmission vehicle of enteric pathogens to fresh produce. In 2002 and 2005, two outbreaks of S. Newport infection in the United States were associated with eating tomatoes and the outbreak strain was isolated from the pond water used to irrigate the tomato fields (Greene et al., 2008). Irrigation of fields with contaminated irrigation waters was also indicated as a possible source of contamination of imported cantaloupe associated with an outbreak of S. Poona infection in the United States in consecutive years during 2000-2002 (CDC, 2002). Given the often sporadic nature of contamination of irrigation water, these documented cases linking irrigation water to an outbreak may represent only a small fraction of the contamination events that actually occur. Worldwide, it is estimated that 17 percent of the world's cropland (1.4 billion hectares) is irrigated and, of that, 20 million hectares are irrigated with untreated wastewater (Jimenez et al., 2010). In the United States and the United Kingdom, extensive irrigation of fresh produce crops occurs and, of the acreage irrigated, 48 percent and 78 percent, respectively, are derived from non-groundwater sources (Knox et al., 2011; USDA NASS, 2009), which are subject to intermittent inputs of pathogens from animal husbandry operations.

Contribution of Bioaerosols to Dissemination of Enteric Pathogens from Animal Production Operations to Produce Fields

Aerosolization of microbial pathogens is an inevitable consequence associated with animal production operations as well as the handling and disposition of animal manure. However, estimating the impact of bioaerosol dispersal on pathogen dissemination has been hampered by the notable absence of standardized and validated methods for enumeration of various types of microorganisms in outdoor bioaerosols. Hence, there has been a wide range of prevalence and cell number values reported across very diverse types of animal operations and landscapes (Millner, 2009).

Studies addressing bioaerosol levels in outdoor air generally address fecal indicator organisms because they are more abundant and easily identified in the aerosols, although it is acknowledged that they may behave differently than the pathogens. The general trend that has been observed is decreasing airborne microorganism concentrations as the distance from the source increases with relative humidity, temperature, and solar irradiance being major factors affecting viability (Dungan, 2010). Other pertinent observations made in studies addressing the levels of the indicator organism, E. coli, in aerosols of poultry houses are that the levels of airborne bacteria are intricately linked to the levels of those bacteria in the litter (Chinivasagam et al., 2009; Smith et al., 2012) and the type of ventilation system affects the distance that E. coli is disseminated, with E. coli traversing 11.1 and 7.5 m downwind from houses using tunnel and conventional fans, respectively (Smith et al., 2012).

Limited studies have been conducted addressing bioaerosol transport following land application of animal manures in contrast to those addressing the application of municipal wastes (Pillai and Ricke, 2002). Although there may be some similar behavior between these two sources, there could be differences given that they vary in their organic matter content that can provide differences in the degree of protection against ultraviolet radiation and drying (Dungan, 2010). In one of the few studies addressing land application of cattle and swine slurry and the method used to disperse the wastes, total bacterial counts in the air were greater at greater distances from spray guns that discharged the slurry upward into the air compared to tank spreading that sprayed the slurries closer to the ground (Boutin et al., 1988). In another study in which swine manure was applied through a center pivot irrigation system, coliform concentrations decreased to near background concentrations at 23 m downwind (Kim et al., 2008). Wind speed and topography, however, are likely to also factor into the distances traversed by pathogens and, hence, safe distances between produce fields and animal production activities will likely be site specific.

Wildlife as a Vehicle to Transmit Pathogens from Domesticated Animal Waste to Produce Fields

The recent focus on wildlife as a potential source of pathogen contamination of produce fields was driven by the isolation of E. coli O157:H7 from feral swine that occupied areas near spinach fields and cattle farms in California following the 2006 spinach outbreak (Jay et al., 2007). More recently, Campylobacter jejuni was isolated both from Sandhill crane feces and raw peas and several of the isolates had pulsed-field gel electrophoresis (PFGE) patterns indistinguishable from clinical samples obtained during a C. jejuni gastroenteritis outbreak that occurred in Alaska in 2008 (Gardner et al., 2011). Attention was again focused on wildlife as a potential source of contamination when E. coli O157:H7 isolated from deer feces was determined to have an identical PFGE pattern as the isolates responsible for 15 people who were ill from eating contaminated fresh strawberries in Oregon in 2011 (IEH Laboratories & Consulting Group, 2011). Given that the same strain was also isolated from soil raises the question as to whether the deer were actually the source of the outbreak or were infected when they ate the contaminated strawberries. Most evidence indicating that wildlife is a potential source of food-borne contamination is from the isolation of clinically relevant pathogens from the animal's feces. In one example, Renter et al. (2006) isolated from deer fecal samples four Salmonella serovars (Litchfield, Dessau, Infantis, and Enteritidis) known to be pathogenic to humans and animals. In another example, subtyping of STEC isolates from wildlife meat in Germany identified virulence genes associated with severe clinical outcome (stx2, stx2d, and eae) in 46 of the 140 STEC samples (Miko et al., 2009). More definitive proof that specific types of wildlife could be transmission vectors of pathogens from domesticated animal facilities was obtained with a study of European starlings (Williams et al., 2011). In that study, distinct molecular types of E. coli O157:H7 were similar in starlings and cattle on different farms, and these birds were capable of shedding the pathogen in their feces for more than 3 days (Kauffman and LeJeune, 2011). Hence, it is reasonable to assume that European starlings could serve as a vector of pathogens from cattle and dairy farms to produce fields.

In response to the limited studies linking wildlife to produce contamination, processors and buyers have become overreactive in many cases in requiring the absence of many types of wildlife from farms. To illustrate this trend, the percentage of growers that reported being told by their processors or buyers that feral pigs, deer, birds, rodents, and amphibians were a significant risk was 19, 28, 44, 47, and 28 percent, respectively (Lowell et al., 2010). Several studies, however, have revealed that some groups of animals have a very low prevalence of contamination with relevant human enteric pathogens (Table A3-11). It is likely that all animal groups have the potential to be contaminated with a food-borne pathogen, but whether they are significant harbingers of human enteric pathogens is likely dependent on their access to animal husbandry sites as well as on their social behavior (i.e., existence of a social group and its size). This would also be the case with insects. For example, filth flies collected in leafy green fields were believed to have originated from nearby rangelands that contained fresh cattle manure (Talley et al., 2009).

Persistence of Pathogens on Produce in Fields Requires a Systems Approach to Prevent and Monitor Pathogen Introduction

Many field studies have revealed the persistence of human enteric pathogens, albeit typically at low levels, in a number of different vegetables contaminated at various points during their cultivation (Erickson et al., 2010; Gutiérrez-Rodriguez et al., 2011; Islam et al., 2004a, 2004b, 2004c, 2005; Moyne et al., 2011). This is noteworthy because chemical disinfectants typically used during minimal processing of fresh produce are not fully effective in eliminating pathogen contamination (Doyle and Erickson, 2008). Hence, it is paramount to prevent the introduction of these pathogens into produce fields. The primary approach currently used to reduce the risk of pathogen contamination in fields is the application of good agricultural practices (GAPs). To prevent the introduction of pathogens through nontraditional vehicles (storm runoff, intrusions by pathogen-carrying wildlife) will require the development of novel approaches in addition to GAPs. Given that the environment surrounding the produce field would likely be impacted by these pathogen control practices, it is important to implement a systems approach and consider all ramifications to the adoption of any intervention practices. It is also important to be cognizant that storm runoff and fecal deposits from wildlife may only contaminate the plants at discrete locations within a field. The ability to detect this contamination by current sampling plans that rely on uniform contamination is therefore limited and efforts are needed to develop new monitoring systems that can detect contamination when such pathogen introductions occur.

Concluding Comments

Vegetables, fruits, and a variety of plant foods and ingredients are now recognized as major vehicles of food-borne disease outbreaks, and a primary source of pathogen contamination of this commodity group is animal manure. There are several routes by which pathogens can be transmitted from animal production sites to produce fields. The vehicles likely presenting the greatest risk are manure-contaminated soil amendments and irrigation water. Wildlife, insects, and vermin, however, may also serve as intermediate vectors of pathogens from animal wastes to plants in the field. The multifaceted routes by which pathogens may be transmitted to produce crops illuminates the value of a One Health approach to minimize pathogen contamination in the production environment while ensuring that adverse effects to the environment be minimized.


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Center for Food Safety, University of Georgia, 1109 Experiment Street, Griffin, GA 30223, USA.


TABLE A3-1Food-Borne Disease Outbreaks Attributed to a Single Commodity by Leading Food Vehicles, 2006-2008

YearRankFood vehicleOutbreaks (%)

SOURCE: CDC (2009a, 2010c, 2011e).

TABLE A3-2Number of Outbreaks (illnesses) Reported Between 2000 and 2009 in the United States That Were Associated with Selected Fresh Produce Items as a Function of their Etiologya,b

Produce itemBacterial agentsViral agentsOther agentsTotal
Salmonella spp.Escherichia coli O157:H7cShigella spp.Campylobacter jejuniOtherdNorovirusHepatitis AProtozoan parasitesUnknown
Cabbage1 (8)1 (41)2 (68)3 (78)1(16)3 (16)11 (227)
Lettuce10 (456)14 (364)1 (4)2 (16)3 (114)39 (999)1 (22)10 (60)80 (2,035)
Spinach2 (223)1 (6)3 (9)6 (238)
Sprouts12 (441)4 (46)1 (20)1 (2)18 (509)
Herbs3 (70)1 (592)1 (20)5 (682)
Leafy green salads23 (997)15 (280)7 (190)7 (42)10 (145)257 (8,520)3 (47)114 (1,419)436 (11,640)
Coleslaw1 (26)4 (22)20 (676)1 (8)1 (11)27 (743)
Peppers4 (1,643)1 (5)2 (17)1 (2)8 (1,667)
Tomatoes25 (1,867)1 (886)1 (13)2 (10)15 (399)1 (23)45 (3,198)
Cantaloupe/melons19 (1,180)1 (5)1 (56)1 (55)12 (502)6 (79)40 (1,877)

Data compiled from the CDC website on outbreak surveillance (http://www​​/surveillance_data.html).


Outbreaks/illnesses attributed to each pathogen group includes both confirmed and suspected.


Includes other Shiga toxin–producing Escherichia coli.


Includes where multiple bacterial pathogens have been found and cases involving the agents of Bacillus cereus, Clostridium botulinum, C. perfringens, Listeria monocytogenes, and Staphylococcus aureus.


TABLE A3-3Selected Food-Borne Disease Outbreaks Attributed to Produce During the Period of 2006-2011

YearPathogenNumber of casesCountry of originAffected regionsImplicated foodReference
2007Salmonella Weltevreden45Italy, seed originNorway, Denmark, FinlandAlfalfa sproutsEmberland et al., 2007
2009Salmonella Saintpaul228Domestic, seed companyU.S., multistateAlfalfa sproutsCDC, 2009b
2010S. Newport44Domestic, processorU.S., multistateAlfalfa sproutsCDC, 2010a
2010Salmonella I 4,[5],12:i:-112DomesticU.S., multistateAlfalfa sproutsCDC, 2011a
2011E. coli O104:H43,911EgyptMultinationalFenugreek sproutsEFSA, 2011
2007S. Senftenberg51IsraelU.K., U.S., Denmark, NetherlandsBasilElviss et al., 2009
2007S. Senftenberg74IsraelU.K., Denmark, Netherlands, U.S.Basil, freshPezzoli et al., 2008
2006S. Saintpaul36DomesticAustralia, multijurisdictionCantaloupeMunnoch et al., 2009
2008S. Litchfield51HondurasU.S., multistateCantaloupeCDC, 2008a
2011S. Panama20GuatemalaU.S., multistateCantaloupeCDC, 2011b
2011Listeria monocytogenes146DomesticU.S., multistateCantaloupeCDC, 2011c
2006Clostridium botulinum4DomesticU.S., GeorgiaCarrot juiceCDC, 2006a
2006Yersinia pseudotuberculosis427Domestic, traced to vegetable distributorFinlandCarrots, gratedRimhanen-Finne et al., 2009
2006Norovirus43ChinaSwedenFrozen raspberriesHjertqvist et al., 2006
2006E. coli O157:H771Not knownU.S., multistateLettuceFDA, 2006
2006E. coli O157:H781DomesticU.S., multistateLettuceFDA, 2007
2011E. coli O157:H760DomesticU.S., multistateLettuce, romaineCDC, 2011c
2008Salmonella Newport and Reading77 (Newport)
30 (Reading)
DomesticFinlandLettuceLienemann et al., 2011
2010Norovirus and E. coli ETEC264FranceDenmark, NorwayLettuce, lollo biondo typeEthelberg et al., 2010
2010E. coli O14533Domestic, processorU.S., multistateLettuce, shredded romaineCDC, 2010b
2007E. coli O157:H-, PT850Netherlands, processing plantNetherlands, IcelandLettuce, shredded, prepackedFriesema et al., 2007
2008Cryptosporidium parvum21ItalySwedenParsleyInsulander et al., 2008
2008S. Saintpaul1,442MexicoU.S., CanadaPeppers (jalapeño and Serrano), tomatoesCDC, 2008b
2007Shigella sonnei227ThailandDenmark, AustraliaRaw baby cornLewis et al., 2009
2006E. coli O157:H7204U.S.U.S., CanadaSpinachCalvin, 2007
2006S. Typhimurium183Not knownU.S, multistateTomatoesCDC, 2006b


TABLE A3-4Prevalence of Salmonella in Lettuce, Cabbage, and Mixed Salads Throughout the World (2001-2011)

Produce itemCountrySampling siteNumber positive/number sampledPrevalence (%)Reference
CabbageIndiaFields4/3312.1Rai and Tripathi, 2007
IndiaStreet vendors2/825.0Viswanathan and Kaur, 2001
IrelandSupermarkets0/40McMahon and Wilson, 2001
MexicoSupply station1/1001.0Quiroz-Santiago et al., 2009
U.S.Packing sheds, southern U.S.0/1090Johnston et al., 2006
U.S.Farms, organic, conventional, semiorganic0/2910Mukherjee et al., 2004, 2006
LettuceCanadaRetail distribution centers/farmer's markets1/5300.2Arthur et al., 2007
IrelandSupermarkets0/80McMahon and Wilson, 2001
ItalyProducers2/623.2De Giusti et al., 2010
KoreaDepartment store, supermarket, restaurant1/303.3Seo et al., 2010
MexicoMarkets, supermarkets10/7513Castañeda-Ramírez et al., 2011
NigeriaFields0/550Okago et al., 2003
NorwayProducers, organic0/1790Loncarevic et al., 2005
SpainFarms, organic, conventional0/720Oliveira et al., 2010
SpainRetail establishments1/293.4Abadias et al., 2008
U.S.Farms, organic, conventional, semiorganic0/2610Mukherjee et al., 2004, 2006
U.S.Supermarkets, farmer's markets0/100Thunberg et al., 2002
U.S.Markets and wholesale distribution centers2/5,4530.04USDA, 2007, 2008, 2009
Mixed salads/vegetablesBrazilRetailers1/214.8Fröder et al., 2007
CyprusProduction sites, retail outlets6/2942.0Eleftheriadou et al., 2002
KoreaDepartment store, supermarket, restaurant1/1290.8Seo et al., 2010
MalaysiaWet markets40/11235.7Salleh et al., 2003
U.K.Catering, retail outlets5/10,0020.05Sagoo et al., 2001, 2003a, 2003b


TABLE A3-5Prevalence of Helminth and Protozoan Parasites in Leafy Greens from 2005-2010

Produce itemCountrySampling targetAscaris spp.Cryptosporidium spp.Giardia spp.Taenia spp.Toxocara spp.Reference
Number positive/number of samples%Number positive/number of samples%Number positive/number of samples%Number positive/number of samples%Number positive/number of samples%
CabbageGhanaRetail fruit, vegetable markets33/6055.0Amoah et al., 2006
SpainFields2/633.32/633.3Amorós et al., 2010
TurkeyWholesale markets0/140Kozan et al., 2005
LettuceGhanaRetail fruit, vegetable markets36/6060.0Amoah et al., 2006
LibyaWholesale, retail markets26/2796.31/273.79/2733.323/2785.2Abougrain et al., 2010
SpainFields10/1376.98/1361.5Amorós et al., 2010
TurkeyField6/1540.03/1520.0Erdoğrul and Şener, 2005
TurkeyWholesale markets2/355.72/355.7Kozan et al., 2005


TABLE A3-6Prevalence and Cell Numbers of Salmonella spp. in Manures from Domesticated Animals

PathogenSourceLocationPrevalence (% of total samples positive)
(average cell numbers in positive samples)
SalmonellaCattle fecesU.K., England and Wales7.7% of 810 samples were positive
(4.6 log CFU/g for positive samples)
Hutchison et al., 2004
Salmonella spp.Cattle fecesBritain7.7-10.0% samples were positive;
3.3-3.4 log CFU/g in positive samples
Hutchison et al., 2005
SalmonellaCattle, beef and dairy farms, rectal fecalU.S., TN, NC, AL, WA, CA0.2% of 480 beef cattle samples were positive;
0.4% of 480 dairy cattle samples were positive;
8.5% of 18 beef farms had at least one positive sample;
17.9% of 18 dairy farms had at least one positive sample
Rodriguez et al., 2006
Salmonella spp.Cattle, beef, abattoir, fecesIreland, northern3% of 200 samples were positiveMadden et al., 2007
SalmonellaCattle, dairy fecesNew Zealand0% of 155 samples positiveMoriarty et al., 2008
S. entericaCattle, feedlot fecesAustralia6% of 32 samples were positiveKlein et al., 2010
S. entericaCattle fecesCalifornia0.13% of 795 samples were positiveGorski et al., 2011
SalmonellaChicken fecesU.K., England and Wales17.9% of 67 samples were positive
(3.7 log CFU/g for positive samples)
Hutchison et al., 2004
Salmonella spp.Poultry fecesBritain11.5-17.9% samples were positive
(2.3-3.6 log CFU/g in positive samples)
Hutchison et al., 2005
SalmonellaTurkey feces and litterU.S., NC70% of 48 composite fecal samples were positive;
79% of 48 composite litter samples positive
(<1 – 5.3 log MPN/g in positive samples)
Santos et al., 2005
SalmonellaChicken litterLebanon0% of 24 samples were positiveOmeira et al., 2006
SalmonellaChicken/turkey farms, rectal fecesU.S., TN, NC, AL, WA, CA0.2% of 480 samples were positive;
16.2% of 18 farms had at least one positive sample
Rodriguez et al., 2006
SalmonellaChickens, laying hens fecesBelgium0% of fecal samples collected on farm were positiveVan Hoorebeke et al., 2009
SalmonellaChicken fecesFrance8.6% of 370 flocks had at least one positive sample;
most prevalent serovar was S. hadar followed by S. anatum and S. mgandaka
Le Bouquin et al., 2010
SalmonellaPig fecesU.K., England and Wales7.9% of 126 samples were positive
(4.0 log CFU/g for positive samples)
Hutchison et al., 2004
Salmonella spp.Pig fecesBritain5.2-7.9% samples were positive
(2.8 log CFU/g in positive samples)
Hutchison et al., 2005
SalmonellaSwine farms, rectal fecesU.S., TN, NC, AL, WA, CA6.0% of 480 samples were positive;
57.3% of 18 farms had at least one positive sample
Rodriguez et al., 2006
SalmonellaSwine, finishing pigs, fecesItaly, Piedmont9% of 75 fecal samples were positiveLomonaco et al., 2009
S. entericaSwine fecesJapan3.1% of 169 samples were positive
22.0% of farms had positive samples
Kishima et al., 2008
SalmonellaSheep fecesU.K., England and Wales8.3% of 24 samples were positive
(3.0 log CFU/g in positive samples)
Hutchison et al., 2004
Salmonella spp.Sheep fecesBritain8.3-11.1% samples were positive
(2.8-3.8 log CFU/g in positive samples)
Hutchison et al., 2005

NOTE: CFU, colony forming unit; MPN, most probable number.

TABLE A3-7Prevalence and Cell Numbers of Campylobacter spp. in Manures from Domesticated Animals

PathogenSourceLocationPrevalence (% of total samples positive)
(average cell numbers in positive samples)
CampylobacterCattle fecesU.K., England and Wales12.8% of 810 samples were positive
(3.9 log CFU/g in positive samples)
Hutchison et al., 2004
Campylobacter spp.Cattle fecesBritain9.8-12.8% samples were positive
(2.5-2.7 log CFU/g in positive samples)
Hutchison et al., 2005
CampylobacterCattle, dairy fecesNew Zealand64% of 155 samples were positive
(5.6 log CFU/g in positive samples)
Moriarty et al., 2008
Campylobacter spp.Cattle, beef, abattoir, fecesIreland, northern24.8% of 220 samples were positiveMadden et al., 2007
C. jejuniCattle, feedlot fecesAustralia94% of 32 samples were positiveKlein et al., 2010
CampylobacterChicken fecesU.K., England and Wales19.4% of 67 samples were positive
(3.6 log CFU/g for positive samples)
Hutchison et al., 2004
Campylobacter spp.Poultry fecesBritain7.7-19.4% samples were positive;
(2.4-2.8 log CFU/g in positive samples)
Hutchison et al., 2005
CampylobacterBroilers cecaSweden47% of 540 ceca samples were positive;
proportion of positive samples ranged from 10 to 100% within a flock.
(1.7 to 8.6 log CFU/g in positive samples)
Hansson et al., 2010
CampylobacterDuck, Mallard, fecesU.K.93.3-100.0% of two groups of 60 farmed ducks tested at 28-56 days of age were positiveColles et al., 2011
CampylobacterPig fecesU.K., England and Wales13.5% of 126 samples were positive
(3.3 log CFU/g for positive samples)
Hutchison et al., 2004
Campylobacter spp.Pig fecesBritain10.3-13.5% samples were positive
(2.5-3.2 log CFU/g in positive samples)
Hutchison et al., 2005
CampylobacterSheep fecesU.K., England and Wales20.8% of 24 samples positive
(2.9 log CFU/g for positive samples)
Hutchison et al., 2004
Campylobacter spp.Sheep fecesBritain11.1-20.8% samples were positive
(2.0-2.6 log CFU/g in positive samples)
Hutchison et al., 2005

NOTE: CFU, colony forming unit.

TABLE A3-8Prevalence and Cell Numbers of Shiga Toxin–Producing E. coli in Manures from Domesticated Animals

PathogenSourceLocationPrevalence (% of total samples positive)
(average cell numbers in positive samples)
E. coli O157:H7Cattle, beef feedlot, fecesCanada, Alberta1.9% of 8,682 samples were positiveBerg et al., 2004
E. coli O157Cattle, dairy beef, farms, rectal fecesMexico, central1.2% of 240 samples were positiveCallaway et al., 2004
E. coli O157:H7Cattle, beef and dairy, rectal fecesU.S., TN, NC, AL, WA, CA3.9% of 408 dairy cattle samples were positive
4.7% of 408 beef cattle samples positive
Doane et al., 2007
E. coli O157:H7Cattle, cow and calf farms, rectal fecesU.S.2.5% of 408 samples were positive;
17.2% of 29 cow-calf farms positive
Dunn et al., 2004
E. coli O157Cattle fecesU.K., England and Wales13.2% of 810 samples were positive
(6.5 log CFU/g for positive samples)
Hutchison et al., 2004
E. coli O157Cattle, beef and dairy farms, fecesKorea1.7% of 864 beef cattle samples were positive;
6.7% of 990 dairy cattle samples were positive
Jo et al., 2004
E. coli O157:H7Cattle, feedlotU.S., midwest10.2% of 10,622 cattle were positive;
52.0% of 711 pens had a positive animal;
95.9% of 73 feedlots sampled had a positive animal
Sargeant et al., 2004
E. coli O157Cattle fecesBritain9.1-13.2% samples positive
(2.4-3.1 log CFU/g in positive samples)
Hutchison et al., 2005
E. coli STECCattle, beef fecesJapan23% of 272 samples positiveKijima-Tanaka et al., 2005
E. coli O157:H7Cattle, dairySwitzerland4.2% of 966 samples were positiveKuhnert et al., 2005
E. coli O157:H7Cattle fecesNorway7.0% of 156 samples were positiveWasteson et al., 2005
E. coli O157:H7Cattle, feedlot fecesU.S., KS9.2% of 891 samples were positiveAlam and Zurek, 2006
E. coli STECCattle, organic and conventional dairy fecesU.S., MN32.% of 2208 samples were positive;
71.4% of dairy farms had at least one positive sample
Cho et al., 2006
E. coli O26 and O111Cattle, beef and dairy farms, fecesKorea6.7%, 4.6%, and 2.0% of 809 samples tested positive for O26, O111, and both O26 and O111, respectively.Jeon et al., 2006
E. coli O157Cattle, dairy, mature, rectal fecesU.S., OH and Norway0.7% of 750 samples were positive in Ohio;
8% of 50 herds had at least one positive sample;
0% of 680 samples positive in Norway
LeJeune et al., 2006
E. coli STECCattle, dairy, mature, rectal fecal samplesU.S., OH and Norway14% of 750 samples were positive in Ohio;
70% of 50 herds had at least one positive sample;
61% of 680 samples were positive in Norway;
100% of herds had at least one positive sample
LeJeune et al., 2006
E. coli O157Cattle, feedlot and abattoir, fecal pats and rectal fecesU.S., CO, NE24.7% of 450 fecal pats were positive;
27.6% of 145 rectal fecal samples were positive
Woerner et al, 2006
E. coli O157:H7Cattle, rectal, fecesU.S., TX64.3% of 8 cattle were positiveEdrington et al., 2007
E. coli O157Cattle, 12-30-month-old beef, fecesScotland7.9% of 14,856 samples were positive;
22.8% of 952 farms had at least one positive sample
Gunn et al., 2007
E. coli O157:H7Cattle fecesU.S., Central California33.8% of 77 samples tested were positiveJay et al., 2007
E. coli non-O157Cattle, feedlot, fecesCanada, Alberta0.7% of 2099 samples were positive;
57% of 21 feedlots sampled had positive samples
Renter et al., 2007
E. coli, non-O157 STECCattle, beef, rectal fecalSpain, northern46.0% of 124 samples were positiveOporto et al., 2008
E. coli, non-O157 STECCattle, dairy, rectal fecalSpain, northern20.7% of 82 samples were positiveOporto et al., 2008
E. coli O157:H7Cattle, beef, abattoir, fecesIreland, northern0.9% of 220 samples were positiveMadden et al., 2007
E. coli, STECCattle, dairy fecesNew Zealand1.3% of 155 samples were positiveMoriarty et al., 2008
E. coli O157:H7Cattle, beef, rectal fecalSpain, northern1.6% of 124 samples were positive;
6.7% of herds had positive samples
Oporto et al., 2008
E. coli O157:H7Cattle, dairy, rectal fecalSpain, northern7.0% of 82 samples were positiveOporto et al., 2008
E. coli O157:H7Cattle feces (perineal swab)Canada7.2% of 2,125 cattle were identified as supershedders of E. coli O157:H7 (> 4 log CFU/g) in the spring/summer;
0.5% of 2,000 cattle were identified as supershedders of E. coli O157:H7 (> 4 log CFU/g) in the fall/winter
Stephens et al., 2009
E. coli O157:H7Cattle, beef GI tractU.S., KS20.3% of 815 samples were positiveWalker et al., 2010
E. coli O157Chickens fecesKorea0% of 418 samples were positiveJo et al., 2004
E. coli STECChicken broiler fecesJapan0% of 158 samples were positiveKijima-Tanaka et al., 2005
E. coli O157:H7Chicken fecesNorway13.6% of 22 samples were positiveWasteson et al., 2005
E. coli O157:H7Chicken and turkey, rectal fecal samplesU.S., TN, NC, AL, WA, CA2.7% of 444 samples were positiveDoane et al., 2007
E. coli O157Swine, rectal fecesMexico, central2.1% of 240 samples were positiveCallaway et al., 2004
E. coli O157Pig fecesUK, England and Wales11.9% of 126 samples positive
(4.8 log CFU/g for positive samples)
Hutchison et al., 2004
E. coli O157Swine fecesKorea0.3% of 345 samples were positiveJo et al., 2004
E. coli O157Pig fecesBritain11.9-15.5% samples were positive
(3.1-3.6 log CFU/g in positive samples)
Hutchison et al., 2005
E. coli STECSwine fecesJapan14% of 179 samples were positiveKijima-Tanaka et al., 2005
E. coli O157:H7Swine, rectal fecal samplesU.S., TN, NC, AL, WA, CA8.8% of 426 samples were positiveDoane et al., 2007
E. coli O157:H7SwineSpain, northern0% of 17 samples were positiveOporto et al., 2008
E. coli, non-O157 STECSwineSpain, Northern0% of 17 samples were positiveOporto et al., 2008
E. coli O157Sheep fecesUK, England and Wales20.8% of 24 samples positive
(4.0 log CFU/g for positive samples)
Hutchison et al., 2004
E. coli O157Sheep fecesBritain20.8-22.2% samples were positive
(2.4-2.9 log CFU/g in positive samples)
Hutchison et al., 2005
E. coli O157:H7Sheep fecesNorway17.1% of 117 samples positiveWasteson et al., 2005
E. coli O157Sheep fecesTurkey9.1% of 175 samples positive;
47% of 15 flocks had at least one positive sample
Turutoglu et al., 2007
E. coli, non-O157 STECSheep, dairy, rectal fecesSpain, northern50.7% of 122 samples were positiveOporto et al., 2008
E. coli O157:H7Sheep, dairy, rectal fecesSpain, northern8.7% of 122 samples were positive;
7.3% of herds had positive samples
Oporto et al., 2008

NOTE: CFU, colony forming unit; STEC, Shiga toxin–producing Escherichia coli.

TABLE A3-9Prevalence and Cell Numbers of Cryptosporidium spp. in Manures from Domesticated Animals

PathogenSourceLocationPrevalence (% of total samples positive)
(average cell numbers in positive samples)
C. parvumCattle fecesU.K., England and Wales5.4% of 810 samples were positive
(2.4 log CFU/g for positive samples)
Hutchison et al., 2004
CryptosporidiumCalf fecesAustralia, Sydney watersheds57.1% of 7 samples were positiveCox et al., 2005
CryptosporidiumCattle, adult, fecesAustralia, Sydney watersheds22.2% of 9 samples were positiveCox et al., 2005.
C. parvumCattle fecesBritain2.8-5.4% samples were positive
(1.0-1.3 log CFU/g in positive samples)
Hutchison et al., 2005
CryptosporidiumCattle, dairy fecesNew Zealand5.2% of 155 samples were positiveMoriarty et al., 2008
Cryptosporidium spp.Cattle, feedlot fecesAustralia13% of 32 samples were positiveKlein et al., 2010
CryptosporidiumChicken fecesAustralia, Sydney watersheds0% of 7 samples were positiveCox et al., 2005
C. parvumPig fecesU.K., England and Wales13.5% of 126 samples were positive
(2.5 log CFU/g for positive samples)
Hutchison et al., 2004
CryptosporidiumPig fecesAustralia, Sydney watersheds77.8% of 9 samples were positiveCox et al., 2005
C. parvumPig fecesBritain5.2-13.5% samples were positive
(1.5-1.8 log CFU/g in positive samples)
Hutchison et al., 2005
CryptosporidiumPig slurrySpain40% of 5 pig farms were positiveBornay-Llinares et al., 2006
C. parvumSwine waste lagoonsU.S., Southeast1.2% of 407 samples were positiveJenkins et al., 2010
CryptosporidiumSwine fecesChina, Shanghai34.4% of 2,323 samples were positive; 82.6% of positive samples were C. suis and 8.7% of positive samples were Cryptosporidium pig genotype II;
100% of 12 pig farms were infected with prevalence ranging from 14.1 to 90.6%
Chen et al., 2011
CryptosporidiumSwine manureCanada55.7% of 122 pooled samples from 10 farms were positive; 55.4% of positive samples were C. parvum and 37.5% of positive samples were Cryptosporidium sp. pig genotype IIFarzan et al., 2011
C. parvumSheep fecesU.K., England and Wales29.2% of 24 samples were positive
(1.7 log CFU/g for positive samples)
Hutchison et al., 2004
CryptosporidiumSheep fecesAustralia, Sydney watersheds66.6% of 9 samples were positiveCox et al., 2005
C. parvumSheep fecesBritain29.2% samples were positive
(1.0 log CFU/g in positive samples)
Hutchison et al., 2005

NOTE: CFU, colony forming unit.

TABLE A3-10Prevalence of Salmonella spp., STEC, and Protozoan Parasites in Environmental Waters

PathogenWater sourceLocationPrevalence (% positive of number of samples analyzed)Reference
Salmonella spp.Water, pond/creeksAustralia, Brisbane3% of 32Ahmed et al., 2009
Water, riverCanada, Ontario62% of 32Droppo et al., 2009
Water, riverU.S., GA79.2% of 72Haley et al., 2009
Water, surfaceNetherlands14.3% of 49Heuvelink et al., 2008
Watersheds, swineU.S., NC41.7% of 12Patchanee et al., 2010
Watersheds, agriculture cropsU.S., NC50% of 12Patchanee et al., 2010
Watersheds, forestryU.S., NC57.1% of 28Patchanee et al., 2010
Water, surfaceCanada, Alberta6.2% of 1429Johnson et al., 2003
Water, irrigationNigeria8.2% of 196Okago et al., 2003
E. coli O157Water, surfaceNetherlands2.0% of 49Heuvelink et al., 2008
E. coli O157:H7Water, surfaceU.S., Central CA3.8% of 79Jay et al., 2007
Water, wellU.S., Central CA0% of 19Jay et al., 2007
Water, surfaceCanada, Alberta0.9% of 1483Johnson et al., 2003
STECWater, pond and creeksAustralia, Brisbane9-15% of 32Ahmed et al., 2009
CryptosporiumWater, reclaimedU.S.70% of 30Harwood et al., 2005
Water, irrigationMexico18% of 11Thurston-Enriquez et al., 2002
Water, irrigationU.S.<1 of 3Thurston-Enriquez et al., 2002
GiardiaWater, reclaimedU.S.80% of 30Harwood et al., 2005
Water, irrigationMexico64% of 11Thurston-Enriquez et al., 2002
Water, irrigationU.S.67% of 3Thurston-Enriquez et al., 2002
MicrosporidiaWater, irrigationU.S.67% of 3Thurston-Enriquez et al., 2002

TABLE A3-11Prevalence of Enteric Food-Borne Pathogens in Wildlife and Insects

AnimalPathogenCountryPrevalence (% positive of number of samples analyzed)Reference
Wild boars/pigsE. coli O157:H7U.S.14.9% of 87Jay et al., 2007
E. coli O157:H7Spain3.3% of 212Sánchez et al., 2010
Non-O157 STECSpain5.2% of 212Sánchez et al., 2010
C. parvumAustralia0% of 5Cox et al., 2005
GiardiaAustralia0% of 5Cox et al., 2005
CoyotesSalmonellaU.S.5% of 40Gorski et al., 2011
RabbitsE. coli (VTEC)U.K.15.5% of 129Scaife et al., 2006
CryptosporidiumAustralia50% of 2Cox et al., 2005
GiardiaAustralia0% of 2Cox et al., 2005
RaccoonsSalmonellaU.S.0% of 2Gorski et al., 2011
SkunksSalmonellaU.S.30.7% of 13Gorski et al., 2011
DeerE. coli O157:H7U.S.0% of 4Jay et al., 2007
CryptosporidiumAustralia100% of 1Cox et al., 2005
GiardiaAustralia0% of 1Cox et al., 2005
SalmonellaU.S.1% of 500Renter et al., 2006
Pigeons/sparrowsE. coli O157Czech Rep.0% of 70Čižek et al., 1999
Birds (cattle farm)E. coli O157:H7U.S.0.5% of 200 pooledHancock et al., 1998
Geese, CanadianE. coli (EHEC)U.S.6.0% of 151Kullas et al., 2002
Multiple birds (domestic animal farms)SalmonellaDenmark1.5% of 1285Skov et al., 2008
Multiple birdsSalmonellaU.S.6.6% of 105Gorski et al., 2011
ReptilesSalmonellaSpain41.5% of 94Briones et al., 2004
AmphibiansSalmonellaSpain0% of 72Briones et al., 2004
RatsE. coli O157Czech Rep.40% of 10Čižek et al., 1999
Mice, woodE. coli O157Czech Rep.0% of 7Čižek et al., 1999
RodentsE. coli O157:H7U.S.0% of 300 pooledHancock et al., 1998
RodentsToxoplasma gondiiNetherlands9.1% of 77Kijlstra et al., 2008
RodentsSalmonellaDenmark2% of 135Skov et al., 2008
Mice/ratsS. EnteritidisU.S.16.2% of 715Henzler and Opitz, 1992
SquirrelsS. entericaU.S.0% of 28Gorski et al., 2011
Mice/ratsC. parvumU.S.27.8% of 241Li et al., 2011
FliesE. coli O157:H7U.S.3.3% of 60Hancock et al., 1998
HousefliesE. coli O157:H7U.S.2.2% of 3,440Alam and Zurek, 2004
FliesSalmonellaDenmark22.6% of 31Skov et al., 2008
Houseflies/dump fliesSalmonellaU.S.18% of 22Olsen and Hammack, 2000
HousefliesS. EnteritidisU.S.∼50% of 120Holt et al., 2007
CockroachesSalmonellaU.S.14.4% of 90Kopanic et al., 1994
SlugsE. coli O157:H7U.K.0.2% of 33Sproston et al., 2006
Copyright © 2012, National Academy of Sciences.
Bookshelf ID: NBK114507