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Quantitative microbial risk assessment of haemolytic uremic syndrome associated with Argentinean kosher beef consumption in Israel

  • Victoria Brusa ,

    Roles Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing

    Affiliation IGEVET—Instituto de Genética Veterinaria “Ing. Fernando N. Dulout” (UNLP-CONICET LA PLATA), Facultad de Ciencias Veterinarias UNLP, La Plata, Argentina

  • Sergio Dolev,

    Roles Data curation, Investigation, Visualization, Writing – review & editing

    Affiliation Veterinary Services and Animal Health, Ministry of Agriculture and Rural Development, Tel Aviv, Israel

  • Marcelo Signorini,

    Roles Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – review & editing

    Affiliation IdICaL–Instituto de Investigación de la Cadena Láctea (INTA–CONICET). EEA Rafaela, Instituto Nacional de Tecnología Agropecuaria (INTA), Santa Fe, Argentina

  • Gerardo Leotta

    Roles Funding acquisition, Investigation, Methodology, Supervision, Visualization, Writing – original draft, Writing – review & editing

    Affiliation ICYTESAS—Instituto de Ciencia y Tecnología de Sistemas Alimentarios Sustentables (INTA-CONICET), Buenos Aires, Argentina


The aim of this study was to perform a quantitative microbial risk assessment (QMRA) of Shiga toxin-producing Escherichia coli hemolytic uremic syndrome (STEC-HUS) linked to the consumption of Kosher beef produced in Argentina and consumed in Israel in children under 14 years. A probabilistic risk assessment model was developed to characterize STEC prevalence and contamination levels in the beef supply chain (cattle primary production, cattle transport, processing and storage in the abattoir, for export and at retail, and home preparation and consumption). The model was implemented in Microsoft Excel 2016 with the @Risk add-on package. Results of 302 surveys with data collected in Israel were as follows: 92.3% of people consumed beef, mostly at home, and 98.2% preferred levels of cooking that ensured STEC removal from the surface of beef cuts. The preferred degree of ground beef doneness was “well-done” (48.2%). Cooking preference ranged from red to “medium-well done” (51.8%). Median HUS probability from Argentinean beef cut and ground beef consumption in children under 14 years old was <10−15 and 8.57x10-10, respectively. The expected average annual number of HUS cases and deaths due to beef cut and ground beef consumption was zero. Risk of infection and HUS probability correlated with salting effect on E. coli count, processing raw beef before vegetables, ways of storage and refrigeration temperature at home, joint consumption of salad and beef cuts, degree of beef doneness and cutting board washing with detergent after each use with beef and vegetables. The STEC-HUS risk in Israel from consumption of bovine beef produced in Argentina was negligible. The current QMRA results were similar to those of previous beef cut consumption QMRA in Argentina and lower than any of the QMRA performed worldwide in other STEC-HUS linked to ground beef consumption. This study confirms the importance of QMRA to estimate and manage the risk of STEC-HUS from beef consumption. The impact variables identified in the sensitivity analysis allowed us to optimize resources and time management, to focus on accurate actions and to avoid taking measures that would not have an impact on the risk of STEC-HUS.


Hemolytic uremic syndrome (HUS) is a group of diseases characterized by the triad of nonimmune microangiopathic hemolytic anemia, thrombocytopenia and decreased renal function [1]. Several classifications have been proposed for HUS, such as infection-induced HUS (mainly caused by Shiga toxin producing Escherichia coli (STEC-HUS) and less frequently by Streptococcus pneumoniae and a number of other microorganisms), atypical HUS and HUS with coexisting diseases [24].

The presentation of STEC-HUS epidemiological data is uneven because surveillance strategies differ [5]. Some countries report STEC-HUS cases every 100,000 people, as is the case of New Zealand (0.8), Argentina (0.6), China (0.57), Ireland (0.54) and France (0.49) [4, 68]. In addition, STEC-HUS prevalence is reported per 100,000 children under 5 years (Argentina, 5.95; Belgium, 4.5; France, 3.1; New Zealand, 2.6; the US, 1.4; China, 0.38) [4, 611]. The incidence of HUS is lower in Israel than in most countries, especially because STEC-HUS is very rare, accounting for 0.01 cases per 100.000 children under 18 years old [3].

A wide range of animal species and foods have been identified as STEC reservoirs [12]. The relative importance of selected sources and transmission pathways for STEC infections at regional or global level has been assessed using outbreak data [11, 13], case-control studies of sporadic infections [14, 15], microbial subtyping [16] and expert elicitations [17]. A recent study summarizes evidence on risk factors for sporadic STEC infection through the meta-analysis of outcomes from case-control studies [18]. The main risk factors identified were foreign travel, contact with ill people, farm animals or their environment, food consumption and exposure to untreated drinking water. Regarding foodborne transmission, the consumption of meat and dairy products, especially undercooked, appeared as a risk factor in all the studied populations.

The prevalence of STEC in cattle has been reported in several countries, including Argentina [1923]. Shiga toxin (stx) detection and STEC isolation from hides, carcasses, beef cuts, ground beef and hamburgers have also been evaluated [2429]. However, the presence of STEC in cattle or meat products should not be interpreted as a direct risk to meat consumers. Quantitative microbial risk assessment (QMRA) models should be performed to establish the risk of HUS from beef consumption [22, 3033].

The transfer of STEC to beef may occur at different stages of processing. In this sense, abattoirs are the first step in the production chain where beef may be contaminated [3436]. The current preventive systems comply with the hazard analysis and critical control point (HACCP) principles to avoid contamination with hazards such as STEC. In the case of Kosher slaughter, the method of slaughtering animals for meat according to the Jewish law, the presence of defects is verified according to religious definitions [37]. If the carcass qualifies as Kosher, beef must go through the salting process to remove any blood remainings [38].

Israel imports some 100,000 tons of Kosher beef each year. In 2021, Argentina exported 30,707 tons to Israel from abattoirs applying HACCP (IPCVA, pers comm). In 2019, although the risk of occurrence of STEC illnesses and HUS due to beef consumption was not estimated with a QMRA, the State of Israel implemented the zero tolerance criteria for the top 7 STEC serogroups (O26, O45, O103, O111, O121, O145 and O157) on beef products.

The aim of this study was to perform a QMRA of STEC-HUS associated with the consumption of Kosher beef produced in Argentina and consumed in Israel in children under 14 years.

Materials and methods

Study design

The prevalence and contamination levels of STEC through the beef supply chain were characterized using a probabilistic risk assessment model (Fig 1). The beef supply chain comprised four production modules: cattle primary production, cattle transport, processing and storage in the abattoir, for export and at retail, and home preparation and consumption. The modules were used to model two beef products: ground beef (any foodstuff containing ground beef, excepting commercial hamburgers) and intact beef cuts. The model was implemented in Microsoft Excel 2016 with the @Risk add-on package (version 7.5, Palisade Corporation, New York, USA) using inputs derived from data collected in Argentina and Israel and information gathered from experts, whenever possible. A Monte Carlo simulation with Latin Hypercube Sampling was used to assess all potential scenarios. Each simulation performed 5,000 iterations of the model, which allowed to achieve an adequate level of convergence (<1%). Model outputs were estimated as risk per serving of contaminated beef and population risk (median and 95.0% confidence intervals). The validity of the model was analyzed by comparing the predicted number of HUS cases with data published by Alfandary et al. [3].

Fig 1. Beef supply chain conceptual model and relevant input variables.

1 S1, S2 Tables, 2 S3 Table, 3 S4 Table, 4 Table 2 and S4 Table.

Hazard identification

Shiga toxin-producing Escherichia coli (STEC) comprise a group of zoonotic food- and waterborne pathogens whose main virulence factor is the Shiga toxin (Stx) codified by stx genes [39]. The optimum temperature for STEC growth ranges between 35 °C and 42 °C; their growth is retarded at 7 °C and they continue to be viable but without developing at -20 °C. For STEC survival, water activity should be between 0.95 and 0.99 [39, 40]. Although optimum pH for STEC growth is between 6.5 and 7, they can survive in the 2.5–9.0 pH range. STEC growth is retarded above 2.5% NaCl at 37 °C and inhibited by 8.5% NaCl. The amount of salt required for STEC inhibition reduces as other factors such as temperature and pH become more appropriate for STEC growth [40]. Due to intrinsic factors such as rich nutrients, high water activity and pH, beef provides favorable conditions for STEC development [41, 42].

All STEC were included in the model, assuming a similar pathogenic potential. This definition considered the worst scenario, since all the analyses in samplings from Argentinean abattoirs exporting to Israel were negative for Top 7 STEC [43]. The STEC prevalence at different production stages of the beef supply chain in Argentina was obtained by screening results of stx genes and/or STEC isolation reported in the literature (S1S3 Tables).

Hazard characterization

The relationship between the ingested dose of STEC from beef consumption and the probability of health endpoints of interest was described using a dose-response model. The probability of illness (Pill) was estimated using a Beta-Poisson model relating the ingested dose of the pathogen and the probability of illness [44, 45]. The variability in parameters α and β was modelled using PERT distributions based on the 5%, 50% and 95% percentiles estimated by Teunis et al. [45].

The probability of evolution to HUS (PHUS|ill) of all STEC cases (0.3%) and HUS mortality rate (Pmort|HUS) (3/51 cases) were estimated from the data reported for the Israel region [46] and Israel [3].

Exposure assessment

The four production modules of the beef supply chain were characterized by inputs (Fig 1). They were connected so that output distributions from each module served as inputs to the next module or as final outputs of the estimated ingested STEC dose (CFU) per serving portion (Table 1).

Table 1. Input parameters used in the risk assessment model of STEC due to beef consumption.

Cattle primary production.

The model reported by Brusa et al. [22] was used, ‘except for the “age of animals”, which was not considered as category because only adult animals (>18 months) slaughtered for export to Israel were included. This classification resulted in four different production scenarios (Table 1). The proportion of animals in each season (P(Se)) was modelled using 2018 cattle census data [74] (S2 Table). The probability that a slaughtered animal belonged to a feedlot or semi-intensive production system (P(PS)) was modelled using slaughter data from feedlot animals (S2 Table). The probability of occurrence of both variables (P(Se) and P(PS)) was modelled using Beta distributions. Data describing stx prevalence in cattle feces were available from several peer-reviewed studies performed in Argentina (S3 Table). The combination of P(Se) and P(PS) allowed to model stx prevalence considering potential risk factors. A syllogism was used to combine the probability of occurrence of the four level combinations (P1, P2, P3 and P4). Applying the method of moments [75], these data were used to determine parameters α and β of Beta distributions and to estimate stx prevalence in each combination of factors.

Cattle transport.

The results of the meta-analysis reported by Brusa et al. [22] were used to incorporate the effect of cattle transport on stx prevalence.

Processing and storage at abattoir, exportation and retail.

The prevalence of stx and STEC levels was modelled at various stages along the production process, from arrival of live cattle to beef storage at retail (Fig 1 and Table 1). All licensed Argentinean kosher abattoirs that export beef to Israel must apply HACCP for STEC (HACCP-STEC), which include the production of vacuum-packaged beef cuts. The prevalence of stx in carcasses was modelled using scientific publications conducted in Argentina in HACCP-STEC abattoirs (S3 Table). The odds ratio (OR) from cross-contamination during slaughtering was calculated using stx prevalence in carcasses and live cattle jointly for abattoirs (TT(s)) (S3 Table), using the following equation (Eq 1): (1) where P is the new stx prevalence after a specific scenario (e.g., stx prevalence in carcasses at abattoir) and Pi is the stx prevalence before the specific scenario (e.g., stx prevalence in beef cattle in the abattoir after transport) and OR is the value between the scenarios compared.

Enumeration levels of STEC were estimated by using generic E. coli counts in carcasses from abattoirs (C(c)) [55]. This was considered as the most conservative scenario as STEC enumeration levels are expected to be much lower than generic E coli counts. The levels of STEC during cold chamber storage (C(stg)) were estimated using the growth equation reported by Huang et al. [56].

Cold chamber temperature (Temp(a)) and storage times (Ti(a)) of abattoirs were provided by the participating plants (Industry communication). The growth of STEC in beef cuts, commercial hamburgers and ground beef in the cold chamber and at retail was estimated using the same equation.

Beef cuts.

Operators, equipment, the environment and beef are sources of STEC contamination during cutting and deboning. To model the effect of salting on bovine forequarter, results of Top 7 STEC detection and isolation and of E. coli counts in abattoirs exporting kosher beef to Israel were used [43].

To estimate the prevalence of STEC in salted bovine forequarter, the effect of salting on the concentration of E. coli (Eff(sal-ec)) was modeled with results informed by Brusa et al. [43], using a Pert distribution. The concentration of E. coli in salted bovine forequarter (C(fq-sal)) was also estimated.

Salting effect on bovine forequarter was modeled (Eff(sal-stx)). The stx prevalence in salted bovine forequarter (P(fq-sal)) was calculated from the stx prevalence in carcasses stored in cold chambers (C(stg)) and the salting effect (Eff(sal-stx)) (Table 1). The OR value due to cross-contamination during deboning to obtain beef cuts (OR(deb)) was modelled with data obtained in Argentina by Brusa et al. [43] in exporting Argentinean kosher beef abattoirs.

The OR(deb) was calculated using STEC prevalence in salted beef cuts (P(bc-a)) and salted bovine forequarter (P(fq-sal)) jointly (S3 Table), using the previously mentioned Eq 1. The STEC concentration in beef cuts (C(bc)) was modeled using the previously mentioned growth equation [56]. The Final STEC concentration in beef cuts at retail in Israel (FC(bc-g)) was estimated per 100 cm2 of beef cuts and considered as superficial contamination. To convert load per cm2 (log CFU/cm2) to load per gram of product (log CFU/g), the relationship between the two measures was estimated. According to previous estimates, a gram of beef corresponds to 0.1–0.5 cm2 cut surface (Sa) [33]. Temperature (Temp(a)) and storage time (Ti(a)) values in beef cuts were provided by the abattoirs (Table 1) (Industry communication). Considering that Argentinean kosher beef for export to Israel is shipped chilled (34%) and frozen (66%), both forms of storage were modeled until they reach consumers (National Service of Agrifood Health and Quality of Argentina pers. comm). Chilled cuts are kept for 80 to 85 days at an average temperature of 0.4 °C (variation range, 0.2 °C to 0.5 °C). Frozen cuts are kept until they reach the consumer (80 to 450 days) at an average temperature of -20.1 °C (range, -2 °C to -40°C).

Home and consumption.

Beef consumption habits in Israel were surveyed (S4 Table) using a descriptive epidemiological design. The survey was anonymous and self-administered. It consisted of 16 closed questions with different options to evaluate frequency and preference of beef consumption, place of acquisition, habit of beef storage and preparation. Informed consent was attached regarding anonymity, non-mandatory participation and use of research results.

Beef cuts.

The growth of STEC and cross-contamination at home (Stg(Hom)) were modelled as described by Brusa et al. [43]. The probability that consumers prepared salads together with beef (Salad), hand washing (P(WH)), cutting board washing (P(Wcb)), the effect of cooking at home on STEC concentration and the STEC concentration after cooking (C(bccons)) (CFU/g) were modeled and estimated using the survey of Israeli consumers (S4 Table), according to Brusa et al. [43].

Ground beef.

Argentine kosher beef exported to Israel is not subjected to any processing at the outlet level and is not marketed as minced beef (Veterinary Services and Animal Health, Ministry of Agriculture and Rural Development, pers comm). Since the mincing process occurs at home, aspects related to the way of storing ground beef at home, the temperature of refrigerators and freezers, the simultaneous consumption of salads and cross-contamination during food preparation were modeled in a similar way to that described for beef cuts. The effect of cooking during the preparation of ground beef was modelled using the survey of Israeli consumers (S4 Table) [43].

Risk characterization

The QMRA model used the specific conditions for the production of Argentinean kosher beef for export to Israel, considering the intrinsic variability and uncertainties of each process. Risk characterization was expressed as probability of HUS and death and number of HUS cases after consuming STEC-contaminated beef products. Children under 14 years were considered the target population of this study as they represent the age group with the highest STEC-HUS incidence in Israel [3]. Final exposure to STEC was estimated as the combination of the ingested dose (CFU) in a beef serving (beef cuts and ground beef) and the dose ingested during salad consumption in case both were consumed together. Due to the lack of available information about the portion size of beef cut (PS(bc)) and ground beef (PS(gb)) consumed by children under 14 years of age in Israel, information from Argentine children aged 6 to 15 years was used to model this stage (beef cut mean portion size: 120.8 g, SD: 68.7 g; Ground beef mean portion size 91.9 g, SD: 69.3 g) [72, 86]. Similarly, since the percentage of Kosher beef consumed as beef cut or minced meat in Israel is not known, both were assessed independently, i.e., assuming that all the exported Kosher beef is consumed as beef cut and as minced meat. In 2020, 29,082 tons of Argentine kosher beef were exported to Israel (National Service of Agrifood Health and Quality of Argentina pers. comm). The number of servings of Argentine kosher beef consumed by the total population in Israel was calculated with the amount of Argentine kosher beef exported to Israel in 2020 and PS(bc) and PS(gb). Children under 14 years of age represent 28% of the total population in Israel [73]. To calculate the number of servings of Argentine kosher beef consumed by children under 14 years of age in Israel, the number of servings of Argentine kosher beef consumed by the total population in Israel was multiplied by 0.28. The number of annual HUS cases due to beef consumption (N(HUSbc), N(HUSgb), N(HUSH)) was estimated considering the probability of acquiring the disease (P(HUS|ill)) and the frequency of beef consumption.

Sensitivity analysis

The sensitivity analysis was performed using @Risk (Palisade Inc.) to identify the processing steps with the greatest impact on the risk of acquiring STEC infection and thereby identify the risk management strategies that would generate the greatest impact on public health.


Cattle primary production

The stx prevalence during primary production for all production scenarios (season and production system) was 17.57% (0.80%–38.80% 95.0% CI). Results differed when stx prevalence was calculated for each specific scenario, as follows: 29.83% (27.84%–31.86%) in autumn-winter, 21.49% (19.70%–31.86%) in spring-summer, 25.84% (24.50%–27.20%) in semi-intensive and 15.38% (4.50%–31.20%) in feedlot production system (Table 2).

Table 2. Results of the estimations for the input and output variables of the risk model.

Processing and storage at abattoir, exportation and retail

The prevalence of stx and the enumeration of STEC levels on carcass surfaces before salting were 20.53% (16.96–26.53%) and 0.59 (0.199–0.996) log CFU/100 cm2, respectively. The prevalence of stx and STEC concentration in salted bovine forequarters were 17.89% (7.42%–33.66%) and 0.912 (0.06–2.84) log CFU/g cm2, respectively. The stx prevalence in beef cuts was estimated after deboning (19.83%; 6.0%-43.0%). Finally, STEC concentration in packaged beef cut at retail stores in Israel was 0.9022 (0.06–2.79) log CFU/100 cm2 and 2.71x10-3 (1.4x10-4–9.86x10-3) CFU/g (Table 2).

Home and consumption

A total of 302 surveys from Israel were collected in February and March 2021 (S4 Table). Of the total surveyed population, 92.3% consumed beef cuts once a week (64.5%; 192/298) and ground beef less than once a week (61.6%; 183/297). Of these, 75.3% and 84.7% consumed beef cuts and ground beef at home, respectively. From those who consumed beef cuts at home, 45.5% bought it at a butcher shop and 46.5% at the supermarket. Those who consumed ground beef at home bought it at a butcher shop (37.5%) and the supermarket (62.7%). At retail, beef cuts were frozen and refrigerated according to 19.6% and 74.5% of surveyed consumers. Beef cuts and ground beef are stored frozen according to 67.3% and 79.1% of surveyed consumers, respectively. The time elapsed from the purchase of beef cuts and ground beef to consumption was less than 1 week (70% and 64% of the surveyed consumers, respectively). Most consumers (98.2%) preferred levels of cooking that ensured STEC removal from the surface of beef cuts. The preferred degrees of doneness were well-done" (26.4%), “medium-well done” (34.6%) and “medium-well” (25.7%). In the case of ground beef, the preferred degree of doneness was “well-done” (48.2%) and cooking preference ranged from red to “medium-well done” (51.8%). Both beef products were consumed sometimes or always with fresh vegetables (97%; 267/275); 59.5% reported having two separate tables to prepare beef and vegetables, whereas 26% of those who used the same table for both, sometimes or never washed the table with detergent in between handling these foods. After handling beef, 87.7% of consumers reported to wash their hands and 93% reported to wash the utensils.

The STEC concentration in raw beef cuts and ground beef was -1.89 (-3.79–3.59) and -2.51 (-3.8–1.99) log CFU/g, respectively. The STEC transfer rates from beef cuts and ground beef to salad was 10.26 (0.0–30) and 16.63 (0.0–36) CFU/g, respectively (Table 2).

Risk characterization

Median HUS probability from consumption of Argentinean beef cuts and ground beef in children under 14 years old was <10−15 (<10−15–0.003, 90.0% CI) and 8,57x10-10 (7,01x10-14–0.003, 90% CI), respectively (Table 2). The expected average annual number of HUS cases and deaths due to beef cut and ground beef consumption was 0.

Sensitivity analysis

Beef cuts.

The risk of STEC infection from beef cut consumption and subsequent outcomes correlated with processing raw beef before vegetables (r = 0.18), storage form (r = 0.15), refrigeration temperature at home (r = 0.09), joint consumption of salad and beef cuts (r = 0.02), beef degree of doneness (r = 0.02) and table washing with detergent between use for beef and vegetables (r = -0.18) (Fig 2).

Fig 2. Beef cut sensitivity analysis.

Model inputs on the probability of developing HUS due to beef cut consumption.

Ground beef.

The risk of STEC infection from ground beef and subsequent outcomes correlated positively with salting effect on E. coli count (r = 0.37), storage form (r = 0.23) and temperature at home (r = 0.09). Ground beef cooking preference was the only input with a negative correlation (r = -0.42) (Fig 3).

Fig 3. Ground beef sensitivity analysis.

Model inputs on the probability of developing HUS due to ground beef consumption.


The QMRA provided a mathematical representation of the beef chain from Argentina to Israel and was used to estimate current risks of STEC-HUS of Israeli children under 14 years from intact beef cut and ground beef consumption. The QMRA included all the available information throughout the Argentinean beef production chain until home consumer habits in Israel. A QMRA of HUS from intact beef cut and ground beef consumption had already been carried out in Argentina [22]. The relevance of the current QMRA is concerned with the innovative information about obtained kosher beef processing in two countries: a) one that produces the cattle and kosher beef, and b) another that imports and consumes this kosher beef. The current study includes new information, such us risk factors associated with the presence of STEC from cattle to kosher beef production; identification of risks associated with processing and storage at kosher beef abattoirs and exportation; and application of a survey to assess beef consumption habits at home in Israel.

In the present QMRA, the mean probability of HUS and death from Argentinean Kosher beef cut consumption in children under 14 years in Israel was <10−15, with an expected number of zero HUS cases per year (95.0% CI 0–0). Few studies have been published on HUS-QMRA due to intact beef cut consumption. Previous QMRAs conducted in Argentina estimated a similar probability of HUS due to beef cut consumption [22]. Other QMRAs performed in Canada estimated 2.9x10-9, six orders of magnitude greater than in the present study [76]. According to the bibliographic search carried out by these authors, the present study and the report by Brusa et al. [22], the only QMRA in beef cuts was conducted in Canada 9 years ago [76]. Unlike Argentinean studies, E. coli O157:H7 was the hazard evaluated in Canadian QMRAs [76].

In the present QMRA, the mean probability of HUS and death from consumption of ground beef elaborated with Argentinean Kosher beef in children under 14 years in Israel was 8.57x10-10 and 5.87x10-11, respectively, with zero HUS cases expected per year (95.0% CI 0–0). In the last 25 years, several HUS-QMRA from ground beef and hamburger consumption have been published. They were performed in Canada [76, 77], Australia [78], the Netherlands [79], the US [80, 81], and Ireland [82]. The two most recent studies were carried out in the USA and Argentina [22, 80]. The probability estimates reported in those studies (PHUS, 4.2×10−9–6.4×10−5; Pdeath 5.9×10−10–2.3×10−6) were between 1–6 orders of magnitude greater than in the present study. In a previous QMRA carried out in Argentina [22], the probability of HUS (5.4x10-8) and death (6.4x10−9) from ground beef consumption was two orders of magnitude greater than in Israel. Also, in contrast with the zero expected annual HUS cases in Israel, 28 HUS cases per year due to ground beef consumption were expected in Argentina. Differences in the probability and number of expected cases due to the consumption of ground beef in Israel and Argentina could be determined by the production process and consumption habits [22]. In the present QMRA, ground beef was made from Argentine vacuum-packed kosher cuts provided by abattoirs applying HACCP-STEC. However, Brusa et al. [22] considered abattoirs applying and not applying HACCP-STEC and modeled the effect of handling and cross-contamination at retail. The significantly increased bacterial load in ground beef was identified due to cross-contamination at retail [22]. The lack of sanitation standard operating procedures (SSOP) and Good Manufacturing Practices (GMP) resulted in significantly higher public health risk associated with ground beef consumption [22].

Sensitivity analysis

Israeli consumption habits and preferences were incorporated into the model based on information from the survey carried out on the specific population. According to the present QMRA, the risk of HUS from both beef products evaluated as very low (negligible), estimating zero cases per year. However, some model input variables were identified in the sensitivity analysis, associated with higher or lower HUS risk. In coincidence with Brusa et al. [22], the joint consumption of salad and beef cuts was associated with higher HUS risk. According to surveys, the percentage of respondents who affirmed to have consumed beef cuts with vegetables was similar in Israel (64.0%) and Argentina (66.8%). Other QMRA did not consider or identify the joint consumption of salads with beef as a risk factor for HUS [30, 32].

The only variable identified with protective effect against HUS due to beef cut consumption was cutting board washing with detergent between use for beef and vegetables. In coincidence with Brusa et al. [22] (92.3%), the majority of Israeli respondents (92.8%) conducted this procedure. Cross-contamination during simultaneous beef and vegetable preparation has been previously proposed as a factor associated with illness and increased HUS risk [31, 33, 83]. Other correct food handling practices are also performed in Israel and Argentina. For example, 98.9% and 99.5% of Israeli and Argentinean people reported washing knives or other utensils used to process raw beef with detergent after use, respectively [22]. In the same way, after handling beef, 97.8% and 88.7% of Argentine and Israeli consumers reported washing their hands, respectively [22]. In contrast, 68.6% of New Zealander consumers reported washing their hands, and 41% and 28% of New Zealand respondents used knives and kitchen surfaces in a manner that could allow cross contamination [84].

In agreement with other authors, storage type and temperature at home were identified as having a positive correlation in the sensitivity analysis [22, 79]. The effect of hygiene measures, such as cutting board surface sanitization, the transference between contaminated surfaces with STEC (including hands) and vegetables and beef correct storage have also been studied [8587].

In beef cuts, STEC contamination is superficial and can be easily destroyed by cooking since STEC are not heat-resistant [22]. Exposure to the recommended cooking temperatures eliminates STEC from beef cut surfaces [88]. However, ground beef requires thorough cooking to ensure removal of STEC from the entire product [89]. According to surveys, 1.8%, 0.7% and 0.3% of Israeli, New Zealand and Argentine consumers prefer to consume beef cuts "raw" [22, 84]. In the present QMRA, beef cut cooking preferences had a positive correlation with HUS in the sensitivity analysis. This variable was not identified by Brusa et al. [22]. Surveys conducted in New Zealand, Argentina, Ireland, Israel and Norway reported that 82.2%, 79.1%, 65.0%, 48.2% and 45.7% of consumers ate ground beef or hamburgers well-done [22, 84, 90, 91]. The same as in other risk assessments, ground beef cooking degree was identified in the sensitivity analysis and was the only input with a negative correlation with STEC-HUS probability [22, 31, 76, 77, 92].

Brusa et al. [22], reported that the impact of consumers´ habits during food preparation at home was lower than other variables in different stages of the beef chain. The present QMRA agrees with Brusa et al. [22] in that the primary production variables did not impact in the risk of HUS for any beef product. However, only one input (salting effect on E. coli counts) of processing and storage in the abattoir, at export and retail was identified in the sensitivity analysis for all the evaluated products. Few studies have evaluated the effect of salting on E. coli counts in bovine forequarter. The present QMRA applied results published by Brusa et al. [43], who did not identify statistically significant differences due to the salting effect. This could explain why this variable showed a positive correlation with the risk of HUS. More studies evaluating the salting effect could be useful to improve the present QMRA. On the other hand, the finding of several input variables in the abattoir stage in the sensitivity analysis by Brusa et al. [22] could be due to the fact that two beef abattoir categories corresponding to two different sanitary standards were modeled. However, in the present QMRA, only one type of abattoir authorized to export to Israel and with high sanitary standards was modeled.

The published information about HUS cases around the world is scarce [22]. Few primary studies and notifiable disease data from different World Health Organization (WHO) regions, and population estimates on exposure, age distribution and clinical course of illness are available [46]. Sometimes, the available epidemiological data do not discriminate HUS cases from other STEC illnesses (diarrhea, bloody diarrhea, hemorrhagic colitis), making it difficult to interpret and compare information, such as little quantitative information from countries and regions, paucity of data for incidence and DALY estimates, and lack of specification of number of outbreaks versus sporadic cases of disease [93, 94].

In Israel, the mean annual incidence of pediatric HUS is 1.5 cases per million/year. The annual incidence of STEC-HUS is 0.02 cases per 100,000 children, and the annual incidence of HUS associated with STEC was 0.01 cases per 100,000 children. Routine surveys conducted by the Israeli Ministry of Health revealed a low prevalence of STEC isolation in stool samples (<10 cases per year) [3]. According to Alfandary et al. [3], a possible explanation for the low incidence of STEC-HUS in Israel is the low level of consumption of beef compared to chicken meat, assuming that the endemicity of STEC-HUS in Argentina is due to the high consumption of beef. Beef consumption in Argentina, is 47.8 kg/person/year [95] and 16 kg/person/year (Veterinary Services and Animal Health, Ministry of Agriculture and Rural Development, pers comm) in Israel. However, the analysis would not be so simple and linear. In Argentina, the report of HUS cases is mandatory [96] and the annual incidence rate of HUS in the general population in 2021 was 0.6 cases per 100,000 inhabitants [6]. In Argentina only 4 (0.07%) STEC-HUS cases were associated with beef consumption in a period of 13 years (2002–2015) [97]. According to the last QMRA carried out in Argentina, it is estimated that only 10% of HUS cases would be due to the consumption of beef [22], with ground beef being the meat product with the highest risk. In this context, the highest prevalence of STEC-HUS in Argentina is in children ≤1 year of age, whose consumption of ground beef is low [72]. Therefore, the endemicity of STEC-HUS would not be associated directly and solely with the consumption of beef [98100]. It is therefore necessary to consider HUS transmission in a transdisciplinary way, consolidate epidemiological studies (outbreaks and cases) and, if possible, perform QMRA to manage risks with scientific evidence.


According to our study, STEC-HUS risk in Israel due to the consumption of bovine beef produced in Argentina is negligible. Estimates in the present assessment are similar to those quoted in the beef cut consumption QMRA performed in Argentina (Median<10−15). In addition, estimates in the present assessment from ground beef consumption are lower than any of the estimates quoted in other STEC-HUS risk assessments performed in Argentina, Canada, Australia and USA [22, 32, 7678]. This study confirms the importance of making a QMRA to estimate and manage the risk of STEC-HUS from beef consumption. Knowledge of the impact variables identified in the sensitivity analysis allows optimizing resources and time, directing actions with greater certainty, as well as avoiding taking measures that will not have an impact on the risk of STEC-HUS.

Supporting information

S1 Table. Bovine slaughtered during 2018.

Total amount and amount provided by feedlots.


S2 Table. Peer-reviewed sources of stx prevalence in Argentinean cattle feces used for input into the model.


S3 Table. Scientific publications of samplings conducted in Argentinean HACCP-STEC abattoirs used to model the prevalence of stx-positive carcasses after slaughter.


S4 Table. Survey of Israeli beef consumption habits.



We thank A. Di Maggio for correcting and editing the manuscript.


  1. 1. Karmali MA, Gannon V, Sargeant JM. Verocytotoxin-producing Escherichia coli (VTEC). Vet Microbiol. 2010;140(3–4):11. pmid:19410388
  2. 2. Loirat C, Fakhouri F, Ariceta G, Besbas N, Bitzan M, Bjerre A, et al. HUS International. An international consensus approach to the management of atypical hemolytic uremic syndrome in children. Pediatr Nephrol. 2016; 31(1): 15–39. pmid:25859752
  3. 3. Alfandary H, Rinatt C, Guerevich E, Eisenstein I, Golberg O, Kropach N, et al. Hemolytic Uremic Syndrome: A Contemporary Pediatric Experience. Nephron. 2020; 144(3):109–117. pmid:31935726
  4. 4. Feng J, Xu K, Shi X, Xu L, Liu L, Wang F, et al. Incidence and cost of haemolytic uraemic syndrome in urban China: a national population-based analysis. BMC Nephrology. 2022; 23:122. pmid:35354386
  5. 5. Torres AG. Escherichia coli in the Americas. 1st ed. Switzerland: Springer International Publishing; 2016.
  6. 6. Área de Vigilancia de la Salud de la Dirección de Epidemiología. Ministerio de Salud de la Nación. Boletín Integrado de Vigilancia N° 611 –SE 29–2022. Ciudad Autónoma de Buenos Aires 2022.
  7. 7. Minstry for Primary Industries. New Zealand Government. Annual report concerning Foodborne Diseases in New Zealand 2020. New Zealand Food Safety Technical PaperNo: 2021/16. ISBN No: 978-1-99-101934-9 (online) ISSN No: 2624-022X (online). 2021
  8. 8. ECDC. Shiga toxin-producing Escherichia coli (STEC) infection. In: ECDC. Annual epidemiological report for 2019. Stockholm: ECDC; 2021.
  9. 9. Jacquinet S, De Rauw K, Pierard D, Godefroid N, Collard L, Van Hoeck K, et al. Arch. Haemolytic uremic syndrome surveillance in children less than 15 years in Belgium, 2009–2015. Public Health. 2018; 76:41. pmid:30128150
  10. 10. Center for Disease Control. FoodNet Fast. Foodborne Diseases Active Surveillance Network (FoodNet). HUS. 2022 [cited 2022 Aug 9]. Available from:
  11. 11. Greig JD and Ravel A. Analysis of foodborne outbreak data reported internationally for source attribution. Int. J. Food Microbiol. 2009; 130:77–87. pmid:19178974
  12. 12. EFSA-BIOHAZ. Scientific Opinion on VTEC-seropathotype and scientific criteria regarding pathogenicity assessment. EFSA J. 2013; 11:3138.
  13. 13. Pires SM, Majowicz S, Gill A, Devleesschauwer B. Global and regional source attribution of Shiga toxin-producing Escherichia coli infections using analysis of outbreak surveillance data. Epidemiol Infect. 2019;147(e236):9. pmid:31364563
  14. 14. Devleesschauwer B, Pires SM, Young I, Gill A, Majowicz SE. Associating sporadic, foodborne illness caused by Shiga toxin-producing Escherichia coli with specific foods: a systematic review and meta-analysis of case-control studies. Epidemiol Infect. 2019; 147:e235. pmid:31364553
  15. 15. Kintz E, Byrne L, Jenkins C, McCarthy N, Vivancos R, Hunter P. Outbreaks of Shiga Toxin–Producing Escherichia coli Linked to Sprouted Seeds, Salad, and Leafy Greens: A Systematic Review. J Food Prot. 2019;82(11):9. pmid:31638410
  16. 16. Mughini-Gras L, van Pelt W, van der Voort M, Heck M, Friesema I, Franz E. Attribution of human infections with Shiga toxin-producing Escherichia coli (STEC) to livestock sources and identification of source-specific risk factors, The Netherlands (2010–2014). Zoonoses Public Health. 2018; 65:e8–e22. pmid:28921940
  17. 17. Hoffmann S, Devleesschauwer B, Aspinall W, Cooke R, Corrigan T, Havelaar A, et al. Attribution of global foodborne disease to specific foods: Findings from a World Health Organization structured expert elicitation. PLoS One. 2017; 12:e0183641. pmid:28910293
  18. 18. Augustin JC, Kooh P, Mughini-Gras L, Guillier L, Thébault A, Audiat-Perrin F, et al. Risk factors for sporadic infections caused by Shiga toxin-producing Escherichia coli: a systematic review and meta-analysis. Microb Risk Anal. 2021; 17: e100117.
  19. 19. Mir RA, Weppelmann TA, Elzo M, Ahn S, Driver JD, Jeong KC. Colonization of beef cattle by Shiga toxin-producing Escherichia coli during the first year of life: a cohort study. PLoS One. 2016;11(2):16. pmid:26849041
  20. 20. Thiry D, De Rauw K, Takaki S, Duprez JN, Iguchi A, Pierard D, et al. Low prevalence of the ‘gang of seven’ and absence of the O80:H2 serotypes among Shigatoxigenic and enteropathogenic Escherichia coli (STEC and EPEC) in intestinal contents of healthy cattle at two slaughterhouses in Belgium in 2014. J Appl Microbiol. 2017; 124:867–873. pmid:29280544
  21. 21. Rivelli Zea SM, Padola NL, Etcheverria AI, Florentin M, Acuna P, Rodriguez F, et al. Molecular characterization of Shiga toxin producing Escherichia coli isolated from 2 livestock establishments of Paraguay. Rev Argent Microbiol. 2020; 52(2):131–135. pmid:31635897
  22. 22. Brusa V, Costa M, Padola NL, Etcheverría A, Sampedro F, Fernandez PS, et al. Quantitative risk assessment of haemolytic uremic syndrome associated with beef consumption in Argentina. PLoS One. 2020; 13;15(11): e0242317. pmid:33186398
  23. 23. Costa M, Londero A, Brusa V, Galli L, Van Der Ploeg C, Roge A, et al. Characterization and molecular subtyping of Shiga toxin–producing Escherichia coli strains in provincial abattoirs from the Province of Buenos Aires, Argentina, during 2016–2018. Prev Vet Med. 2020; 183:105133. pmid:32979660
  24. 24. Etcheverria AI, Padola NL, Sanz ME, Polifroni R, Kruger A, Passucci J, et al. Occurrence of Shiga toxin-producing E. coli (STEC) on carcasses and retail beef cuts in the marketing chain of beef in Argentina. Meat Sci. 2010;86(2):4. pmid:20646836
  25. 25. Monaghan Á, Byrne B, Fanning S, Sweeney T, McDowell D, Bolton DJ. Serotypes and virulotypes of non-O157 shiga-toxin producing Escherichia coli (STEC) on bovine hides and carcasses. Food Microbiol. 2012;32:7. pmid:22986184
  26. 26. Baeza Quiroz CB. Aislamiento y caracterización de cepas de Escherichia coli productor de toxina Shiga desde carne de vacuno nacional e importada, distribuída en los principales supermercados de la provincia de Santiago. Chile: Escuela de Salud Pública. Universidad Mayor; 2013.
  27. 27. Brusa V, Restovich V, Galli L, Teitelbaum D, Signorini M, Brasesco H, et al. Isolation and characterization of non-O157 Shiga toxin-producing Escherichia coli from beef carcasses, cuts and trimmings of abattoirs in Argentina. PLoS One. 2017;12(8):16. pmid:28829794
  28. 28. Cap M, Carbonari CC, D’Astek BA, Zolezzi G, Deza N, Palladino MP, et al. Frequency, characterization and genotypic analysis of Shiga toxin-producing Escherichia coli in beef slaughterhouses of Argentina. Rev Argent Microbiol. 2019; 51(1):32–38. pmid:29937134
  29. 29. Varcasia BM, Tomassetti F, De Santis L, Di Giamberardino F, Lovari S, Bilei S, et al. Presence of Shiga toxin-producing Escherichia coli (STEC) in fresh beef marketed in 13 eegions of ITALY (2017). Microorganisms. 2018;6(126):12. pmid:30563244
  30. 30. Delignette-Muller ML, Cornu M, the AFSSA STEC study group. Quantitative risk assessment for Escherichia coli O157:H7 in frozen ground beef patties consumed by young children in French households. Int. J. Food Microbiol. 2008; 128:158–164. pmid:18617285
  31. 31. Duffy G, O’ Brien S, Carney E, Butler F, Cummins E, Nally P, et al. A quantitative risk assessment of E. coli O157:H7 in Irish minced beef. In: Teagasc, editor. Ireland2005.
  32. 32. Ebel E, Schlosser W, Kause J, Orloski K, Roberts T, Narrod C, Malcolm S, et al. Draft risk assessment of the public health impact of Escherichia coli O157:H7 in ground beef. J Food Prot. 2004; 67(9):1991–9. pmid:15453594
  33. 33. Signorini M, Tarabla H. Quantitative risk assessment for verocytotoxigenic Escherichia coli in ground beef hamburgers in Argentina. Int J Food Microbiol. 2009;132(2–3):9. pmid:19446904
  34. 34. Buncic S, Nychas GJ, Lee MR, Koutsoumanis K, He´braud M, Desvaux M, et al. (2014). Microbial pathogen control in the beef chain: recent research advances. Meat Sci. 97: 288–297. pmid:23688797
  35. 35. Burfoot D, Tinker D, Thorn R, Howell M. Use of fluorescence imaging as a hygiene indicator for beef and lamb carcasses in UK slaughterhouses. Biosyst. Eng. 2011; 109(3):175–185.
  36. 36. Wheeler TL, Kalchayanand N, Bosilevac JM. Pre- and Post-Harvest Interventions to Reduce Pathogen Contamination in the U.S. Beef Industry. Meat Sci. 2014; 98(3):372–8. pmid:25027798
  37. 37. Farouk MM, Al-Mazeedi HM, Sabow AB, Bekhit AED, Adeyemi KD, Sazili AQ, et al. Halal and kosher slaughter methods and meat quality: a review. Meat Sci. 2014; 98(3):505–19. pmid:24973207
  38. 38. Zurek J, Rudy M, Kachel M, Rudy S. Conventional versus Ritual Slaughter–Ethical Aspects and Meat Quality. Processes. 2021; 9:1381.
  39. 39. Brusa V, Colello R, Galli L. Escherichia coli productor de toxina Shiga. In: Stanchi NO, editor. Microbiología Veterinaria; 2019. pp. 1066–73
  40. 40. Ministry for Primary Industries. Shiga toxin-producing Escherichia coli. New Zealand Food Safety. ESR Science for Communities. 2018. Available from:
  41. 41. EFSA-BIOHAZ. Scientific Opinion on the microbiological safety of aged meat. EFSA J. 2023; 21(1):7745. pmid:36698487
  42. 42. Reid R, Fanning S, Whyte P, Kerry J, Lindqvist R, Yu Z, et al. The microbiology of beef carcasses and primals during chilling and commercial storage. Food Microbiol. 2017; 61:50–57. pmid:27697169
  43. 43. Brusa V, Blainq L, Brasesco H, Brussone M, Buezas J, Contardi I, et al. Detection and isolation of Shiga toxin-producing Escherichia coli and microbial population counts on Argentinean Kosher beef for export to Israel. Analecta Vet. 2023; Forthcoming In press.
  44. 44. Área de Vigilancia de la Salud de la Dirección de Epidemiología. Ministerio de Salud de la Nación Argentina. Boletín Integrado de Vigilancia. N˚ 439—SE 06–2018. Ciudad Autónoma de Buenos Aires2018.
  45. 45. Teunis PF, Ogden ID, Strachan NJ. Hierarchical dose response of E. coli O157:H7 from human outbreaks incorporating heterogeneity in exposure. Epidemiol Infect. 2008; 136:10.
  46. 46. Majowicz SE, Scallan E, Jones-Bitton A, Sargeant JM, Stapleton J, Angulo FJ, et al. Global incidence of human Shiga toxin-producing Escherichia coli infections and deaths: a systematic review and knowledge synthesis. Foodborne Pathog Dis. 2014; 11(6):17. pmid:24750096
  47. 47. Barham RA, Barham BL, JoHNSon AK, Allen DM, Blanton JR, Miller MF. Effects of the transportation of beef cattle from the feedyard to the packing plant on prevalence levels of Escherichia coli O157 and Salmonella spp. J Food Prot. 2002; 65(2):4. pmid:11848558
  48. 48. Arthur TM, Bosilevac JM, Brichta-Harhay DM, Guerini MN, Kalchayanand SD, Shackelford SD, et al. Transportation and lairage environment effects on prevalence, numbers, and diversity of Escherichia coli O157:H7 on hides and carcasses of beef cattle at processing. J Food Prot. 2007; 70(2):7. pmid:17340859
  49. 49. Stanford K, Bryan M, Peters J, Gonzaléz LA, Stephens TP, Schwartzkopf-Genswein KS. Effects of long- or short-haul transportation of slaughter heifers and cattle liner microclimate on hide contamination with Escherichia coli O157. J Food Prot. 2011; 74(10):6. pmid:22004805
  50. 50. Dewell GA, Simpson CA, Dewell RD, Hyatt DR, Belk KE, Scanga JA, et al. Impact of transportation and lairage on hide contamination with Escherichia coli O157 in finished beef cattle. J Food Prot. 2008; 71(6):5. pmid:18592735
  51. 51. Fegan N, Higgs G, Duffy LL, Barlow RS. The effects of transport and lairage on counts of Escherichia coli O157 in the feces and on the hides of individual cattle. Foodborne Pathog Dis. 2009; 6(9):8. pmid:19630516
  52. 52. Jaros P, Cookson AL, Reynolds A, Withers H, Clemens R, Brightwell G, et al. The effect of transportation and lairage on faecal shedding and carcass contamination with Escherichia coli O157 and O26 in very young calves in New Zealand. Epidemiol Infect. 2018; 146(9):12. pmid:29789035
  53. 53. Minihan D, O’Mahony M, Whyte P, Collins JD. An Investigation on the effect of transport and lairage on the faecal shedding prevalence of Escherichia coli O157 in cattle. J Vet Med B. 2003; 50:5. pmid:14633207
  54. 54. Reicks AL, Brashears MM, Adam KD, Brookes JC, Blanton JR, Miller MF. Impact of transportation of feedlot cattle to the harvest facility on the Prevalence of Escherichia. J Food Prot. 2007;70(1):17–21. pmid:17265854
  55. 55. Signorini M, Costa M, Teitelbaum D, Restovich V, Brasesco H, Garcia D, et al. Evaluation of decontamination efficacy of commonly used antimicrobial interventions for beef carcasses against Shiga toxin-producing Escherichia coli. Meat Sci. 2018; 142:8.
  56. 56. Huang L, Tu SI, Phillips J, Fratamico P. Mathematical modeling of growth of non-O157 Shiga toxin-producing Escherichia coli in raw ground beef. J Food Sci. 2012; 77(4):9. pmid:22515248
  57. 57. Signorini ML, Frizzo LS. Quantitative risk model for verocytotoxigenic Escherichia coli cross-contamination during homemade hamburger preparation. Rev Argent Microbiol. 2009; 41:9.
  58. 58. Evans EW, Redmond EC. Analysis of older adults’ domestic kitchen storage practices in the United Kingdom: identification of risk factors associated with listeriosis. J Food Prot. 2015; 78(4):9. pmid:25836399
  59. 59. James C, Onarinde BA, James SJ. The Use and Performance of Household Refrigerators: A Review. Compr Rev Food Sci F. 2017; 16(1):20. pmid:33371545
  60. 60. Montville R, Schaffner DW. Inoculum size influences bacterial cross contamination between surfaces. Appl Environ Microbiol. 2003; 69(12):6. pmid:14660365
  61. 61. Barril PA, Soto SA, Jaureguiberry MV, Gottardi G, Bascur I, Leotta GA, et al. Microbiological risk characterization in butcher shops from the province of Neuquen, Patagonia Argentina. LWT. 2019; 107:6.
  62. 62. Llorente P, Barnech L, Irino K, Rumi MV, Bentancor A. Characterization of Shiga toxin-producing Escherichia coli isolated from ground beef collected in different socioeconomic strata markets in Buenos Aires, Argentina. BioMed Res Internat. 2014; 2014:9. pmid:25006586
  63. 63. Montero D, Bodero M, Riveros G, Lapierre L, Gaggero A, Vidal RM, et al. Molecular epidemiology and genetic diversity of Listeria monocytogenes isolates from a wide variety of ready-to-eat foods and their relationship to clinical strains from listeriosis outbreaks in Chile. Front Microbiol. 2015; 6:384. pmid:25983727
  64. 64. Salinas Ibáñez AG, Lucero Estrada C, Favier GI, Vega AE, Stagnitta PV, Mattar MA, et al. Characterization of Shiga-toxin producing Escherichia coli isolated from meat products sold in San Luis, Argentina. J Food Saf. 2018; 38(5):1–10.
  65. 65. Ruiz MJ, Sanz M, Elichiribey L, Villalobo C, Kruger A, Colello R, et al. Carnicerías saludables: detección de Escherichia coli O157:H7 y no-O157:H7 en carne picada fresca y en instalaciones de comercios minoristas. XIII Congreso Argentino de Microbiología II Congreso de Microbiología Agrícola y Ambiental; Ciudad Autónoma de Buenos Aires, Argentina, 2013.
  66. 66. Chen Y, Jackson KM, Chea FP, Schaffner DW. Quantification and variability analysis of bacterial cross-contamination rates in common food service tasks. J Food Prot. 2001; 64(1):9. pmid:11198444
  67. 67. Anderson ME, Marshall RT, Dickson JS. Estimating deppths of bacterial penetration into post-rigor carcass tissue during washing. J Food Saf. 1991; 12:191–8.
  68. 68. Sporing SB. Escherichia coli O157:H7 risk assessment for production and cooking of blade tenderized beef steaks. M.Sc. Thesis, Kansas State University. 1996. Available from:
  69. 69. Stringer SC, George SM, Peck MW. Thermal inactivation of Escherichia coli 0157:H7. Journal of Applied Microbiology Symposium Supplement 2000; 88:11.
  70. 70. Jackson TC, Hardin MD, Acuff GR. Heat resistance of Escherichia coli O157:H7 in a nutrient medium and in ground beef patties as influenced by storage and holding temperatures. J Food Prot. 1996; 59:8. pmid:10463438
  71. 71. Juneja VK, Snyder OP, Williams AC, Marmer BS. Thermal destruction of Escherichia coli 0157:H7 in hamburger. J Food Prot. 1999; 60:4. pmid:31207730
  72. 72. Ministerio de Salud. Encuesta Nacional de Nutrición y Salud 2004/5. Argentina2012. p. 54.
  73. 73. World Bank. 2022 [cited 2022 Aug 9]. Available from:
  74. 74. IPCVA. [2019] [cited 2022 Aug 9]. Available from:
  75. 75. Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev. 2015; 4:9. pmid:25554246
  76. 76. Smith BA, Fazil A, Lammerding AM. A risk assessment model for Escherichia coli O157:H7 in ground beef and beef cuts in Canada: Evaluating the effects of interventions. Food Control. 2013; 29(2):18.
  77. 77. Cassin MH, Lammerding AM, Todd ECD, Ross W, Stephen McColl R. Quantitative risk assessment for Escherichia coli O157:H7 in ground beef hamburgers. Int J Food Microbiol. 1998; 41 23. pmid:9631335
  78. 78. Lammerding AM, Fazil A, Paoli G, Desmarchelier P, Vanderlinde P. Shiga-toxinproducing E. coli in ground beef manufactured from Australian beef: Process improvement. Australia: Meat and Livestock. Australia., 1999.
  79. 79. Nauta MJ, Evers EG, Takumi K, Havelaar A. Risk assessment of Shiga-toxin producing Escherichia coli O157 in steak tartare in the Netherlands. In: Environment NIfPHat, editor. The netherlands: RIVM; 2001. p. 169.
  80. 80. Kiermeier A, Jenson I, Sumner J. Risk assessment of Escherichia coli O157 illness from consumption of hamburgers in the United States made from Australian manufacturing beef. Risk Anal. 2015; 35 (1):13. pmid:24984959
  81. 81. USDA, Assessment FtPR, Academies IoMotN. Escherichia coli O157:H7 in ground beef: Review of a draft risk assessment. In: Board BoHPaDPFaN, editor. Washington, D.C.: The National Academies Press; 2002.
  82. 82. EFSA-BIOHAZ. Scientific Opinion on the public health risks related to the maintenance of the cold chain during storage and transport of meat. Part 2 (minced meat from all species). EFSA J. 2014; 12 (7):30. doi: 10.2903/j.efsa.2014.3783
  83. 83. Duffy G, Cummins E, Nally P, O’ Briena S, Butler F. A review of quantitative microbial risk assessment in the management of Escherichia coli O157:H7 on beef. Meat Sci. 2006; 74(1):13. doi: 10. 1016/j.meatsci.2006.04.011
  84. 84. Gilbert SE, Whyte R, Bayne G, Paulin SM, Lake RJ, van der Logt P. Survey of domestic food handling practices in New Zealand. Int. J. Food Microbiol. 2007; 117(3): 306–311. pmid:17566578
  85. 85. Wachtel MR, Mcevoy JL, Luo Y, Williams-Campbell AM, Solomon MB. Cross-contamination of Lettuce (Lactuca sativa L.) with Escherichia coli O157:H7 via Contaminated Ground Beef. J Food Prot. 2003; 66(7):8. pmid:12870750
  86. 86. Schaffner DW, Schaffner KM. Management of risk of microbial cross-contamination from uncooked frozen hamburgers by alcohol-based hand sanitizer. J Food Prot. 2007; 70(1):5. pmid:17265868
  87. 87. Jackson LA, Keene WE, McAnulty JM, Alexander R, Diermayer M, Davis MA, et al. Where’s the Beef? The Role of Cross-contamination in 4 Chain Restaurant–Associated Outbreaks of Escherichia coli O157:H7 in the Pacific Northwest. Arch intern med. 2000; 160(15):6. pmid:10927738
  88. 88. FSAI. Microbiological safety of raw minced beef and beef burgers on retail sale in Ireland (11NS1). MicroBiology ed2013. Available from:
  89. 89. Shen C. Cooking inactivation of Shiga toxin producing Escherichia coli (STEC) in non-intact beefs—a mini-review. J Food Processing & Beverages. 2014;2(1): 5.
  90. 90. FSAI. An investigation of the most appropriate z-value to be used in calculating ‘equivalent cooks’ for beef burgers in food business establishments. Dublin, Ireland. 2018. p. 36. Available from:
  91. 91. Røssvoll E, Sorheim O, Heir E, Moretro T, Olsen NV, Langsrud S. Consumer preferences, internal color and reduction of shigatoxigenic Escherichia coli in cooked hamburgers. Meat Sci. 2014; 96:9. pmid:24200560
  92. 92. Rangel JM, Sparling PH, Crowe C, Griffin PM, Swerdlow DL. Epidemiology of Escherichia coli O157:H7 outbreaks, United States, 1982–2002. Emerg Infect Dis. 2005; 11(4):7. pmid:15829201
  93. 93. Kirk MD, Pires SM, Black RE, Caipo M, Crump JA, Devleesschauwer B, et al. World Health Organization estimates of the global and regional disease burden of 22 foodborne bacterial, protozoal, and viral diseases, 2010: A data synthesis. PLoS Med. 2015; 12(12):21. pmid:26633831
  94. 94. JEMRA. Joint FAO/WHO. Shiga toxin-producing Escherichia coli (STEC) and food: attribution, characterization, and monitoring. 2018. Microbiological risk assessment series 31. Available from:
  95. 95. Bolsa de Comercio de Rosario. 2021 [cited 2022 Aug 9]. Available from:
  96. 96. Rivas M, Chinen I, Guth BEC. Enterohemorrhagic (Shiga Toxin-Producing) Escherichia coli. In: Torres AG, editor. Escherichia coli in the Americas; 2016. pp. 97–124.
  97. 97. Área de Vigilancia de la Salud de la Dirección de Epidemiología. Ministerio de Salud de la Nación Argentina. Boletín Integrado de Vigilancia. N˚ 329—SE 39–2016. Ciudad Autónoma de Buenos Aires2016.
  98. 98. Rivas M, Miliwebsky E, Chinen I, Deza N, Leotta GA. Epidemiología del Síndrome Urémico Hemolítico en Argentina. Diagnóstico del agente etiológico, reservorios y vías de transmisión. Medicina (B Aires). 2006; 66(III):17.
  99. 99. Rivero M, Passucci P, Lucchesi P, Signorini M, Alconcher L, Rodríguez E, et al. Epidemiología del síndrome urémico hemolótico en dos regiones de la provincia de Buenos Aires. Medicina (B. Aires). 2013; 73:127–135
  100. 100. Fernandez-Brando RJ, Bentancor LV, Mejias MP, Ramos MV, Exeni A, Exeni C, et al. Antibody response to Shiga toxins in Argentinean children with enteropathic hemolytic uremic syndrome at acute and long-term follow-up periods. PLoS One. 2011; 6(4):7. pmid:21559455