Comparison of human health risks resulting from exposure to fungicides and mycotoxins via food

Food and Chemical Toxicology 47 (2009) 2963–2974

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Comparison of human health risks resulting from exposure to fungicides and mycotoxins via food Stefan D. Muri a,*, Hilko van der Voet b, Polly E. Boon c, Jacob D. van Klaveren c, Beat J. Brüschweiler a a

Federal Office of Public Health, Nutritional and Toxicological Risks Section, Stauffacherstrasse 101, CH-8004 Zurich, Switzerland Biometris, Wageningen University and Research Centre, P.O. Box 100, NL 6700 AC Wageningen, The Netherlands c RIKILT – Institute of Food Safety, Wageningen University and Research Centre, P.O. Box 230, NL 6700 AE Wageningen, The Netherlands b

a r t i c l e

i n f o

Article history: Received 14 July 2008 Accepted 26 March 2009

Keywords: Deoxynivalenol Margin of exposure Risk perception Spiroxamine Tebuconazole Zearalenone

a b s t r a c t The interest in holistic considerations in the area of food safety is increasing. Risk managers may face the problem that reducing the risk of one compound may increase the risk of another compound. An example is the potential increase in mycotoxin levels due to a reduced use of fungicides in crop production. The Integrated Probabilistic Risk Assessment (IPRA) model was used to compare the estimated health impacts on humans caused by crops contaminated with the fungicides spiroxamine (SPI) and tebuconazole (TEB) or with the mycotoxins deoxynivalenol (DON) and zearalenone (ZEA). The IPRA model integrates a distribution characterising the exposure of individuals with a distribution characterising the susceptibility of individuals towards toxic effects. Its outcome, a distribution of Individual Margins of Exposure (IMoE), served as basis to perform comparisons of compounds, effects, countries, and population groups. Based on the available data and the assumptions made, none of the four compounds was found to have impact on human health in the addressed scenarios. The IMoE distributions were located as follows: DON < TEB = ZEA < SPI, showing DON to be the compound with the highest potential for negative health impacts. The presented approach can help risk managers to prioritise risk-reduction measures. Ó 2009 Elsevier Ltd. All rights reserved.

1. Introduction Until now, risk managers have mainly dealt with health risks related to one compound by comparing the outcome of an exposure assessment with a health based guidance value like the Acceptable Daily Intake (ADI) or the Acute Reference Dose (ARfD). It has become clear in the last few years that – apart from traditional risk assessment – more holistic considerations are desirable with many foods, like in a risk-benefit analysis (EFSA, 2006). An example is eating fish containing important nutrients such as polyunsaturated fatty acids, but also contaminants such as dioxins. Another example is the potential increase in mycotoxin levels due to a reduced Abbreviations: ADI, Acceptable Daily Intake; AGH, adrenal gland hypertrophy; ARfD, Acute Reference Dose; BW, body weight; CES, critical effect size; CED, critical effect dose; CZ, Czech Republic; DK, Denmark; DON, deoxynivalenol; FBW, foetal body weight; FOPH, Federal Office of Public Health; HIC, health impact criterion; ICED, individual critical effect dose; IMoE, individual margin of exposure; IPRA, integrated probabilistic risk assessment; JECFA, FAO/WHO Joint Expert Committee on Food Additives; JMPR, FAO/WHO Joint Meeting on Pesticide Residues; LOR, limit of reporting; MRL, maximum residue level; NL, The Netherlands; NOAEL, noobserved-adverse-effect-level; OCL, oestrous cycle length; PMTDI, provisional maximum tolerable daily intake; RAC, raw agricultural commodity; RBC, red blood cell; SCF, Scientific Committee on Food; SPI, spiroxamine; TDI, tolerable daily intake; TEB, tebuconazole; ZEA, zearalenone. * Corresponding author. Tel.: +41 43 322 21 18; fax: +41 43 322 21 99. E-mail addresses: [email protected], [email protected] (S.D. Muri). 0278-6915/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.fct.2009.03.035

use of fungicides in crop production (risk–risk comparison). To come to a balanced decision in these cases, risk managers need to weigh all potential consequences of a certain policy or advice. In order to compare risks when dealing with two compounds with different toxicological adverse effects (as in the example of fungicides and mycotoxins), the Integrated Probabilistic Risk Assessment (IPRA) model may be a useful tool (van der Voet and Slob, 2007). This model integrates a distribution characterising the diverse exposure between individuals with a distribution characterising the variable susceptibility between individuals towards toxic effects. Its outcome is a distribution of Individual Margins of Exposure (IMoE). Calculating the IMoE distribution for each compound may help risk managers to weigh risk and benefit of a certain policy and to prioritise risk-reduction measures. In this paper, we will demonstrate the use of the IPRA model as a case study to compare human health risks resulting from exposure to fungicides and mycotoxins via food. These compounds may be regarded as counteracting agents since fungicides are applied to protect crops from being infected by fungi and thus prevent the growth of mycotoxin producing organisms, but potentially lead to residues of the applied fungicides on or in the crop. Fungicides are a type of pesticides which are thoroughly studied concerning their toxic potential. For each pesticide a full toxicological data package produced according to regulatory guidelines is mandatory for market approval. In contrast, mycotoxins are

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generally far less studied and thus, only little data – often from non-standard toxicity tests – are available. Although the toxicity of fungicides is thoroughly tested, a substantial part of the data is confidential and therefore not available in the public domain. In contrast, mycotoxins occur naturally; there is no registration process and no company is responsible for a specific compound. Toxicity tests for mycotoxins are therefore conducted on a voluntary basis and the results are usually published in scientific journals, making them available in the public domain. It has been claimed that the occurrence of mycotoxins is higher in organic agriculture compared to conventional agriculture. The reason for this claim seems to be logical since the use of fungicides is forbidden in organic farming and therefore neither the growth of fungi nor the production of mycotoxins is inhibited. However, several studies showed that the type of agricultural system has a lower impact on the occurrence of mycotoxins than other parameters. That is to say, weather was found to be the most important factor. Warm and humid weather promotes fungal growth and thus the occurrence of mycotoxins in or on the crops. Moreover, the preceding crop and differences between varieties have a stronger influence on the occurrence of mycotoxins than the type of agricultural system (Schachermayr and Fried, 2000). Another interesting feature of the comparison between fungicides and mycotoxins is the difference in risk perception of the compounds by consumers and scientists. Consumers often object to synthetic compounds present in food, whereas the awareness of mycotoxins is much lower. In fact, the general public perceives risks related to pesticides as posing a greater hazard than mycotoxins (Williams and Hammitt, 2001). Most involved scientists on the other hand consider the health risks associated with fungicide residues in foods as low compared to the potential impacts of mycotoxins on public health. In the present paper, only risks to human health are included; potential risks of ecological, economical or social nature are outside the scope of this work. Furthermore, potentially important factors influencing levels of fungicide residues and mycotoxin contamination such as weather, season, produce origin or type of agricultural system are not considered.

sponse, termed the critical effect size (CES)1. This CEDanimal was then extrapolated to a CED for the average human (CEDhuman) based on an allometric relation as proposed by Bokkers and Slob (2007). Interspecies differences can be divided into two aspects: differences in body size, and differences in toxicokinetics and/or toxicodynamics: CED ratios between different animal species were found to conform to lognormal distribution with a geometric mean explained by allometric scaling and a geometric standard deviation 2.0 (Bokkers and Slob, 2007). This distribution was taken to describe uncertainty regarding toxicodynamics and toxicokinetics (see van der Voet et al., 2009). Table 1 lists the basic data on body weight (BW) and the derived interspecies extrapolation factors (EFinter). A lognormal distribution around this average CEDhuman value was assumed to represent the variation in human sensitivity. This was based on the default assumption that a sensitive individual (defined as the 5th percentile in the sensitivity distribution) would be 2–10 times more sensitive than the average human. A value drawn from this distribution is termed individual critical effect dose (ICED). The ICED is the dose level which results in exactly the CES in a particular person. See van der Voet et al. (2009) for details. Variability and uncertainty in the different input parameters (both exposure and effect) are modelled separately in IPRA by a two-dimensional Monte Carlo approach. Modelling variability makes it possible to quantify risks for the population in terms of percentage of people having an IMoE below a certain value. In the IPRA model, variability in consumption, BWs and sensitivities of individuals is modelled as well as the variability in fungicide residue concentrations of different foods. Uncertainty is modelled for all numerical inputs of the model: consumption data, concentration data, animal dose–response data and inter- and intraspecies extrapolation. It is performed either by re-sampling datasets (e.g. for consumption, concentration and dose–response data) or by sampling from an explicit uncertainty distribution (e.g. for the interspecies factor and the parameter describing the amount of intraspecies variation). The fact that the Monte Carlo simulation is performed with a finite number of iterations adds a sixth source of uncertainty (labeled MC) to all results, but if this number is large enough this will have a very small effect. For explanation of the uncertainty factors in detail see van der Voet and Slob (2007). Modelling uncertainty provides the possibility to specify a confidence interval for IMoE percentiles. The lower 95% confidence limit on a particular percentile (e.g. p1) may then be considered in risk management. The total quantified uncertainty in any specific model output can often be partitioned to relative contributions from each of the six sources of uncertainty (Monte Carlo, consumption data, concentration data, animal dose–response data, interspecies extrapolation, intraspecies factor). This procedure has been fully described in van der Voet and Slob (2007). Shortly, the whole uncertainty computation is repeated 25 times in a full factorial structure with the uncertainty in the latter 5 factors being switched on or off. The results of each of the 32 calculations was summarised as the uncertainty variance in a selected model output. This set of 32 variances was then analysed by a simple linear model with terms indicating the switch status of the factors, to find the relative uncertainty contributions for each of the 5 factors (the slopes), as well as the Monte Carlo component of uncertainty which was present in all 32 analyses (the intercept).

2. Methods

2.2. The selected compounds

2.1. The integrated probabilistic risk assessment

The compounds were selected according to the following two criteria: First, data on concentration levels, consumption levels and toxicity of the compound must be available. Secondly, the mycotoxins selected must be produced on the field. By contacting several experts in the area of fungicides and mycotoxins, the following four compounds were selected: The fungicides spiroxamine (SPI) and tebuconazole (TEB) as well as the mycotoxins deoxynivalenol (DON) and zearalenole (ZEA). TEB is active against fungi producing both mycotoxins, whereas SPI is used to enhance the uptake of TEB in plants. The mixture of SPI and TEB tends to be more effective in preventing DON formation (Suty-Heinze, personal communication). Compared to other mycotoxins, DON and ZEA have qualitatively and quantitatively better data on concentration, consumption and toxicity. Fungicides against Fusarium fungi, the producers of DON and ZEA, are effective only as preventive measures, e.g. by preventing fungal growth (Frei, personal communication). They are not effective anymore when there are any infestations in or on the crop. Therefore, their effectiveness depends highly on the time of application: the closer the treatment is to the time of infection, the higher the effectiveness (Ellner, 2006). Other fungicides to be applied explicitly against Fusarium fungi in spikes are for example prothioconazole and metconazole (Poiger, personal communication). But they were not considered here because concentration and toxicity data were not available to us. Aflatoxins and ochratoxin A, mycotoxins occurring in food and being of importance due to their carcinogenic potency (IARC, 1993), were not selected because they are produced mainly during storage and not on the field (JECFA, 2001).

Van der Voet and Slob (2007) proposed a model for an integrated probabilistic risk assessment (IPRA). The IPRA model integrates exposure assessment and hazard characterisation in a probabilistic way. The basic idea of this model is to estimate the distribution of both the individual critical effect dose (ICED) and the individual exposure (IEXP) in a population. The ICED and IEXP distribution are combined via Monte Carlo analysis, which randomly unites ICED levels with IEXP levels, resulting in a distribution of individual margins of exposure (IMoE). An IMoE = 1 indicates that ICED and IEXP are equal. The IMoE distribution can then be used for the various comparisons. Assuming that, for example, the first percentile (p1) of the IMoE distribution equals 1, this would mean that 1% of the population is estimated to be at risk of having an exposure at levels modelled to represent a possible health concern. Exposure assessments in IPRA probabilistically integrate data on concentration levels measured in foods (see Section 2.3.3) with data on consumption levels of those foods (see Section 2.3.4). If available, data on processing effects on the concentrations may be incorporated, but were not used in this study. Depending on the type of risk, exposure can be calculated for both acute and chronic risks. In assessments of acute risk, a population of individuals at one random day is considered whereas in chronic assessments the usual intake over a longer (unspecified) period of time is taken into account. Exposure calculations in IPRA are the same as those in the Monte Carlo Risk Assessment (MCRA) program using the betabinomial-normal model for chronic risk assessments (de Boer and van der Voet, 2007). The hazard characterisation part of IPRA consists of a probabilistic integration of dose–response modelling, interspecies (animal to human) extrapolation and intraspecies extrapolation accounting for the variation in sensitivity within a human population. More specific, for each relevant combination of compound and toxicological effect an appropriate dose–response model was fitted to toxicity data from animal studies to derive a critical effect dose (CEDanimal) according to Slob (2002). The CED is defined as the dose corresponding to a predetermined change in re-

1 The terms CES and CED were introduced by Slob and Pieters (1998) to distinguish dose–response modelling with continuous data from that with quantal data (see Section 2.3.1). Both terms are used also for quantal data in this paper.

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b

c

M+F F

11.5 11.1

75.4 70.7

1.76 1.74

Mouse Dog

F M+F

0.06 12.5

70.7 75.4

8.43 1.71

Developmental toxicity Chronic toxicity

Mouse Mouse

F M+F

0.06 0.04

70.7 75.4

8.22 9.40

Reproduction toxicity Developmental toxicity

Pig Rat

F F

130 0.32

70.7 70.7

0.83 5.07

Compound

Study type

Species

Sexes

SPI

Chronic toxicity

Dog

TEB

Developmental toxicity Chronic toxicity

DON ZEA

a

BWhum (kg)

EFinter

SPI, spiroxamine; TEB, tebuconazole; DON, deoxynivalenol; ZEA, zearalenone; M, males; and F, females. a Body weight averages obtained from animal studies. b Body weight averages calculated from Dutch National Food Consumption Survey, ages 20–80 years (both sexes, or females only, i.e. the factor is also applied to women of child-bearing age because the use of a restricted age range (e.g. 15–45 years) would have required the definition of an equivalent age range for animals, which in turn would have called for an animal study performed exactly on animals in the same age range). c Interspecies extrapolation factor estimated according to the formula EFinter = (BWhum/BWani)1 0.7, for details see Bokkers and Slob (2007).

2.2.1. Spiroxamine SPI is a foliar fungicide for use on barley, wheat, rye and triticale. It inhibits the steroid synthesis in fungi. Its acute toxicity is low, but it provokes severe skin irritation due to alkalinity. An ADI of 0.025 mg/kg body weight/day was established based on changes in the liver observed in a 1-year oral study in dogs and a no-observed-adverse-effect-level (NOAEL) of 2.5 mg/kg BW/day. Hepatocytomegaly was one of the observed changes in the liver. An ARfD was not considered necessary to derive (EC, 1999). 2.2.2. Tebuconazole TEB is a triazole fungicide acting by inhibition of ergosterol biosynthesis (WHO, 1995). It can be used as a seed dressing against various cereal diseases and as a foliar spray to control numerous pathogens in various crops (BCPC, 1997). TEB has low acute toxicity, but it provokes adrenal gland hypertrophy in chronic dog studies as well as developmental effects (reduced BW of foetus) and, at higher doses, also teratogenic effects (increase of common malformations) in mice. A 1-year dietary study in dogs served as the basis to establish an ADI of 0.03 mg/kg BW/day (WHO, 1995), based on histopathological alterations in the adrenals at 4.5 mg/kg BW and a NOAEL of 3.0 mg/kg BW/day. 2.2.3. Deoxynivalenol The mycotoxin DON is produced mainly by the species Fusarium graminearum and F. culmorum and may occur in various cereal crops such as wheat, maize, barley, oats, and rye. Infections mainly take place at the time of flowering. The most critical factor for toxin production is the time of rainfall, rather than the amount (Codex, 2002). DON inhibits the synthesis of DNA, RNA and proteins (Schlatter, 2004), but does not present a carcinogenic hazard as it was stated in the latest available evaluation by JECFA (2001). The induction of apoptosis particularly in lymphatic and haematopoietic tissues and the haemolysis of erythrocytes are other common effects (SCF, 2002). Abdominal pain, dizziness, headache, throat irritation, nausea, diarrhea, and blood in the stool are signs of DON poisoning in humans (Pieters et al., 2002). Since another characteristic feature of DON is the induction of vomiting, it is also called vomitoxin (SCF, 2002). The former Scientific Committee on Food (SCF) established a tolerable daily intake (TDI) of 0.001 mg/kg BW/day, based on a NOAEL of 0.1 mg/kg BW/day for growth retardation in a 2-year study in mice (SCF, 2002). 2.2.4. Zearalenone ZEA is a mycotoxin produced by several Fusarium species. Mould invasion and subsequent toxin production occurs mainly at the pre-harvest stage. It nearly always co-occurs with other Fusarium toxins such as DON (EFSA, 2004). ZEA is found worldwide in a number of cereal crops such as maize, barley, oats, wheat, rice and sorghum. By binding to oestrogen receptors in mammals, ZEA induces various oestrogenic but no teratogenic effects. Its acute toxicity after oral administration is low. A temporary TDI of 0.0002 mg/kg BW/day was established by SCF (2000), whereas JECFA established a provisional maximum tolerable daily intake (PMTDI) of 0.0005 mg/kg BW (JECFA, 2000). Both decisions were based on estrogenic effects and a NOAEL of 0.04 mg/kg BW/day obtained in a 15-day study in pigs, the most sensitive species. 2.3. Data 2.3.1. Toxicity data Table 2 gives an overview of the effects considered in the comparisons of the selected compounds. We aimed at selecting the effect with the lowest NOAEL determined in an experimental animal study and one additional effect. For this purpose,

toxicological evaluations by international bodies (e.g. EFSA, JECFA, JMPR) and national authorities (FOPH, Switzerland) were searched for the four selected compounds. Then, the original study containing detailed data was retrieved. Additional effects were chosen in order to compare risks between effects of the same compound and because hardly any compound provokes only one particular effect. The additional effects were chosen on the basis of the same toxicological evaluation as for the corresponding critical effect, i.e. they are effects that occurred at slightly higher doses in the same experimental animal study, but are preferably of different nature. The toxicity of a compound is influenced by the duration of the exposure. A short exposure to a high dose (e.g. by a meal) may provoke different effects than the repeated exposure over years to low doses. Furthermore, most compounds provoke various effects depending on dose and duration of exposure. Acute exposure is usually defined as exposure for a period up to 24 h, whereas the term ‘chronic’ refers to an exposure covering a substantial amount of an animal’s life span. In this paper, we treat developmental and teratogenic effects as acute effects (relevant for TEB, DON and ZEA) because they might potentially occur after a single exposure when the compound is administered during pregnancy in the period of organogenesis. Particularly malformations, skeletal effects, and resorptions are regarded as relevant endpoints (van Raaij et al., 2003). The effect ‘prolonged oestrous cycle length’ caused by ZEA was measured in a period of 15 consecutive days during oestrus. Therefore, it can not be regarded as an acute effect. On the other hand, 15 days is too short to consider it as chronic exposure. Thus, it was decided to address it in the calculations as both an acute and chronic effect. The effects measured in toxicity studies can result in different data types: continuous, quantal, ordinal and multinomial data. Only the former two types are considered here. Continuous data relate to a numerical measure of an effect (e.g. decrease in foetal BW), whereas quantal data express the incidence of a particular effect (e.g. the incidence of malformations in a group of animals). Quantal data are derived from binary data which can have only two values: a positive or negative response. The population addressed was dependent on the relevancy of the adverse effects (see Table 2). For example, a reduction of foetal BW was supposed to be relevant only for women of child-bearing age. The availability of data on concentration levels and on food consumption levels, together with the restriction in terms of people potentially affected, led to a set of calculations given in Tables A1 and A2 of the appendix. It should be noted that the occurrence of an adverse effect does not necessarily result in a negative health impact which can be related directly to a disease. However, some of the endpoints are indicators for or precursors of a disease. For example, decreased red blood cell (RBC) count may indicate anaemia or decreased BW may lead to growth retardation. 2.3.2. The health impact criteria One established concept to evaluate health impacts is the Disability Adjusted Life Years (DALY) approach (Murray, 1994). However, the DALY approach is mainly applicable for ranking human diseases and life years lost. For many toxicological effect parameters it is hardly possible to specify the consequences in terms of life years lost. To compare risks when dealing with compounds with different adverse effects (like in the comparison of fungicides and mycotoxins), a semi-quantitative model for health impact valuation was developed (Bos et al., 2009). In this model, the potential impact on human health is divided into four categories which are separated by three levels of health impact, called health impact criteria (HIC):  HIC-1, level separating the categories ‘no health impact’ from ‘low health impact’  HIC-2, level separating the categories ‘low health impact’ from ‘moderate health impact’

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Table 2 Exposure duration, data type, data source, groups supposed to be more sensitive for the specified effect than the normal population, and health impact criteria for the selected effects. Compound

Effect

Exposure

Data type

Data source

Sensitive population group

SPI

Decreased RBC count Hepatocytomegaly* Decreased foetal body weight Adrenal gland hypertrophy* Malformations Decreased body weight* Prolonged oestrous cycle length* Decreased foetal body weight

Chronic

Continuous Quantal Continuous Quantal Quantal Continuous Continuous Continuous

Notifier dossier

Children + adolescents (aged Children + adolescents (aged Females (aged 15–45 years) Children + adolescents (aged Females (aged 15–45 years) Children + adolescents (aged Females (aged 15–45 years) Females (aged 15–45 years)

TEB DON ZEA

Acute Chronic Acute Chronic Sub-acute Acute

Notifier dossier Khera et al. (1982) Iverson et al. (1995) Edwards et al. (1987) Collins et al. (2006)

4–19 years) 4–19 years) 4–19 years) 4–19 years)

HIC-1

HIC-2

HIC-3

5% HIC-1 5% HIC-2 HIC-3 5% 10% 5%

10%

20%

10%

20%

10% 20% 10%

20% 40% 20%

SPI, spiroxamine; TEB, tebuconazole; DON, deoxynivalenol; ZEA, zearalenone; RBC, red blood cell; and HIC, health impact criterion. Effect with lowest NOAEL.

*

 HIC-3, level separating the categories ‘moderate health impact’ from ‘high health impact’ The HICs used for this risk comparison are listed in Table 2 for the selected effects. It is assumed that HICs at the same level for different effects have an equally adverse health impact. For example, 5% decrease in RBC count and 5% decrease in BW are both assumed to have a low health impact. HIC-1 corresponds to the CES used for dose–response modelling. The CES indicates the dose level between nonadverse and adverse changes in toxicological endpoints; values of 5% or 10% change are usually selected. In this paper, we used a CES of 5% for all continuous effects except for ‘prolonged oestrous cycle length’ induced by ZEA. For this effect, we regarded a CES of 10% as being appropriate because the effect was considered less severe than the other continuous effects. However, it should be noted that there is no consensus on the value of a CES to be used for a specific effect (Dekkers et al., 2001). It is stressed that CES and HIC values used here serve only as examples for the application of the approach and are not meant to represent definitive values for the effects considered. In this work, HIC-2 was set to twice the value of HIC-1 (=CES) and HIC-3 to twice the value of HIC-2. We aimed at setting three HIC levels per effect following the concept of Bos et al. (2009). This was however only meaningful for effects with continuous data. With quantal data, there is no information about doses with a lower or higher impact than the scored effect. The shape of the dose–response curved is highly influenced by variation between animals which is irrelevant for the variation between models. Therefore, the ED50 (=dose at which 50% of the tested animals show an effect) is used to represent the CED. The occurence of the event itself is the health effect, and a judgment of its severity has been used to define its HIC level (Bosgra, 2007). Depending on the assumed health impact being low, moderate or severe, the ED50 for a toxicological endpoint was considered to represent the CED at HIC-1, HIC2 or HIC-3 respectively. It is stressed that the allocation of quantal effects to impact levels serves only for illustrative purposes and does not represent a definitive toxicological assessment.

via two 24-h recalls. The repeated recall was within a period of one to six months after the first recall and addressed another day of the week. Amounts consumed were estimated using either photographs of portions for the most frequently consumed meals or measuring guides such as spoons, cups, etc. Since part of the analyses for the selected compounds was performed in raw agricultural commodities (RAC), the consumption of foods as recorded in the three different food surveys were translated to the consumption of RACs. For more details see Boon et al. (2009).

3. Results 3.1. Calculation of ICED and IEXP distributions Dose–response modelling was applied to all compounds, effects and HIC levels to derive the corresponding CEDanimal values (see Fig. 1). These data were combined with the inter- and intraspecies extrapolation to estimate the distributions of the ICED (see Fig. 2). For both CEDanimal and ICED, the 90% confidence intervals were estimated and are represented by the outer curves (thin red lines) in Fig. 2. Table 3 lists the CEDanimal and the CEDhuman, which is the central value of the ICED distribution, for every considered HIC level. Note that the CEDhuman for the hormonal effect of ZEA is higher than the CEDanimal due to an EFinter below 1 (see Table 1). The occurrence data of the four compounds differed in the year in which the analyses were performed: either the samples were

2.3.3. Occurrence data Concentration data used in the assessments were all derived from national monitoring programmes performed in the different countries. An overview of the occurrence data used for the different compounds is provided in Table A3 of the appendix. The Dutch concentration data are stored in the Quality Agricultural Products Database (KAP, van Klaveren et al., 2006). 2.3.4. Consumption data The food consumption data included in the present study are from the Netherlands (NL), Denmark (DK) and Czech Republic (CZ). Besides the whole population of these countries, the health impact on specific groups was also analysed, including children (up to age 19) and pregnant women. However, due to lack of consumption data on this group, it was assumed that the food pattern of women of child-bearing age (15–45 years) was representative for this group. Data from NL were derived from the Dutch National Food Consumption Survey of 1997/1998 (Anonymous, 1998; Kistemaker et al., 1998). In this survey, 6250 respondents aged 1–97 years (of which 1638 children aged 1–19 and 1603 women of child-bearing age) recorded their food consumption over two consecutive days. The amount eaten was weighed. For DK, we used food consumption data from the National Food Consumption Survey conducted during 2000–2002 (Lyhne et al., 2005). In this survey, 4120 persons aged 4–75 years (of which 1059 children aged 4–19 and 992 women of childbearing age) recorded their food consumption during seven consecutive days (7days dietary record). Amounts consumed were estimated using photographs of portion sizes. Food consumption data of CZ were derived from a food consumption survey (SISP04) conducted between November 2003 and November 2004 (Ruprich et al., 2006). In this study, 2590 persons aged 4–90 years (of which 772 children aged 4–19 and 473 women of child-bearing age) were asked about their eating habits

Fig. 1. Dose–response of SPI in dogs, effect ‘decreased RBC count’; triangles indicate geometric mean responses, squares the arithmetic responses; diamond shows the CEDanimal at HIC-1 = 5%.

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Fig. 2. ICED distribution of SPI, effect ‘decreased RBC count’ (bold line); 90% uncertainty limits (small lines).

from several consecutive years (NL and DK) or from only one year. There were also differences in the total number of analyses per-

formed (much higher in NL than in CZ and DK) and in the limit of reporting (LOR). The number of analyses above the LOR was similar among the three countries, except for DON where 73% (DK) and 81% (CZ) positive samples were found compared to 11% in NL. See Table A3 of the appendix for details where also the five products with the highest percentage levels above LOR for each compound and each country are listed. It can be seen in Table A4 of the appendix that the estimated intakes of SPI, TEB and ZEA were very low, even at the 99th percentile. Only the intakes of DON reached values between 0.1 and 0.4 lg/kg BW/day at the 50th percentile and partly more than 1 lg/kg BW/day at the 99th percentile. The IMoE distributions were calculated by combining the ICED and the IEXP distributions for each effect with continuous data at HIC-1 and with quantal data at HIC-1, -2 or -3 (Table 2). The resulting IMoE values for both effects of SPI and TEB and for the effect ‘prolonged oestrous cycle length’ of ZEA were very high (see Tables A1 and A2 of the appendix), thus representing no risk. Since it was clear that the IMoEs for these effects at HIC-2 and HIC-3 levels would be even higher, we did not perform the calculations for these levels. Moreover, for the effect ‘decreased foetal BW’ of TEB, the dose–response curve was so steep that the resulting CEDanimal at the HIC-2 and HIC-3 level was outside the range of observations in the animal experiment. Consequently, CEDanimal values would have been derived by extrapolation, which means additional uncertainty.

Table 3 CEDanimal and CEDhuman for every HIC value, in mg/kg BW/day. Compound

Species

Sexes

Effect

HIC

CEDanimal

CEDhuman

SPI

Dog

TEB

Mouse Dog Mouse Mouse

M+F F F M+F F M+F

Decreased RBC count Hepatocytomegaly Decreased foetal body weight Adrenal gland hypertrophy Malformations Decreased body weight

Pig Rat

F M+F

Prolonged oestrous cycle length Decreased foetal body weight

5% n.a. 5% n.a. n.a. 5% 10% 20% 10% 5% 10% 20%

25.8 25.1 96.0 3.47 2.21 0.27 0.56 1.18 2.04 1.48 3.04 6.49

14.7 14.3 11.1 1.98 0.29 0.03 0.06 0.09 2.31 0.28 0.63 1.26

DON

ZEA

(18.6–50.8) (17.2–30.7) (79.2–122) (2.54–4.43) (2.04–2.35) (0.25–0.30) (0.52–0.61) (1.10–1.29) (1.91–2.21) (1.46–1.51) (2.99–3.09) (6.40–6.60)

(5.00–86.8) (4.10–45.4) (3.52–39.8) (0.72–6.48) (0.07–0.96) (0.01–0.10) (0.02–0.20) (0.03–0.42) (0.75–8.15) (0.10–0.77) (0.21–1.91) (0.50–4.28)

SPI, spiroxamine; TEB, tebuconazole; DON, deoxynivalenol; ZEA, zearalenone; M, males; F, females; RBC, red blood cell; HIC, health impact criterion; and CEDanimal, critical effect dose in the average animal; CEDhuman, critical effect dose in the average human; n.a., not applicable; a HIC value cannot be applied to quantal endpoints; these endpoints are directly categorised as low, moderate or severe health impact. See text for further explanation. The 90% confidence interval around the estimated CEDanimal and the CEDhuman are given in brackets.

Fig. 3. IMoE distributions of SPI and DON for children and adolescents in NL; bars show IMoE distribution (p1-p99) representing variability in the population groups; error bars show uncertainty, and extend to the lower 95% confidence limit on p1 and the upper 95% confidence limit on p99. HEPA, hepatocytomegaly; RBC, decreased red blood cell count; and BW, decreased body weight.

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Fig. 4. Uncertainty sources of P (IMoE < 106) for SPI (RBC/5%), at the low health impact level and limited to Dutch children and adolescents; MC, Monte Carlo simulation; cons, consumption data; conc, concentration data; animal, animal dose–response data; inter, interspecies factor; and intra, intraspecies factor.

tion the distributions could be compared directly. The IMoEs for SPI were three to four orders of magnitude higher than the IMoEs for DON. The lower uncertainty bar of the IMoE distribution for DON at HIC-1 level was close to 1. Figs. 4 and 5 show the percental contribution of the six input parameters to the overall uncertainty regarding specific characterisations of the IMoE distributions of SPI (RBC/5%) and DON (BW/ 5%), respectively. For this purpose, the probability of an IMoE to be lower than a certain threshold value was considered. In both cases, interspecies extrapolation contributed most to the overall uncertainty of the analyses: 64% for SPI and 95% for DON. For TEB and ZEA it was possible to make a comparison between a fungicide and a mycotoxin causing the same effect. The IMoE distributions given in Fig. 6 concern the effect ‘decreased foetal BW’ in Danish women of child-bearing age (aged 15–45). The estimated low health impact from TEB and ZEA were almost equal and far from representing a risk. The distributions for ZEA at HIC-2 and HIC-3 were shifted to higher IMoEs. For both TEB and ZEA, interspecies extrapolation contributed for nearly 90% to the overall uncertainty in P (IMoE < 104) and P (IMoE < 105) respectively (results not shown). The remaining 10% resulted from intraspecies variation (TEB) or from concentration data (ZEA). 3.3. Other comparisons Comparisons of health impacts using the IMoE distribution figures can also be made between effects, between countries or between population groups. Below, some examples of such comparisons are shortly addressed.

Fig. 5. Uncertainty sources of P (IMoE < 100) for DON (BW/5%), at the low health impact level and limited to Dutch children and adolescents; MC, Monte Carlo; cons, consumption data; conc, concentration data; animal, animal dose–response data; inter, interspecies factor; and intra, intraspecies factor.

3.3.1. Comparison of effects Fig. 7 shows the IMoE distributions for the two effects of ZEA limited to Czech women of child-bearing age. It can be seen that the distributions for the effect ‘decreased foetal BW’ were at lower IMoEs than for the effect ‘prolonged oestrous cycle length’. Addressing the effect ‘prolonged oestrous cycle length’ as either an acute or a chronic effect did not affect this conclusion; the distributions were only shifted to lower IMoE values. In the acute model, consumption levels of relevant foods on an arbitrary day were combined with concentrations of these foods. High levels of consumption on a certain day may have resulted in a high exposure level. In the chronic model, high consumption levels were mostly equalised by combining days of high with days of low or zero consumption, resulting in lower exposure levels, and thus higher IMoE values.

3.2. Comparison among compounds Fig. 3 gives the IMoE distributions of SPI and DON in NL for children and adolescents (aged 4–19). Due to the same target popula-

3.3.2. Comparison of countries By comparing IMoE distributions of a particular effect by the same compound at the same HIC level in the same population

Fig. 6. IMoE distributions of TEB and ZEA for effect ‘foetal body weight’ for women of child-bearing age in DK; bars show IMoE distribution (p1–p99) representing variability in the population groups; error bars show uncertainty, and extend to the lower 95% confidence limit on p1 and the upper 95% confidence limit on p99.

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Fig. 7. IMoE distributions for ZEA for women of child-bearing age in Czech Republic; FBW, decreased foetal body weight; OCL, prolonged oestrus cycle length; acute, addressed as acute effect; chronic, addressed as chronic effect; bars show IMoE distribution (p1–p99) representing variability in the population groups; error bars show uncertainty, and extend to the lower 95% confidence limit on p1 and the upper 95% confidence limit on p99.

Fig. 8. IMoE distributions for DON on the effect ‘body weight’ at HIC level 1; bars show IMoE distribution (p1–p99) representing variability in the population groups; error bars show uncertainty, and extend to the lower 95% confidence limit on p1 and the upper 95% confidence limit on p99.

group, similarities or differences in exposure between countries became apparent. In Fig. 8, we have plotted the IMoE distributions for NL, DK and CZ for the effect of DON on BW at HIC-1 (5% decrease). The distributions of the countries differed only slightly in the magnitude; the distribution for NL was shifted more to higher IMoE levels. Since the same toxicity data were used for all countries, the result indicated that exposure levels tended to be lower in NL compared to DK and CZ. However, although it might seem that children and adolescents in DK and CZ were potentially more at risk, it should be noted that differences between countries were small compared to variation within countries and uncertainty. Several factors may have influenced this result. The LOR of CZ was lower than in NL resulting in a higher fraction of levels reported to be above the LOR (81% in CZ vs. 11% in NL). In addition,

100% of the samples of three food items consumed on more than 50% of the days included in the survey were positive in CZ (data not shown). Finally, the Czech occurrence data were only from the year 2006 whereas the Dutch data included the years 2002 until 2006. This averaging may have reduced the influence of one year with many rainy days leading to an increased mycotoxin production. 3.3.3. Comparison of population groups Another option is to compare IMoE distributions of population groups within the same country. It may be important to know which group is estimated to have the lowest IMoE. To illustrate such a comparison, the IMoE distributions for the two effects of TEB were compared between different population groups in DK. Fig. 9 shows that Danish children and adolescents had a slightly

Fig. 9. IMoE distributions for TEB in DK; AGH, adrenal gland hypertrophy; FBW, decreased foetal body weight; bars show IMoE distribution (p1–p99) representing variability in the population groups; error bars show uncertainty, and extend to the lower 95% confidence limit on p1 and the upper 95% confidence limit on p99.

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Fig. 10. First percentile (p1) of IMoE distributions for all compounds.

lower IMoE for the effect ‘adrenal gland hypertrophy’ than the whole population. The IMoE distribution for the effect ‘decreased foetal BW’, potentially affecting only women of child-bearing age, was shifted to the right. Although the latter could not be directly compared with the other distributions due to different HIC levels (HIC-2 vs. HIC-1), a conclusion may be that the estimated health impact for women aged 15–45 was less important than the health impact for the whole population. Since the lower uncertainty bar of all distributions was far away from an IMoE of 1, TEB exposure was not expected to cause any health impact. 3.4. Summary comparison Fig. 10 summarises the calculations performed for each compound. It gives p1 of the IMoE distributions. None of the values indicated a health concern for the different compounds, effects, countries and population groups for at least 99% of the population. The IMoEs for DON were about two to three orders of magnitude lower than the IMoEs for TEB and ZEA; overall, the values were smallest for DON followed by TEB, ZEA and SPI. The outlier of TEB at an IMoE of 107 related to the effect ‘decreased foetal BW’ in CZ. A very low exposure was the reason for the large difference compared to the other values of TEB (see Table A4 of the appendix).

4. Discussion 4.1. Risk comparisons We demonstrated in this case study how distributions of IMoE estimated by combining distributions of IEXP levels with distributions of ICEDs could be used to compare risks between compounds, effects, countries and population groups. The IMoE distributions were truncated at 107, which is considered a very high level of safety. An IMoE of 1 (where IECD = IEXP) was taken as the level below which there may be a health concern. Based on this cut-off point, all IMoE distributions revealed that the estimated exposure to all compounds is safe, i.e. health impacts of the studied effects are not expected to occur for at least 99% of the population. DON was the compound with the lowest IMoEs. The graphical representation of the IMoE distribution helps to identify potential differences between compounds, effects, countries and population groups. Comparisons of effects based on continuous and quantal data can not be made unconditionally unless the selected HICs are at the same impact level. The summary figure can be used to compare several compounds with each other. All conclusions are dependent on assumptions regarding the statistical distributions, and even where empirical data are available they allow only a limited view on these distributions. Therefore, one should realise that with other assumptions conclusions may turn out different (a sensitivity analysis could address some

of these concerns). A common concern in risk assessment is that a large amount of extrapolation is needed to predict health effects to be expected for very small proportions of the population, e.g. one in a million. The example shown here illustrates a more modest approach where no attempt is made to see below the first percentile of the distribution of individual margins of exposure. If this percentile is far above 1 this might give some confidence that there is no real risk for the overwhelming majority of the population, but we do not attempt an exact quantification of the true probabilities or health effects at very low levels. If concentrations of different compounds are analysed in the same (group of) foods, it is possible to generate separate IMoE distributions per food (group) and to compare the corresponding health impacts. Or it can be analysed whether the health impact of the food item contributing most to exposure is different between the whole population and specific groups such as children. By comparing risks between countries, differences in the way food consumption data were collected (e.g. dietary assessment method, food coding) and differences in monitoring practices (e.g. targeted vs. random sampling, LOR) should also be taken into account. During this work, several data gaps were observed. The unavailability of concentration and toxicity data influenced the compound selection (see Section 2.1). For SPI, there were only concentration data from NL, which made it impossible to compare IMoE distributions between countries. Particularly for mycotoxins, toxicity data suitable for dose–response modelling were hardly available. The situation is comparable or even worse for natural toxins (Muri et al., 2009). In case of poor or incomplete datasets an approach allowing for differences in data quality (e.g. by choosing a higher cut-off point than 1) may be needed. Uncertainty was taken into account in all comparisons, shown as error bars of the IMoE distribution. The input parameter contributing most to the overall uncertainty was the interspecies extrapolation, which was specified to be mainly uncertain due to toxicokinetic and toxicodynamic differences between experimental animals and humans as shown by Bokkers and Slob (2007). Thus, it remains a challenge to gain further insight into this field and to incorporate the knowledge into approaches for risk assessment. The selection of HIC levels is a crucial step in the presented approach. It may be difficult to assess the severity of an effect and select appropriate HIC levels consistent with HICs for other effects. For example, ‘prolonged oestrous cycle length’ caused by ZEA might be considered an endocrine disrupting effect. In this case, it might be more appropriate to select lower HIC levels than we did. Furthermore, it might be argued that selecting only one HIC level for quantal data seems unbalanced compared to continuous data. This issue must be seen in connection with the characteristics of quantal data. As described in Section 2.3.2, it is only meaningful to determine the ED50 as HIC level although it has to be stressed that the ED50 as point of departure is rather high and therefore

S.D. Muri et al. / Food and Chemical Toxicology 47 (2009) 2963–2974

needs to be dealt with deliberately. But quantal data are very common and need to be taken into account in risk assessment. We did not use an additional safety (or uncertainty) factor to derive the ICED for quantal effects, which might be a point of discussion in the further development of the IPRA model. The proposed procedure is recommended as long as no other approach is available. Furthermore, it is beyond the scope of this paper how to set HIC levels; its scope was to show the principle of this approach in risk–risk comparisons. HIC levels may be set by consensus of a group of scientists from multiple disciplines, risk managers and other involved stakeholders. 4.2. Risk perception of scientists and consumers As shown in Fig. 10 and discussed in Section 4.1, the IMoE distributions for SPI and TEB (and ZEA) are at such high levels (>1000) that they are far from representing a risk to human health. Nevertheless, some consumers are concerned about the potential health effects caused by pesticides. For example, pesticide residues were mentioned among the most worrying factors in a survey on risk issues (EC, 2006). Furthermore, risks caused by external factors (e.g. pesticides) are perceived as involuntary and uncontrollable, which in turn may lead to the perception that the risks are more threatening (Houghton et al., 2006). Basically, consumers judged natural compounds to be less risky than synthetic ones. Thus, it is not surprising that they also believe that organically grown produces will pose fewer risks than conventionally grown produces (Williams and Hammitt, 2001). Many consumers feel safe although it is known in science that cereal products from organic agriculture are not automatically free of health risks (BfR, 2007). In November 2007, the German Federal Institute for Risk Assessment (BfR) organised a conference on risk assessment and risk management issues where risk perception was one of the topics. It was mentioned that perceived risks may occur because consumers do not understand or misinterpret scientific risk assessments. Secondly, people may think that all risks and associated uncertainties can be overcome if technological and administrative efforts are high enough. Finally, risk communication may be blamed because it has failed to clearly inform the public about the risks (BfR, 2007). Furthermore, it was discussed whether and how perceived risks should be taken into account in risk analysis. Even if public concerns are unfounded from a scientific point of view, risk managers should take them seriously and consider them in decision making. Otherwise, the public may lose confidence in the institutions. In order to avoid that, perceived risks should be addressed by an open, comprehensible risk communication taking into account the position of science on the one hand and the positions of the various stakeholders on the other. But not only the results of a risk assessment should be communicated, it is also important to disclose associated uncertainties and potential gaps in data and knowledge (BfR, 2007). With regard to the latter requirement, the IPRA model is an improvement because uncertainty is quantitatively taken into account. The model also specifies the data source contributing most to the overall uncertainty. This allows identifying where more effort is needed for improving the database and in turn, reducing uncertainty. Furthermore, the IMoE distribution figures help to visualise differences in potential health risks and may thus be a comprehensible medium for risk communication.

pare risks, to weigh risks and to prioritise risk-reduction measures. We are well aware that there are also ecological, economical or social aspects associated with fungicides and mycotoxins. For example, fungicides may have impacts on soil and groundwater, or a shortage of corn contaminated with mycotoxins may have economical consequences. If it is aimed to perform a comparison as complete as possible, these aspects need to be also addressed (http://www.safefoods.nl). It may be of interest to know whether the selected four compounds may have combined effects. Because in reality, humans are simultaneously exposed to a number of compounds via food, and such a combined exposure could be of concern even if the exposure to either of the compounds individually does not pose a health risk. However, combined effects of SPI, TEB, DON and ZEA are not likely to occur because the four compounds have different modes of action. A review in the scientific literature yielded only one study where DON, ZEA and ochratoxin A were administered to pigs, either alone or in combination. It was concluded that a clear additive or synergistic effect was not observed (Speijers and Speijers, 2004). 4.4. Conclusions This case study demonstrated that for the selected effects at the HIC levels examined neither the two fungicides nor the two mycotoxins gave reason for concern to cause a health risk in humans for at least 99% of the population. The IMoEs were smallest for DON followed by TEB, ZEA and SPI. The selection of HIC levels is a crucial step in the presented approach. It may be difficult to assess the severity of an effect and select appropriate HIC levels consistent with HICs for other effects. In the present case study, the input parameter contributing most to the overall uncertainty was the interspecies extrapolation. The IMoE distribution figures help to visualise differences in potential health risks and can help risk managers to prioritise riskreduction measures. Conflict of interest statement The authors declare that there are no conflicts of interest. Acknowledgments The authors gratefully acknowledge financial support by the European Commission under the project SAFE FOODS (CT-2004506446). The authors thank Josef Schlatter (FOPH) for the critical review of the manuscript; Anne Suty-Heinze, Bayer CropScience, Monheim, Germany, Peter Frei, Federal Office for Agriculture (FOAG), Switzerland and Thomas Poiger (FOAG) for information on fungicides; Susanne Vogelgsang (FOAG) and Gabriela Wyss, Research Institute of Organic Agriculture (FIBL), Frick, Switzerland, for information on mycotoxins; Gerie van der Heijden and Waldo de Boer, Wageningen University and Research Centre (WUR), the Netherlands, for implementation of the IPRA method; Gemma Janer and Bas Bokkers, Dutch National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands, for support in defining the interspecies factor distributions. Appendix

4.3. Applications in practice and possible extensions The presented approach is intended as a tool for risk managers to serve as a basis for decision making in situations where different risks need to be compared and prioritised. It can help them to com-

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See See See See

Table Table Table Table

A1. A2. A3. A4.

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Table A1 First (p1), 50th (p50) and 99th (p99) percentile of the IMoE distribution for SPI and TEB. Compound

Effect

HIC level

HIC value

Country

Population affected

IMoE p1

IMoE p50

IMoE p99

SPI

Decreased RBC count

1

5%

NL

Hepatocytomegaly

1

n.a.

NL

Decreased foetal body weight

1

5%

Adrenal gland hypertrophy

2

n.a.

CZ DK NL CZ

Whole population 4–19 Whole population 4–19 Females 15–45 Females 15–45 Females 15–45 Whole population 4–19 Whole population 4–19 Whole population 4–19

1.85E+06 1.65E+06 1.82E+06 1.21E+06 9.69E+06 2.28E+04 7.06E+04 3.05E+05 2.70E+05 1.19E+04 9.82E+03 4.61E+04 3.37E+04

9.78E+06 9.73E+06 9.78E+06 9.58E+06 9.84E+06 9.76E+06 9.81E+06 3.40E+06 1.39E+06 8.97E+04 7.69E+04 3.15E+05 2.41E+05

1.00E+07 1.00E+07 1.00E+07 9.99E+06 1.00E+07 1.00E+07 1.00E+07 9.98E+06 7.19E+06 8.64E+05 7.70E+05 2.35E+06 1.95E+06

TEB

DK NL

SPI, spiroxamine; TEB, tebuconazole; RBC, red blood cell; HIC, health impact criterion; n.a., not applicable; a HIC value cannot be applied to quantal endpoints; these endpoints are directly categorised as low, moderate or severe health impact. See text for further explanation. NL, the Netherlands; CZ, Czech Republic; and DK, Denmark.

Table A2 First (p1), 50th (p50) and 99th (p99) percentile of the IMoE distribution for DON and ZEA. Compound

Effect

HIC level

HIC value

Country

Population affected

IMoE p1

IMoE p50

IMoE p99

DON

Malformations

3

n.a.

Decreased body weight

1

5%

2

10%

3

20%

1

10%

CZ DK NL CZ DK NL CZ DK NL CZ DK NL CZ

Females 15–45 Females 15–45 Females 15–45 4–19 4–19 4–19 4–19 4–19 4–19 4–19 4–19 4–19 Whole population Females 15–45 Whole population Females 15–45 Females 15–45 Females 15–45 Females 15–45 Females 15–45 Females 15–45

1.57E+02 1.36E+02 1.50E+02 1.16E+01 1.43E+01 2.91E+01 2.42E+01 3.00E+01 6.05E+01 5.04E+01 6.36E+01 1.27E+02 2.51E+05 3.05E+05 2.07E+05 2.44E+05 3.38E+04 2.90E+04 7.11E+04 6.10E+04 1.57E+05

1.61E+03 2.35E+03 8.94E+03 6.51E+01 8.76E+01 2.15E+02 1.33E+02 1.81E+02 4.41E+02 2.83E+02 3.84E+02 9.38E+02 9.83E+06 9.83E+06 9.72E+06 9.72E+06 9.83E+06 2.10E+06 9.83E+06 4.49E+06 9.83E+06

9.95E+06 9.95E+06 9.98E+06 3.92E+02 5.85E+02 1.62E+03 8.01E+02 1.22E+03 3.35E+03 1.70E+03 2.59E+03 7.17E+03 1.00E+07 1.00E+07 9.99E+06 9.99E+06 1.00E+07 9.99E+06 1.00E+07 9.99E+06 1.00E+07

ZEA

Prolonged oestrous cycle length

DK Decreased foetal body weight

1

5%

2

10%

3

20%

CZ DK CZ DK CZ

DON, deoxynivalenol; ZEA, zearalenone; HIC, health impact criterion; n.a., not applicable; a HIC value cannot be applied to quantal endpoints; these endpoints are directly categorised as low, moderate or severe health impact. See text for further explanation. CZ, Czech Republic; DK, Denmark; and NL, the Netherlands.

Table A3 Summary of the occurrence data per compound. Compound

Country

Years

Total number of analyses

LOR (mg/kg)

Number of analyses above LOR (%)

Mean positive concentration (mg/kg product)

Top five products with highest percentage levels above LOR

SPI

NL

3693

0.05

59 (1.6)

0.0497

TEB

NL

2002– 2006 2002– 2003 2002– 2003 2003

1765

0.05

100 (5.7)

0.1139

839

0.01

75 (8.9)

0.0702

Table grapes; plums; potato; pear; tomato/ strawberry Japanese persimmon; Savoy cabbage; garden peas; leek; Brussel sprouts Tomato; celeriac; sweet pepper; leek; peach

2 (3.4)

0.2100

Peach; leek

2002– 2006 1998– 2003 2006

3880

0.02 – 2.00* 0.05

415 (11)

0.1351

157

0.02

115 (73)

0.1185

Wheat; semolina powder; Dutch rusk; maize; barley Wheat; rye bread

243

0.0185

197 (81)

0.0964

1998– 1999 2006

86

0.0005

24 (28)

0.0011

0.00075

21 (18)

0.0034

DK CZ DON

NL DK CZ

ZEA

DK CZ

59

116

Bread; coarse semolina; wheat germ; wheat flour; muesli White bread; wheat; rye bread Ready-to-eat cereals; toasting bread; corn flakes; rice; wholemeal bread

SPI, spiroxamine; TEB, tebuconazole; DON, deoxynivalenol; ZEA, zearalenone; NL, the Netherlands; CZ, Czech Republic; DK, Denmark; and LOR, limit of reporting. Depending on the food analysed.

*

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S.D. Muri et al. / Food and Chemical Toxicology 47 (2009) 2963–2974 Table A4 50th (p50), 90th (p90) and 99th (p99) percentile of the intake distribution for SPI, TEB, DON and ZEA in lg/kg BW/day. Compound

Country

People affected

Exposure

Intake p50

Intake p90

Intake p99

Mean intake

Maximum intake

SPI

NL

TEB

CZ

Whole population 4–19 Whole population 4–19 Females 15–45 Whole population 4–19 Females 15–45 Whole population 4–19 Females 15–45 4–19 Females 15–45 4–19 Females 15–45 4–19 Females 15–45 Whole population

Chronic Chronic Chronic Chronic Acute Chronic Chronic Acute Chronic Chronic Acute Chronic Acute Chronic Acute Chronic Acute Acute Chronic Acute Chronic Acute Chronic Acute Chronic

0.001 0.001 0.000 0.000 0.000 0.020 0.030 0.000 0.010 0.010 0.000 0.440 0.173 0.330 0.118 0.130 0.032 0.000 0.000 0.000 0.000 0.000 0.001 0.000 0.001

0.002 0.003 0.000 0.000 0.000 0.050 0.060 0.094 0.010 0.020 0.006 0.690 0.454 0.560 0.472 0.270 0.233 0.000 0.001 0.000 0.000 0.003 0.001 0.002 0.001

0.004 0.004 0.000 0.000 0.000 0.080 0.100 0.325 0.020 0.030 0.129 0.950 0.914 0.820 1.186 0.490 1.345 0.007 0.001 0.006 0.000 0.008 0.002 0.006 0.002

n.c. n.c. n.c. n.c. 0.001 n.c. n.c. 0.027 n.c. n.c. 0.008 n.c. 0.217 n.c. 0.197 n.c. 0.114 0.000 n.c. 0.000 n.c. 0.001 n.c. 0.001 n.c.

n.c. n.c. n.c. n.c. 2.038 n.c. n.c. 2.731 n.c. n.c. 5.480 n.c. 3.372 n.c. 5.045 n.c. 32.26 0.149 n.c. 0.030 n.c. 0.034 n.c. 0.024 n.c.

DK

NL

DON

CZ DK NL

ZEA

CZ

Females 15–45 DK

Whole population Females 15–45

SPI, spiroxamine; TEB, tebuconazole; DON, deoxynivalenol; ZEA, zearalenone; NL, the Netherlands; CZ, Czech Republic; DK, Denmark; and n.c., not calculated.

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