Any quantitative understanding of human risk from exposure to pesticides requires knowledge of both hazard (the intrinsic ability of a pesticide to cause harm) and exposure (absorbed dose), i.e., risk is directly proportional to the product of hazard and exposure. Thus, regardless of potential high hazard, risk may be insignificant if exposure is very low, and exposure-driven risk assessment is increasingly being recognized as being the best path forward for the protection of human health. In fact, regulatory agencies did not start doing quantitative risk assessments for pesticides using endpoints other than lethality until the 1970s in part because the analytical tools to sensitively measure exposure were lacking.

Quantifying exposure to pesticides required analytical methods such as gas chromatography and liquid chromatography that weren?t commercially available until the mid-1960s to early 1970s, respectively. With the advent of quadrapole mass spectroscopy in the early 1970s the ability to quantify sub milligram per kilogram bodyweight exposures to a wide variety of pesticides with confidence became commonplace. Analytical capability has continued to improve, and it is now possible to measure exposures in the nanogram and sometimes pictogram per kilogram range. As our quantitative knowledge of human exposure matured, it was desirable to extrapolate the knowledge from one chemical that had been measured to others that had not. Indeed, by the early 1980s it became evident that handler exposure to conventional pesticides was generic and not chemical specific. Part of the driving factor to do this modeling was that definitive exposure measurements for one chemical under one set of conditions was costly (>?100,000) and time consuming (months), and the combinations and permutations of exposure scenarios and pesticides are staggering.

Models allow us to estimate the exposure to a new active substance or rank exposure of one pesticide to others used in similar conditions. The objective of this paper is to present a brief overview of the range of human exposure models that are available, and the route or pathway of exposure for which they estimate dose with the hope that it provides an appreciation of the basic approaches, chronology and effort expended in developing them.

The choice of suitable normal foods is limited for individuals with particular medical conditions, e.g., inborn errors of metabolism (phenylketonuria – PKU) or severe cow’s milk protein allergy (CMPA). Patients may have dietary restrictions and exclusive or partial replacement of specific food groups with specially formulated products to meet particular nutrition requirements. Artificial sweeteners are used to improve the appearance and palatability of such food products to avoid food refusal and ensure dietary adherence. Young children have a higher risk of exceeding acceptable daily intakes for additives than adults due to higher food intakes kg^–1 body weight. The Budget Method and EFSA’s Food Additives Intake Model (FAIM) are not equipped to assess partial dietary replacement with special formulations as they are built on data from dietary surveys of consumers without special medical requirements impacting the diet. The aim of this study was to explore dietary exposure modelling as a means of estimating the intake of artificial sweeteners by young PKU and CMPA patients aged 1–3 years. An adapted validated probabilistic model (FACET) was used to assess patients’ exposure to artificial sweeteners. Food consumption data were derived from the food consumption survey data of healthy young children in Ireland from the National Preschool and Nutrition Survey (NPNS, 2010–11). Specially formulated foods for special medical purposes were included in the exposure model to replace restricted foods. Inclusion was based on recommendations for adequate protein intake and dietary adherence data. Exposure assessment results indicated that young children with PKU and CMPA have higher relative average intakes of artificial sweeteners than healthy young children. The reliability and robustness of the model in the estimation of patient additive exposures was further investigated and provides the first exposure estimates for these special populations.

Background:

The feasibility of using a retailer fidelity card scheme to estimate food additive intake was investigated in an earlier study. Fidelity card survey information was combined with information provided by the retailer on levels of the food colour Sunset Yellow (E110) in the foods to estimate a daily exposure to the additive in the Swiss population. As with any dietary exposure method the fidelity card scheme is subject to uncertainties and in this paper the impact of uncertainties associated with input variables including the amounts of food purchased, the levels of E110 in food, the proportion of food purchased at the retailer, the rate of fidelity card usage, the proportion of foods consumed outside of the home and bodyweights and with systematic uncertainties was assessed using a qualitative, deterministic and probabilistic approach. An analysis of the sensitivity of the results to each of the probabilistic inputs was also undertaken. The analysis identified the key factors responsible for uncertainty within the model and demonstrated how the application of some simple probabilistic approaches can be used quantitatively to assess uncertainty.