Polychlorodibenzo-4-dioxin

All pollutant data posted by the NCHS before December 2008 were accessed and downloaded, which yielded 196 pollutants from 17 subclasses as categorized by the NCHS: serum perfluorinated compounds; urinary heavy metals; urinary total arsenic and speciated arsenics; urinary total (elemental plus inorganic) mercury; serum organochlorine pesticides; serum polybrominated diphenyl ethers (PBDEs); urinary polyaromatic hydrocarbons; urinary phthalates; serum polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), and coplanar polychlorinated biphenyls (PCBs); serum non–dioxin-like PCBs; urinary organophosphate insecticides; urinary perchlorate; urinary environmental phenols; urinary iodine; blood lead, mercury (total and inorganic), and cadmium; serum cotinine; and blood volatile organic compounds [for the full list of chemicals in each subclass, see Supplemental Material, Table 1 (doi:10.1289/ehp.1002720)]. An additional subclass, coplanar PCBs, was constructed by selecting only these chemicals from the broader “PCDDs, PCDFs, and coplanar PCBs” subclass. A second subclass for total PCBs was then created by combining the non–dioxin-like PCBs and coplanar PCBs subclasses.
All ALT and pollutant levels were measured in biologic samples collected on the same day from each individual participant. We evaluated only pollutants with a ≥ 60% detection rate [111 of 196 pollutants; see Supplemental Material, Table 1 (doi:10.1289/ehp.1002720)] to avoid bias in estimation for those pollutants with levels < the lower limit of detection (Lee et al. 2007a (link), 2007b (link)). Concentrations of organic pollutants measured in serum (non–dioxin-like PCBs; dioxins, furans, coplanar PCBs; PBDEs; organochlorine pesticides) were lipid adjusted, and concentrations of pollutants measured in urine were adjusted for creatinine [Supplemental Material, Table 1 (doi:10.1289/ehp.1002720)] (Schwartz et al. 2003 (link)).

The method used to perform the human exposure assessment is described in the strategy in Annex A.1 of this Scientific Opinion.
The CONTAM Panel considered that only chronic dietary exposure had to be assessed. As suggested by the EFSA Working Group on Food Consumption and Exposure (EFSA, 2011a), dietary surveys with only one day per subject were excluded from the current assessment because they are not adequate to assess repeated exposure. Similarly, subjects who participated only 1 day in the dietary studies, when the protocol prescribed more reporting days per individual, were also excluded from the chronic exposure assessment. When, for one particular country and age class, two different dietary surveys were available only the most recent one was used.
For calculating the chronic dietary exposure, food consumption and body weight data at the individual level were accessed in the Comprehensive Database. Occurrence data and consumption data were linked at the relevant FoodEx level (see also Section 3.2.1). For each individual of the selected surveys, the mean occurrence values of the different food samples collected (pooled European occurrence data) were combined with the average daily consumption of the corresponding food items, and the resulting exposures per food were summed in order to obtain the total chronic exposure at individual level (divided by the respective individual body weight). The mean and the 95th percentile of the individual exposures were subsequently calculated for each dietary survey and each age class separately. All analyses were performed using the SAS Statistical Software (SAS enterprise guide 5.1).
It is well‐known that fish from certain areas may contain relatively high levels of PCDD/Fs and DL‐PCBs. This applies not only to eel from contaminated rivers and lakes, but also to various fatty fish species from, e.g. the Baltic Sea. When included in the occurrence data this may result in an overestimation of the exposure from such fish in areas where they are not consumed, and vice versa. Fish from, e.g. the Baltic Sea should be monitored to a higher extent and as such there may be a bias towards relatively high levels in the data submitted to EFSA. However, when compliant with the MLs (including measurement certainty), this fish can be put on the EU market and as such it seems not correct to exclude them from the database. It is known that in the EU, the large majority of the salmon and trout on the market is farmed, and it is widely consumed.18 In order to investigate the impact of excluding wild salmon and trout from the data set, exposure was calculated including and excluding wild salmon and trout (see Section 3.5.1).
For matching the occurrence and the consumption data, occurrence data from samples of solid tea and herbs for infusions, and infant formulae were converted to the corresponding beverages by applying specific factors (75 and 8, respectively) which are commonly used in other similar EFSA opinions.
To perform the exposure assessment, the occurrence data that are expressed on a fat weight basis, were combined with the fat per cent of the consumed foods as it is reported in the national consumption surveys in the Comprehensive Database. Fat contents available in the Comprehensive Database are included according to the national composition tables of Member States. Where the fat content was missing in the consumption database, the random hot‐deck imputation method was used to complete this information. This technique consists of replacing the missing value with an observed one, which is randomly drawn from values corresponding to samples sharing ‘similar’ characteristics. In the case of fat content, the ‘similar’ characteristic was defined by the kind of food or food group, according to the different levels of hierarchy of the FoodEx1 catalogue.