Combining dietary and non-dietary exposures
When individuals are exposed to dietary and/or non-dietary exposure sources, the exposure is combined and aggregated at the internal level. Three methods are available for aggregating the exposure of individual(days) coming from oral, dermal and/or inhalation exposure routes. Individuals are matched:
completely at random,
based on individual properties like age, gender, or other properties.
based on their individual ID,
For method 1, no additional information is required, while method 3 is the most restrictive one, see also population alignment methods.
In general, dietary and non-dietary exposures are available for surveys that are independently taken (different populations, populations in different countries or separated in time, etc…). What should be noted is that each survey has a unique set of individuals and that there are no individuals that appear in two or more surveys. For such data, the only way to relate individuals in various surveys is to use surveys properties like sex, age group or other propertie, e.g. males, females between 18-45 years, children in the age of 0 to 5 years, etc.
When various surveys do not have common individuals matching based on the individual ID (method 3) is not possible and matching of individuals in different surveys should be based on individual properties (method 2) or completely at random (method 1).
Matching completely at random
For each individual in the dietary survey a random nondietary individual(day) from the nondietary surveys is sampled or vice versa and the exposures are aggregated. Note that even when surveys contain common individuals, matching completely at random is still possible.
Matching based on individual ID
When both dietary and non-dietary surveys contain common individuals, those individuals that have identical ID’s are matched Each individual in the food survey is matched to a nondietary exposure of an individual with an identical ID.
Matching based on individual properties
When both dietary and non-dietary surveys contain common individuals, dietary and non-dietary exposures of indiviuals can be matched based on the individual ID, e.g. for all Individuals of the food survey also a nondietary exposure is available or vice versa and the exposures are aggregated (method 3: individuals are matched based on the individual Id). Any nondietary exposure that does not correspond to an individual in the food survey will be ignored and vice versa (see Example 2). This may be too restrictive and the assumption can be relaxed by specifying the individual ID in the nondietary survey as Id = General. In that case, the individual properties of the dietary individual should meet the survey properties of the non-dietary survey.
When multiple non-dietary surveys are available, option Match individuals of multiple non-dietary surveys is relevant. When matching (correlation) is chosen, the exposure contributions of non-dietary individuals with identical Id in different surveys are combined and allocated to a dietary individual. When no correlation is chosen, the non-dietary exposures of randomly selected individuals from different surveys are combined and allocated to a dietary individual.
The user may also define demographic criteria for the assignment (for each source of non-dietary exposure) to indicate that those exposures are relevant only to a defined sub-population. Only those individuals in the food survey who meet the criteria of the non-dietary survey will be assigned non-dietary exposures from that source e.g. only males aged 18 to 65 (see Example 1). The simplest assessment consists of a single (deterministic) non-dietary exposure estimate which is assigned to all individuals in the food survey (Id = General). This case, and more complex possibilities are illustrated below using hypothetical examples (method 2: individuals are match based on individual properties).
Figure 69 Aggregate exposure distributions.
Example 1
Deterministic cumulative (multi-substance) non-dietary exposure input, adult male sub-population. Unmatched case.
idIndividual |
idNonDietarySurvey |
idSubstance |
Dermal |
Oral |
Inhalation |
|---|---|---|---|---|---|
General |
1 |
011003001 |
10 |
5 |
17 |
General |
1 |
011003002 |
34 |
20 |
18 |
General |
1 |
011003003 |
56 |
43 |
19 |
idNonDietarySurvey |
Description |
Location |
Date |
NonDietaryIntakeUnit |
|---|---|---|---|---|
1 |
BROWSE, acute, cumulative, operators |
York |
09/10/2012 |
\(\mu g/day\) |
idNonDietarySurvey |
PropertyIndividualName |
IndividualPropertyTextValue |
IndividualPropertyDoubleMinValue |
IndividualPropertyDoubleMaxValue |
|---|---|---|---|---|
1 |
Age |
18 |
65 |
|
1 |
Gender |
Male |
In this example, there are exposure values for multiple substances in Table 188 and the user has provided Table 190 which specifies that the non-dietary exposures given in survey number 1 relate to males aged 18 to 65.
When this assessment is performed, only those individuals whose property values fit the criteria in Table 190 will receive the non-dietary exposures in survey 1. The use of idIndividual = General indicates that this is the default exposure. All individuals in the dietary survey meeting the criteria receive all exposures given in the 3 rows, corresponding to 3 substances. The following should be noted:
There should only ever be one General entry in the non-dietary exposures table per substance x survey combination.
The property names and the values of any text properties must precisely match those given in the IndividualProperties and IndividualPropertyValues tables for this to work.
The minimum and maximum values for numeric properties are both inclusive boundaries.
So in this example, all males aged 18 to 65 will receive the given exposures of all three substances and the other members of the population will receive no non-dietary exposure. Note that example 1 describes the unmatched case.
Example 2
Variability (but no uncertainty) in cumulative non-dietary exposure input (matched to dietary survey individuals).
idIndividual |
idNonDietarySurvey |
idSubstance |
Dermal |
Oral |
|---|---|---|---|---|
5432 |
1 |
011003001 |
10 |
5 |
5432 |
1 |
011003002 |
33 |
22 |
5433 |
1 |
011003001 |
12 |
7 |
5433 |
1 |
011003002 |
34 |
23 |
5434 |
1 |
011003001 |
18 |
9 |
5434 |
1 |
011003002 |
35 |
25 |
5435 |
1 |
011003001 |
10 |
5 |
5435 |
1 |
011003002 |
33 |
21 |
idNonDietarySurvey |
Description |
Location |
Date |
NonDietaryIntakeUnit |
|---|---|---|---|---|
1 |
BROWSE, acute, cumulative, operators |
York |
09/10/2012 |
\(\mu g/day\) |
In this example, the non-dietary exposures are being specified explicitly for individuals in the dietary population. Switch ‘matching’ on to allow exposure variability to be specified at the individual level. For the purposes of illustration, the population is extremely small, consisting of only four individuals. The values in the idIndividual column of Table 191 match those in the Individuals table (dietary population).
It is not mandatory to specify exposures for every individual in the population. Those not included will simply receive a zero non-dietary exposure, unless there is also a default exposure value (General row(s) in Table 191) and the individual matches the specified demographic criteria for the survey, as specified in Table 190. (In this example, a default exposure would apply to all individuals not listed in Table 191 because Table 190 has not been used).
There is variability between individuals in this example, but no uncertainty in exposure. Note that these data could also be used with matching switched off. This would be the same as treating the idIndividual values as generic individuals, so that each pair of exposure lines would be assigned at random to individuals meeting the criteria.
Example 3
Variability (no uncertainty) in cumulative non-dietary exposure input (unmatched individuals).
idIndividual |
idNonDietarySurvey |
idSubstance |
Dermal |
Oral |
Inhalation |
|---|---|---|---|---|---|
ND1 |
1 |
011003001 |
10 |
5 |
17 |
ND1 |
1 |
011003002 |
33 |
22 |
45 |
ND2 |
1 |
011003001 |
12 |
7 |
18 |
ND2 |
1 |
011003002 |
34 |
23 |
47 |
ND3 |
1 |
011003001 |
18 |
9 |
19 |
ND3 |
1 |
011003002 |
35 |
25 |
49 |
ND4 |
1 |
011003001 |
10 |
5 |
17 |
ND4 |
1 |
011003002 |
33 |
21 |
45 |
idNonDietarySurvey |
Description |
Location |
Date |
NonDietaryIntakeUnit |
|---|---|---|---|---|
1 |
BROWSE, acute, cumulative, operators |
York |
09/10/2012 |
\(\mu g/day\) |
idNonDietarySurvey |
PropertyIndividualName |
IndividualPropertyTextValue |
IndividualPropertyDoubleMinValue |
IndividualPropertyDoubleMaxValue |
|---|---|---|---|---|
1 |
Age |
50 |
65 |
|
1 |
Gender |
Female |
This example is similar to example 2, except that the values in the idIndividual column of Table 193 do not match those in the Individuals table. In this instance, ‘matching’ would not be an option, and the non-dietary exposures would be randomly assigned to individuals who meet the criteria in Table 195. (In fact for the same result rather than changing the values in the idIndividual column in Table 191 from the previous example may be used with matching switched off). Exposures in Table 193 will be recycled if the number of exposure rows is less than the number of dietary records with which to aggregate exposures.
Again, there is variability between individuals in this example, but no uncertainty in exposure.
By allowing generic idIndividual values in this way, correlations between different sources (within individual) can be accounted for even in the unmatched case. If the same idIndividual value is used in different surveys, then the corresponding exposure values will be kept together and assigned to an eligible individual as a combined exposure.
So for option matching switched of, it is relevant whether individuals are correlated or not. In the following example, two non-dietary surveys are available, per survey three individuals are specified.
idIndividual |
idNonDietarySurvey |
idSubstance |
Dermal |
Oral |
Inhalation |
|---|---|---|---|---|---|
ND0 |
1 |
011003001 |
10 |
5 |
17 |
ND1 |
1 |
011003001 |
23 |
22 |
45 |
ND2 |
1 |
011003001 |
12 |
7 |
18 |
ND0 |
2 |
011003001 |
34 |
23 |
47 |
ND3 |
2 |
011003001 |
18 |
9 |
19 |
ND4 |
2 |
011003001 |
33 |
16 |
35 |
When a correlation is applied, the non-dietary exposure for individual ND0 from survey 1 and 2 are combined and allocated to a dietary individual. For individual ND1, ND2, ND3 and ND4 just a single non-dietary exposure is found and allocated to a dietary individual.
When no correlation is applied, the exposure for individual ND0 from survey 1 is combined with one of the exposures of ND0, ND3 or ND4 from survey 2; exposure of ND1 from survey 1 is combined with one of the exposures of ND0, ND3 or ND4 from survey 2, etc.
When the intention is to sample just one exposure for a dietary individual, specify per survey different codes, e.g. ND1, ND2, ND3 for survey 1, ND4, ND5, ND6 for survey 2 and apply correlation, or specify 6 different individual codes and just one idNonDietarySurvey. Then, options with or without correlation are irrelevant and sampling results are identical no matter which option is chosen.
Figure 70 Contributions by route to aggregate exposure distributions.