Human monitoring analysis settings
Calculation settings
| Name | Type | Description |
|---|---|---|
Seed for pseudo-random number generator
|
Numeric |
A value of 0 will use a pseudo-random seed in each run, a value > 0 will provide the same results in a repeated run. |
Exposure type
|
ExposureType | The type of exposure considered in the assessment; acute (short term) or chronic (long-term). |
Multiple substances analysis
|
Boolean |
Specifies whether the assessment involves multiple substances. |
Compute cumulative exposures
|
Boolean |
Specifies whether the assessment involves multiple substances and results should be cumulated over all substances. |
Report consumptions and exposures per individual instead of per kg body weight
|
Boolean |
Specifies whether body weights should be ignored and consumptions and exposures should be expressed per individual. Otherwise, the consumptions and exposures are per kg body weight. |
Sampling method
|
AlphaNumeric |
The sampling method that should be included in the action. |
Censored values handling method (also used as fallback for censored lognormal approach)
|
NonDetectsHandlingMethod | Method for dealing with censored value samples in human monitoring data. Note that this method is also used as a fallback when fitting a censored lognormal model to the concentration data fails. |
Fraction for censored value replacement
|
Numeric |
Factor used for replacing the censored value. |
Imputation method for non detect values
|
NonDetectImputationMethod | Imputation method for non detect values: replace nondetects based on by f*LOD/LOQ) or from left tail censored lognormal distribution. |
Missing value imputation method
|
MissingValueImputationMethod | Imputation method for missing values: 1) By zero, 2) Impute from data, 3) No missing value imputation |
Biological matrix
|
BiologicalMatrix | The target biological matrix (internal compartment) for which the results are computed. Biological matrices from kinetic conversions will become available after selecting a data source in the kinetic models module. |
Apply kinetic conversions
|
Boolean |
Convert substance concentrations from other biological matrices using kinetic conversion models. The substances for conversion are designated within the kinetic models module. Substance conversion proves valuable when a biomarker was not directly measured for a matrix of interest. |
Convert to single exposure surface (biological matrix or external route)
|
Boolean |
Convert all substance concentrations from other biological matrices to the same target biological matrix. This conversion is applied when the number of substances measured on the target biological matrix is limited. Substances measured on other matrices can be converted using kinetic conversion models. |
Exposure surface level (external or internal)
|
TargetLevelType | The targeted exposure surface level of the HBM analysis. |
Specify the minimum percentage of non-missing values (%)
|
Numeric |
Specify the minimum percentage of non-missing values required for imputation. No imputation is done when the percentage of non-missing values in the data is smaller than the specified percentage. |
Standardise blood concentrations for lipid-soluble substances
|
Boolean |
Standardise blood concentrations for lipid-soluble substances: 1) Standardise by total lipid measured via gravimetric analysis, 2) Standardise by total lipid measured via enzymatic summation, 3) Standardise by derived total lipid content of Triglycerides/Cholesterol (Bernert et al. 2007). |
Specify the standardisation method of blood concentrations for lipid-soluble substances
|
StandardiseBloodMethod | Specify the standardisation method of blood concentrations for lipid-soluble substances. |
Subset selection: exclude substances from lipid standardisation
|
Boolean |
Select this option to exclude one or more lipid-soluble substances from standardisation. |
Select substances to exclude from lipid standardisation
|
AlphaNumeric |
The selected (lipid-soluble) substances will be excluded from lipid standardisation. |
Normalise or standardise urine concentrations for specific gravity or creatinine
|
Boolean |
Normalise or standardise urine concentrations for specific gravity or creatinine: 1) Normalise by specific gravity, 2) Standardise by creatinine concentration. |
Specify the normalisation/standardisation method of urine concentrations for specific gravity or creatinine
|
StandardiseUrineMethod | Specify the normalisation/standardisation method of urine concentrations for specific gravity or creatinine. |
Subset selection: exclude substances from urine normalisation/standardisation
|
Boolean |
Select this option to exclude one or more substances from normalization for specific gravity or creatinine standardisation. |
Select the substances to exclude from urine normalisation/standardisation
|
AlphaNumeric |
The selected substances will be excluded from urine normalization/standardisation. |
Substance weighting in mixtures
|
ExposureApproachType | Risk based: exposures in equivalents of the reference substance; standardised: standardised exposures per substance have variance 1; or unweighted exposures: RPFs are equal to 1. |
Perform MCR analysis
|
Boolean |
Perform a Maximum Cumulative Ratio (MCR) analysis to determine co-exposure between substances. |
Display ratio total exposure/ maximum (in MCR plot)
|
Numeric |
For MCR plot: specify ratio total exposure/ maximum for individual(day) exposures . |
Show tail percentiles (MCR plot) for:
|
Numeric |
Give specific percentiles of exposure distribution (%), e.g. 97.5 99 (space separated). |
Set minimum percentage contribution per substance to the tail exposure (MCR plot)
|
Numeric |
Set minimum percentage contribution per substance to the tail exposure. |
Cutoff MCR
|
Numeric |
For selection of individual(day) exposures with maximum cumulative ratio (MCR = total exposure/maximum) above the cutoff. |
Cutoff percentage in population ranked on total exposure
|
Numeric |
For selection of individual(day) exposures above the cutoff percentage in the set of individual(day)s ranked on total exposure. |
Apply exposure biomarker conversions
|
Boolean |
Use this option to translate HBM concentrations derived from measured substances (biomarkers) to concentrations of other substances. This can be usefull when the measured substance is a combination of multiple substances, e.g., to translate measured total arsenic (t-As) to toxicologically relevant arsenic (TRA). This option can also be used to translate between different expression types (e.g., from measured urine concentration to urine concentrations standardized for specific gravity), but not for translation between different biological matrices. |
Biological matrix
|
BiologicalMatrix | The target biological matrix (internal compartment) for which exposures are computed. |
Specific gravity conversion factor
|
Numeric |
A specific gravity adjustment is applied by multiplying a creatinine adjusted concentration with a factor (default 1.48 for adults 18 - 68 year). |
Output settings
| Name | Type | Description |
|---|---|---|
Exclude privacy sensitive data from outputs
|
Boolean |
Use this setting to not report the parts of the results (i.e., figures, tables, or sections) that are marked as (potentially) privacy sensitive. |
Lower percentage for variability (%)
|
Numeric |
The default value of 25% may be overruled. |
Upper percentage for variability (%)
|
Numeric |
The default value of 75% may be overruled. |
Percentage for upper tail
|
Numeric |
Gives detailed output for this upper percentage of the exposure distribution. |
Store simulated individual day exposures
|
Boolean |
Store the simulated individual day exposures. If unchecked, no additional output will be generated. If checked, the output will contain an additional section with the simulated individual day exposures. |
Uncertainty settings
| Name | Type | Description |
|---|---|---|
Lower uncertainty limit (%)
|
Numeric |
Percentage lower bound, e.g. 2.5%. |
Upper uncertainty limit (%)
|
Numeric |
Percentage upper bound, e.g. 97.5%. |
Monte Carlo iterations per uncertainty run
|
Numeric |
Specifies the number of Monte Carlo iterations in each uncertainty cycle (default 10,000). |
Resample HBM individuals
|
Boolean |
HBM individual data are resampled from the original database using the bootstrap methodology (Efron 1979, Efron & Tibshirani 1993). |