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Transcript of Modelling cumulative risk Hilko van der Voet Biometris, DLO, Wageningen University and Research...
![Page 1: Modelling cumulative risk Hilko van der Voet Biometris, DLO, Wageningen University and Research Centre Third ACROPOLIS consortium meeting 31 March 2011,](https://reader030.fdocuments.us/reader030/viewer/2022032702/56649cda5503460f949a4a51/html5/thumbnails/1.jpg)
Modelling cumulative risk
Hilko van der VoetBiometris, DLO, Wageningen University and Research Centre
Third ACROPOLIS consortium meeting
31 March 2011, Milano
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Contents
• State of the art for modelling of single pesticides– Exposure assessment– Risk assessment
• ACROPOLIS: modelling for multiple pesticides in a Common Assessment Group– Cumulative exposure assessment– Cumulative risk assessment
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Risk assessment
A.G. Renwick et al. (2003)integrated risk assessment
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Exposure AssessmentMCRA
– MCRA 7 calculates exposure distributions for single compounds
– Percentiles and number of people exceeding a limit value (e.g. ARfD)
– Acute or chronic risks– Processing factors, variability factors,
modelling of non-detects, covariates, ...– Drill-downs– Uncertainty analysis
1 10 100 1000 10000
Individual Margin of Exposure
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Risk Assessment: the IPRA model
van der Voet & Slob (2007), Risk Analysis 27: 351-371
1 10 100 1000 10000
Individual Margin of Exposure
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Individual Margin of Exposure Exposure assessment and hazard characterisation combined into an
integrated probabilistic model (IPRA)
Margin of Exposure replaced by Individual Margin of Exposure (IMoE)
Analysis of variability and uncertainty kept separate
Proposed instruments for risk managers: IMoE safety bar, IMoEp1 and/or IMoEL
1 10 100 1000 10000
Individual Margin of Exposure
IMoEp1IMoEL
Van der Voet et al. (2009)
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Example: Comparison of risks
• Decisions of fungicide use are an example of risk-benefit analysis– Fungicides may have toxic effects (hazard)– Fungicides may reduce risk of mycotoxin production (benefit)
Muri et al. (2009)
1 10 100 1000 10000 100000 1000000 10000000
5% effect on BW frommycotoxin
5% effect onerythrocyte count from
fungicide
50% cases ofhepatocytomegaly from
fungicide
Individual Margin of Exposure
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Cumulative assessments
• Common Assessment Groups refer to multiple compounds with for the purpose of the assessment will be assumed to have the same health effect
• Potency differences are captured in Relative Potency Factors (RPFs)– Estimated from data– Therefore RPF estimates will be not exactly
known but uncertain
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Estimating RPFs from dose-response data• Example Organophosphates (Bosgra et al. 2009)
– Dose-response data EPA– Parallel curves fitted by PROAST
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Probabilistic models cumulative exposure
• It is important to describe the variation between persons (Person Oriented Models) in the relevant population
• Which population is used?– Models with predefined populations or
subpopulations thereof: e.g. US models DEEM/Calendex, LifeLine, CARES, SHEDS
– Model applicable to user-defined populations: Acropolis model based on MCRA
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Data for cumulative exposure
• Consumption data: national survey data• Residue data: need to collect at the level of
individual samples so that correlations between pesticides are represented– use of pesticides A and B may be exclusive– or they may be used always together– or anything in between ...
• Problem: residue data matrix contain many missing values (MVs) and non-detects (NDs)
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Cumulative exposure: residue data
positive value
non-detect
(< 0.05)
missing value (non-
measurement)
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Cumulative exposure assessment
In the EFSA triazole project (van Klaveren et al. 2009) two approaches for cumulative exposure assessment using single-residue modelling methods were compared:
1. First add, then analyse Calculate RPF-weighted sum of concentrations per sample then exposure assessment for ‘single’ compound
2. First analyse, then add Parallel exposure assessment runs for the compounds then RPF-weighted summing of intakes using same sequence
of simulated consumers
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Approach 1: First add, then analyse
Assumes that the total set of samples is representative for a food
Advantage: – incorporates correlations between compounds
• negative correlation: lower exposure• positive correlation: higher exposure
• Disadvantage:– requires data for all compounds in all samples
• for non-measured compounds effectively a concentration 0 is assumed• estimated exposure may be too low
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Approach 2: First analyse, then add
Assumes that per compound the set of samples with measurements is representative for a food
• Advantage: – each compound may have its own set of samples
• Disadvantage:– does not incorporates correlations between
compounds
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Example triazoles
• Netherlands: not much difference– most samples were analysed for most triazoles
• France: Approach 2 more conservative– many samples analysed for only part of triazoles
van Klaveren et al. (2009)
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ACROPOLIS approach
• Combine advantages of Approaches 1 and 2 by– Fitting a multivariate model to the combined residue data– Allow for patterns of missing information– Allow for measurements below a Limit of reporting (non-detects)
• Detailed models are under investigation– Correlation between pesticides may exist
• Regarding the use frequencies• Regarding the resulting concentrations
– We know fairly certain that each pesticide is only used in a fraction of cases, so there must be many ‘true zeroes’
– Some models may allow the use of additional data from Pesticide Usage Surveys
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FERA PUS data. Example : Wheat (GB, 2008)
• Proportion of wheat fields treated with a triazole is 0.95
• 12 different triazoles are used for wheat in GB
• 111 different combinations of up to 6 triazoles used
• Most fields use a combination of 2 or 3 triazoles
• Only 25 fields were treated with 6 different triazoles
• Conclusion: many of the non-detects and missing values must be true zeroes
1 2 3 4 5 6
Wheat
Number of triazoles per field
Nu
mb
er
of f
ield
s
02
00
40
06
00
80
01
00
01
20
01
40
0
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Example : Wheat GB, 2008 (FERA)
• Prothioconazole is applied most (in total 272.88 kg/ha) either individually (2 fields) or in combination (1465 fields)
• Prothioconazole used in GB but not in The Netherlands
• Suggests GB data for wheat may not be appropriate to make assumptions for some countries in Europe
• Data available for other countries?
Bro
muc
onaz
ole
Cyp
roco
nazo
le
Epo
xico
nazo
le
Flu
quin
cona
zole
Flu
sila
zole
Flu
tria
fol
Met
cona
zole
Pro
pico
nazo
le
Pro
thio
cona
zole
Teb
ucon
azol
e
Tet
raco
nazo
le
Tria
dim
enol
Wheat Overall UsageT
otal
tria
zole
app
lied
(kg/
ha)
0
50
100
150
200
250
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Example modelling of correlation
simulated from bivariate normal distribution,
means 3 and 7
sds 2 and 3
correlation 0.8
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Which distributions are appropriate?
• We need a statistical model for cumulative exposure
• Options:– multivariate lognormal (convenient)– Mixture of true zeroes and lognormal– other parametric or non-parametric
multivariate distributions
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Uncertainty approaches
• Uncertainty about inputs and model form uncertainty about quantities of interest – e.g. fraction of population exceeding a limit value
• Sources of information on uncertainty– Data, e.g. implicit in small sample or s.e. from literature– Expert judgment (needs ‘elicitation’)
• Main approaches to address uncertainty: – modelling based on available data or expert judgment – qualitative assessment of uncertainties by experts, summarized
in uncertainty tables
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Quantitative and qualitative approaches
0
5
10
15
20
25
30
35
% c
on
trib
uti
on
MC cons conc proc anim al inter intra
Uncertainty source
Uncertainty PoCE
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Updated view on data needed for cumulative
assessments• Consumption survey data• Residue monitoring data or field trial data (pre-
registration)• Food conversion (linking food as eaten to food
as measured)• Data on processing, unit variability• Pesticide usage data• Dose response data for critical health effect to
estimate RPFs (or for direct use)
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Cumulative Risk Assessment• For integrating exposure assessment and hazard
characterisation two approaches are possible:– Two-step approach:
• First, perform cumulative exposure assessment using RPF-weighted sum
• Secondly, calculate MoE or IMoE distribution using toxicology data for the index compound
• Examples in Bosgra et al. (2009), Müller et al. (2009)
– One-step approach:• single-pesticide IMoE distributions from a cumulative IPRA
analysis can be directly combined into a cumulative IMoE distribution (for details see van der Voet et al. 2009)
• This would circumvent the explicit calculation of RPFs
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Conclusions
• Modelling cumulative exposure and risk already possible, further developed in ACROPOLIS
• Patterns and amount of missing values and non-detects may be a problem
• Pesticide usage survey data may be useful• Future: Integrated models may replace separate
estimation of RPF and use of RPF models• ACROPOLIS system: bring many data together
in one platform, accessible to all stakeholders