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Modelling cumulative risk Hilko van der Voet Biometris, DLO, Wageningen University and Research Centre Third ACROPOLIS consortium meeting 31 March 2011,

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Presentation on theme: "Modelling cumulative risk Hilko van der Voet Biometris, DLO, Wageningen University and Research Centre Third ACROPOLIS consortium meeting 31 March 2011,"— Presentation transcript:

1 Modelling cumulative risk Hilko van der Voet Biometris, DLO, Wageningen University and Research Centre Third ACROPOLIS consortium meeting 31 March 2011, Milano

2 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

3 Risk assessment A.G. Renwick et al. (2003) integrated risk assessment

4 Exposure Assessment MCRA –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

5 Risk Assessment: the IPRA model van der Voet & Slob (2007), Risk Analysis 27: 351-371

6 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 IMoEp1IMoEL Van der Voet et al. (2009)

7 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)

8 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

9 Estimating RPFs from dose- response data Example Organophosphates (Bosgra et al. 2009) –Dose-response data EPA –Parallel curves fitted by PROAST

10 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

11 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)

12 Cumulative exposure: residue data positive value non-detect (< 0.05) missing value (non- measurement)

13 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

14 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

15 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

16 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)

17 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

18 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

19 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? Bromuconazole Cyproconazole Epoxiconazole Fluquinconazole Flusilazole Flutriafol Metconazole Propiconazole Prothioconazole Tebuconazole Tetraconazole Triadimenol

20 Example modelling of correlation simulated from bivariate normal distribution, means 3 and 7 sds 2 and 3 correlation 0.8

21 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

22 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

23 Quantitative and qualitative approaches

24 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)

25 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

26 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

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