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AY,yTiG xsPhRN7V+M+Ȉ1-QV&Rb)d-֭ e#yJ#ž.!ju.JJ<3-@5Tȡzj%V] 'xQI8VɰQR)SRRRRRR,W4}bN(,kV}(`  & [eDocument Word.Document.80.Microsoft Word DocumentfDocument Word.Document.80.Microsoft Word DocumentTPicture Word.Picture.60,Microsoft Word PictureUPicture Word.Picture.60,Microsoft Word Picture,www.risk-modelling.com<http://www.risk-modelling.com/vvDocument Word.Document.80.Microsoft Word Document} Chart Excel.Chart.80*Microsoft Excel Chart= Chart MSGraph.Chart.80*Microsoft Graph ChartARegneark Excel.Sheet.800Microsoft Excel-regnearkBDiagram Excel.Chart.80.Microsoft Excel-diagramEWorksheet Excel.Sheet.802Microsoft Excel WorksheetH/0(  0;[0 0 000$([\{b00 000000000  0=] 0 0 0000 2 3 !A0C0E0G0I0c00000000000000000!%),.:;?]}acdeghijklmnop|DTimes New Roman(0(z[ 0 DTahomaew Roman(0(z[ 0 " DSymbolew Roman(0(z[ 0 0DArialew Roman(0(z[ 0 @DArial Narrowan(0(z[ 0 "@ .@  @@``  @n?" dd@  @@`` $X$ 1   ##  $% +,5 6 <=DEHI TVWnopq2 t "$j/8 2$0gM:3?[ڬ7I@i S@ gN1vIENDB` nX3WOj!7C- b rMicrobial risk assessment is a scientifically-based process consisting of four steps: Hazard Identification The identification of known or potential health affects associated with a particular agent; Exposure Assessment The qualitative and/or quantitative evaluation of the degree of intake likely to occur; Hazard Characterization The quantitative and/or qualitative evaluation of the nature of the adverse effects associated with biological, chemical and physical agents that may be present in food& For biological agents& a dose-response assessment should be performed if the data are available; Risk Characterization Integration of Hazard Identification, Hazard Characterization and Exposure Assessment into an estimation of the adverse effects likely to occur in a given population, including attendant uncertainties.<W P" PWcg3]cg3Ycg3 cg3ccccccuc :  +Codex based on NAS chemical risk guidelines,,+ b .What have we done with these CODEX guidelines?//  !Current microbial risk assessment,"! b Microbial QRA is a developing discipline Mostly producing  farm-to-fork (F2F) Model the  whole system but very poorly Not designed to model any decision question well One pathogen in one food commodity directly consumed Often rely on poor data, surrogates, and guesses Almost never is a decision question posed beforehand Assessors have over-sold F2F QRA s usefulness Managers have expected too much Focused on there being exposure and D-R models (eg see WHO guidelines and developed models) xPfPPPY3f\  ` 4  , 0* 6Dutch observations on past QRA Havelaar, Jansen (2002):7 bThe lessons learnt from risk analysis experiences: Risk management has not always been an integral part of risk analysis so far; Risk managers should be trained to understand risk assessment, and risk assessors should be trained to explain their work; Available data are often of limited use for risk assessment and communication of data needs between risk assessors, food scientists and risk managers is a critical issue; The risk manager questions usually require rapid results, whereas (farm-to-fork) risk assessment projects require several years to complete. Solving this conflict requires open communication; Uncertainty is often large. \3 P"  3cPcc  +Completion times of some farm-to-fork QRAs,,+cb"'  g$USDA-FSIS-FDA Salmonella EnteritidisP%c c c b $  Although the goal was to make the model comprehensive, it has some important limitations. & . For many variables, data were limited or nonexistent. Some obvious sources of contamination, & were not included. And, as complex as the model is, it still represents a simplistic view of the entire farm-to-table continuum. & Much more work is needed to address & all & limitations. Vy P^c4gcc y jConflict between microbiology and risk assessment focus (Adapted from presentation by Maarten Nauta, 2002)k83@V     fMicrobiology About the detectable Micro-world Selected strains Against variability Qualitative Science. Z 3Z g  sRisk Assessment What is there (prevalence, load) Macro-world All strains Pro variability Quantitative Decision tool.d3d t :Where do we go wrong? Separation of managers and assessors,;:b0CODEX recommended the separation of the roles of managers and assessors To allow science to evaluate risks unhindered by politics To maintain transparency of decisions BUT the idea has been taken too far: Managers and assessors not allowed to talk to each other! Assessors given too narrow a focus (in terms of risk management options, or risk scenario) or too broad (model everything because management actions not specified) No guidance to assessors when data are not available Managers don t get early warning that r.a. request is impossible or useless Assessors can t seek guidance, or suggest new risk management options when new information becomes available Managers make assessors follow exposure pathway & dose-response approach to ensure that a  risk assessment has been performedHP`P&P:P%PPH`&:  % #Remedies (1): focusing on decisions>$   b Management must state acceptable level of risk All agree zero-risk world not possible, but management don t like to be explicit Without a threshold risk, any risk can be considered  unacceptable  a greater threat to transparency than assessment/management not separated Focus on the objectives  making food safer Not performing a  risk assessment Prioritise by potential benefits of risk management actions, not necessarily same as prioritising by total health impact Consider what is known about the risk problem, and data available immediately or within acceptable time frame Use epidemiological data as much as possible, rather than lab data Collect more epi data (e.g. Japan, Denmark for good examples) Consider what analysis could be done with this knowledge i.e. a risk-based reasoned argument for evaluating particular actions Critically evaluate assumptions necessary for analysing each option Look at feasibility, political acceptability and compliance issues (risk management overlap)/PP,PPnPP9PP/J,;    3Y  n9P  K    . i #Remedies (2): focusing on decisions>$   b Estimate the possible pros and cons of a risk action Secondary risks may outweigh possible benefit Recognise that some actions will have impact over two or more pathogens Note that it may not be possible to evaluate all actions Perform a cost-benefit analysis on these actions Perform a Value of Information analysis Determines whether it is worth (in time and money) collecting more data before making a decision Consider strategy to validate whether predicted improvement occurs And make public intention to review decision if prediction incorrect Train data producers to supply maximally useful data E.g. microbiologists taken more than one cfu from a plate 5PPYPbPCPEP5P;P5Yb  CE5;`5 . >8 D_    *Draw the problem out& 23f3$Key component is attribution of risk,%$ bMultiple pathogen sources Feedback loops Need to use more sophisticated methods for risk attribution Eg DAGs, classification trees, logistic regression Need to look at total exposure, eg human-to-human exposures and imported food products Need to use epidemiological studies, eg: Case:control studies Typing of pathogens in food-producing animals and humans Outbreak investigation Comparative risk arguments Translate number of reported cases into number of illnesses, and then into some consistent measure of impact, eg QALY or veP3PPP|Pe3  |teLZ  0Example for Salmonella of risk attribution modelP1    b  @Prevalence and phage-type distribution of S. Typhimurium in 1999AAc.-    :Consumed amount of relevant food stuffs in Denmark in 1999;;c: The mathematics  + lij= Mj * pij * qi * aj, where lij = the predicted no. of cases/year of type i from source j Mj = the amount of source j available for consumption/year pij = the prevalence of type i in source j qi = the bacteria-dependent factor for type i aj = the food-source dependent factor for source j8!      > < ( , 1            =  <  (  -  1 >Danish Vet Service Salmonella QRA  A Bayesian Approach to Quantify the Contribution of Animal-food Sources to Human Salmonellosis - Hald, Vose, Koupeev (2002)L!ccccHW +    z7Fluoroquinolone-resistant Campylobacter risk assessment*8   7 x In summary&  $Risk assessment is a decision tool It needs: Good questions Good ideas, creativity Rational, decision-focused thinking Good use of available data Continuous evaluation of fitness-for-purpose Continuous communication2. 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Number of cases & impact for a case -> total disease burden Risk attribution: Domestic, imported food, environment, human-to-human, etc Risk factors -> clue to possible management options&0 oF23 pj   Ta ??6 q,$ 0 (Change in operations that maximise human health benefit So look at effect over portfolio of risks Might involve removing expensive, ineffective standards Need to consider reactions to change in the world (economic, social) Look at secondary risks RANK risk management options, use risk registers &)0 oF2)3& p   Tp ??6 q ,$  0 Eg chemicals used in crops food = risk + environmental risk Eg bacteria from food-producing animal = meat, vegetable, environment risk &0 oF230;I p   T p ??6 q ,$  0 $Partnership v regulation, eg Danish elimination of use of growth promotants Who has data? 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(could be several) Animal testing for chemicals, species-to-species dose-response issue (10 fingers) Single-hit (microbial) v chronic (chemical) exposure Pathogens are discrete (eg detection difficulties) Pathogens grow and die, need to be cultured for detection Usually surface contamination, so concentration not ideal measure Pathogens naturally occur in environment and animal gut, not a  contaminant Pathogens can mutate (resistance issues) Single pathogen can cause infection, no threshold7ZZZMZRZZ7M  3R3$[     QO  S 3 ,X`hpH  0޽h ? ̙3357-7___PPT10 7+:v7D6' = @B Dt6' = @BA?%,( < +O%,( < +D' =%(D' =%(Du' =A@BB BB0B%(E5' =1B B`BPB,54*3>B ppt_c='`B@BPB<*7D' =1:Bvisible*o3>+B#style.visibility<*7%(D' =-g6B fade*<3<*7Du' =A@BB BB0B%(E5' =1B B`BPB,54*3>B ppt_c='`B@BPB<*7hD' =1:Bvisible*o3>+B#style.visibility<*7h%(D' =-g6B fade*<3<*7hDu' =A@BB BB0B%(E5' =1B B`BPB,54*3>B ppt_c='`B@BPB<*hD' =1:Bvisible*o3>+B#style.visibility<*h%(D' =-g6B fade*<3<*hDu' =A@BB BB0B%(E5' =1B B`BPB,54*3>B ppt_c='`B@BPB<*D' =1:Bvisible*o3>+B#style.visibility<*%(D' =-g6B fade*<3<*Du' =A@BB BB0B%(E5' =1B B`BPB,54*3>B ppt_c='`B@BPB<*D' =1:Bvisible*o3>+B#style.visibility<*%(D' =-g6B fade*<3<*D#' =%(D2#' =%(Du' =A@BB BB0B%(E5' =1B B`BPB,54*3>B ppt_c='`B@BPB<*D' =1:Bvisible*o3>+B#style.visibility<*%(D' =-g6B fade*<3<*Du' =A@BB BB0B%(E5' =1B B`BPB,54*3>B ppt_c='`B@BPB<*?D' =1:Bvisible*o3>+B#style.visibility<*?%(D' =-g6B fade*<3<*?Du' =A@BB BB0B%(E5' =1B B`BPB,54*3>B ppt_c='`B@BPB<*?D' =1:Bvisible*o3>+B#style.visibility<*?%(D' =-g6B fade*<3<*?Du' =A@BB BB0B%(E5' =1B B`BPB,54*3>B ppt_c='`B@BPB<*D' =1:Bvisible*o3>+B#style.visibility<*%(D' =-g6B fade*<3<*Du' =A@BB BB0B%(E5' =1B B`BPB,54*3>B ppt_c='`B@BPB<*D' =1:Bvisible*o3>+B#style.visibility<*%(D' =-g6B fade*<3<*Du' =A@BB BB0B%(E5' =1B B`BPB,54*3>B ppt_c='`B@BPB<*4D' =1:Bvisible*o3>+B#style.visibility<*4%(D' =-g6B fade*<3<*4Du' =A@BB BB0B%(E5' =1B B`BPB,54*3>B ppt_c='`B@BPB<*4vD' =1:Bvisible*o3>+B#style.visibility<*4v%(D' =-g6B fade*<3<*4vDu' =A@BB BB0B%(E5' =1B B`BPB,54*3>B ppt_c='`B@BPB<*vD' =1:Bvisible*o3>+B#style.visibility<*v%(D' =-g6B fade*<3<*vDu' =A@BB BB0B%(E5' =1B B`BPB,54*3>B ppt_c='`B@BPB<*D' =1:Bvisible*o3>+B#style.visibility<*%(D' =-g6B fade*<3<*Du' =A@BB BB0B%(E5' =1B B`BPB,54*3>B ppt_c='`B@BPB<*D' =1:Bvisible*o3>+B#style.visibility<*%(D' =-g6B fade*<3<*+8+0+p +   &p(  ~  s *p  p H  0޽h ? ̙33___PPT10e.+D=' = @B +8,  H(  H H HHp   p  H c $Dp]<$ 0 p H H 0޽h ? ̙33))___PPT10).+qqDh)' = @B D#)' = @BA?%,( < +O%,( < +D%' =%(D' =%(Du' =A@BB BB0B%(E5' =1B B`BPB,54*3>B ppt_c='`B@BPB<*H)D' =1:Bvisible*o3>+B#style.visibility<*H)%(D' =-g6B fade*<3<*H)D%' =%(D' =%(Du' =A@BB BB0B%(E5' =1B B`BPB,54*3>B ppt_c='`B@BPB<*H)OD' =1:Bvisible*o3>+B#style.visibility<*H)O%(D' =-g6B fade*<3<*H)OD ' =%(D ' =%(Du' =A@BB BB0B%(E5' =1B B`BPB,54*3>