Makita, K.

CGSpace

Participatory risk assessment I - Introduction -  ‘Learning Event’ on risk analysis and participatory methods CSRS, November 28, 2014 Kohei Makita Associate Professor of Veterinary Epidemiology at Rakuno Gakuen University (OIE Joint Collaborating Centre for Food Safety) Joint Appointment Veterinary Epidemiologist at International Livestock Research Institute (ILRI) Outline • Food safety in informal market • Participatory Risk Analysis • Monte Carlo simulation • Key distributions 2 3 4 Why food safety? • Every year, at least 2 billion cases of diarrhea occur and 1.5 million children under 5 yrs die worldwide  • Poor, young, elderly, pregnant women and immune-suppressed most affected  • Food borne diseases include non-diarrheal severe zoonoses Why animal source foods? • Two-thirds of human pathogens are zoonotic – many of these transmitted via animal source food  • Animal source food is a single most important cause of food-borne disease  • Many food-borne diseases cause few symptoms in animal host  • Many zoonotic diseases controlled most effectively in animal host/reservoir Formal marketing Informal marketing in sub-Saharan Africa (90-95%) Dominance of informal markets in developing countries “Absence of structured sanitary inspection”  8 Informal ≠ Illegal Outline • Food safety in informal market • Participatory Risk Analysis • Monte Carlo simulation • Key distributions 9 10 Risk analysis A tool for decision-making  under uncertainty  11 OIE Import Risk Analysis Release  assessment Exposure  assessment Consequence  assessment Risk  management Risk  assessment Hazard  identification                          Risk communication                       OIE Risk analysis for antimicrobial resistance Release  assessment Exposure  assessment Consequence  assessment Risk  management Risk  assessment Hazard  identification                          Risk communication                       Use of antimicrobials at farm and selection of resistant bacteria Food chain and consumption of the foods contaminated Weaker response of  antimicrobials in treatment Hazard Analysis and Critical Control Point (HACCP) system • Adopted by Codex Alimentarius Commission • Science based and systematic system which identifies specific hazards and measures for their control to ensure the safety of food 13 14 Risk  Assessment Risk  Management  Risk  Communication Codex Alimentarius Commossion Food safety risk analysis A tool for decision-making under uncertainty *Risk is a probability of occurrence of a scenario and its size of impact (Vose, 2008) 15 Risk  Assessment Risk  Management  Risk  Communication Food safety risk analysis in informal marketing system Participatory methods 16 Advantage of  participatory risk assessment • -Speed • -Affordability • -Flexibility in application • -Understanding of culture • -Best control option • -Potential to change behavior 17 Food safety risk assessment Codex Alimentarius Commission system Hazard identification Hazard characterization Exposure assessment Risk characterization Participatory methods fit well Risk communication Codex Alimentarius Commission Risk assessment framework (CAC/GL-30 (1999)) Hazard  identification  Exposure  assessment  Hazard  characterization  Risk  characterization 18 Statement of purpose of  risk assessment • Clear statement of the specific purpose of the particular risk assessment • Output form – Prevalence of illness – Annual incidence rate (eg. case/10,000)  • Preliminary investigation phase may be required Hazard  identification  Exposure  assessment  Hazard  characterization  Risk  characterization 19 Hazard identification • The identification of biological, chemical, and physical agents –  • capable of causing adverse health effects –  • and which may be present in a particular food or group of foods Hazard  identification  Exposure  assessment  Hazard  characterization  Risk  characterization 20 Exposure assessment • Assessment of the extent of actual or anticipated human exposure  • Based on potential extent of food contamination by a particular agent or its toxins, and on dietary information  Hazard  identification  Exposure  assessment  Hazard  characterization  Risk  characterization 21 Hazard characterization • Qualitative or quantitative description of the severity and duration of adverse effects that may result from the ingestion of a microorganisms or its toxin in food • A dose-response assessment should be performed if the data are obtainable Hazard  identification  Exposure  assessment  Hazard  characterization  Risk  characterization 22 Factors that need to be considered in hazard characterization • Factors related to the microorganism – Replication – Virulence and infectivity – Delay of onset following exposure – Attributes altering pathogenicity, e.g., high fat content of a food vehicle • Factors related to the host – Genetic factors – Host susceptibility characteristics • Age, pregnancy, nutrition, immune status etc. – Population characteristics • Population immunity, access to and use of medical care etc.  Hazard  identification  Exposure  assessment  Hazard  characterization  Risk  characterization 23 Dose-response Assessment • Determination of the relationship between the magnitude of exposure (dose) to a chemical, biological or physical agent and the severity and/or frequency of associated adverse health effects (response) Hazard  identification  Exposure  assessment  Hazard  characterization  Risk  characterization 24 Risk characterization • Integration of previous three steps to obtain a Risk Estimate • A qualitative or quantitative estimate of the likelihood and severity of the adverse effects which could occur in a given population • Degree of confidence: uncertainty and variability • Influence of the estimates and assumptions by sensitivity analyses  Hazard  identification  Exposure  assessment  Hazard  characterization  Risk  characterization 25 Uncertainty and variability • “Variability is a phenomenon in the physical world to be measured, analysed and where appropriate explained. By contrast, uncertainty is an aspect of knowledge” 26 Sir David Cox Uncertainty and variability 27 • Each of them are uncertainty distributions • The variety of uncertainty distributions shows variability • Variability in this case is the growth speed of S. aureus Uncertainty • Assessor’s lack of knowledge about the parameters that characterise the physical system that is being modeled • Level of ignorance, degree of belief • Sometime reducible through further measurement or study • In our (food safety risk assessor) case; – Prevalence of pathogens/ antibodies in foods – Temperature of foods – Cooking and consumption behavior (could be dealt as variability if you know well) – Time from production to processing/ consumption  28 Variability • A function of the system – In our (food safety risk assessors) case; • Variety in virulence • Variety in speed of bacteria multiplication • System of the value chain • Amount of foods passing through value chains • Seasonal variety • Inter-individual variability – Height – Weight – Sex  29 Documentation • Systematic documentation for communication to risk manager • Reporting the limitations is important in transparent process of the decision making • Description of the model for other risk assessors to repeat and critique the work Hazard  identification  Exposure  assessment  Hazard  characterization  Risk  characterization 30 Types of Risk assessment • Qualitative risk assessment – Qualitative statement (Negligible, low, medium, high, extremely high etc.)  • Quantitative risk assessment – Deterministic risk assessment • Single point estimates – Stochastic risk assessment • Probability distribution 31 Outline • Food safety in informal market • Participatory Risk Analysis • Monte Carlo simulation • Key distributions 32 What is Monte Carlo simulation? • Imagine this EcoHealth work; – Probability that vegetable A on farm is contaminated with Salmonella came from pig waste: 30% (PA) – Probability of reduction of Salmonella prevalence by washing at home: 90% (PB) – After washing, contaminated vegetable contains 20 Salmonella/g (CFU) – A person ingests 50g of salad of vegetable A at a time (Cons) – In the country, this salad is consumed once a week (Rate) – Dose-response (DR) relationship known – What is the probability of salmonellosis due to consumption of vegetable A as salad? Probability of illness = PA x (1-PB) x CFU x Cons x DR x Rate Deterministic model  = 0.3 x (1-0.9) x 20 x 50 x DR x 1/7 Stochastic model assigns distributions for each parameter Then how we calculate? 33 Then how the distribution of a risk is made? • Assign stochastic distributions to each variable • Pick up values randomly from each distribution based on the probability and calculate a risk • Repeat the risk calculation automatically 5000-10,000 times until the results become stable (Monte Carlo simulation)  34 X = Outline • Food safety in informal market • Types of Risk Analysis (Codex, OIE, HACCP) • Monte Carlo simulation • Key distributions 35 Types of data Head or tail 0 or 1 Healthy or diseased Binomial data Milk yield Body weight Continuous data Animal breed Districts Categorical data Bacteria load Number of animals Count data 36 Parametric and non-parametric statistics E.g. Milk yield E.g. Rank of psychological stress  during FMD outbreak among vets Parametric Non-parametric 37 Parametric and non-parametric statistics Does not make any distributional  assumptions about the data   Parametric tests Non-parametric tests Make assumptions about the underlying distribution of the observations and the  parameters which define that distribution 38 39 • Mean – Arithmetic mean • Median – Central value of the observations which have been arranged in rank order • Mode – The most commonly occurred observation in a set of observations Mean, median and mode 40 Normal distribution • Continuous data • Two parameters  – Mean – Standard deviation • Mean, mode and median are same Where Probability density function 41 Binomial distribution • Two parameters – Trial, probability • Logit-link • (E.g.) 10% chicken carcasses is known to be contaminated with Campylobacter  • You sampled four carcasses • How many were contaminated? Probability mass function Where x success in n trials Probability P of success • Binomial/ Binary response data • Two parameters – Trial – Success • (E.g.) Prevalence of E. coli in foods • (E.g.) Prevalence of brucellosis among cattle herds Beta (2,30) Beta (30,2) Beta (6,26) Beta (26,6) Beta (16,16) Probability density function for Beta (a, b) 101111)1()1()(dtttxxxfbaba42 Poisson distribution • Count data • One parameter – Shape • Log-link • (E.g.) Number of customer per day λ=10 λ=0.9 λ=3 λ=6 !)()(xtexfxtλλProbability mass function 43 Gamma distribution 44 • Gamma (α,β) • Two parameters – Number of events α – Time between events β • Mean: αβ • Variance:αβ 2 Gamma(1,2) Gamma(1,1) Gamma(2,1) Gamma(4,1) Probability density function )()/exp()(1αΓββ αα xxxfWhen 01)( dtetn txΓExponential distribution 45 Expon (1) Expon (2) Expon (5) • Exp(β)=Gamma (1, β) – Special case of Gamma distribution when α=1  • Time between successive occurrences  ββ/)(xexfProbability density function Key parametric distributions • Normal distribution • Binomial distribution • Beta distribution • Poisson distribution • Gamma distribution • Exponential distribution  46 There are more parametric distributions but they are key ones to relate other distributions used for food safety risk assessment Any questions? 47 

Participatory risk assessment: Introduction

https://cgspace.cgiar.org/bitstream/10568/58480/2/Participatory%20Risk%20Assessment%20Intro.pdf

Participatory risk assessment: Introduction

Abstract

Similar works

Full text

Available Versions

CGSpace