Multifactorial Risk Prioritization

As outlined in the preceding sections, one or two aggregate measures are defined for each of the four areas of risk assessment: public health (DALY and COI values), market-level (economic value of domestic market activities), consumer (aggregate measure of perception and acceptance of risk), and social sensitivity (consumer and firm). While we recognize that other measures could be used, we consider these six measures to be comprehensive and consistent with the principles outlined by FAO/

WHO. Using our framework, multidimensional risk profiles can be created for pathogen–food combinations, food categories, and different pathogens. In all cases, a ranking process must be able to compare each one on the basis of multiple criteria.

Multi-criteria decision analysis (MCDA) techniques are structured, consistent, and transparent, and therefore helpful in dealing with large amounts of complex information. We have explored a number of MCDA tools for use in the prioriti-zation framework (Daza Donoso2008; Ruzante et al.2009). Daza Donoso (2008) compared a number of MCDA techniques on the basis of their expandability, ability to incorporate uncertainty and variability, and interactions with decision-makers (e.g., level of knowledge, inputs and time requirements, analytical skills).

On this basis, outranking methods had definite advantages over techniques such as the multi-attribute utility theory and the analytic hierarchy process. Outranking methods are based on pair-wise comparisons of alternatives. For each criterion, alternative “a2” is compared to alternative “a1” using a preference or outranking relation defined in an appropriate scale for the criterion. This permits the use of ordinal scales and binary values for risk criteria. Furthermore, the outranking relations can be constructed to reflect uncertainty and varying degrees of prefer-ence (weak to strong) or higher ranking. Figure7.5shows an outranking relation for the criterion related to consumer risk perception and acceptance of risk. Two discrimination thresholds are defined: indifference (r) and strict preference (s).

When two alternatives (a1 anda2) are compared, there is insufficient evidence to rank a2higher when the difference in the criterion scores is less than 0.05 (indifference threshold r). This threshold value is defined specifically for the criterion related to consumer perception and acceptance of risk; it is based on our estimate of the consistency in opinions from our Delphi panel. For score differ-ences in the interval between 0.05 and 0.33, there is weak but increasing preference (or higher priority) fora2; above 0.33 (strict preference thresholds)

a2 clearly ranks as a higher risk than a1. The characteristics of the outranking relations (thresholds and transitions between thresholds) can be used to reflect some aspects of uncertainty and variability in the risk measures.

Daza Donoso et al. (2008) used two outranking methods, ELECTRE III (Elimi-nation et Choix Traduisant la Re´alite´) and PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) to rank six pathogen–food combinations. The multidimensional risk profiles for the six examples are summar-ized in Table7.2. The ELECTRE III method developed by Roy (1978) is available inWindows-compatible software from the LAMSADE research group (Laboratoire d’analyse et mode´lisation pour l’aide a` de´cision1994). PROMETHEE is an out-ranking method developed by Brans and colleagues (Brans and Vincke1985; Brans et al. 1986) and implemented in Decision Lab software (Visual Decision Inc., Montreal, QC, Canada). The same discrimination thresholds were defined for PROMETHEE and ELECTRE III ranking relations as shown in Table 7.3.

Although the ELECTRE III method allowed for a “veto” effect, this component was not used in ranking the six pathogen–food combinations since it was assumed that all six pairs posed risk, regardless of how much lower its performance in any one criterion may be. The final rankings were based on the equal weightings for the four risk factors (Table7.2).

As shown in Table7.4,Escherichia coli O157 in beef is ranked as the highest priority in both outranking methods. At the number two rank, there are differences depending on the ranking method. In ELECTRE III, two pathogen–food

Strict preference for a2

Weak preference for a2

Indifference

Difference in consumer perception and acceptance scores (a2 – a1) Fig. 7.5 Outranking relation for consumer perception and acceptance of risk criterion

Table 7.2 Risk profiles for six pathogen–food combinations (Canadian data) Pathogen–food

combination

Public health Market Consumer perception and acceptance of risk

Social sensitivity DALY^a COI^b Domestic

size^c

Salmonella/chicken 449 79.4 5472 0.25 0 0

Salmonella/spinach 1 0.2 118 0.5 0 0

Escherichia coli O157/spinach

3 0.5 118 0.8 1 0

E. coli O157/beef 260 40.2 5264 0.6 1 0

Listeria

monocytogenes/

ready-to-eat meats

58 12.7 974 0.6 1 1

Criterion weights 0.125 0.125 0.25 0.25 0.125 0.125

aDALY – Disability-adjusted life years

bCOI – Cost of illness

cDomestic market size (annual basis)¼ value of retail sales + value of exports – value of imports

Table 7.3 Characteristics of outranking relations in ELECTRE III and PROMETHEE analysis

Criterion Discrimination thresholds Transition between

thresholds Indifference Strict

preference

DALY (years) 10 78 Linear

COI (CAN$ 10⁶) 0.1 9 Linear

Economic value of domestic market activities (CAN$ 10⁶)

100 1000 Linear

Consumer risk perception and acceptance of risk

0.05 0.33 Linear

Social sensitivity – consumer 0 1 Not applicable

Social sensitivity – firm 0 1 Not applicable

Table 7.4 Ranking of six pathogen–food combinations

Rank position ELECTRE III PROMETHEE I PROMETHEE II

Partial ranking Complete ranking 1 Escherichia coli O157/beef E. coli O157/beef E. coli O157/beef 2 Campylobacter/chicken Campylobacter/chicken Listeria monocytogenes/

RTEm L. monocytogenes/RTEm L. monocytogenes/RTEm

3 Salmonella/chicken Salmonella/chicken Campylobacter/chicken E. coli O157/spinach

4 Salmonella/spinach E. coli O157/spinach Salmonella/chicken

5 Salmonella/spinach E. coli O157/spinach

6 Salmonella/spinach

combinations –Campylobacter in chicken and Listeria monocytogenes in ready-to-eat mready-to-eats – are considered to be of equivalent risk priority but are actually incomparable. The basis for this “incomparability” can be understood by compar-ing the risk profiles in Table7.2.Campylobacter in chicken has high scores in the factors related to public health and market-level impact but much lower scores (relative to the other five pairs) in consumer perception and acceptance of risk and social sensitivity.L. monocytogenes in ready-to-eat meats is the highest risk case in terms of social sensitivity factors and higher than Campylobacter in chicken in terms of consumer perception and acceptance of risk. The PROMETHEE I ranking is referred to as a partial ranking and positions the same two pathogen–food combinations at number two. In the partial ranking procedure, the six pairs are ordered based on the degree to which each pair outranks the other pairs (positive preference flows) as well as the degree to which each pair is outranked by the others (negative preference flows). Incomparability in PROMETHEE I arises when there is an inconsistency in the ordering based on the two analyses – the two pairs change positions in the positive and negative preference orders. The PROMETHEE II ranking is referred to as a complete ranking and is based on the net preference flow (positive preference flow – negative preference flow). It may be easier to interpret because there are no incomparable cases. However, the complete rankings can mask diversity in the risk profiles that should be appreciated by risk managers in making their decisions.

Overall, this is a limited data set but it is useful in demonstrating some of the limitations and the value of MCDA methods in ranking microbial risks in the food system. Future work includes refinement of ranked lists to reflect uncertainty in ranking and feasibility of interventions to reduce risks.

7.4 Food Engineering at Risk Management/Risk Assessment