Modeling Cross-contamination in Quantitative Microbial Risk Assessment

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Briana Henderson
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1 Modeling Cross-contamination in Quantitative Microbial Risk Assessment Don Schaffner Food Risk Analysis Initiative Rutgers University 1of 19 Modeling Cross-contamination in Quantitative Microbial Risk Assessment Don Schaffner Food Risk Analysis The Initiative Achilles heel Rutgers University of risk assessment - G. Paoli 7/24/02 2of 19

2 Laboratory Experiments Nalidixic acid resistant Enterobacter aerogenes with attachment characteristics similar to Salmonella More than 30 participants dice inoculated chicken, wash hands and/or wear gloves, slice lettuce Sample hands, foods, faucet spigots cutting boards for Enterobacter 3of 19 Why these studies? Practical consideration A company was interested in showing efficacy of a touch-free faucet they provided funding! Our research philosophy Variability matters, especially for modeling and risk assessment 4of 19

3 Things to think about A surface can either be sampled or be used to contaminate another surface Relative numbers and rates Dirty hands -> clean faucet handles Dirty hands <-> dirty faucet handles Clean hands <- dirty faucet handles How many observations at one set of conditions are needed? 5of 19 Data Analysis CFU on Spigot Transfer rate (%) = 100 CFU on Hand Log transformation of % transfer Frequency histogram in Excel Best distribution using BestFit Normal distributions fit the data 6of 19

4 Think about data transformation 8 Frequency Percent Transfer Log Percent Transfer 7of 19 Cross contamination results Log Reduction Hand-to-spigot Handwashing Hand-to-lettuce Spigot-to-hand Chicken-to-board Chicken-to-hand -1 SD Mean +1 SD Log Transfer rate (%) Transfer rate (%) 8of 19

5 Glove barrier: Chicken to hand 16 Frequency With glove Without glove Transfer Rate (%) 9of 19 Our Published Work Chen, Y., Jackson, K.M. Chea, F.P. and Schaffner, D.W Quantification and variability analysis of bacterial cross-contamination rates in the kitchen. Journal of Food Protection. 64(1): Montville, R., Chen, Y., and Schaffner, D.W Glove barriers to bacterial cross-contamination. Journal of Food Protection. 64(6), Montville, R., Chen, Y. and Schaffner, D.W., Risk assessment of handwashing efficacy using literature and experimental data. International Journal of Food Microbiology 73(2-3), of 19

6 Investigator Organization Project Griffith University of Wales Behavior frequencies, high risk surfaces Jaykus Kasuga Currently ongoing research with application on microbial behavior in the kitchen environment North Carolina State University National Institute of Infectious Diseases Cross contamination with viruses and pathogenic bacteria Cross contamination with naturally occurring bacteria, different foods Mattick University of Bristol Dishwashing effectiveness Michaels Georgia Pacific Handwashing and cross contamination Sobsey Univ NC, Chapel Hill Simultaneous Serratia and Phage transfer Todd Michigan State Cross contamination with Listeria 11 of 19 Other recent publications L. L. Gibson, J. B. Rose, C. N. Haas, C. P. Gerba, and P. A. Rusin. Quantitative assessment of risk reduction from hand washing with antibacterial soaps. J.Appl.Microbiol. 92:136S- 143S, The objective of this study was to examine the risk reduction achieved from using different soap formulations after diaper changing using a microbial quantitative risk assessment approach. T. A. Cogan, J. Slader, S. F. Bloomfield, and T. J. Humphrey. Achieving hygiene in the domestic kitchen: the effectiveness of commonly used cleaning procedures. J.Appl.Microbiol. 92 (5): , Aims: To quantify the transmission of Salmonella and Campylobacter to hands, cloths, and hand- and food-contact surfaces during the preparation of raw poultry in domestic kitchens, and to examine the impact on numbers of these bacteria of detergent-based cleaning alone, or in conjunction with thorough rising. 12 of 19

7 Things to consider in QMRA Cross-contamination must be handled differently than other increases Two log increase due to growth: = CFU added from crosscontamination: = 110 Modeling the non-linear nature of the process 13 of 19 Non-linear process Initial Storage effect log increase Raw chicken Cooking effect log decrease Cooked chicken raw to hand hand to cooked raw to board Hand hand to mouth Exposure hand to lettuce Cutting board board to lettuce Lettuce 14 of 19

8 Model interface Initial 100 Storage effect log increase 2 Model Cooking effect log decrease 5 xrate mean -2 Xrate module xrate sd 1 Log Exposure Log exposure stats Calc Calc Exp where less than 1 is zero Calc 15 of 19 Probability Density High Rate Results Log Exposure initial: 1000 storage increase: 1 cooking decrease: 5 log cross contamination rate mean: -1 (10%) log cross contamination standard deviation: 1 16 of 19

9 Low Rate Results initial: 1000 storage increase: 1 cooking decrease: 5 log cross contamination rate mean: -3 (0.1%) log cross contamination standard deviation: 1 Statistic Dose Minimum 0 Median 0 Mean 5 Maximum of 19 Where to we go from here? What factors are important in controlling transfer rate? Soil type, organism, pressure, concentration, etc. What routes are important? Hand to mouth, cutting board to raw product, etc. What behaviors are important? Handwashing, cleaning frequency, etc. Once we know what s important, we can ignore what s not important, and include a useful, simplified cross contamination module in our risk assessments 18 of 19

10 Acknowledgements Lee Budd for stimulating discussions Sloan Valve company for support and funding The Food Risk Analysis Initiative was funded in part by the New Jersey Agricultural Experiment Station Members of the FoRAI team: Yuhuan Chen, Rebecca Montville, Kristin Jackson, Siobain Duffy, Purvi Vora, Lihui Zhao 19 of 19

Risk assessment of hand washing efficacy using literature and experimental data

International Journal of Food Microbiology 73 (2002) 305 313 www.elsevier.com/locate/ijfoodmicro Risk assessment of hand washing efficacy using literature and experimental data Rebecca Montville, Yuhuan