use ofmicrobial risk modeling to determine the benefit ......intervention tests due to logistical...

Use of Microbial Risk Modeling to Determine the

Benefit of Topical Antimicrobial Products

Prepared by:Charles N. Haas and Jason R. Marie

Drexel UniversitySchool of Environmental Science, Engineering and Policy

Philadelphia, PA 19104

Joan B. RoseUniversity of South FloridaCollege of Marine Sciences

St. Petersburg, FL 33701

and

Charles P. GerbaUniversity of Arizona

Department of Soil, Water and Environmental ScienceTucson, AZ 85721

Prepared for:The Soap and Detergent Association

Washington, DC 20005

and

The Cosmetic, Toiletry and Fragrance AssociationWashington, DC 20036

June 11, 2002

2

SECTION 1: INTRODUCTION .4

SECTION 2: RISK ASSESSMENT FRAMEWORK 5

SECTION 3: DATABASE REFERENCES 7

SECTION 4: ESTIMATION OF MICROBIAL REDUCTION 8

SECTION 5: GENERAL STATISTICAL ANALYSIS 9

SECTION 6: RISK CHARACTERIZATION 17

SECTION 7: SELECTING THE RIGHT DISTRIBUTIONS 23

SECTION 8: PROBABILITY OF INFECTION RESULTS 30

SECTION 9: SUMMARY 52

SECTION 10: SIGNIFICANCE 54

SECTION 11: REFERENCES 56

SECTION 12: APPENDIX 57

Section 1: Introduction

Topical antimicrobial products for hand use containing a variety of active

ingredients have been increasingly used among the general public, and represent a

growing area among consumer product companies. It is difficult to confirm the

quantitative benefits for use of such products using classical, direct epidemiological

intervention tests due to logistical problems of controlling use behavior in a large number

of subjects over a long duration trial. In other applications, the methods of quantitative

microbial risk assessment (QMRA) have been useful in estimating levels of risks or

benefits difficult to establish by direct epidemiological study. The objective of this work

was to demonstrate the applicability of QMRA for estimation of benefits from use of

topical antimicrobial hand products.

A risk assessment model for the reduction of infectious disease in the general

population by the use of topical antimicrobial products was developed. An extensive

literature search was completed in order to obtain the necessary data to perform the risk

assessment. The essential information required to complete the risk assessment included:

natural occurrence of microorganisms on chicken and ground beef~ transfer rates of

microorganisms from food to hands and hands to mouth, and reduction of

microorganisms by the use of antimicrobial hand products. These inputs were used in

conjunction with available dose-response information to formulate an integral model for

infection prevention.

5

Section 2: Risk Assessment Framework

A paradigm or “framework” must be used in order to integrate microbial risk

assessment and risk management. Industry, state, local, and federal agencies use risk

management. If the microbial risk assessment process is understood at all levels it can

become an effective tool for risk managers. In 1996, the International Life Science

Institute developed a conceptual framework to assess the risk of human disease following

exposure to pathogens. Figure 1 shows this “framework” in detail.

The analysis box in Figure 1 is highlighted in more detail on the right side of the

figure. This “analysis box” of distributions includes human health effects, dose-response

modeling, exposure analysis, and occurrence assessment. Table 1 presents examples of

the distributions considered for the analysis phase of risk assessment. The information

displayed in Table 1 was taken from (Haas et al., 1999).

Figure 1. Fundamental Paradigm for Microbial Risk Assessment (ILSI).

6

Table 1. Distribution Examples in the Analysis Phase of Risk Assessment.

DistributionsHealth Effects

ExamplesSymptomatic and Asymptomatic Infection

SeverityDuration

HospitalizationMedical Care

MortalityHost Immune Status

Susceptible PopulationsVehicleAmountRoute

Single verses MultipleDemographics of those Exposed

MethodsConcentrations

FrequencySpatial

Temporal VariationRegrowthDie-off

Transt~ort

There are many QMRA applications where these techniques can be applied.

QMRA can answer questions such as:

Should I disinfect the water?

How thoroughly should I wash the chicken?

When I handle poultry, will it cross-contaminate vegetables in the kitchen?

What is the risk from “agent X” released into the community (terrorist/warfare)?

This is just a brief review of other QMRA applications. There are many more scenarios

where QMRA techniques can be applied.

Dose-Response ModelingExposure Analysis

Occurrence Assessment

7

Section 3: Database References

As mentioned in the previous section, an extensive literature search was

completed in order to obtain the necessary data to perform the risk assessment. This

literature search used three different methods to obtain the data. The first source of data

included references currently in the files of the investigators that are available in the open

literature. The second source was from an extensive computer bibliographic search.

Index Medicus, Biological Abstracts, SCISEARCH and Web ofScience were databases

used to acquire relevant information from studies. SCISEARCH and Web ofScience are

particularly useful in that once a key study is located, these search engines can be used to

locate all subsequent papers that have cited the key study — thus it can “extend” the range

of associated studies forward in time from the publication date of a key study. Thirdly,

unpublished information provided by product manufacturers was used in this analysis.

A database was created containing the relevant information obtained from the

data gathering. Several criteria were established in order to determine if a reference

(study) would be included in the database. References that generated data from hospital

or clinical studies were not included. This study focused on consumer products not

products used in the hospital setting. In the hospital setting there are different skin floras

present along with increased strength of germicidal ingredients. Criteria for useful

references included use of the glove juice methodology and sufficient data to quantify the

reduction ratio (N/No). N is the concentration of microorganisms on the hand after using

antimicrobial hand wash products. N0 is the initial concentration of microorganisms on

the hand. Much of the information in the literature was not usable for several reasons.

8

These reasons included lack of results to quantify N/No and incomplete description of the

methodology used for the study.

Section 4: Estimation of Microbial Reduction

The bacterial inactivation data, which were extracted from the extensive literature

search, were grouped by active ingredient. The active ingredient groups along with their

abbreviations can be found in Table 2.

Table 2. Inactivation/Reduction Groups With Designated Abbreviations.

Abbreviations Group NameALC Alcohols

PCMX ParachlorometaxylenolCHEX Chiorhexidine/HibiclensTSAN TriclosanfHexachlorophene

I lodine/PovidoneNONG Non-Germicidal

Table 3 is a summary table, which shows a detailed breakdown of the entire data

set cross-tabulated by active ingredient and microorganism tested. This table shows the

number of observations associated with each microorganism type/active ingredient group

combination. Table 22 in Section 12: Appendix identifies the source of each data point.

The 10gb reductions found in Table 22 where calculated by taking the —logio of the

reduction ratio (N/No).

9

Table 3. SununarY of All Observations Used According to Microorganism and

Active Ingredient Group.

Active Ingre4ientMicroorga~

Acinetobacter baumanniilida albicans~

Diphteroid, bacilliEscherip~i~ coli

Enterobacter aerogenesB199A

K.aero~Native anaerobes

P.aerugi]Non~pathOgemcstaphylococcuS!

rnicrocC0cl~5S. aureus~

Serratia marcesCeflsS.

Total Aerobic CountALL DATA

X- No observation.

Section 5: General Statistical Analysis

General descriptive statistics were calculated for the logio reductions. The mean

and standard deviation for each group can be found in Table 4. The frequency of

observations for all groups is present along with the number of observations for each

individual group.

10

Table 4. Statistical Summary of Logio Reductions by Active Ingredient Group.

StandardGroup Mean Deviation Frequency

ALC 2.973 1.258 66CHEX 2.360 1.171 78

I 1.908 1.726 42NONG 1.136 0.922 23PCMX 2.519 1.102 48TSAN 1.740 0.840 43

ALL DATA 2.274 1.318 300

Figure 2 below is a box and whisker plot of the logio reductions sorted by active

ingredient group. These groups are also sorted from highest mean to lowest mean. The

line in the middle of the box represents the median or 50th percentile of the data. The

ends of the box depict the interquartile range (IQR), 25th and 75th percentiles. The

whiskers are the upper and lower adjacent values. The upper adjacent value is defined as

the largest data point less than or equal to the 75th percentile + 1 .5*IQR. Similarly, the

lower adjacent value is the smallest data point greater than or equal to the 25th percentile

- l.5*IQR.

11

LoglO Reductions10

(1)C00

-o

0 i~1L1 -ALC 2-PCMX 3-CHEX 4-I 5-TSAN 6-NON~

Group Identification

Figure 2. Box and Whisker Plot of Logio Reductions Arranged by Active Ingredient

Group.

A one way analysis of variance (ANOVA) was performed on the data to

determine if there was a statistical difference between logio reductions and active

ingredient groups. The null hypothesis (H0) for this test is that there is no statistically

significant effect of active ingredient groups on logio reductions

It was noted that there was considerable heterogeneity present, i.e., differences in

standard deviations between groups. Hence it was decided to transform the data to

reduce heterogeneity.

A Box-C0X transformation, as defined by equation 1 was used. The best value of

?~ computed by maximum likelihood was found to be 0.6530. This value was then used

in equation 1.

12

Log10Reduction~ —1Box — Cox Transformation = (1)

The results for the Box-Cox transformation are shown in Table 5 and Table 6.

Table 5. Statistical Summary of the Box-Cox Transformation of Logio Reductions

by Active Ingredient Group.

StandardGroup Mean FrequencyDeviation

ALC 1.552 0.884 65CHEX 1.068 0.895 78

I 0.673 1.243 41NONG -0.014 0.929 23PCMX 1.203 0.820 48TSAN 0.595 0.754 43

ALL DATA 0.989 1.014 298

Table 6. Analysis of Variance for the Box-Cox Transformation of Logio Reductions

(Inactivation/Reduction).

Source SS df MS F Prob>FBetween Groups 57.203 5 11.441 13.47 7.890e-12Within Groups 248.008 292 0.849

Total 305.212 297 1.028

Homogeneity of variances between groups was tested using Bartlett’s test (Sokal

and Rohlf, 1995). Although the variances were still heterogeneous (~2=13.24, p=O.O2l),

the transformation substantially improved the homogeneity between groups.

The Sidak test compares the logio reductions of each active ingredient group

against the logio reductions of each of the other active ingredient groups separately, The

first value found is the row mean minus the colunin mean. The second value is the F test

probability value. If this value is less than or equal to 0.05 then there is a significant

13

difference between these two groups. Table 7 shows the results from the Sidak test for

the logio reductions by active ingredient group. For example the third row (NONG), first

column (ALC) shows that antimicrobial products with alcohol as the active ingredient

have a 1.838 log greater reduction than antimicrobial products with no active ingredient.

This difference is highly significant because the F test probability value is less than

0.001.

Table 7. Comparison of Logio Reductions by Active Ingredient Group.

Row Mean- ALC CHEX I NONG PCMXColumn_Mean

CHEX -0.6140.041

I -1.065 -0.4520.000 0.562

NONG -1.838 -1.224 -0.7720.000 0.000 0.204

PCMX -0.455 0.159 0.611 1.3830.536 1.000 0.242 0.000

TSAN -1.233 -0.619 -0.168 0.605 -0.7780.000 0.111 1.000 0.575 0.038

Now that it is shown that there is a statistically significant effect of active

ingredient on the logio reductions, a second question must be addressed. Is the loglo

reduction different for different microorganisms with the same active ingredient? This

question was answered by using similar procedures as with the active ingredient groups.

Table 8 shows the microorganism names and abbreviations used in the ANOVA analysis.

A one-way ANOVA was done within each active ingredient group to determine if

there was a statistically significant effect of microorganism upon logio reductions within

14

that group. For example the alcohol active ingredient group included observations from

ten different types of organisms.

Table 8. Microorganism Identifications and Abbreviations.

Organism Organism ID Organism Abbreviation

Acinetobacter baumanni (hospital isolate) Acm_baum AcmCandida albicans (4 strains) Cand_alb Cand

Diphteroid bacilli Diph_bac Diph_bacEscherichia coli Ecoli Ecoli

Enterobacter aerogenes B 1 99A Enter_aerog Ent_aerK. aerogenes K_aerogenes K_aerog

Native anaerobes Native_ana Nat_anaP.aeruginosa P_aeruginosa P_aerug

Non-pathogenic staphylococcus! microccocus Staphy_micro StaphyS. aureus Saureus S_aureus

Native and MRSA-S. aureus S_aureus S_aureusSerratia marcescens Smarcescens Smarces

S. saprophyticus Ssaprophyticus S_saprophTotal Aerobic Count TAerobic Count T_Aerob_C

Figure 3 is a box and whisker plot, which displays an interesting comparison

between the alcohol, chlorhexidine, and triclosan active ingredient groups. The logio

reductions for all common microorganisms between these three groups were separated

from the data set in order to show this comparison. The logio reductions for alcohol and

chiorhexidine were always greater than the logio reductions for triclosan. This was true

for every common microorganism between the three groups.

~Iog1Ored

6.61— 0

H

_ T Ii —

I 1T

_ I~I1~ I t 1

:11-Ecoli-ALC 1-Ecoli-TSAN ~2-S_aureus-QHEX 3-S_marces-ALC 3-S_marces-TSAN 4-T_Aerob_C-CHEX 5-P_aerug-ALC S-P_aerug-TSI\N

1-Ecoli-CHEX 2-S_aureus-ALC 2-S_aureus-TSAN 3-S_marces-CHEX 4-T_Aerob_C-ALC 4-T.Aerob_C-TSAN 5-P_aerug-CHEXMicroorganism Identification

Figure 3. Box and Whisker Plot Comparing ALC, CIIEX, and TSAN Active Ingredient Groups Showing Logio Reductions by

Microorganism.

An ANOVA was performed on the ALC-E. coil and TSAN-E. coli logic, reduction

data. The results, found in Table 9, show that active ingredient group has a statistically

significant effect on E. coil logic, reductions.

Table 9. Analysis of Variance for the E. coil Logio Reductions Comparing ALC and

TSAN Active Ingredient Groups.

Source SS df MS F Prob>FBetween Groups 18.986 1 18.986 24.19 0.0002Within Groups 11.772 15 0.785

Total 30.758 16 1.922

As mentioned previously, homogeneity of variances between groups was tested

using Bartlett’s test. The variances were homogenous (~2=093 p=O.336), which

satisfies a formal assumption of ANOVA.

The ANOVA results for each active ingredient group can be found in the

Appendix (Table 23 through Table 28). Three out of the six active ingredient groups

showed that microorganism type had a statistically significant effect on logic, reductions.

The ANOVA for the lodine/Povidone, PCMX, and CHEX groups did not show a

statistically significant effect. Several transformations were completed which did not

change the results for the three groups previously mentioned. Statistically significant

results were found for the ALC, TSAN, and NONG active ingredient groups without

performing any transformations. All formal assumptions of ANOVA were also satisfied.

Table 10 qualitatively summarizes the ANOVA results determining whether

microorganism type had a statistically significant effect on logic, reductions.

17

Table 10. Summary of ANOVA results for Microorganism Type and Logio

Reductions.

Active Ingredient Group ANOVA ResultsALC Microorganism Type Significant

NONG Microorganism Type SignificantTSAN Microorganism Type SignificantPCMX Microorganism Type Not Significant

I Microorganism Type Not SignificantCHEX Microorganism Type Not Significant

One explanation for the three active ingredient groups which did not show

microorganism type having a statistically significant effect on logio reductions is that

there was not enough data. If the number of observations in each group increased along

with the number of microorganism types it is believed that these active ingredient groups

would also display statistically significant results, which implies that microorganism

types do have an effect on logio reductions for all active ingredients.

Section 6: Risk Characterization

Three scenarios were evaluated for the risk characterization. Each scenario

estimated the probability of infection from preparing raw meat in the kitchen. Once the

raw meat was prepared, the concentration of microorganisms that were transferred from

the raw meat to the person’s hands and then to their lip, where infection can occur, was

determined. Within each scenario the efficacy of using antimicrobial or non

antimicrobial hand products was evaluated to determine the magnitude in which the

probability of infection would be reduced as opposed to not using antimicrobial or non

antimicrobial hand products.

18

Salmonella is a common bacteria found on raw chicken. When preparing chicken

during food preparation there is a chance for Salmonella and other bacteria to be

transferred from the chicken to a person’s hands and subsequently from hands to mouth.

One way to lower the probability of infection is for the subject to reduce the level of

microbes on the hands by washing with soap and water by use of antimicrobial-

containing soaps or other hand products immediately after this handling activity. A

QMRA model was constructed in order to assess the probability of infection after

preparing raw chicken. The beta-Poisson model was used to model the probability of

infection for three different scenarios. The subject either washed hands with

antimicrobial or non-antimicrobial products or did not wash their hands at all after

handling raw chicken. The beta-Poisson model equation along with the description of its

parameters can be found below in Equation 2.

F1(d) =1— + ~(2’~ — (2)

N50 is the median infectious dose, a is a non-negative value that represents the slope of

the curve and d is the dose of microorganisms.

The beta-Poisson model takes into account the variations that exist in pathogen

host interactions and approaches the exponential model when alpha approaches infinity.

It has been found that the infectivity of Salmonella to humans is well described by the

beta-Poisson model with best estimates of a0.3 126 and N50=2.36*~ (Fazil and Haas,

1996).

A Salmonella bootstrap distribution of 1000 trials was used for the alpha (a) and

N50 parameters in the beta-Poisson model. The Salmonella bootstrap distribution and

19

best-fit parameters were taken from (Fazil and Haas, 1996). The uniform distribution

was used in the Monte Carlo analysis in order to randomly pick a row from 1 to 1000 that

corresponded to an alpha and N50 value from the bootstrap. The uniform distribution was

selected because every row has an equal chance of being chosen. Figure 4 is a scatter

plot of the N50 versus a parameters from the Salmonella bootstrap used in the beta-

Poisson model.

~‘ Bootstrap estimation (1000 iterations)

• Best-fit parameters

ii I ~

1111111111 IIIIIII~IIJIII~IIIIIi{III~II!~NIIIIIII!IIIII!IjIl!IIl ~~

IIIIIIHU I!IIIII~IIIIll~HHI Illll~llI~IHhIIc~~II I II ~

1111111111 I~IIIIII~IIIIII~IIIII~

0 :..........~ ~

z I~

102

101

100

101

102

io-3

111111111 — 11111 liii III II H IIIIIIIIIIIIIIIIIIIIIIIIIIIIH TI IIITTITTIIIITTIII IlIllIllIll 1111110 IIIIflhllIll 11111 liii III hi ii iiihiiiilihihihiiiiiiiiiiiliii Ii IIhiiihiiiIIiiihi iiiiiiiiiii iiiiiiii iii :i I liii lii

11!!!~~ — I!! II II~ ..‘. ~ £ £,,,,,,,,,.. ,,.,~.., .,,,,, .,,

10~ 100

a

Figure 4. Salmonella Bootstrap Distribution of N50 versus Alpha (a).

A second case study was performed using the beta-Poisson model to evaluate the

probability of infection after preparing raw ground beef. The same scenarios were run

for this case study as in the Salmonella-Chicken case study. Instead of evaluating

Salmonella-Chicken this study evaluated ground beef with either non 0157:H7

20

enteropathogenic Eseherichia coli or Escherichia coli 0157 :H7. The former group

represents the non 0157:H7 pathogenic E. coli minus the 0111 strain.

A non 0157:H7 enteropathogenic Escherichia coli and Escherichia coli

01 57:H7 bootstrap distributions of 1000 trials each were used for the alpha (ce) and N50

parameters in the beta-Poisson model for each respective case. The non 0157:H7

enteropathogenic Escherichia coli and Escherichia coli 0157:H7 bootstrap distributions

were taken from (Haas et al., 1999) and (Hans et al., 2000), respectively. Figure 5 and

Figure 6 are scatter plots of the N50 versus ci~ parameters from the E. coli bootstraps used

in the beta-Poisson model.

21

K~’ Bootstrap estimation (1000 iterations)


— —

K

“~‘““““ F

1010

0

It) in8z

106

iolcx

Figure 5. Non 0157:117 Enteropathogenic E. coli Bootstrap Distribution of N50 versus alpha

(00.

100

22

.~.1

~Js~

cc2~

>1..~.

~ k~.°t1~

i0~ 10.2 10l 100 10’ 102

Figure 6. E. coil 0157:117 Bootstrap Distribution of N50 versus Alpha (a).

(3 points are outside of graph range)

The bootstrap estimations found in Figure 6 show that there is scattering present

within the distribution. This is due to the particular experimental design of the E. coli

01 57:H7 dose response experiment itself. This “scattering” will effect the results of the

Monte Carlo analysis. The results of the Monte Carlo analysis will be presented in the

section entitled Probability of Infection Results.

The best-fit values for the dose-response parameters in each bootstrap distribution

can be found in Table 11. These values are also represented in the N50 versus alpha plots

by a single circle point.

0 Bootstrap esthnation (1000 iterations)


WQ~,~\ ,.‘

Lt,

1010

108

ios

102

100

z

23

Table 11. Best-Fit Dose Response Parameters for all Bootstrap Distributions.

Bootstrap N50Salmonella 2.36e4 0.31

E. coli 8.60e7 0.18E. coli 0157:H7 5.96e5 0.49

Section 7: Selecting the Right Distributions

Several statistical distributions were incorporated into this model to describe

specific parameters. This model was constructed in Microsoft Excel® and used in

conjunction with an add in statistical software package, Crystal Ball®, which performs

Monte Carlo analysis. The best point estimate for each case study can be found in the

appendix. Statistical distributions were fitted to the data using Crystal Ball®. The

Kolmogorov-Smimov test was used to determine which distribution best described the

data.

During distribution fitting Crystal Ball matches the data against each continuous

probability distribution. Continuous probability distributions, such as the normal and

Weibull distributions, describe values over a range or scale. Continuous probability

distributions assume the existence of every possible intermediate value over a range; i.e.

there are an infinite continuum of values between any two points in a distribution. Once

the data is matched against a continuous probability distribution a mathematical fit is

performed which determines the set of parameters for each distribution that best describe

the characteristics of the data. This mathematical fit is done by computing Maximum

Likelihood Estimators (MLE5). The MLEs method chooses values for the parameters of

the distribution that maximize the probability of producing the actual data set. Once the

24

parameters for each distribution are calculated a goodness of fit test is performed in order

to determine which distribution best represents the data.

Cumulative frequency plots are shown in Figure 7 and Figure 8 in order to better

describe how well the chosen distributions fit the data. The x-axis in the cumulative

frequency plots found in Figure 7 is the % transfer rate while the x-axis in Figure 8 shows

the logio reductions. For Figure 7 the upper cumulative comparison is for the % transfer

rate (chicken-hand) parameter while the bottom plot describes the fit for the % transfer

rate (finger tips-mouth).

The % transfer rate from chicken to hand data was found in (Chen et al., 2001)

and the % transfer rate, fingertip to mouth, was obtained from (Rose et aL). Additional

data that were used in the Monte Carlo simulations for the Salmonella-Chicken case

study were taken from (Dufrenne et al., 2001). These data represent the CFU/carcass

parameter. The data were extracted from the article and the log-normal distribution was

determined to represent the data best. This data set describes the natural occurrence of

Salmonella on chicken.

25

~L0OO

750 ~ Lognorma~ Distribt~jtionMe~n~=15~~8lStdDav 48,40

.250

~o000.00

~ Lqgn~tj~I Di~rjbutic~n2’ = 2B~8

St~d 0ev = 4~.28

~ tnpuU~a

Figure 7. Cumulative Frequency Plots for % Transfer Rate (Chicken-Hand and Finger Tips-

Mouth).

The log-normal distributions which were chosen for the % transfer rate

parameters between chicken and hands in the model fit the data well. Figure 8 is a series

of three cumulative frequency plots, which show the fit for the logio reduction parameter

(active ingredient-alcohol). The three distributions listed were the top three best-fit

distributions for this model parameter. The Weibull distribution displayed the lowest

Kolmogorov-Smirnov statistic followed by the logistic and beta distributions,

respectively.

CumüIativ€i Compayj~on

1PpUtD~ta

~0,00 4~JJJQ ~O,00 80.00

bumuiativ~ Con~pa~ison

0.00 27.5~J 55.00 82.50 110.00

26

~t00O?

~750.

.500

.250

~00o~~50

1.000

.750

I• 000

11300

.750

~500

.000~

W~i~lJ Qf~f~nLap,~~c~1~w 8~75~S~Ejape ~7.34

~ lhpLD~t~

~ tog~ic Di~t~bu1JonMear) = 4~27Scale~

~ input D~a

~ Beta DIstiib~.4ionAlpha = 5:63’Beta 2.58Scale = 6.21

~ Input Data

Figure 8. Cumulative Frequency Plots for Alcohol Active Ingredient Logio

Reductions (E. colt).

For the Salmonella-Chicken case study a total of six different simulations (runs)

were performed with each simulation consisting of 500 iterations. All distributions for

each variable of the model remained the same for each simulation except for the logio

Cumuhilive Copipør~p

~a38 4.25 &00

2F~fl 425 !~1~ t~flflCumulative Comparison

2.50 3.38 4.25 5.13 6130

27

reduction distribution. The first four simulations contained logio reduction data taken

from the alcohol active ingredient — E. coil microorganism group. The best-fit

distribution was the Weibull (simulation 1) followed by the logistic (simulation 2), and

beta (simulation 3). The log-normal distribution was used for simulation 4. The

distribution for the logio reduction in simulation 5 was from the chlorhexidine/Hibiclens

active ingredient — E. coil group. Simulation 6 used the non-germicidal-E. coil logio

reduction data where the gamma distribution was determined to be the best-fit

distribution. Logio reduction data for Salmonella was unable to be obtained. The E. coli

data was used in its place due to the anticipated similarity of inactivation, since both

organisms are enterobacteriacae,

For the Escherichia coli-Ground Beef case study three different simulations were

performed with each simulation consisting of 500 iterations. These simulations were

executed using two different studies to characterize the initial concentration of

Escherichia coil on ground beef. The first set of data represents non 0157 :H7

enteropathogenic Escherichia coli while the second represents Escherichia coil 0157:H7.

All distributions for each variable of the model remained the same for each simulation

except for the logio reduction data. The first simulation was taken from the alcohol active

ingredient — E. coli microorganism group while the second was from the

chiorhexidine/Hibiclens active ingredient — E. coil group. The third simulation was the

non-germicidal-E. coli scenario. The best-fit distribution for the ALC active ingredient

group was the Weibull while the Extreme Value distribution represented the CHEX

active ingredient group. The Gamma distribution represented the NONG ingredient

group. In the open literature, a transfer rate for Escherichia coli from ground beef to

28

hands was unable to be found. Due to this absence, the transfer rate (ground beef to

hands) model parameter was used from a Salmonella and chicken transfer study (Chen et

al., 2001).

The non 0157:H7 enteropathogenic Escherichia coli data was taken from Russell

(2000). The data were extracted from the article and the beta distribution was determined

to represent the data best. The Escherichia coli 0157:H7 data was found in (Tuttle et al.,

1999).

Additional transfer rate data were obtained from the Dial Corporation describing

the transfer rate from chicken breast to hand and lean ground beef to hand. These data

were first analyzed using the Kolmogorov-Smirnov test in order to determine if the

transfer data came from the same distribution as the transfer data used in previous

models. As mentioned earlier, the data representing the % transfer rate from chicken to

hand was found in (Chen et al., 2001). The results of the Kolmogorov-Smirnov test

showed that the unpublished transfer rate data from Dial Corporation and the Chen et al.

data did not come from the same distribution and that the unpublished company data

contains smaller values than the transfer data previously used. Due to these results the

unpublished company transfer rate data was fit to its own distributions because it could

not be pooled with the Chen et al. (2001) data. Table 12 is a summary table that contains

the distributions and their respective parameters used in all models and simulations.

Table 12. Best Fit Distributions and Parameters for All Models and Simulations.

Sc

ECB

% Transfer Rate~Co*(chicken-H~d)

% Transfer Rate~Co*(Ground Beef-Hand)

% Transfer Rate(Finger Tips-Mouth)

scsalmonella~chicken case StudyECBE. coli-Ground Beef case Study*Unpublished company Data

All

Simulation Std.Parameter Distribution Mean Alpha Beta Scale Loc. Shape ModeNumber Dev.

1SC 1ECB Logio Reduction~ Weibull 6.75 -2.10 7.94

2~’~ Logio Reduction~ Logistic 4.27 0.5435C Logio Reduction~ Beta 5.63 2.58 6.2145C Logio Reduction~ Log Normal 4.27 1.04

5SC 2~ Logio Reduction~ Extreme Value 0.45 3.16

~ 3ECB Logio Reduction~ Gamma 1.29 1.22 0.41Sc cFU!carcass Log Normal** -1 3

ECB Logio MPN Beta 1.05 0.97 6.04

MPN/g Weibull 2.47 0.10 0.64ECB (E. coli 0157:H7)

All % Transfer Rate Log Normal 15.81 48.40

Log Normal 1.25 3.69

Weibull 0.12 0.00 0.72

Log Normal 26.58 49.29

**For cF’U/carcass mean and standard deviation values are Log Mean and Log Standard Deviationtklcohol was the germicidal ingredient

chlorhexidine/Hibiclens was the germicidal ingredient~Non-germicidal hand products was the ingredient

Section 8: Probability of Infection Results

The Salmonella-Chicken and E. coli-Beef case studies produced similar results.

In each case study using antimicrobial products after food preparation lowered the

probability of infection when compared to the no hand-washing scenario. The results for

the Salmonella-Chicken case study will be presented first followed by the E. coil-Beef

case study.

It was previously mentioned that each simulation had 500 iterations. Therefore,

each simulation will have 500 probability of infection estimates, which are shown in

Figure 9 and Figure 10. The log-normal probability plots shown in Figure 9 and Figure

10 display probability of infection estimates over the entire range of x-axis values, from

approximately 0.1 % to 99.9 %.

Figure 9 is a log-normal probability plot for all of the simulations where the

antibacterial and non-antibacterial hand washing scenarios were modeled. Most of the

simulations appear to have similar results except at the lower and upper tails. However,

these tails (<1%,>99%) are not well estimated in the 500 replicate Monte Carlo study.

The differences at the tails may be due to the different distributions used for the logio

reduction parameter. However, regardless of distribution, a substantial risk reduction is

shown. Another observation from this plot is that using alcohol as the active ingredient

reduces the probability of infection more than CHEX. Additionally, the use of alcohol or

CHEX as an active ingredient reduces the probability of infection more than the non

germicidal scenario.

31

100

1 02

1~

10b0

1 012

1014

1016

E ALC J~og1~ R~duØtiØn (~ogn~ri~a1) . ~ +~ ~11TRë~lIi~iöh (B~>

-I ALC Log 1OReduc~tion (L~ogi$tic)..>~~

L

. : :

.01 .1 1 5 10 2030 50 7080 9095 99 99.999.99Percent

Figure 9. Probability of Infection for All Simulations Using Antibacterial Hand

Wash Products (Salmonella-Chicken Case Study).

Figure 10 is a probability plot, which describes the probability of infection for the

no hand-washing scenario. The legends in Figure 9 and Figure 10 match in order to

compare the hand washing and no hand-washing scenarios by simulation (run). Overall,

it is assumed and clearly seen that there is a higher probability of infection in the no

hand-washing scenario when compared to the antibacterial hand-washing scenario. The

only difference found between simulations in Figure 10 is the difference in the tails of

each distribution. This difference is the only one that would be expected considering all

other parameters in the model are the same.

All Runs With Antibacterial Hand Wash Products

32

Figure 10. Probability of Infection for All Simulations with No Hand

Washing (Salmonella-Chicken Case Study).

After a simulation ended, a sensitivity chart was produced in order to clearly

determine which parameters in the model were the most sensitive. Figure 11 is the

sensitivity chart for all six simulations. The most influential parameter in all simulations

was the CFU/carcass parameter. The other parameters in the model also influence the

probability of infection however, a reduction in the range of the initial concentration of

microorganisms on chicken would have the greatest impact on reducing the range of the

output (e.g. probability of infection).

All Runs With No Hand Wash

10~l

1~

1~

;.. i~-~

1 0’

1 ~

1

0 CHEXLo~19 Reduc~tion (Extreme Value)L~ NONG~Log1O R~du~ticin (Garnn~a)~0 ~LC LbglD Reductrnn (Lognorn~iai~ ATX~ LbglO Redaictrbn ?~Betá).+ ~~

.01 .1 1 5 10 2030 50 7080 9095 99 99.999.99Percent

33

Figure 11. Sensitivity Chart for All Salmonella Chicken Runs.

The Monte Carlo runs for each case study were also performed using the best-fit

dose response parameters instead of N50 and c~ being randomly selected from the

bootstrap estimations. The dose response parameters were fixed at their respective MLE

value. The cumulative distribution functions (CDF) of the probability of infection

estimates were calculated after the Monte Carlo runs. The CDF’ s within each case study

were compared (i.e. the fixed alpha and N50 simulations verses the alpha and N50

simulations that were randomly selected from the bootstrap distributions). Figure 12

shows the comparison between the CDF’s for the Salmonella-Chicken case study, where

the probability of infection estimates are on the x-axis and the cumulative distribution

functions are on the y-axis.

Sensitivity Chart

% Transfer Rate(Finger Tips-Mouth)

• % Transfer Rate(Chicken-Hand)

D LoglO Reduction

~ CFU/carcass

-0.5 -0.3 -0.1 0.1 0.3 0.5 0.7

Measured Rank Correlation

34

Figure 12. CDF Comparison for the Salmonella-Chicken Case Study.

Statistical summaries for all simulations and scenarios are found in Table 14 (no

hand washing) and Table 15 (antibacterial products). A comparison table for the best fit

no hand washing, antibacterial and non-antibacterial product scenarios can be found in

Table 13. These summaries include mean, standard deviation, variance, skewness, mm

and max ranges, etc.

Table 16 gives a detailed description of the percentiles for each simulation. The

percentiles were 2.5, 5.0, 50.0, 95.0, 97.5. Each table is captioned to easily compare the

values for each simulation. Overall, there are not many dramatic changes in results

between each simulation.

CDF Comparison, Salmonella-Chicken Scenario

~CDF, Fixed MLE —cOF

1

0.75

0.5

1E-16 1E-14 1E-12 1E-10 1E-08

P1(d)

0.2 5~

1E-06 0.0001 0.01 10

35

Table 13. P1(d) Descriptive Statistics of Best Fit Simulations for All Scenarios

(Alcohol Active Ingredient, Salmonella-Chicken Case Study).

. . . Non-AntibacterialStatistics Antibacterial Products . No Hand Washing(Non-Germicidal)

Trials 5.OOE+02 5.OOE+02 5.OOE+02Mean 8,29E-06 3.30E-05 l.40E-03

Median 7.37E-12 2.72E-09 l.48E-07Mode 2.OOE-15

Standard Deviation 1 .82E-04 5.91E-04 2.08E-02Variance 3.32E-08 3.49E-07 4.31E-04Skewness 2.22E+O1 2.16E+O1 1.90E+O1Kurtosis 4.96E+02 4.76E+02 3.86E+02

Coeff of Variability 2.20E+O 1 1 .79E+O 1 1 .48E+O 1Range Minimum O.OOE+OO O.OOE+OO 2.29E-13Range Maximum 4.07E-03 l.31E-02 4.35E-O1

Range Width 4.07E-03 l.31E-02 4.35E-O1Mean Std. Error 8.15E-06 2.64E-05 9.29E-04

From Table 13 it is apparent that the use of alcohol and non-antibacterial-

containing active ingredient hand wash products reduce the median risk from 1.48*1 O~ to

737* 1012 (ALC) and 2.72* iO~ (NONG). Similar conclusions can be made from the

other Salmonella-Chicken Monte Carlo simulations found in Table 14 and Table 15.

36

Table 14. P1(d) Descriptive Statistics for No Hand Washing Scenario (Salmonella-Chicken

Case Study).

Statistics Run 1 a Run 2b Run 3C Run 4d Run 5e Run 6~Trials 5.OOE+02 5.OOE+02 5.OOE+02 5.OOE+02 5.OOE+02 5.OOE+02Mean 1.40E-03 1.40E-03 1.18E-03 2.56E-03 l.40E-03 7.25E-04

Median 1.48E-07 1.48E-07 1.32E-07 1.23E-07 1.48E-07 1.1OE-07Mode

Standard Deviation 2.08E-02 2.08E-02 2.24E-02 2.42E-02 2.08E-02 8.65E-03Variance 4.31E-04 4.31E-04 5.OOE-04 5.85E-04 4.31E-04 7.49E-05Skewness l.90E+O1 1.90E+O1 2.21E+O1 1.12E+O1 1.90E-l-O1 L49E+O1Kurtosis 3.86E+02 3.86E+02 4.92E+02 1.36E+02 3.86E+02 2.41E+02

Coeff. of Variability l.48E+O1 1.48E+O1 1.90E+O1 9.45E+OO 1.48E+O1 1.19E+O1RangeMinimum 2.29E-13 2.29E-13 2.98E-12 9.1OE-15 2.29E-13 1.11E-13Range Maximum 4.35E-O1 4.35E-O1 4.99E-O1 3.46E-O1 4.35E-O1 1.57E-O1

Range Width 4.35E-O1 4.35E-O1 4.99E-O1 3.46E-O1 4.35E-O1 1.57E-O1Mean Std. Error 9.29E-04 9.29E-04 1.OOE-03 1.08E-03 9.29E-04 3.87E-04

aRun 1- ALC active ingredient group (Weibull Distribution)bRun 2- ALC active ingredient group (Logistic Distribution)cRun 3~ ALC active ingredient group (Beta Distribution)dRun 4~ ALC active ingredient group (Log Normal Distribution)eRun 5~ CHEX active ingredient group (Extreme Value Distribution)~Run 6- NONG ingredient group (Gamma Distribution)

37

Table 15. P1(d) Descriptive Statistics for Antibacterial Hand Products Scenario

(Salmonella-Chicken Case Study).

Statistics Run l~’ Run 2b Run 3C Run 4d Run 5e Run 6~Trials 5.OOE+02 5.OOE+02 5.OOE+02 5.OOE+02 5.OOE+02 5.OOE+02Mean 8.29E-06 8.79E-06 2.62E-04 6.36E-05 l.86E-04 3.30E-05

Median 7.37E-12 8.45E-12 8.76E-12 5.54E-12 2.20E-lO 2.72E-09Mode 2.OOE-15 O.OOE+OO O.OOE+OO O.OOE+OO 2.53E-14

Standard Deviation l.82E-04 l.93E-04 5.85E-03 l.25E-03 4.11E-03 5.91E-04Variance 3.32E-08 3.73E-08 3.42E-05 1.57E-06 l.69E-05 3.49E-07Skewness 2.22E+O1 2.22E+O1 2.22E+Ol 2.17E+O1 2.22E+O1 2.16E+O1Kurtosis 4.96E+02 4.96E+02 4.96E+02 4.79E+02 4.96E+02 4.76E+02

Coeff. of Variability 2.20E+Ol 2.20E+Ol 2.24E+Ol 1.97E+Ol 2.21E+O1 1.79E+O1Range Minimum O.OOE+OO O.OOE+OO O.OOE±OO O.OOE+OO 4.44E-16 O.OOE+OORange Maximum 4.07E-03 4.32E-03 1.31E-O1 2.78E-02 9.19E-02 l.31E-02

Range Width 4.07E-03 4.32E-03 l.31E-Ol 2.78E-02 9.19E-02 l.31E-02Mean Std. Error 8.15E-06 8.64E-06 2.62E-04 5.60E-05 l.84E-04 2.64E-05

aRun 1- ALC active ingredient group (Weibull Distribution)bRun 2- ALC active ingredient group (Logistic Distribution)cRun 3- ALC active ingredient group (Beta Distribution)dRun 4~ ALC active ingredient group (Log Normal Distribution)eRun 5~ CHEX active ingredient group (Extreme Value Distribution)~Run 6- NONG ingredient group (Gamma Distribution)

38

Table 16. P1(d) Percentiles for Antibacterial Hand Products and No Hand Washing

Scenarios (Salmonella-Chicken Case Study).

AntibacterialProducts

Percentile Runi Run 2 Run 3 Run 4 Run 5 Run 62.5% 1.11E-15 8.88E-16 1.78E-15 2.22E-16 4.24E-14 2.32E-135.0% 3.55E-15 2.89E-15 5.77E-15 1.11E-15 1.24E-13 2.85E-1250,0% 7.37E-12 8.45E-12 8.76E-12 5.54E-12 2.20E-10 2.72E-0995.0% 1.98E-08 1.79E-08 1.96E-08 3.25E-08 2.26E-07 6.30E-0697.5% 8.48E-08 8.12E-08 7.90E-08 1.83E-07 1.05E-06 3.91E-05

No Hand WashingPercentile Runi Run 2 Run 3 Run 4 Run 5 Run 6

2.5% 5.49E-1 1 5.49E-1 1 5.O1E-1 1 2.04E-1 1 5.49E-1 1 7.82E-1 15.0% 1.49E-10 1.49E-10 2.50E-10 1.33E-10 1.49E-10 2.97E-1050.0% 1.48E-07 1.48E-07 1.32E-07 1.23E-07 1.48E-07 1.1OE-0795.0% 2.22E-04 2.22E-04 2.06E-04 2.94E-04 2.22E-04 2.36E-0497.5% 6.21E-04 6.21E-04 6.24E-04 2.33E-03 6.21E-04 1.52E-03

Figure 13 is a log-normal probability plot for the Escherichia coli simulations

where the antibacterial, non-antibacterial, and no hand washing scenarios were modeled.

39

1 02

1~

1 06

~ 1O~

1O’~

1 012

1016

.01 .1 1 5 10 2030 50 7080 9095 99 99.999.99Percent

Figure 13. Probability of Infection for Escherichia coil Simulations for All

Scenarios.

As mentioned previously, it is clearly seen that there is a higher probability of

infection in the no hand-washing scenario when compared to the antibacterial and non

antibacterial hand-washing scenarios. This plot also shows that using alcohol as the

active ingredient reduces the probability of infection when compared to CHEX and non

antibacterial (NONG).

Figure 14 is a log-normal probability plot for the Escherichia coli 0157:H7

simulations where the antibacterial, non-antibacterial, and no hand washing scenarios

were modeled.

40

....,

~1.1.~ No 1~and ~thhiikg (NONG’~~~~

::::::::::~ . .

~AA~

.1 1 5 10 2030 50 7080 9095 99 99.999.99Percent

Figure 14. Probability of Infection for Escherichia coli 0157:H7

Simulations for All Scenarios.

The maj or difference between the simulations is seen in the mid to lower

percentiles of the probability of infection plot. These percentiles display the results

which are similar to the previous case studies. ALC being more effective as an active

ingredient than CHEX which was more effective than NONG. The E. coli 0157:H7

upper tail is likely due to the extreme bootstrap distribution shown in Figure 6.

Figure 15 is the sensitivity chart for all simulations. The most influential

parameter in the E. coli simulations was the Logao MPN or MPN/g parameter. The Logio

Reduction parameter was the most influential for the E. coli 0157:H7 simulations except

for the NONG simulation. The most influential parameter in the NONG simulation was

the % transfer from ground beef to hands. The other parameters in the model also

influence the probability of infection however; a reduction in the range of a parameter

100

1 02

1~

106

•o -~~-8

1’j

0~~10b0

1 012

1016

.01

41

with a high measured rank correlation would have the greatest impact on reducing the

range of the output (e.g. probability of infection).

Sensitivity Chart

Ec~’II 0157: 17- _________________

U fl ~

Ecoli-Ru i 3 _____________________ _______ ______~ /o Transfer Rate

(Finger Tips-Mouth)Ec ILOl~: !1_ •% Transfer Rate[ _____________ (Ground Beef-Hand)

_____________ DLog1O Reduction

__________________ ___________________ ~— ii ~ ~ ___________________ __________________ =_________ DLog1OMPNor

Ec Ii 0157: 7- ~ MPN/g

________ I ________mT~-rr~-r1-rrrrrrrrrrrr~r rr~rrrrrr ~

-0.5 -0.3 -0.1 0.1 0.3 0.5 0.7 0.9

Measured Rank Correlation

Figure 15. Sensitivity Chart for Eschericlzia coli and Escherichia coti 0157:H7

Ground Beef Runs.

Figure 16 and Figure 17 display the comparison between the CDF’s for the E. coli

Ground Beef and E. coli 015 7:H7-Ground Beef case studies, respectively.

42

E

CDF Comparison, Generic Ecoli-Hamburger Scenario

Fixed MLE —GOF

1

0.75

0.5

0.25

00.00011E-16 1E-14 1E-12 1E-lO 1E-08 0.000001

P1(d)

Figure 16. CDF Comparison for the Generic E. coli-Ground Beef Case Study.

43

Figure 17. CDF Comparison for the E. coli 0157:H7-Ground Beef Case Study.

As the probability of infection increases, in Figure 17, the CDF curves become

further apart. This shows the results of the very wide bootstrap samples for the E. coil

01 57:H7-Ground Beef case study. This result was previously discussed when the E. coil

0157:117 bootstrap distribution was presented in the Risk Characterization section.

Statistical summaries for all simulations and scenarios are found in Table 19

(antibacterial products) and Table 20 (no hand washing). A comparison table for the best

fit no hand washing, antibacterial, and non-antibacterial scenarios can be found in Table

17 and Table 18 for E. coil and E. coli 015 7.H7, respectively. These summaries include

mean, standard deviation, variance, skewness, mm and max ranges, etc. Table 21 gives a

detailed description of the percentiles for each simulation. The percentiles were 2.5, 5.0,

CDF Comparison, Ecoli 0157:H7-Hamburger Scenario

Fixed MLE ~~DF

0.25

4 ~ 1J

1E-16 1E-14 1E-12 1E-lO 1E-08 0.000001 0.0001 0.01 1

P1(d)

44

50.0, 95.0, 97.5. Each table is captioned to easily compare the values for each

simulation.

Table 17. E. coli P1(d) Descriptive Statistics of Best Fit Simulations for All

Scenarios (Alcohol Active Ingredient).

‘ Antibacterial Non-Antibacterial No HandProducts (Non-Germicidal) Washing

Statistics E. coli E. coli E. coliTrials 5 .OOE+02 5 .OOE+02 5 .OOE+02Mean 3.58E-08 2.12E-06 7.66E-05

Median 7.89E-12 3.69E-09 9.61E-08Mode 4.44E-16

Standard Deviation 4.53E-07 1 .78E-05 6.OOE-04Variance 2.05E-1 3 3.1 7E-1 0 3.60E-07Skewness l.73E+01 1.67E+01 1.48E+01Kurtosis 3.29E+02 3.21E+02 2.56E+02

Coeff. of Variability 1 .26E+01 8.42E+00 7.84E+00Range Minimum 0.OOE+00 2.22E-16 1.89E-12Range Maximum 9.08E-06 3.57E-04 1.13E-02

Range Width 9.08E-06 3.57E-04 1.13E-02Mean Std. Error 2.03E-08 7.97E-07 2.68E-05

45

Table 18. E. coli 0157:H7 P1(d) Descriptive Statistics of Best Fit Simulations for All

Scenarios (Alcohol Active Ingredient).

From Table 17 it is apparent that the use of alcohol and non-antibacterial-

containing active ingredient hand products reduce the median risk from 9.61 * 10.8 to

7.89*10.12 (ALC) and 3.69*10-9 (NONG) for theE. coli case study. Table 18 displays

results for the E. coli 0157:H7 case study which show reduction in median risk from

6.91 * 1 0~ (No Hand Washing) to 7.02*10.13 (ALC) and 1.35*10.10 (NONG). Similar

conclusions can be made from the other E. coli-Ground Beef Monte Carlo simulations

Antibacterial

E.

No Hand WashingNon-Antibacterial

Products (Non-Germicidal)Statistics coli 0157:H7 E. coli 0157:H7 E. coli 0157:H7

Trials 5.OOE+02 5.OOE+02 5.OOE+02Mean 1.54E-02 1.66E-02 1.80E-02

Median 7.02E-13 1.35E-10 6.91E-09Mode 0.OOE+00 0.OOE+00 0.OOE+00

Standard Deviation 8.40E-02 8.83E-02 9.27E-02Variance 7.05E-03 7.80E-03 8.59E-03Skewness 5.36E+00 5 .42E+00 5 .30E+00Kurtosis 2.99E+01 3.13E+01 3.05E+01

Coeff. of Variability 5.46E+00 5.33E+00 5.14E+00Range Minimum 0.OOE+00 0.OOE+00 0.OOE+00Range Maximum 4.91E-01 6.76E-01 7.30E-01

Range Width 4.91E-01 6.76E-01 7.30E-01Mean Std. Error 3.75E-03 3.95E-03 4.14E-03

found in Table 19 and Table 20.

46

Table 19. P1(d) Descriptive Statistics for Antibacterial Hand Wash Scenario.

Antibacterial a b e a b cRun 1 Run 2 Run 3 Run 1 Run 2 Run 3Products

. E. coli E. coli E. coliStatistics E. coli E. coli E. coli Q]57H7 0157.•H7 0157.117

Trials 5.OOE+02 5.OOE+02 5.OOE+02 5.OOE+02 5.OOE+02 5.OOE+02Mean 3.58E-08 2.40E-07 2.12E-06 l.54E-02 1.60E-02 1.66E-02

Median 7.89E-12 1 .52E-1O 3.69E-09 7.02E-13 1 .25E-1 1 1 .35E-1OMode 4.44E-16 4.66E-15 O.OOE+OO O.OOE+OO O.OOE+OO

Standard Deviation 4.53E-07 2.73E-06 1.78E-05 8.40E-02 8.65E-02 8.83E-02Variance 2.05E-13 7.47E-12 3.17E-1O 7.05E-03 7.49E-03 7.80E-03Skewness l.73E+O1 1.70E+O1 1.67E+O1 5.36E+OO 5.34E+OO 5.42E+OOKurtosis 3.29E+02 3.15E+02 3.21E+02 2.99E+O1 2.99E+O1 3.13E-l-O1

Coeff. of Variability l.26E+O1 1.14E+Ol 8.42E+OO 5.46E+OO 5.40E+OO 5.33E+OORange Minimum O.OOE+OO 4.44E-16 2.22E-16 O.OOE+OO O.OOE+OO O.OOE+OORange Maximum 9.08E-06 5.39E~O5 3.57E-04 4.91E-O1 5.83E-Ol 6.76E-O1

Range Width 9.08E-06 5.39E-05 3.57E-04 4.91E-O1 5.83E-O1 6.76E-O1Mean Std. Error 2.03E-08 1.22E-07 7.97E-07 3.75E-03 3.87E-03 3.95E-03

aRUn 1- ALC active ingredient group (Weibull Distribution)bRun 2- CHEX active ingredient group (Extreme Value Distribution)cRun 3~ NONG ingredient group (Gamma Distribution)

47

Table 20. Pi(d) Descriptive Statistics for No Hand Washing Scenario.

No Hand Washing Run la Run 2b Run 3° Run la Run 2b Run 30

. . . . . E. colt E. colt E. coliStatistics E. coli E. coli E. coli 0157:117 0157:117 0157:117

Trials 5.OOE+02 5.OOE+02 5.OOE+02 5.OOE+02 5.OOE+02 5.OOE+02Mean 7.66E-05 7.66E-05 6.08E-05 1.80E-02 l.80E-02 l.84E-02

Median 9.61E-08 9.61E-08 l.52E-07 6.91E-09 6.91E-09 7.99E-09Mode --- --- --- O.OOE+O0 0.OOE+O0 0.OOE+00

Standard Deviation 6.OOE-04 6.OOE-04 3.98E-04 9.27E-02 9.27E-02 9.27E-02Variance 3.60E-07 3.60E-07 l.58E-07 8.59E-03 8.59E-03 8.59E-03Skewness l.48E+01 1.48E+Ol 1.46E+0l 5.30E+00 5.30E+0O 5.25E+O0Kurtosis 2.56E+02 2.56E+02 2.64E+02 3.05E+01 3.05E+01 3.03E+01

Coeff. of Variability 7.84E+0O 7.84E+OO 6.54E+0O 5.l4E+00 5.14E+0O 5.03E+00Range Minimum l.89E-l2 1.89E-12 4.25E-12 0.OOE+00 O.OOE+0O O.OOE+00Range Maximum 1.13E-02 1.13E-02 7.62E-03 7.30E-0l 7.30E-O1 7.43E-Ol

Range Width l.13E-02 1.13E-02 7.62E-03 7.30E-Ol 7.30E-Ol 7.43E-OlMean Std. Error 2.68E-05 2.68E-05 l.78E-05 4.14E-03 4.14E-03 4.l4E-03

aRUfl 1- ALC active ingredient group (Weibull Distribution)bRun 2- CHEX active ingredient group (Extreme Value Distribution)°Run 3- NONG ingredient group (Gamma Distribution)

Table 21. P1(d) Percentiles for Antibacterial Hand Wash and No Hand Washing Scenarios.

Antibacterial Products Run 1 Run 1 Run 2 Run 2 Run 3 Run 3. . E. coil . E. colt . E. coil

Percentile E. coli 0157117 E. coli 0157:117 E. coli 0157:117

2.5% 4.44E-16 2.22E-16 l.80E-14 8.22E-15 4.11E-13 4.31E-l45.0% 1.55E-15 6.66E-16 6.62E-14 2.91E-14 l.34E-12 2.06E-13

50.0% 7.89E-l2 7.02E-13 l.52E-l0 1.25E-11 3.69E-09 1.35E-1O95.0% 1.11E-08 9.36E-07 2.42E-07 8.11E-06 5.06E-06 1.99E-0497.5% 4.91E-08 4.84E-01 9.96E-07 4.87E-01 1.15E-05 4.89E-0l

No Hand Washing Run 1 Run 1 Run 2 Run 2 Run 3 Run 3. . E. coii . E. colt . E. coil

Percentile E. coiz 0157:117 E. coli O157.~H7 E. coli 0157.H7

2.5% 2.65E-l1 1.14E-11 2.65E-11 1.14E-ll 2.03E-11 5.29E-125.0% 4.88E-ll 4.17E-ll 4.88E-11 4.17E-11 7.27E-11 2.40E-11

50.0% 9.61E-08 6.91E-09 9.61E-08 6.91E-09 1.52E-07 7.99E-0995.0% 1.93E-04 3.27E-03 l.93E-04 3.27E-03 1.74E-04 4.73E-0397.5% 5.OOE-04 4.91E-01 5.OOE-04 4.9lE-01 8.l4E-04 4.91E-Ol

48

The simulations that were run using the unpublished company transfer rate data

produced the same results as previously shown in the Salmonella-Chicken and E. coli

Ground Beef case studies. As mentioned in “Section 7: Selecting the Right

Distributions” it was found that the unpublished company transfer rate data contained

smaller values than the transfer rate data used from (Chen et al., 2001). The probability

of infection plots showing the comparison between the unpublished company transfer

rate and the Chen et al. (2001) transfer rate support these results.

The logio reduction data for the unpublished company transfer rate comparison

simulations were taken from the ALC-E. coli logio reduction group. The Weibull

distribution was the best fit for this data set. Figure 18, Figure 19, and Figure 20 are the

probability of infection plots showing this comparison.

49

100

102

106

108

10~10

1012

1016

Salmonella-Chicken Comparison

.01 .1 1 5102030 50 70809095Percent

99 99.9)9.99

Figure 18. Salmonella-Chicken Case Study Comparison Between Transfer

0.0

Company P1(d)-Antibacterial ProductsCompany P1(d)-No Hand WashingP1(d)-Antibacterial ProductsP1(d)-No Hand Washing

0~

Rate Data For Chicken to Hand (ALC Active Ingredient Group).

50

0

0~

1012

io-14

Generic B. coli-Beef Comparison

99 99.999.99

Figure 19. E. coli-Ground Beef Case Study Comparison Between Transfer

102

io-4106

108

Company P1(d)-Antibacterial ProductsCompany P1(d)-No Hand WashingP1(d)-Antibacterial ProductsP1(d)-No Hand Washing

1O~16

.01 .1 1 5 10 2030 50 7080 9095Percent

Rate Data For Ground Beef to Hand (ALC Active Ingredient Group).

51

100

10..2

10~

‘fl-8‘~ 1’J

0~10b0

1012

10.~16

.01 .1 1 5 10 2030 50 7080 9095 99 99.999.99Percent

Figure 20. E. coli 0157:H7-Ground Beef Case Study Comparison Between

Transfer Rate Data For Ground Beef to Hand (ALC Active Ingredient Group).

The final comparison between active ingredient groups was done using the

median E. coli -logio reductions for each active ingredient group. The Salmonella-

Chicken case study was used to determine the median probability of infection using the

median logio reductions. Figure 21 displays the results from this analysis. The results

from lowest median probability of infection to highest were as follows: ALC, CHEX, I,

TSAN, NONG (non-germicidal), PCMX. This shows that there is a strong direct

relationship between the logio reduction and the estimated median risk of infection.

B. coli 0157:H7-Beef Comparison

o Company P1(d)-Antibacterial Products• Company P1(d)-No Hand WashingEl P1(d)-Antibacterial Products• P1(d)-No Hand Washing

52

Salmonella Chicken Case Study-Median LoglO Reduction With E. coli

1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.501.005-08 liii’ I

1.005-09

•ALC~cDHEX

1.005-10

•TSANOPGtvIX

1.005-11

1.005-12

Median LoglO Reduction

Figure 21. Median Probability of Infection Using Median Logio Reduction for Each

Active Ingredient Group (E. coil).

The median probability of infection for the no hand-washing scenario was

1.37*10-7. The term “median logrn reduction” refers to the median value of the logio

reduction observations for each active ingredient group. For example, if the ALC-E. coli

group has 13 observations then the median value of these observations would be the

“median logio reduction.”

Section 9: Summary

In general, when performing risk assessments the biggest obstacle to overcome is

finding enough data to complete the analysis. Overall, there were sufficient data for this

project with respect to the scenarios considered. There were several data gaps where

53

more data would have been very useful, for example, logio reduction data for Triclosan as

the active ingredient and E. coli as the microorganism.

Several conclusions can be made from the general statistical analysis. The active

ingredient groups from highest to lowest mean logio reductions were as follows: ALC,

PCMX, CHEX, I, TSAN, NONG (refer to Table 4). The ANOVA results indicated that

active ingredient group does have a statistically significant effect on logio reductions.

Furthermore, microorganism type has a statistically significant effect on logio reductions

for ALC, TSAN, and NONG. However, there were insufficient data available in the

PCMX, I, and CHEX active ingredient groups to support a statistical evaluation to

examine this potential effect.

Within each active ingredient group there were different concentrations of the

active ingredient and possibly small differences in experimental procedure. The pooling

of such parameters was done because it was difficult to obtain inactivation data sets

where all procedures and concentrations were the same, which also provided sufficient

amount of observations for analysis.

In the healthcare industry, iodine and chlorhexidine are recognized as topical

antiseptics. Based on the work reported here alcohol is more effective as an active

ingredient than chlorhexidine and iodine (refer to Figure 2 and Figure 3). Therefore, if

chiorhexidine and iodine are “acceptable” then alcohol based antimicrobial products,

which appear to be superior, should also be “acceptable.”

Each case study produces similar estimates for the effect of antimicrobial agents

on the probability of infection. The general trend showed that washing hands with a

product containing ALC or CHEX as an active ingredient after handling raw meat greatly

54

reduced the probability of infection when compared to not washing at all. Using a non-

antimicrobial product also reduced the probability of infection when compared to not

washing at all. However, ALC and CHEX as active ingredients were more effective than

non-antimicrobial products. Another finding was that ALC as an active ingredient

reduces the probability of infection lower than CHEX. The most sensitive parameter in

each simulation was the natural occurrence of Salmonella on chicken orE. coli on ground

beef with only one exception. The logio reduction parameter for the E. coli 0157:H7

simulations was the most sensitive excluding the NONG simulation. The % transfer rate

from ground beef to hands was the most sensitive parameter in theE. coli 0157:H7-

NONG simulation.

Section 10: Significance

Quantitative microbial risk assessment is an effective method for determining the

benefits from using hand hygiene products, including topical antimicrobial products. It is

difficult to confirm the quantitative benefits from using topical antimicrobial products by

classical, direct epidemiological intervention tests due to logistical problems of

controlling use behavior over a long duration trial in a study population. The methods of

quantitative microbial risk assessment (QMRA) have been useful in estimating levels of

risks or benefits difficult to establish by direct epidemiological study. This was clearly

shown in the three case studies presented in this report. QMRA does not have to be

limited to predicting the probability of infection for the raw meat, hand washing case

studies. Other daily activities could be modeled to assess their benefits. This theme can

lead to future work.

55

The preparing of raw meat in the kitchen is just one of many daily routine

scenarios that the average person completes throughout his or her day which result in

potential exposure to infectious agents. The scenarios that could be modeled are

numerous, provided that adequate data are present for the QMRA. For example, how

many orders of magnitude would the probability of infection be reduced if a subject

washed their hands with specified products after taking out the trash? On a much larger

scale, what about the average daily life of a college student. What degree is the

probability of infection reduced if the student’s hands are washed throughout the day

during his or her daily routine activities?

56

Section 11: References

Chen, Y., K.M. Jackson, F.P. Chea and D.W. Schaffner. 2001. Quantification andVariability Analysis of Bacterial Cross-Contamination Rates in Common FoodService Tasks. Journal ofFood Protection, 64(1) :72-80.

Dufrenne, J., W. Ritmeester, E. Delfgou-van Asch, F.v. Leusden and R.d. Jonge. 2001.Quantification of the Contamination of Chicken and Chicken Products in theNetherlands with Salmonella and Campylobacter. Journal ofFood Protection,64(4): 538-541.

Fazil, A.M. and C.N. Haas. 1996. A Quantitative Risk Assessment Model forSalmonella. Drexel University.

Haas, C.N., J.B. Rose and C.P. Gerba. 1999. Risk Assessment Paradigms. InQuantitative Microbial Risk Assessment. Edited by New York: John Wiley &Sons, Inc.

Haas, C.N., J.B. Rose and C.P. Gerba. 1999. Compendium of Data. In QuantitativeMicrobial Risk Assessment. Edited by New York: John Wiley & Sons, Inc.

Haas, C.N., A. Thayyar-Madabusi, J.B. Rose and C.P. Gerba. 2000. Development of aDose-Response Relationship for Escherichia coli 0157 :H7. International JournalofFood Microbiology, 56(2:3):153-159.

Rose, J.B., C.N. Haas, L.L. Gibson, C.P. Gerba and P.A. Rusin. QuantitativeAssessment ofRisk Reduction from Hand Washing with Antibacterial Soaps.University of South Florida.

Russell, S.M. 2000. Comparison of the Traditional Three-Tube Most Probable NumberMethod with the Petrifilm, SimPlate, BioSys Optical, and BactometerConductance Methods for Enumerating Escherichia coli from Chicken Carcassesand Ground Beef. Journal ofFood Protection, 63(9):1 179-1183.

Sokal, R.R. and F.J. Rohlf. 1995. Homogeneity of Variances. In Biometry: ThePrinciples and Practice ofStatistics in Biological Research. Edited by NewYork: W. H. Freeman and Company.

Tuttle, J., T. Gomez, M.P. Doyle, 1G. Wells, T. Zhao, R.V. Tauxe and P.M. Griffin.1999. Lessons From a Large Outbreak of Escherichia coli 0157:H7 Infections:Insights Into the Infectious Dose and Method of Widespread Contamination ofHamburger Patties. Epidemiology and Infection, 122(2): 185-192.

57

Section 12: Appendix

Table 22. Source Identification for the Logio Reduction Data.

LoglO GroupReference Microorganism Reduction Abbreviation

Cardoso, C. L., H. H. Pereira, J. C. Zequim, and M.Guilhermetti 1999. Effectiveness of hand-cleansing Acinetobacteragents for removing Acinetobacter baumannii strain baumanni 1.97 ALC

from contaminated hands Am J Infect Control. 27:327-31.

Burnie, J. P. 1986. Candida and hands J. Hosp. Infect.Candida albicans 4.00 ALC8:1-4.

Rotter M. Hand washing and hand disinfection. In:Mayhall CG, editor. Hospital epidemiology and Escherichia coli 5.00 ALCinfection control. First ed. Baltimore, Maryland:

Williams & Wilkins. 1996. p. 1052-68.Rotter M. Hand washing and hand disinfection. In:

Mayhall CG, editor. Hospital epidemiology and Escherichia coli 3.90 ALCinfection control. First ed. Baltimore, Maryland:











Williams & Wilkins. 1996. p. 1052-68.

58

Rotter M. Hand washing and hand disinfection. In:Mayhall CG, editor. Hospital epidemiology and Escherichia coli 3.80 ALCinfection control. First ed. Baltimore, Maryland:





Williams & Wilkins. 1996. p. 1052-68.Ayliffe, G. A., J. R. Babb, J. G. Davies, and H. A. Lilly

1988. Hand disinfection: a comparison of various Escherichia coli 2.90 ALCagents in laboratory and ward studies J Hosp Infect.

11:226-43.Ayliffe, G. A., J. R. Babb, J. G. Davies, and H. A. Lilly

1988. Hand disinfection: a comparison of various Escherichia coli 2.50 ALCagents in laboratory and ward studies 3 Hosp Infect.

11:226-43.Ayliffe, G. A., 3. R. Babb, 3. G. Davies, and H. A. Lilly

1988. Hand disinfection: a comparison of various Escherichia coli 3.50 ALCagents in laboratory and ward studies 3 Hosp Infect.

11:226-43.Mackintosh, C. A., and P. N. Hoffman 1984. An

extended model for transfer of micro-organisms via the K. aerogenes 4.19 ALChands: differences between organisms and the effect of

alcohol disinfection 3 Hyg (Lond). 92:345-55.Mackintosh, C. A., and P. N. Hoffman 1984. An




alcohol disinfection J Hyg (Lond). 92:345-55.Larson, E. L., P. I. Eke, and B. E. Laughon 1986.

Efficacy of alcohol-based hand rinses under frequent- Native anaerobes 2.93 ALCuse conditions Antimicrob Agents Chemother. 30:542-

4.Larson, E. L., P. I. Eke, and B. E. Laughon 1986.

Efficacy of alcohol-based hand rinses under frequent- Native anaerobes 3.44 ALCuse conditions Antimicrob Agents Chemother. 3 0:542-

4.Larson, E. L., P. I. Eke, and B. E. Laughon 1986. Native anaerobes 1.45 ALC

59

Efficacy of alcohol-based hand rinses under frequent-use conditions Antimicrob Agents Chemother. 3 0:542-


Efficacy of alcohol-based hand rinses under frequent- Native anaerobes 2.58 ALCuse conditions Antimicrob Agents Chemother. 3 0:542-

4.Ojajarvi, J. 1980. Effectiveness of hand washing anddisinfection methods in removing transient bacteria P. aeruginosa 1.60 ALC

after patient nursing 3 Hyg (Lond). 85:193-203.Ojajarvi, 3. 1980. Effectiveness of hand washing anddisinfection methods in removing transient bacteria P. aeruginosa 1.12 ALC

after patient nursing 3 Hyg (Lond). 85:193-203.Mackintosh, C. A., and P. N. Hoffman 1984. An

extended model for transfer of micro-organisms via theP.aeruginosa 3.66 ALC

hands: differences between organisms and the effect ofalcohol disinfection 3 Hyg (Lond). 92:345-55.

Mackintosh, C. A., and P. N. Hoffman 1984. Anextended model for transfer of micro-organisms via the

P.aeruginosa 3.23 ALChands: differences between organisms and the effect of


extended model for transfer of micro-organisms via the P.aeruginosa 4.21 ALChands: differences between organisms and the effect of

alcohol disinfection 3 Hyg (Lond). 92:345-55.Ojajarvi, 3. 1980. Effectiveness of hand washing anddisinfection methods in removing transient bacteria S. aureus 0.63 ALC

after patient nursing 3 Hyg (Lond). 85:193-203.Ojajarvi, 3. 1980. Effectiveness of hand washing anddisinfection methods in removing transient bacteria S. aureus 2.70 ALC

after patient nursing 3 Hyg (Lond). 85:193-203.Oj aj arvi, 3. 1980. Effectiveness of hand washing anddisinfection methods in removing transient bacteria S. aureus 0.82 ALC

after patient nursing 3 Hyg (Lond). 85:193-203.Mackintosh, C. A., and P. N. Hoffman 1984. An

extended model for transfer of micro-organisms via theS. saprophyticus 3.80 ALC

hands: differences between organisms and the effect ofalcohol disinfection J Hyg (Lond). 92:345-55.

Mackintosh, C. A., and P. N. Hoffman 1984. Anextended model for transfer of micro-organisms via the S. saprophyticus 2.53 ALChands: differences between organisms and the effect of

alcohol disinfection J Hyg (Lond). 92:345-55.Mackintosh, C. A., and P. N. Hoffman 1984. An

extended model for transfer of micro-organisms via the S. saprophyticus 2.61 ALChands: differences between organisms and the effect of

60

alcohol disinfection J Hyg (Lond). 92:345-55.Kimberly Clark Corporation. KinCare® Instant Hand

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alcohol disinfection J Hyg (Lond). 92:345-55.Gojo Industries, Inc., Akron, OH. Technical Guide:

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Personnel Hand Rinse. Undated.The Soap and Detergent Association and The

Cosmetic, Toiletry, and Fragrance Association. 1995. Serratia marcescens 2.91 ALCLetter to W. Gilbertson and attachments. FDA Public

Docket 75N-183H. June 13, 1995.Johnson Wax. Deliver Alcohol Gel Health Care Serratia marcescens 1.80 ALC

Personnel Hand Rinse. Undated.Dyer DL, Gerenraich KB, Wadhams PS. Testing a

New Alcohol-Free Hand Sanitizer to Combat Infection. Serratia marcescens 2.80 ALCAORN. 1998; 68: 239-25 1.

Mackintosh, C. A., and P. N. Hoffman 1984. Anextended model for transfer of micro-organisms via the Serratia marcescens 4.44 ALChands: differences between organisms and the effect of

alcohol disinfection J Hyg (Lond). 92:345-5 5.Dyer DL, Gerenraich KB, Wadhams PS. Testing a

New Alcohol-Free Hand Sanitizer to Combat Infection. Serratia marcescens 2.50 ALCAORN. 1998; 68: 239-25 1.

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New Alcohol-Free Hand Sanitizer to Combat Infection. Serratia marcescens 2.80 ALCAORN. 1998; 68: 239-251.

Johnson & Johnson Medical, Arlington, TX, 2000. Serratia marcescens 3.04 ALCHuntington Laboratories, Inc., Huntington, IN. Letter

to W. Gilbertson. FDA Public Docket 75N-183H. Serratia marcescens 2.59 ALCDecember 13, 1995.

Dyer DL, Gerenraich KB, Wadhams PS. Testing aNew Alcohol-Free Hand Sanitizer to Combat Infection. Serratia marcescens 2.00 ALC

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extended model for transfer of micro-organisms via the Serratia marcescens 3.61 ALChands: differences between organisms and the effect of

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Larson, E. L., P. I. Eke, and B. E. Laughon 1986.Efficacy of alcohol-based hand rinses under frequent- Total Aerobic 3.17 ALC

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Cosmetic, Toiletry, and Fragrance Association. 1995. Serratia marcescens 2.50 TSANLetter to W. Gilbertson and attachments. FDA Public

Docket 75N-183H. June 13, 1995.Ciba-Geigy Corporation, Greensboro, NC. FDA Serratia marcescens 2.54 TSANPublic Docket 75N-183H. December 14, 1995.


Vivo Antimicrobial Effectiveness of Triclosan, Serratia marcescens 2.28 TSANChiorhexidine, Alcohol! Chlorhexidine, and Polaxamer

Iodine Topical Formulations. AJIC. 1998; 26: 186.,Johnson & John

Bartzokas, C. A., J. E. Corkill, and T. Makin 1987.Evaluation of the skin disinfecting activity andcumulative effect of chiorhexidine and triclosan Serratia marcescens 3.78 TSAN


8:163-7Bartzokas, C. A., J. E. Corkill, and T. Makin 1987.

Evaluation of the skin disinfecting activity andcumulative effect of chiorhexidine and triclosan Serratia marcescens 2.91 TSAN


81

8:163-7Peterson AF, A. Rosenberg, and S.D. Alatary 1978.

Comparative evaluation of surgical scrub preparations. Serratia marcescens 1.66 TSANSurgery, Gynecology & Obstetrics. 146(1):63-5.

Peterson AF, A. Rosenberg, and S.D. Alatary 1978.Comparative evaluation of surgical scrub preparations. Serratia marcescens 1.89 TSAN

Surgery, Gynecology & Obstetrics. 146(1) :63-5.Faoagali, J., J. Fong, N. George, P. Mahoney, and V.

OtRourke 1995. Comparison of the immediate,residual, and cumulative antibacterial effects of Total Aerobic 0 26 TSAN


23:337-4Ojajarvi, J. 1976. An evaluation of antiseptics used for Total Aerobic 1 70 TSAN

hand disinfection in wards J Hyg (Lond). 76:75-82. CountFaoagali, J., J. Fong, N. George, P. Mahoney, and V.

ORourke 1995. Comparison of the immediate,residual, and cumulative antibacterial effects of Total Aerobic 0 89 TSAN








23:337-4Ojajarvi, J. 1976. An evaluation of antiseptics used for Total Aerobic 1 40 TSAN

hand disinfection in wards J Hyg (Lond). 76:75-82. CountNamura, S., S. Nishijima, K. J. McGinley, and J. J.

Leyden 1993. A study of the efficacy of antimicrobial Total Aerobic 0 17 ThANdetergents for hand washing: using the full-hand touch Count

plates method J Dermatol. 20:88-93.Peterson AF, A. Rosenberg, and S.D. Alatary 1978.

. . . . Total AerobicComparative evaluation of surgical scrub preparations. Count 1.21 TSAN

Surgery, Gynecology & Obstetrics. 146(1):63-5.Bartzokas, C. A., J. E. Corkill, T. Makin, and E. Parry

. . . . Total Aerobic1987. Comparative evaluation of the immediate and C ~ 2.79 TSANsustained antibacterial action of two regimens, based oun

82

on triclosan- and chiorhexidine- containing handwashpreparations, on volunteers Epidemiol Infe

Bartzokas, C. A., J. E. Corkill, T. Makin, and E. Parry1987. Comparative evaluation of the immediate and Total Aerobicsustained antibacterial action of two regimens, based 1.03 TSANCounton triclosan- and chiorhexidine- containing handwash

preparations, on volunteers Epidemiol InfePeterson AF, A. Rosenberg, and S.D. Alatary 1978. Total Aerobic

Comparative evaluation of surgical scrub preparations. 2.63 TSANCountSurgery, Gynecology & Obstetrics. 146(1): 63-5.

Table 23. Analysis of Variance for ALC Logio Reductions.

Source SS df MS F Prob>FBetween Groups 46.212 9 5.135 5.08 5.11E-05WithinGroups 56.591 56 1.011

Total 102.803 65 1.582

BartleWs test for equal variances: chi2(7) = 9.3456 Prob>chi2 0.229

Table 24. Analysis of Variance for TSAN Logio Reductions.

Source SS df MS F Prob>FBetweenGroups 11.385 4 2.846 5.93 0.0008Within Groups 18.240 38 0.480

Total 29.626 42 0.705


83

Table 25. Analysis of Variance for NONG Logio Reductions.


Total 18.711 22 0.851

BartleWs test for equal variances: chi2(2) = 6.1709 Prob>chi2 = 0.046

Table 26. Analysis of Variance for PCMX Logio Reductions.


Total 57.080 47 1.214


Table 27. Analysis of Variance for I Logio Reductions.


Total 122.207 41 2.981

Bartlett~s test for equal variances: chi2(4) 9.8401 Prob>chi2 0.043

Table 28. Analysis of Variance for CHEX Logio Reductions.


Total 105.536 77 1.371

Bartletfs test for equal variances: chi2(6) 7.3169 Prob>chi2 = 0.293

84

Point Best Estimate (Salmonella -Chicken Case Studv~

Using Antibacterial Soap (d) Dose (CFU)No Hand Washing (d) Dose (CFU)

beta-Poisson ModelUsing Antibacterial Soap P1(d)

No Hand Washing P1(d)

Parameter Units Value Distribution

Assume 1 MPN=1 CFU ________________________

Initial Concentration Chicken*a CFU/carcass

From Chicken to Hand*b % Transfer Rate (Chick-Hand)

Active Ingredient-Alcohol _______________ _____________LoglO Reduction ____________________________

Microoraanism-Ecoli c ___________________________After Washing CFU/hand 0.0000805Without Washing CFU/hand 1 .4446750

Finger Tip Area/Hand Area*d Conversion Factor (Unitless) 0.13

% Transfer RateFrom Finger Tips to Mouth e (Finoer_TiDs-Mouth) ______

beta-Poisson Model

P1(d) = 1 - [1 + (d/N50) * (2~’~ - 1)]

P1(d)=Probability of Infection

Assume that 1 CFU=1 Organism

Salmonella Pooled beta-Poisson Model Parameters*fAlpha (ci)

N50

Log-normal

Log-normal

Weibull

Log-normal

Unitless

Organisms

0.3126

23600.00

0.000002540.04555461

Forcast Cell

Forcast Cell

RAND NUM _________ UnifcROWNUM 1

alpha 0.3126N50 23600.00

86

Forcast Cell

Forcast Cell

RANDNUMROWNUM

alpha

N50

Point Best Estimate (E. coil -Ground Beef Case Study)Parameter ~ Distribution

Initial Concentration Ground Beef*a LoglO MPN

Initial Concentration Ground Beef MPN 1370.88

From Ground Beef to Hand*b % Transfer Rate Beef-Hand) —~

Active Ingredient-AlcoholLoglO ReductionMicroorganism~EcoIi*c

After Washing CFU/hand 0.01 10389Without Washing CFU/hand 1 98.047861 6

Finger Tip Area/Hand Area*d Conversion Factor (Unitless) 0.13

% Transfer RateFrom Finger Tips to Mouth*e(Finger Tips-Mouth)

Beta

Log-normal

Weibull

Log-normal

beta-Poisson Model

P1(d) = 1 - [1 + (d/N50) * (2~~ - 1 )]~



Assume 1 MPN~1 CFU

fc~jj_ Pooled beta-Poisson Model Parameters*fAlpha (ce) Unitless 0.1778

N50 Organisms 86000000.00

Using Antibacterial Soap (d) Dose (CFU) 0.00034809No Hand Washing (d) Dose (CFU) 6.24499869

beta-Poisson ModelUsing Antibacterial Soap

No Hand Washing

P1(d)

Uniforr

0.1778

86000000.00

P1(d)

Point Best EstimateParameter

Conversion Factor (Unitless)

% Transfer Rate(Finaer Ties-Mouth)

beta-Poisson ModelUsing Antibacterial Soap P1(d)

No Hand Washing P1(d)

Units(E. coil 0157:H7-Ground Beef Case Study)

Value

Assume 1 MPN=1 CFU

Initial Concentration Ground Beef*a MPN/g

From Ground Beef to Hand*b % Transfer Rate (Beef-Hand)

87

Active Ingredient-Alcohol Logl 0 ReductionMicrooraanism~Ecoli*cAfter Washing CFU/hand 0.0000287Without Washing CFU/hand 0.5157490

Distribution

WeibuN

Log-normal

Weibull

Finger Tip Area/Hand Area*d

From Finger Tips to Mouth*e

beta-Poisson Model

P1(d) = 1 - [1 + (d/N50) * (2~’~ - 1)]°



Pnni~H hpt~-Pni~rn Mnd~~i P~rnmptpr~*f

Alpha (a)N50

0.13

Unitless 0.49

Organisms 596000.00

F Log-normal

Using Antibacterial Soap (d) Dose (CFU) 0.0000009 1No Hand Washing (d) Dose (CFU) 0.01626300

RANDNUMROW NUM

alphaN50

_____________ Uniforr

0.49596000.00

Forcast Cell

Forcast Cell

use ofmicrobial risk modeling to determine the benefit ......intervention tests due to logistical...

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