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### NATIONAL INSTITUTE FOR 1



### OCCUPATIONAL SAFETY AND HEALTH 2



### PERSONAL PROTECTIVE TECHNOLOGY PROGRAM 3



### PUBLIC MEETING 4



### Development of Inward Leakage Standards for 5



### Half

- Mask Air

- Purifying Particulate Respirators 6

7

8

9

10



### September 17, 2013 11

12

13

14

15

16

17

18

19



### Commencing at 1:00 p.m. at NIOSH NPPTL 626 20



### Cochrans Mill Road, Pittsburgh, Pennsylvania. 21

222

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### MS. D'ALESSANDRO: Good afternoon, 1



### everyone. And welcome to the National Personal 2



### Protective Technology Laboratory public meeting on 3



### development of inward leakage standards for 4



### half

- mask air

- purifying particulate respirators. 5



### The information for this meeting can be 6



### found in Docket No. 250. 7



### My name is Maryann D'Alessandro, and I'm 8



### the director of NPPTL. 9



### The m ission of NPPTL is to conduct the 10



### research and surveillance that leads to the 11



### standards and certification of personal protective 12



### equipment and conduct the outreach necessary to keep 13



### workers safe. 14



### Today's meeting will start with Jon 15



### Szalajda providing open ing remarks. And Jon is the 16



### technology research branch chief, the acting 17



### technology research branch chief, who has led the 18



### development of these inward leakage requirements. 19



### I will now turn it over to Jon to provide 20



### logistics information for you and the su mmary of 21



### where we will be going today. 223

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### MR. SZALAJDA: Thank you, Maryann. 1



### And I hope everyone can hear me. And I 2



### think we're connected on LiveMeeting as well. 3



### As John had mentioned earlier in terms of 4



### conducting

-- for the LiveMeeting audience in terms 5



### of conducting the meeting, we're going to go through 6



### a process of where we will make a certain number of 7



### presentations. We'll take a break. 8



### And then we'll have hopefully a dialogue 9



### between stakeholders as well as the NIOSH panel 10



### representatives on differ ent topics that were 11



### identified in the Federal Register notice that was 12



### published within the past few months. 13



### As far as the logistics for the 14



### housekeeping for the meeting site, the restrooms are 15



### to the left of the meeting room. The ladies' room 16



### is right here outside the door. The men's is the 17



### next corridor down. 18



### For emergency exits if the fire alarm were 19



### to go off, you exit through the doors at the rear of 20



### the room. There is an exit here to the right as 21



### well as the exit in the lobby. 224

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### And the meeting area for logistics purposes 1



### is there's a parking lot outside of the front lobby 2



### area which is known as the little parking lot. 3



### Cell phones, if you have cell phones, if 4



### you could please either vibrate or mute them as a 5



### courtesy to the speaker and to the dia logue, we 6



### would appreciate that. 7



### And also, for the comment period, there is 8



### a microphone in the center of the room. When we're 9



### at the appropriate times when we want to take 10



### questions or get comments from the audience, if you 11



### could come to the microphone, state your name, and 12



### your organization that you're representing clearly, 13



### and then also your comment. 14



### The meeting is being transcribed and will 15



### become part of our docket proceedings. 16



### And, again, as Maryann had mentioned, the 17



### docket number is 250. And the presentations that 18



### you're going to hear today as well as a copy of the 19



### transcript will be available in the docket. 20



### In terms of how we're going to proceed 21



### through the meeting, we have three presentations 225

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### that different NIOSH researchers will be providing 1



### h ere within the next few minutes. 2



### Dr. Coffey, who is our associate director 3



### for science. Dr. Wassell, who is a statistician, 4



### has been detailed to NPPTL as supporting the 5



### project. And then Dr. Zhuang, who is our 6



### respiratory protection research team leader. 7



### I'm currently the acting branch chief for 8



### technology research. I think if you're familiar 9



### with our organization structure, in the past several 10



### years, I have worked in policy and standards. 11



### But while we go through our reorganization 12



### process, I'm respon sible for the research branch. 13



### But I will continue to support the 14



### development of the proposed rule and the final rule 15



### for the inward leakage for filtering facepieces and 16



### particulate respirators until this project is 17



### complete. 18



### Again, this is the

-- the pu rpose of the 19



### meeting that was identified in the Federal Register 20



### notice. And at this point in time, what we are 21



### looking to do is to get feedback from the 226

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### stakeholders prior to going into introducing a new 1



### proposal for what the performance requirements 2



### sho uld be for inward leakage for filtering 3



### facepieces and half

- mask. 4



### And I wanted to at least talk a little bit 5



### about the history behind why we are here and where 6



### we're going. 7



### And one of the things that I have learned 8



### at least in my tenure with NIOSH is that when you 9



### get into rule making and the finalization and 10



### establishing

-- establishing rules, there's always 11



### an issue or two that's left behind that's unresolved 12



### when the rule is finalized. 13



### And for this particular topic, in 1995, 14



### there was not an inward le akage requirement 15



### established for filtering facepieces and particulate 16



### respirators, and that was primarily technology 17



### driven. And I believe the head of the respirator 18



### branch at the time said, oh, in two or three years, 19



### we'll have this done. 20



### Well, now her e we are a few years later, 21



### but I do think that the time has come for being able 227

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### to establish the rule. 1



### And, again, part of that was technology 2



### driven with regard to identifying how could a 3



### performance requirement be identified and applied in 4



### a repeatable fashion as part of the certification 5



### program for this type of respirator. 6



### So there was a significant amount of 7



### research that went into establishing the initial 8



### NPRM that was published in 2009. 9



### I think most people who have been involved 10



### in the field are familiar with Dr. Zhuang's 11



### anthropometric research in looking at trying to 12



### update the panel that was established for 13



### determining fit of the respirators. And other 14



### research was done in support of the proposed rule 15



### that was published in 2009. 16



### As far as the background, additional 17



### background, I think people who have been more 18



### involved in the field know that the existing 19



### certification policy and the use of panels is based 20



### on decades of work that was done back in the 1960s. 21



### The Air Force had conducted a study, and I 228

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### think many people are familiar with the LANL panel 1



### that was developed. And this currently was 2



### established by NIOSH as part of the certification 3



### testing program for respirators. 4



### Also, along with the establishment of 42 5



### CFR, for every other class of respirators other than 6



### filtering facepieces and half

- mask particulate 7



### respirators, there is a fit test requirement that is 8



### established as part of respiratory protection. 9



### But for these types of respirators, when 10



### they were introduced in 1995, the focus was s olely 11



### on testing filter penetration and resistance. There 12



### was no performance measurement of leakage through 13



### the face seal or any of the other parts of the 14



### respirators, like exhalation valve leakage. 15



### And I think one of the things that we are 16



### looking to do is to raise the bar, to raise the 17



### performance of these types of respirators and treat 18



### filtering facepieces and half

- masks the same as we 19



### treat any other type of respirator that we certify 20



### for the work force. 21



### So where we were in 2009 is that we had 229

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### propos ed a regulation to establish a performance 1



### requirement for filtering facepieces and half

- mask. 2



### And while the testing wasn't considered 3



### that this was going to be a substitute for fit 4



### testing or respiratory protection or respiratory 5



### program requirements in the field or training 6



### necessary to how to properly use the respirators, 7



### NIOSH felt that it was important to establish a 8



### minimum level, a minimum performance requirement 9



### associated with how well these types of respirators 10



### should fit to the individual in ord er to be able to 11



### increase the probably that people could get a fit 12



### that met their requirements in the field. 13



### Also, with the information that was 14



### provided in the NPRM, we also introduced bringing 15



### forward the panels that were developed by Dr. Zhuang 16



### over the past decade incorporating the Principal 17



### Component Analysis as a screening tool and then 18



### introducing the NIOSH Bivariate panel as a means of 19



### identifying appropriate sizes for individuals as 20



### part of the fit test. 21



### And I don't want to go into a lot of the 2210

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### de tails associated with the rule at the time because 1



### at least at this point, we're trying to look forward 2



### and learn from what we did in the past in the last 3



### four years, and in particular, in responding to the 4



### comments that we received from stakeholders with 5



### regard to the NPRM as it was initially crafted in 6



### 2009. 7



### And if you're interested to go and see what 8



### the comments were, they are available in 9



### dockets

-- NIOSH Dockets 36 and 137 that include the 10



### history behind the development of that one proposal. 11



### And a l ot of the focus of what you're going 12



### to hear about today is the discussion of intra

- and 13



### inter

- panel variability. 14



### And over the past couple of months, 15



### Dr. Coffey has led some scientific webinars that 16



### have address some of the research that was conducted 17



### her e at NPPTL over the last year and a half to try 18



### to address that question that was identified by our 19



### stakeholders as part of the initial rulemaking 20



### activity. 21



### Along with the discussion of variability, 2211

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### some of the things that we want to capture later in 1



### the d ay when we have our panel discussion are some 2



### of the other comments that we received with regard 3



### to the test method, panel size, and implementation 4



### type issues, things that were identified in our 5



### notification of this meeting that we wanted to get 6



### your feed back on, at least in terms of ideas that we 7



### should consider in moving forward. 8



### One thing that we aren't going to discuss 9



### today but will be addressed as part of our plan 10



### moving forward is the economic impact. 11



### We did receive some significant comments 12



### from st akeholders with regard to the potential 13



### impact of the rule. 14



### And while we disagree philosophically with 15



### the magnitude of the comment or the magnitude of the 16



### potential costs that were identified as part of the 17



### public comments that were received, that we are 18



### moving forward and have contracted with ERG to help 19



### assist us in development of an economic analysis to 20



### determine what the impact of this rule will be on 21



### the marketplace moving forward. 2212

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### So and to kind of tie up where we're going, 1



### formally, the one point th at we wanted to make to 2



### our stakeholders is that the proposal that was 3



### identified in 2009 is not going to be finalized. 4



### That at this point, we are going to move 5



### forward with development of a new notice of proposed 6



### rule. 7



### And, again, that's part of why we' re here 8



### today is to give stakeholders the opportunity to 9



### provide feedback, tell us what's knew that we should 10



### consider that you haven't already expressed to us in 11



### either response to Dockets 36 or 137. 12



### Now, we have done research. We're going to 13



### continue to share the results of what we did with 14



### the inter

- panel variability study. But at this 15



### point in time, we're looking for any feedback for 16



### any new knowledge that our stakeholders may have 17



### that they may want to share with us with regard to 18



### the makeup of the p anel, the test method, the 19



### potential pass/fail criteria, how we define the user 20



### populations, as well as, you know, what things 21



### should we consider with regard to the implementation 2213

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### in moving forward with the proposed rule. 1



### And with that, what I would like t o do is 2



### introduce Dr. Chris Coffey, who is our Associate 3



### Director for Science at NPPTL. 4



### And what we would like to do, at least in 5



### terms of moving forward for the presentations, there 6



### will be breaks for questions at the end of 7



### presentations, at the end of Dr. Wassell's 8



### presentation and at the end of Dr. Zhuang's 9



### presentation. 10



### I will, you know, try to moderate those as 11



### we go forward. 12



### And then, after the presentations have been 13



### delivered, we'll take a ten, 15

- minute break. And 14



### then we'll reconvene the panel session to address 15



### the questions that were provided in the Federal 16



### Register notice. 17



### When Dr. Coffey was our principal 18



### investigator for the inter

- panel variability study 19



### that we initiated here at NPPTL, he was assisted by 20



### Colleen Miller as the boots on the ground, as we 21



### like to say, with regard to how the testing was 2214

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### conducted here in Pittsburgh. 1



### So with that, Chris. 2



### MR. COFFEY: Thanks, Jon. 3



### I would just like to give a brief synopsis 4



### of the methodology tha t we used in collecting the 5



### data for our inter

- panel variability study. 6



### And first, just give you a definition of 7



### what we consider to be inter

- panel variability is 8



### just basically the degree of agreement among various 9



### panels. 10



### And we are concerned about wh at we are 11



### calling grid variability, which is the assembly of 12



### one panel to another in various time frames, and 13



### then the same panel even running in between 14



### different laboratories. 15



### So as Jon mentioned, we had a number of 16



### public comments on inter

- panel variab ility. That 17



### boils down to two basic ones. 18



### It causes a wide range of results among the 19



### various panels. And to decrease the inter

- panel 20



### variability to an acceptable level, it was suggested 21



### that we increase the number of panel members up to 2215

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### 105. 1



### So we co ntracted with RTI to look at this 2



### issue of panel size. 3



### So our study of objectives basically were 4



### to determine the variability between different 5



### anthropometric panels, to experimentally confirm 6



### what RTI came up with from a mathematical 7



### standpoint, a theore tical standpoint. 8



### And then also, in the process for our 9



### certification program, investigate the ease of 10



### implementing the panels that were proposed, and we 11



### wanted to collect enough data to allow us to do a 12



### statistical analysis. 13



### So we used ten different N9 5 respirators 14



### from seven different manufacturers. They were 15



### evenly distributed between five filtering 16



### facepieces, which consisted of one size fits all, 17



### and two size respirator models and five elastomeric 18



### N95 respirators, each one having three sizes. 19



### We me asured the inward leakage using the 20



### PortaCount Pro+ 8038. We ensured that an ambient 21



### concentration was within the PortaCount range shown 2216

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### here. 1



### Actually, ours was not anywhere near those 2



### ranges. It was in the neighborhood of ten to the 3



### third particles p er cubic centimeter. 4



### Each respirator on each subject performed 5



### two replicates. We had them break the seal 6



### completely in between each replicate. And our 7



### panels consisted of both experienced people, who 8



### performed respirator testing up here in Pittsburgh, 9



### and also we had to recruit new subjects to fill out 10



### the panel. 11



### So the exercises we used were the normal 12



### eight exercises found in the OSHA fit test 13



### protocols. You're all familiar with those. 14



### And we started out looking at an evenly 15



### distributed panel consis ting of 40 subjects with, of 16



### course, four subjects in each of the ten cells. 17



### So in order to gather enough data to do our 18



### analysis, our goal was to recruit 120 subjects, 19



### which we did eventually. 20



### And for each respirator, we arbitrarily, 21



### just arbitrarily, as signed the following criteria: 2217

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### For one size fits all, of course, everybody in cells 1



### 1 through 10 got that size. 2



### For the two size respirators, the filtering 3



### facepieces, people in cells 1 through 5 got the 4



### small size, and then people in cell 6 through 10 5



### we re issued the larger of the two sizes. 6



### And then for the three sizes, cells 1 7



### through 3 received the medium. People in cells 4 8



### through 7, the

-- the small, 1 through 3, were the 9



### smallest cells, 4 through 7 were the medium, and 10



### then of course the large for people in cells 8 11



### through 10. 12



### So we had some recruitment issues. We had 13



### difficulty in finding four people per cell, 14



### especially in a few of the different cells. So we 15



### made the decision about halfway through the study to 16



### move it to Morgantown since we h ad a

-- possibly a 17



### better subject pool down there to draw from. 18



### We also found some issues with the physical 19



### limits of each cell size. So we went about 20



### recruiting enough members to fill out three 21



### 35

- member population based panels, which was the 2218

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### proposal i n the 2009 notice of proposed rulemaking. 1



### So then, the multiple panel distributions 2



### were analyzed, both were evenly distributed and 3



### population based panels. 4



### And with that, I'll turn it over to 5



### Dr. Wassell, and he'll tell you about his data 6



### analysis that he conducted on our data. 7



### MR. WASSELL: Thank you. 8



### Good afternoon. One thing you should not 9



### talk about after a lunch is statistics, but I'll try 10



### to keep you all awake, try and keep it interesting. 11



### I'm Terry Wassell, research statistician. 12



### And I want to describe and present some of 13



### the preliminary results for the comparison of six 14



### different types of panels and to characterize the 15



### relative variability for these six different types 16



### of panels. 17



### Okay. The methodology used here is 18



### nonparametric bootstrap sampl ing. 19



### In contrast to the earlier work that relied 20



### on simulated data, here, we're using actual 21



### laboratory data collected on human test subjects, as 2219

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### Chris has described. 1



### This is an important distinction that 2



### separates these results from the previous work. 3



### N onparametric bootstrap is a data

- based 4



### resampling, computer intensive procedure. This 5



### method was used to generate 500,000 bootstrap 6



### samples, each representing a panel. 7



### I want to mention that there's an 8



### assumption of independence here so that the 9



### replicate s on the same human test subject are 10



### considered independent. 11



### In this situation of bootstrapping 12



### procedures, it's a reasonable assumption. In fact, 13



### bootstrap sampling

-- bootstrap is sampling with 14



### replacement, and that is has been shown to be 15



### appropriate

. 16



### Sampling with replacement means the same 17



### observation could be included in the bootstrap 18



### sample more than once. Sampling with replacement is 19



### recommended as samples with two replicate 20



### measurements from the same test subject as possible. 21



### Chris, Dr. Coffey, has described the data 2220

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### collection procedures. The data set I worked with 1



### consisted of 2,880 observations. That is 144 human 2



### subjects with two replicate measurements each for 3



### ten respirator models. 4



### From each of these bootstrap samples, I 5



### calculate the m ean of the data values, which gives 6



### me 500,000 mean values. 7



### Next, I measure the variability in terms of 8



### the coefficient of variation of these 500,000 mean 9



### values. 10



### The coefficient of variation is defined as 11



### the sample standard deviation divided by the me an. 12



### I did this computation for each of the ten models or 13



### types of respirators included in the study. 14



### We used a second similar procedure to 15



### obtain samples for the 35

- member population based 16



### panel, also the 30

- member population based panel, 17



### and for the 25

- member population based panel. 18



### Also, we used the similar procedure to 19



### generate bootstrap samples for the 40

- member equal 20



### distribution panel, the 30

- member equal distribution 21



### panel, and the 20

- member equal distribution panel. 2221

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### These are the six panels invest igated here. 1



### Each panel's information was collapsed from 2



### 35 or 40 numbers in the panel to a single mean. As 3



### an aside, I also used

-- tried using geometric mean, 4



### but the results were identical to using the usual 5



### sample mean. 6



### Just a word about the coeffic ient of 7



### variation as a statistic. 8



### Intuitively, you can think of it as the 9



### standard deviation adjusted for or standardized to 10



### the sample mean. 11



### In this respect, the coefficient of 12



### variation is better than just comparing standard 13



### deviation estimates. The c oefficient of variation 14



### has no units, so its main advantage is for 15



### comparison to other values of the coefficient of 16



### variation. 17



### Bootstrapping is a methodology. It has 18



### been around for 20 years now or more. Some other 19



### older related and similar techniques ha ve been 20



### around even longer. Here's a reference book that's 21



### a very straightforward introduction by Efron and 2222

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### Tibshirani. 1



### Okay. Moving right to the results. 2



### Here are the results for comparing equal 3



### distribution panels of size and N=40, N=30, and 4



### N=20. 5



### Fo r Model A, which is an FFR type, the 6



### 40

- member panel coefficient of variation estimate is 7



### 0.241. For the 30

- member panel, the coefficient of 8



### variation is 0.278. That number represents a 15.4 9



### percent increase. 10



### For the 20

- member panel, the coefficient of 11



### v ariation is 0.341, which represents a 41.3 percent 12



### increase as compared to the 40

- member panel. 13



### You can quickly see that within each row or 14



### within each model of respirator investigated, that 15



### the percentage increase in the coefficient of 16



### variation are generally similar. In other words, 17



### those red numbers are fairly close. 18



### Just a word of caution. The study is not 19



### intended to compare respirator models. For example, 20



### because Model E has a smaller coefficient of 21



### variation than Model F, it's not a valid com parison 2223

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### of respirator performance. 1



### Recall, the objective of this study is to 2



### determine variability among panels using the largest 3



### N=40, or four cells

-- or four cells of four each as 4



### the reference. 5



### We find that using a smaller sample of size 6



### N=30 results i n 15.3 to 15.7 greater coefficent of 7



### variation for the equal distribution panels. 8



### Likewise, the 20

- member panel has 41.2 to 9



### 42 percent greater variability than a 40

- member 10



### panel. 11



### Okay. This table represents the results 12



### for the population based panels. T he top row shows 13



### the results for Model A. 14



### For the 35

- member panel, the coefficient of 15



### variation is 0.251. And for the 30

- member panel, 16



### 30

- member population based panel, the coefficient of 17



### variation increases to 0.271, which is 7.8 percent 18



### increase. 19



### Th e 25

- member panel coefficient of 20



### variation is 0.297, which is a 17.8 percent increase 21



### from the 35

- member panel. 2224

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### For the population based panels, using the 1



### largest, that's N=35 member panel as a reference, we 2



### find that using a smaller sample size of N=30 3



### re sults in between 6.7 percent to 10.7 percent 4



### greater coefficient of variation. 5



### And the 25

- member panel has between 15.3 6



### percent to 21.6 greater variability as measured by 7



### the coefficent of variation that is relative to the 8



### 35

- member panel. 9



### When the sample sizes are the same, here 10



### when we are considering the 30

- member panels, it's 11



### possible to compare the coefficient of variation 12



### estimates for the equal distribution panel to the 13



### population based panel. 14



### For eight out of ten of the models, the 15



### population base d panels have a lower coefficient of 16



### variation. This can be explained because the 17



### population based panels take a larger sample from 18



### the more homogenous cells, cells 4 and 7. These 19



### cells have a smaller area than shown in the 20



### graphics. 21



### Compared to the equa l distribution panels, 2225

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### the population based panels take a smaller sample 1



### from more heterogeneous cells, that is cells 5 and 2



### 6. 3



### We would need to test more respirator 4



### models to be sure about this result. As it stands, 5



### eight out of ten yielded a binomial tes t p

- value of 6



### 0.11. 7



### Okay. To summarize the statistical 8



### results, we have compared six different types of 9



### panels with different numbers of members. 10



### We have compared three equal distribution 11



### panels to three of the population based panels. The 12



### comparisons a re done within each of the ten 13



### respirator models in this study. 14



### We found that panels with smaller numbers 15



### of subjects have greater variability, and this 16



### result is consistent for all of the respirator 17



### models. 18



### In the cases of 30

- member panels, the 19



### population based panels have smaller variability 20



### than the equal distribution panels, but the result 21



### is not statistically significant. 2226

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### It makes sense because the population based 1



### panels take a larger number of observations from the 2



### more homogeneous cells, and fewer observations from 3



### the more heterogeneous cells compared to the equal 4



### distribution panels. 5



### Okay. We can actually use something called 6



### the design effect, which is a term used in survey 7



### design to measure the efficiency of sampling by 8



### cells to compare the p opulation based to the equal 9



### distribution panels. Or, as we have discussed, but 10



### not actually investigated, combining cells into 11



### what's been termed clouds, so those sorts of 12



### designs. 13



### All these could be compared by using the 14



### design effect. 15



### Basically, the design effect is comparison 16



### of the variance, sample variance, relative to the 17



### sample variance obtained from a simple random sample 18



### of the same data. 19



### Preliminary results indicate that for six 20



### out of ten models investigated, the population based 21



### panels are more efficient. So that work is still 2227

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### ongoing. 1



### In conclusion, our study shows that panel 2



### variability is different for the six panels 3



### investigated here. The next problem will be to 4



### define a certification test and requirement. 5



### Thank you for your attention

. 6



### MR. SZALAJDA: Any questions so far about 7



### this material? 8



### Yes, please. 9



### SPEAKER: Phil Eitzman from 3M. I just 10



### have a quick question. 11



### When you talked about the simulated study, 12



### was that a previous NIOSH study when you were 13



### talking about the simulation? 14



### MR. WASSELL: Yeah. That's an RTI study. 15



### SPEAKER: Okay. All right. So you weren't 16



### talking about something that

-- other studies or 17



### s omething in the comments or something. 18



### I wanted to make sure. 19



### MR. SZALAJDA: Yeah. Phil, could you 20



### repeat that because I think the mic was off. 21



### SPEAKER: Better? Okay. 2228

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### Repeating the question. The question was 1



### Dr. Wassell talked about previous simulated studies 2



### or studies based on simulations, and I just wanted 3



### to make sure I understood what study he was 4



### referring to. 5



### MR. WASSELL: Yeah. I was referring to the 6



### RTI report, which

-- 7



### MS. D'ALESSANDRO: It's on the web. 8



### MR. WASSELL:

-- it's on the web. 9



### SP EAKER: Okay. Thank you. 10



### All right. 11



### MR. WASSELL: Anything else? 12



### MR. SZALAJDA: Ask if there's anything 13



### online. 14



### MR. WASSELL: Anything online, please? 15



### Any questions from the online audience? 16



### Nothing. 17



### Okay. Well, I'll be glad to address 18



### questions later at any time. 19



### Thank you very much. 20



### MR. ZHUANG: Okay, good afternoon. 21



### My name is Ziqing Zhuang, and so, yeah, of 2229

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### the first webinar on July 23, I sat down with 1



### Dr. Coffey and talked about some additional 2



### analysis. And so

-- and I presented some of my 3



### resu lts last month on August 20th, the second 4



### webinar, and so I'm presenting some more result 5



### today. 6



### And so these additional analyses have a new 7



### study design, a new approach to the inter

- panel 8



### variability issue. And so I think they are covering 9



### more

-- rela ting to the comments or the questions 10



### regarding inter

- panel variability. 11



### And then also these additional analyses 12



### will help identify where the inter

- panel variability 13



### is and then also quantify how much the inter

- panel 14



### variability. And then also, more impor tantly, the 15



### analysis will demonstrate how to reduce the 16



### inter

- panel variability. 17



### And also at the second webinar, I have them 18



### explain the, yeah, test data highly variable and the 19



### inter

- subject variability, and some of the study 20



### talking about inter

- subject v ariability dated back 21



### to 1983 or so. And then we also, yeah, we all look 2230

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### at we have differences, different respirator models. 1



### So we have inter

- respirator variability and then 2



### lately we also identify inter

- respirator sample 3



### variability. 4



### So when you take different respirator out 5



### of the same box, you expect to see some differences 6



### as well. 7



### So

-- and for this particular inter

- panel 8



### variability issue, after the first presentation, I 9



### still had some comment or questions. So I thought 10



### it needed some more clarification here. 11



### So what it is, it's like inter

- panel 12



### variability, to me, like, it's the differences like 13



### inward leakage te st results between two respirator 14



### fit test panels, like two geographical locations or 15



### at the same location. 16



### So we had manufacturer throughout the 17



### country or the world, and they have

-- they can 18



### sample panel of the same size, and then they will 19



### get a set of test results. And when we get the 20



### panel at NIOSH, like do we get the same result or 21



### not. 2231

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### So how to measure the inter

- panel 1



### variability should be the same or similar to how to 2



### describe the inward leakage test result. 3



### So at last month's webinar, I 4



### covere d

-- presented the passing rate, which is 5



### expressed as a frequency of number of subjects 6



### passed or percentage, and then also like passing or 7



### failing a respirator in inward leakage test. 8



### So but typically, like from scientific 9



### standpoint, we would compare mean, like

-- and look 10



### at the two panel and look at their mean and look at 11



### their distribution and look at the standard 12



### deviation. 13



### And then most of the like fit factor or 14



### inward leakage data tend to be like normal 15



### distribution, so we transform them and ca lculate 16



### geometric mean and geometric standard deviation. 17



### And then there's another like way to look 18



### at the data is to look at the percentile, the fifth 19



### percentile or 95th percentile. And then 95th 20



### percentile is the one that the I S O TC 94/SC 15 , yeah, 21



### used to li ke

-- for the inward

-- total inward 2232

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### leakage performance. 1



### But then the committee met over the 2



### weekend, like studied on Sunday, and then they are 3



### still meeting today and tomorrow. 4



### So but then I attended the meeting. And, 5



### now, we have like move forward with like binomial 6



### approach, calling a number of subjects required to 7



### pass. 8



### So like getting all of the fit factor data 9



### for a panel of 25 subjects or so, and then look at 10



### that 93 percentile. If it's less than a certain 11



### inward leakage level or so, you fail that 12



### respirator. 13



### So now they changed the approach also. So 14



### next week, Wednesday, they send it out to different 15



### country for comment or so it will be using the 16



### binomial approach. 17



### And so then you also heard from Dr. Wassell 18



### about coefficient of variation. But typically when 19



### we do that, we will estimate like very

-- like 20



### various component and see which one is like smaller, 21



### which one is like larger. And then with the

-- in 2233

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### the panel, coefficient of variation is large

-- what 1



### is large or what is small. 2



### So then we also have like two approach. So 3



### what I presented last time was how I call 4



### deterministic. So we just like recruited like two 5



### or three panel, and we compare the result. And 6



### then

-- but today, I will also talk about 7



### probabilistic approach. 8



### So by using the data that we have, and then 9



### we create like

-- when we compare like three panel, 10



### we can see do they agree or not. But then when 11



### create a 1,000 panel and see how many of them pass 12



### and how many of them fail, and thus (unintelligible) 13



### that you see f or my result today. 14



### So here is a summary of what we found for 15



### the August webinar. 16



### So passing rate was not significantly 17



### different among the three panels for all respirator 18



### combined or by each model. And then this is true 19



### from the all pass/fail levels of . 01, .02 or .05. 20



### And then using 26 or more subjects out of 21



### like 35 to pass the inward leakage test, all three 2234

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### panels have very consistent passing or failing 1



### results for pass/fail level of .01 and .05. 2



### But then some inconsistency was observed 3



### for pass an d fail level .02. And then pass/fail 4



### level is very important factor affecting inter

- panel 5



### variability. So that's what we found. 6



### And then also

-- so, yeah, we found out 7



### that like inter

- panel variability exists, and then 8



### it's like rather small. 9



### And then the also the concern about the 10



### inter

- panel variability and other types of 11



### variability can be alleviated by properly selecting 12



### these three parameter: Pass/fail level .01, .02, or 13



### .05. The example here. 14



### And then also the panel size. Like in that 15



### case, li ke last time, we used 35. But here I'm 16



### going to show you some results for panel size of 25 17



### and see what in fact we see from inter

- panel 18



### variability data. 19



### And then also the minimum number of 20



### subjects required to pass is also another variable 21



### that we can look into. 2235

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### And so just

-- yeah. So this idea will 1



### give you why, yeah, like we are doing the additional 2



### analysis here. 3



### And so when we talk about like the first 4



### like variable is the sample size, like how big is 5



### the panel size. 6



### Yeah, like NIOSH certificati on test methods 7



### for known particulate respirator use like ten 8



### subjects. And then the CBRN standard require at 9



### least like 25 subjects. 10



### So you could go to 29 if you have two size, 11



### or 38 if you have three size. 12



### And then the EN standard only require 10 13



### sub jects. And then the 2009 proposed like rule 14



### proposed like 35 subjects. And then, as I mentioned 15



### earlier, ISO, they propose a 25 subject. 16



### So there's still some need to look at the 17



### science about this, like sample size issue. 18



### So we have two previous approach. Like one 19



### is the binomial distribution approach. And so 20



### Dr. Landsittel and myself and Roland BerryAnn and 21



### then also (unintelligible), we are coauthors for 2236

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### that paper. And has been accepted and it is being 1



### like published very soon in JOEH

. 2



### And t hen also using the information from 3



### that paper, I prepare a presentation at Boston at 4



### the ISRP conference. 5



### And so using that approach, we use standard 6



### statistical calculation to assess results under 7



### different assumptions. So

-- and then calculate 8



### probabi lities using the binomial distribution. 9



### So like we assume the respirator is how 10



### effective it is, and if it can fit a certain 11



### percentage of the population. And then for 12



### different respirator with different fitting 13



### characteristic, we determine how likely i t would be 14



### rejected if used certain sample size. 15



### And so that approach. And then the other 16



### approach is the random effects model approach, and 17



### these are the people like working for FDA. And they 18



### use a closed

- form normal approximation, and then 19



### sample siz e estimates for testing the proportion of 20



### users who will receive a specified level of 21



### protection, so... 2237

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### And then here I'm trying to use the third 1



### just to look at inter

- panel variability and see what 2



### kind of effect we can see. We reduce the sample 3



### size, an d do we still have similar inter

- panel 4



### variability or not. 5



### So the objective of this portion of the 6



### analysis is to determine if panel size affect 7



### passing rate and also consistency of passing or 8



### failing of respirator in inward leakage test. 9



### So

-- and, aga in, this is a deterministic 10



### approach. So from the 144 subjects, we just 11



### randomly assigned them to one of three test panels. 12



### So we only used 25 subjects for one panel, and then 13



### three panels. So we only used 75 subjects. 14



### So here is how we selected the sub jects or 15



### assigned them. 16



### So if you look at this table, we have 12 17



### subjects in cell 1, but we only use six of them. 18



### But then for cell 4, we use five of them for each 19



### panel, and then 7 we use four here. 20



### So we only use like 75 out of 144. I could 21



### have creat ed more panel, but just try to be 2238

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### consistent with what we created for

-- using the 35 1



### subject, so just to see similar pattern. 2



### So a subject was considered to pass the 3



### inward leakage test if one of the two inward leakage 4



### variables was less than or equal to pass and fail 5



### level. And then also calculated passing rate for 6



### all 144 subjects and ten respirator combined using 7



### the three level, .01, .02, and .05. 8



### Just to give an initial estimate of how 9



### good this respirator fit the population. So it's 10



### not a real li ke passing rate, but just some

-- but 11



### it's better than what we assume for the other paper, 12



### when we assume 80 percent or 90 percent. So at 13



### least we have some data to get started with. 14



### And I know we can improve this number 15



### because like Chris Coffey talk a bout earlier, like 16



### for one size fits all, we use all of them. But then 17



### you have two size, if that fail, we can always get 18



### another size to try on. So I'm sure the performance 19



### will be better. Yeah, but just to 20



### use

-- demonstrate a concept here. 21



### And then c alculated passing rate for each 2239

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### panel or for all three, and then each respirator as 1



### well for the same level. 2



### And then we also tested if passing rates 3



### were significantly different among the three 4



### respirator fit test panels. 5



### And a respirator was considered t o pass the 6



### inward leakage test if 19 or more subjects out of 7



### the 25 passed the inward leakage test. And then we 8



### can determine if the passing or failing of the 9



### respirator inward leakage test was consistent with 10



### the three different respirator fit test panel or 11



### not. 12



### So here are the result. 13



### And this is the figure shown

-- like show 14



### the inward leakage data for all ten models and by 15



### panel. So each panel has 25 subjects. Each subject 16



### has two tests. So we have 50 tests. But then ten 17



### respirator models, so we ha ve 500 data points. 18



### So you can see data equal this range from 19



### here, like zero here, all the way to like .5. 20



### So

-- and .5 is approximately like a fit factor of 21



### 2. 2240

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### So

-- but then you look at like 90 percent 1



### also of the data will be less than 1 point somet hing 2



### here. But then the other point that I would like to 3



### make here is that all three curves are very similar. 4



### So if you test their mean, their mean may 5



### not be statistically significant, and

-- but then 6



### when we look at one individual model, this is a goo d 7



### performance model with the lowest mean inward 8



### leakage and the respirator in the presentation here. 9



### So you can see like this is a good model, 10



### like the maximum is like less than .01. And then 11



### like 90 percent of the data is covered less than .02 12



### or so. 13



### And, yeah, and then also you don't see much 14



### like variation among the three panels, so the three 15



### curves are very similar. 16



### And

-- but then, when you look at the model 17



### with the highest like mean inward leakage, for the 18



### three panel, they kind of scattered litt le bit. 19



### Like panel C show more like, oh, yeah, panel A show 20



### more like leakage, like

-- let's say like here, you 21



### have more than 70 percent is less than .2. But then 2241

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### like if you look at the 70 percent or so, it's like 1



### for the other two panels, they are les s than .1. 2



### So

-- and you can see some variation among 3



### the three panels. 4



### And so this is the table showing the 5



### passing rate for all 144 subjects and for the three 6



### level of pass and fail level. 7



### So for respirator A, we have 66 subject 8



### like pass that level. So one of the two inward 9



### leakages is less than .01. And so that is about 46 10



### percent. 11



### So

-- and then

-- so for this column

-- and 12



### you can see like Respirator F is only one that had 13



### like 125 or more than 80 percent. So

-- and in our 14



### previous paper, we have c ategories. Like anything 15



### more than 80 or so, 80 or 90 percent, we consider 16



### them effective respirator model. 17



### And so I'm trying to apply the same concept 18



### here. Also like some of these respirators could be 19



### impacted as well if you do the right test. 20



### And

-- but just demonstrate a concept here. 21



### And so for the respirator in red, I

-- they 2242

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### are less than 60 percent. So they are

-- we call 1



### ineffective. 2



### And then the one somewhere like between 60 3



### or 80, so we have one model that's in that category. 4



### So I do not code anything. Just leave it as red. 5



### But as we change our pass and fail level to 6



### .02, now, like Respirator A has like 109 something 7



### passing. That's about a 76 percent. 8



### And so now you can see like Respirator F 9



### and J, they become green. So they are ef fective now 10



### if we use that pass and fail level. 11



### And so

-- but then the other three, like B, 12



### C, and E, they are still less than 60 percent. So 13



### they are still ineffective. And then

-- but then 14



### those somewhat between 60 and 80, we have five model 15



### in that c ategory. 16



### So but then when we change our

-- the pass 17



### and fail level to .05, now, like seven model become 18



### effective. They all have more than 80 percent 19



### passing. But then only one model is less than 60, 20



### and the other one

-- the other two model is like 60 21



### an d 69. 2243

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### So that's

-- and this is the concept that 1



### we will use throughout the analysis for the next 2



### like few tables. And so now, when we look at inward 3



### leakage, like pass/fail level of .01, and we look at 4



### the three panel now. 5



### So for Respirator A, only 12 subjects pass 6



### here, 11 and 10. So they all

-- they are all less 7



### than 26. So they all failed the test. So very 8



### consistent result. 9



### But then when you look at Respirator F, so 10



### you have 20 subject like 21 and 22, so they are all 11



### greater than 19. So, agai n, like all three panels, 12



### this respirator pass the test. So very consistent 13



### result. 14



### So

-- but then

-- so in this case, like for 15



### this level, like the passing or failing are very 16



### consistent among the three panel. And then the 17



### other one is we did the chi

- sq uare test like just to 18



### check the number and see whether this number are 19



### statistically different, and this are how the p 20



### value will get. And if less than .05, that means 21



### they are different. But if greater than .05, there 2244

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### is no statistical differences ther e. 1



### So

-- and then when we change it to .02, 2



### now, I have a coded the respirator using the results 3



### from Table 2. So F and J are green, and they are 4



### effective. So they all pass. 5



### And then, the red respirator, they all fail 6



### three panel. 7



### And then the one like, A, D, G, H, I, also, 8



### we have some inconsistency here. 9



### For A, they pass like panel A and

-- fail 10



### panel A and C, but pass B. But then for G, the same 11



### failed the first two, but pass the second one

-- or 12



### the third one. 13



### And so you have some inconsistency he re. 14



### But then when the pass and fail level is 15



### changed to .05, go back to something similar to .01 16



### level again. So you only have like all the 17



### green

-- all the effective model, they all pass. 18



### And the red one all fail, but, yeah, the 19



### red color one is pass one and fail two, or for this 20



### case, I

-- this respirator fail all three. 21



### So you only see a little bit of 2245

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### inconsistency. 1



### So passing rate was not significantly 2



### different among the three panels for all respirators 3



### combined or by respirator with exception of 4



### Re spirator E. And then this is true for all 5



### pass/fail levels of .01, .02, or .05. 6



### And the variability is generally smaller 7



### and some were close to .05 when we compared to the 8



### results for panel size of 35. 9



### But using 19 or more subjects to pass the 10



### inward le akage test, like all three panel has 11



### consistent passing/failing results at .01 or .05 12



### level. And then so some inconsistency was observed 13



### for .02. And so passing/failing results are very 14



### similar to those for sample size of 35. 15



### So, yeah. So when panel siz e was decreased 16



### from 35 to 25 subjects passing, it was slightly 17



### affected. Not a whole lot. And consistency of 18



### passing or failing a respirator was not affected by 19



### this decrease in panel size. 20



### And further simulation analyses can be 21



### conducted to confirm th ese findings. So the next 2246

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### portion of my analysis will be, instead of looking 1



### at three at a time, we are going to look at 1,000 2



### panels and see what the results will look like, and 3



### then we will calculate some probability. 4



### So any questions for now? 5



### SPEAKER: Charles Okay (phonetic). 6



### I was curious if you were using the inward 7



### leakage level at the same thing at the confident 8



### interval. 9



### MR. ZHUANG: Confident interval? 10



### SPEAKER: Yeah. Were you using the same 11



### concept? 12



### MR. ZHUANG: No. No. Like, yea h, if you 13



### go back to that figure, like it show you like they 14



### are

-- like inward leakage datas are highly 15



### variable. 16



### So like from different subjects, different 17



### respirator, it can range from near zero all the way 18



### to like .5. So

-- but then if you like

-- b asically 19



### like we have a .01, .02, and .05, they are somewhere 20



### here. 21



### So if you use that like criteria and see 2247

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### how many of the subjects can get like there, like 1



### inward leakage even less than that. And we consider 2



### them to pass or consider them to fail and, right. 3



### So it's not a confident interval. 4



### SPEAKER: My next question. How did you 5



### determine your sample size? 6



### MR. ZHUANG: The sample size like from the 7



### other two approach, we have our calculation. 8



### But in this case, I only look at effect and 9



### just to see like do we have any like

-- if we 10



### decrease from 35 to 25 or so, how much impact do we 11



### have or how much like more inconsistent result we 12



### can get. And if we don't have enough of that or so, 13



### that means that probably we can use either one, just 14



### to look at , yeah, the effects. So we don't use a 15



### certain formula to derive that sample size. 16



### But the other approach we try like the 17



### binomial approach, we look at probability, and we 18



### also look at alpha/beta error. 19



### And then using that, trying to, like I 20



### said, we wo uld like to have the alpha error like 21



### less than 20 percent. Like we try to like to reject 2248

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### a good model like less than 20 percent. And then 1



### also we tried to like accept the effective model 2



### like less than 5 percent and use that part of the 3



### criteria to pick the sample size. 4



### MS. D'ALESSANDRO: Anything on LiveMeeting? 5



### MR. ZHUANG: Yes. Anyone would like

-- or 6



### have any question from LiveMeeting? 7



### Okay. If not, I will move onto my next 8



### power

-- my presentation. 9



### So, again, we look at the same research 10



### questi on or research issue, but using a different 11



### approach, like which is probabilistic approach. 12



### So the objective was to estimate 13



### probability that two panels will pass or fail a 14



### respirator. And this will cover linking the 15



### question that like, yeah, some peopl e would like to 16



### have answered, to answer. And then also to estimate 17



### probability for respirator to pass the inward 18



### leakage test and then investigate the effect of pass 19



### and fail level and sample size also. These are all 20



### the main three parameters to set a g ood like, like 21



### performance criteria. 2249

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### So from the 144 subjects, we use 1



### bootstrapping to generate two 35

- subject panels. 2



### And then in this case, they are not 3



### like

-- there's no replacement. So when you

-- from 4



### the 144, you just create two panels, similar to what 5



### we did for the deterministic approach. But then you 6



### put those subjects back again, and then the next, 7



### yeah, like

-- so, and then a subject was considered 8



### to pass the inward leakage test if one of the two 9



### inward leakage values is less than or equal to the 10



### pass and fail level again. And then also calculated 11



### the passing rate for each panel and each respirator 12



### for the three pass and fail levels. And then do a 13



### test to see if passing rate was significantly 14



### different between the two respirator fit test 15



### pan els. 16



### And the respirator was considered to pass 17



### the inward leakage test if 26 or more subjects out 18



### of 35 passed the test. And then we can determine if 19



### passing or fail a respirator in inward leakage test 20



### was the same for the two panels. 21



### So typically, yeah, we will see if they all 2250

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### passed, they all passed all panel or like they may 1



### pass one and then fail the other, or maybe both 2



### panels. Yeah, the respirator will fail both panel. 3



### So we have three outcomes. 4



### And then we repeat that procedure until 5



### 1,000 pair o f 35

- subject panels was completed. 6



### So

-- and then calibrated the probability 7



### that passing rates were not significantly different 8



### between the two respirator fit test panels for each 9



### respirator for the three pass and fail levels. 10



### And then also calculated t he probability 11



### for the following outcomes, like the respirator 12



### passed both panels or failed both panels or passed 13



### one and failed the other. 14



### And also, with 1,000 like, yeah, panels 15



### created, we can also calculate probability for a 16



### respirator to pass the inwa rd leakage test. And 17



### then also this procedure was repeated for like 18



### 25

- subject panel as well. So we had two set of 19



### results. 20



### So

-- and this table, Table 6, like shows 21



### the probability that passing rates were not 2251

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### significantly different between the two 35

- s ubject 1



### panel. So for like respirator A, so what it show us 2



### is like we have like 1,000 pair. And so like 949 3



### like, when we do the chi

- square test, like 949 pair, 4



### like it no show any difference. And the remaining 5



### like show differences. 6



### So but then, if you look at all the 7



### results, they are all very high. So the probability 8



### of like not getting significant differences between 9



### the two panels is very high. 10



### So the low is like 93.7 here for that 11



### level. But then you can get some hundred percent or 12



### so. So very

- - so it means like we don't have, 13



### like

-- if we use that to look at our inter

- panel 14



### variability, we do see, yeah, inter

- panel 15



### variability here. 16



### And then so, yeah, when we look at the 17



### number of subjects and do the, yeah, the chi

- square 18



### test. But here we lo ok at whether the respirator 19



### passed the panel or not. And so in that case, if 20



### you look at

-- recall what I mentioned earlier, like 21



### the effective model, the passing panel is very high. 2252

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### So here you are not looking at one hundred 1



### percent. So whenever you get a 65, which is like 2



### one panel. If it pass each of the panel at a 3



### probability of .8 or .9 or so, you get a .6 4



### something or .8. So this is still very consistent 5



### here. 6



### But then for the ineffective model, it's 7



### kind of almost

-- yeah, it's 100 percent h ere. Like 8



### they all failed both panel. So only like for 9



### the

-- somewhere like

-- somewhat effective model, 10



### we also

-- it's almost here 100 for this model. 11



### And for a .02 level, so, again, the two 12



### greens, it's very high probability that they pass 13



### both panel . And then for the ineffective, the red 14



### one, they failed 100 percent of the time, the both 15



### panel. And then the somewhat effective, like the A, 16



### D, G, H, and I respirator, you had some variation. 17



### And when we, yeah, look at the .05 level, 18



### now, yeah, all the green like respirator coded with 19



### green color, they have very high probability of 20



### repeating the result. 21



### And the, yeah, ineffective model is hundred 2253

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### percent fail both panel, also. So

-- and so just a 1



### little bit of like disagreement or inconsistency we 2



### can find. 3



### So the probability that passing rates were 4



### not significantly different between two 35

- subject 5



### test panel was very high and range from 93.7 to 100. 6



### And this was true for all pass and fail level of 7



### .01, .02 and .05. And using 26 or more subjects out 8



### of 35, we, yeah, saw good agreement for pass and 9



### fail level of .01 and .05. But then some 10



### inconsistency was observed for .02 pass and fail 11



### level. 12



### So this is the result for subject for panel 13



### size of 25 now. 14



### So, again, like the minimum here, the 15



### smallest i s like 98.3 (sic), and it also goes to 16



### 100. So, yeah, if you compare the result here and 17



### the result for sample size of 35, you see very 18



### little differences. 19



### And, again, like looking at the probability 20



### for the effective model, 61 versus 64 there. And so 21



### i t's good like repeatability. And then for the 2254

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### ineffective model, they all fail a very high 1



### percentage of the time. 2



### And for .02 now, this way, we have some 3



### like ineffective model, and we have some like 4



### low

-- like higher percentage, all probability of 5



### pas sing one panel and then fail the other panel. 6



### And then

-- but for the effective model, they all 7



### have good repeatability. 8



### And the same for .05 level also. 9



### So the probability that passing grades were 10



### not significantly different between the two 11



### 25

- subject te st panel was very high, ranged from 12



### 93.8 to 99.9. And then using 19 or more subjects 13



### out of 25, like, again, we saw good agreement for 14



### pass/fail level .01 and .05. 15



### And only some inconsistency was observed 16



### for .02 and the probability were very similar to 17



### s ample size 35 subject for the panel. 18



### So the next set of results, yeah, will be 19



### the probability for the respirator to pass the 20



### inward leakage test for three different pass and 21



### fail level or two panel size as well. So this is 2255

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### another table. 1



### We generate 1, 000 panel. And, so, let's 2



### say for Respirator F, like 9

-- 63 of the 1,000 3



### panel passed, and that's how we got our probability 4



### here. 5



### But then for respirator, yeah, A and then I 6



### or so, like that passing probability is very small 7



### at this pass and fail leve l. And then J is the one 8



### somewhat effective, but it passed like 83 of the 9



### 1,000 panel, so it's like 8.3 percent. 10



### And so as we increase the pass and fail 11



### level from .01 to .02, now, the two effective model 12



### is like the 90 something percent passing probabi lity 13



### and 100 percent here. 14



### But then ineffective B, C, D, they are 15



### still very low. And they are somewhat effective. 16



### They pass at a certain

-- like this one is like 17



### about 70 percent, and then here is a 20 percent. So 18



### when we increase that pass and

-- pass /fail level to 19



### .05, so more like passing like

-- so all seven 20



### models passed like are effective, pass like at very 21



### high probability right now. And so like 99, 98 and 2256

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### 100 percent. 1



### And then the red

-- the Model 2



### B

-- Respirator B is still not that effective , so it 3



### failed a lot of the time. And then the two somewhat 4



### effective, like 30 or 6 (phonetic) percent of the 5



### time. 6



### So and if you look at the sample size, you 7



### can see like for this particular case, a good model, 8



### like a good model, if you increase your sa mple size 9



### or you decrease like

-- for smaller sample size, it 10



### tend to give you a lower like passing probability. 11



### So in this case, it's about like 2 percent. 12



### And then that's like the same as what we 13



### found in our paper. So as sample size decrease, you 14



### te nd to increase your alpha error, which is like 15



### rejecting good model. So alpha error for this case, 16



### one is like 4 percent. The other 2 percent. But 17



### it's only about two percent differences. 18



### And then on the other hand, like for like 19



### somewhat effective mo del also, like the

-- for 20



### sample size is smaller. 21



### It tend to accept them or reject them like 2257

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### less. So in this case, like you allow them to pass 1



### like a little bit higher than the other. Maybe you 2



### increase your sample size. You only allow this 3



### model to pa ss at like around 5.5 percent of the 4



### time. But for sample size of 25, it will pass at 5



### 8.3 percent of the time. 6



### So you can see that how it affect between 7



### 25 and 35. And most of the time, that kind of trend 8



### is the same. 9



### So

-- and then the other one I want to show 10



### here is that now, like instead of using 19 out of 25 11



### and 26 out of 35, we used 15 out of 25 to pass a 12



### respirator or 21 out of 35 to pass a respirator, and 13



### these are the current probability. 14



### So when

-- for .01, now, even the yeah, the 15



### ineffective m otel, the so

- called ineffective model, 16



### they pass like little bit now. So increase 8 17



### percent here, 3 percent. 18



### But for the effective one is 100 percent. 19



### And then this one somewhat effective is like 60 20



### percent now. 21



### So but any of you look at the pass/fail 2258

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### level of .02, they are all quite high now. So 1



### except the three ineffective model, like still less 2



### than 1 percent. But then the rest of the model is 3



### like 85, 84, 95, 82, or 100. 4



### So they all passed, yeah, like have a high 5



### probability of passing now. 6



### And fo r .05 level, it's almost like 100 7



### percent for those respirators. And even for the, 8



### yeah, for the ineffective, the so

- called ineffective 9



### model

-- like respirator, it also can pass about 10



### like 30 percent of the time now. And here is like 11



### 90 percent and 60 p ercent for the other two models. 12



### So this is like the same as like I show in 13



### the past. 14



### This is the figure. We look at number of 15



### subjects required to pass, and then this is the 16



### percentage of the time a given respirator will fail. 17



### And so you can see that 80 percent, 90 18



### percent effective like by using that 19 out of 25 or 19



### so, they will pass a high percentage of the time. 20



### And then the 60 or 50 percent, the 21



### only

-- the rejection is so high, they only pass 2259

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### about like a few percent of the time. So for 50 1



### percent or so, beta L is less than 5 percent. And 2



### but then, yeah, just like the previous table, if you 3



### shift this line to here, so now like the green line, 4



### those are effective, they will continue to pass like 5



### 100 percent of the time or so. And then even the 6



### som ewhat effective, like between 60 to 80, now they 7



### can also

-- like they will be rejected a very like 8



### small percentage of the time. 9



### And but then, at the same time, you accept 10



### some of the ineffective models at a little bit 11



### higher percentage of the time. 12



### So that's the kind of trend when you change 13



### the like number of subjects required to pass like in 14



### the panel. 15



### So probability for a respirator to pass the 16



### inward leakage test was not significantly different 17



### between 35

- subject test panel and 25

- subject test 18



### pane l. And this was true for all pass and fail 19



### level of .01, .02, and .05. 20



### When number of subjects required to pass 21



### was decreased, probability for respirator to pass 2260

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### the inward leakage test increased. 1



### Yeah, so pass and fail level affects the 2



### classification of the respirator effectiveness and 3



### then also the probability. 4



### And so when panel size was decreased from 5



### 35 to 25 subjects, that probability for repeating 6



### test results was very similar. And then when panel 7



### size was decreased from 35 to 25 subjects, the 8



### pr obability for respirator to pass the inward 9



### leakage test remained similar. So when the number 10



### of subjects required to pass was increased, then 11



### more respirators passed the inward leakage test. 12



### So any questions? Again, what did you say. 13



### SPEAKER: Lisa Bros seau. So that was a lot 14



### of data. 15



### So here's my question. What's a good pass 16



### rate? 17



### MR. ZHUANG: Okay. I think like today, we 18



### may not want to do that yet. But the data show that 19



### like if I'm

-- yeah, to set the formal requirement, 20



### just using the data that we have right now, I think 21



### like .02 and 25 and maybe I would change the number 2261

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### from 50 to 60 because ISO committee was able to 1



### accept the 60 out like 25 on Sunday. So I think 2



### that may be a good number. 3



### SPEAKER: So what you're saying is that 4



### if

-- what yo u're looking for as a passing fit 5



### factor is 50; right? 6



### MR. ZHUANG: Yeah, roughly, yes. If 7



### there's inward leakage like

-- 8



### SPEAKER: I understand. But we can talk 9



### about fit factors for a minute because that means 10



### more to me than penetration. 11



### So you're looking for a fit factor of 50. 12



### MR. ZHUANG: Yes. 13



### SPEAKER: You're saying it doesn't matter 14



### if it's 25 or 35 subjects. 15



### MR. ZHUANG: Right. 16



### SPEAKER: And how many subjects do you 17



### think we should be looking for as a pass? 15? 18



### MR. ZHUANG: This example s how a 15. 19



### SPEAKER: 15 out of

-- 20



### MR. ZHUANG: 60 percent, basically 60 21



### percent. 2262

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### SPEAKER: Okay. 1



### MR. ZHUANG: But I think going up to 60 is 2



### not too bad also. Like 63 or 64 percent. 3



### SPEAKER: Yeah. So the one question I have 4



### about the design of the original experiments is this 5



### sort of forcing people into cells. 6



### MR. ZHUANG: Yes. 7



### SPEAKER: How much effect do you think that 8



### would have? Because it misclassifies some people. 9



### It gives them the wrong respirator. 10



### What's the effect of that on your data 11



### analysis? 12



### MR. ZHUANG: I would expect the results 13



### will improve the data here. 14



### Because like if you

-- let's say with the 15



### two respirator size, and if some subject fail, and 16



### you let them tes t the other size and they pass. So 17



### that number will go up. 18



### So the number of subjects pass, I expect 19



### that number to go up. So I would expect to see less 20



### inter

- panel variability also. 21



### And then for the three size, you know, they 2263

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### are two more chance to p ass. But know like the real 1



### test like if some of them are in the middle, they 2



### could go next or they could go up and maybe one or 3



### two. Either way. 4



### So but just, again, this is for all of us 5



### to consider and think about it. But to me, the 6



### trend is very si milar, and it won't like increase 7



### the inter

- panel variability. It can only help that 8



### like inter

- panel variability will be smaller. 9



### Yeah. 10



### So but very good point, so... 11



### Any questions from the LiveMeeting? 12



### Okay. If not, we are ready for break. 13



### Fifteen minu tes break. So thank you very 14



### much. 15



### (A recess was taken.) 16



### MR. SZALAJDA: All right. What we would 17



### like to do at least for the balance of the meeting 18



### is to have a panel discussion. 19



### And we have people who have been involved 20



### with the process of developing the project and 21



### conducting our research and helping us get to where 2264

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### we are today. 1



### And just for purposes of introduction, we 2



### have three of our presenters, Dr. Wassell on the 3



### end, Dr. Coffey, Colleen Miller, Roland BerryAnn, 4



### who is currently dual hatted as the deputy director 5



### for NPPTL as well as the evaluation branch chief, 6



### and Dr. Zhuang. 7



### And what we'll be doing with regards 8



### to

-- for the next hour or so is to go over the 9



### topics that were identified in the Federal Register 10



### notice. And they fell into

-- th e questions that we 11



### asked fell into these topics. 12



### And what we'll do is we'll go forward. 13



### I'll introduce the topic. There will be 14



### some slides that Colleen will lead the presentation 15



### on with regard to the different criteria. There 16



### will be some background material that's also 17



### incorporated as part of the discussion, and then, we 18



### will elect to engage stakeholders in dialogue. 19



### If you have any particular

-- what I would 20



### encourage you to do

-- because we do not have a 21



### summary planned at the end of the discussi on. If 2265

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### you have comments on any particular area, pass/fail 1



### criteria, test panel, methods, other things, come up 2



### and, you know, identify yourself, your organization, 3



### and make your comment with regard to where your 4



### comment falls within this framework. 5



### We'll handle the meeting internally first, 6



### then we'll go to LiveMeeting, and then we'll just 7



### move through each topic until we go through all the 8



### slides. 9



### And so initially, we would like to

-- you 10



### know, as a follow

- on to Dr. Zhuang's presentation, 11



### we would like to talk about the pass/fail criteria. 12



### And basic

-- the first basic question is, what panel 13



### size would be sufficient for conducting a 14



### facepiece

- to face seal certification test. 15



### And I'll defer to Colleen first for the 16



### initial comments. 17



### MS. MILLER: Thanks , Jon. 18



### I think there are a couple of points that I 19



### wanted to make about this question. 20



### First of all, to remind everyone in the 21



### notice of proposed rule that went out in 2009, and 2266

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### some of the discussions around that, it was 1



### considered important by some of o ur researchers and 2



### contributors that we require at least one pass per 3



### cell as part of the pass/fail criteria. 4



### The comments that we received back from 5



### ISEA and others indicated that perhaps that wasn't 6



### the best methodology. So we are interested in 7



### getting more feedback on that. 8



### And the other thing I think that's 9



### important to remember in terms of where we're coming 10



### from, as Ziqing made reference to, we do have a 11



### criteria right now for the other half

- mask 12



### respirators that we certify in the isoamyl acetate 13



### te st, where we do use the LANL half

- facepiece panel. 14



### And we do require, you know, every

-- a pass in 15



### every cell for all ten cells. 16



### So in opening up this conversation, we 17



### would kind of like you to keep that in mind and hear 18



### what your thoughts are about wheth er or not one pass 19



### per cell is required or not. 20



### Anyone like to contribute? 21



### MR. WASSELL: Well, I just I mean, I know 2267

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### the difficulty in our study to recruit subjects in 1



### certain cells. 2



### So certain cells were all female, right? 3



### That's our requirement; rig ht? 4



### MS. MILLER: Right. 5



### MR. WASSELL: And so in cell one, all the 6



### subjects were female. 7



### So I mean, we do have issues like that that 8



### pertain to this question. And we just, you know, 9



### I'm not really sure to require a pass in each cell, 10



### you know, thinking a bout it from a statistical 11



### perspective, that's a pretty strict criteria, I 12



### think. So we may make the whole certification 13



### process difficult. 14



### SPEAKER: Jim Johnson. 15



### You know, I guess I would like to step back 16



### and say, to me, the value of running a human fi t 17



### test protocol as part of certification is not to be 18



### absolute. 19



### And to me, one fit in each

-- or one pass 20



### in each cell gets a little more than saying look, 21



### you want to make sure that the respirator fits 2268

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### people and can pass on a large number of them, and 1



### le t the final decision be done in the field with the 2



### OSHA required fit test where it is significant and 3



### you have the variability rather than trying to force 4



### the certification process into a really difficult 5



### situation. 6



### And from the

-- anybody that would try a nd 7



### reproduce this, either in research or as a 8



### manufacturer, before they submit it, it gets even 9



### more difficult for them if they want to try and 10



### duplicate what you're going to do before they submit 11



### a respirator. 12



### So I don't see it. 13



### I think it should be made as reasonable and 14



### as easy as possible to evaluate respirators fit on 15



### individuals, on people with variable face sizes, but 16



### not be absolute. 17



### MS. MILLER: So perhaps the focus should be 18



### more on the boundaries of the panel, the outer 19



### boundaries complete rathe r than the interior 20



### separations into cells. 21



### SPEAKER: And, you know, the fact that 2269

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### you're going to say, what was it, a 60 percent pass? 1



### MS. MILLER: That was just something that 2



### Ziqing was giving us

-- 3



### SPEAKER: Okay. But I mean, just saying, 4



### pick out a number, 60, 70 percent. It's not going 5



### to be 95 percent. 6



### So why would you make the cells 100 7



### percent? 8



### I mean, I think it just

-- again, if that 9



### becomes an issue, it will be sorted out in the field 10



### with the person doing the fit testing. 11



### I don't think it's part of certification. 12



### MR. BERRY ANN: Well, let me ask a 13



### different question here. 14



### I think it's along the same lines as where 15



### you were going. 16



### You have got the panel divided up into ten 17



### cells, which are not equally distributed by the 18



### population. 19



### So if you require at least one pass in each 20



### of the

-- in each cell, then aren't you giving 21



### disproportionate weight to some of them, the lesser 2270

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### populated cells that one is a higher percentage of 1



### the population of that cell but a lower population 2



### of the overall? 3



### SPEAKER: I guess for me, Roland, my 4



### perspective is, it's such an improvement to have a 5



### requirement to fit test or do the certification with 6



### people on these air

- purifying respirators that I 7



### think it becomes almost too difficult to go that 8



### way, or too dema nding to come up with those numbers. 9



### And that becomes too restrictive. 10



### MR. ZHUANG: Yes. 11



### I had a comment on that. 12



### I guess I heard about like one cell, at 13



### least one cell in the past is that you worry that 14



### there may not be anyone to pass in that cell. 15



### Bu t that's not true because like, again, I 16



### think fit factor tend to be more normally 17



### distributed. So when you get only two subjects and 18



### you don't get the two to pass or so, there should be 19



### still other people out there that can get a good 20



### fit. 21



### SPEAKER: Lyn n Feiner, Honeywell. 2271

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### My concern with requiring a pass in every 1



### cell for that particular group is you may be 2



### designing a respirator for a very specific facial 3



### type or a facial size. 4



### And I don't want to see a respirator so 5



### limited that it

-- by actually requ iring it to be 6



### all things to all groups. 7



### And let the market decide, and let the 8



### people decide through the OSHA fit testing what's 9



### the right respirator for them. 10



### MS. MILLER: Okay. Does anyone beyond what 11



### Ziqing presented, you know, his examples and his 12



### an alysis of 25 and 35 subjects, was anyone willing 13



### to give a recommendation for a number of test 14



### subjects for the criteria? 15



### Not you Bob. 16



### SPEAKER: Hi, Dan Glucksman , ISEA. 17



### And I don't have a direct response to your 18



### question but to say that, you know, we ob viously saw 19



### the questions in the Federal Register, and we'll 20



### certainly be submitting comments to those. 21



### So I understand that concerning the

-- not 2272

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### disconnect, but the timing here, you're asking these 1



### questions before we have had a chance to sit down as 2



### a group. 3



### We figured we would wait until after this 4



### event to respond. 5



### So the fact that people aren't responding 6



### right away is probably because there's a lot of wait 7



### and see going on wanting to hear and see the 8



### presentations here and then reconnect. 9



### So I jus t wanted to put that out there, the 10



### reason you're hearing the crickets. 11



### MS. MILLER: We expected it. 12



### It's okay. 13



### SPEAKER: Okay. 14



### MS. MILLER: We're trying to improve the 15



### audio quality for our folks on the cheap seats. 16



### MR. SZALAJDA: Are there any comments on 17



### the LiveMeeting or questions on the LiveMeeting? 18



### Yeah. I guess if you come up to the 19



### microphone, if you can just make sure that you're as 20



### loud as possible. 21



### And I guess the one

-- I did get one note. 2273

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### If the panel members, before you speak, if you can 1



### identify yourselves for the transcript that would be 2



### appreciated. 3



### So we'll move on to the next slide, which 4



### includes should the pass/fail criteria require an 5



### overall higher requirement to pass and allow for a 6



### percentag e of failures. 7



### And should the pass/fail criteria require 8



### an overall lower requirement to pass and allow for 9



### no failures. 10



### And I'll defer to Colleen to open the 11



### discussion. 12



### MS. MILLER: Well, I'm hoping that everyone 13



### will loosen up a little bit. 14



### I think th at part of that question was 15



### already addressed by some of the speakers on the 16



### previous question. 17



### But I mean, we're basically saying going 18



### back, you know, should we say fit factor of 100 and 19



### only like, you know, 12 out of 20 subjects just as 20



### an example we would need to pass, or should we lower 21



### the criteria of the fit factor to 50. 2274

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### And, again, go back as we just discussed to 1



### ten out of ten or whatever, a hundred percent pass 2



### rate. 3



### Does anyone have any other comments on the 4



### panel, try and get things going? 5



### In the audience? 6



### How about LiveMeeting? 7



### MR. SZALAJDA: Okay. 8



### Well, I think as

-- oh, thank you, Phil. 9



### Phil Eitzman from 3M. 10



### SPEAKER: Phil Eitzman from 3M. 11



### I just kind of second what Dan was saying. 12



### We will be submitting comments. And just 13



### wanted t o get kind of all the information that you 14



### guys presented today before we did that. 15



### And want to make sure that we have kind of 16



### good complete answers, not just something that I 17



### thought up on the spot. 18



### So we're going to

-- we will be doing that. 19



### So apolog ize for not interacting with you on this, 20



### but want to make sure that we have good answers and 21



### responses to your questions. 2275

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### MR. SZALAJDA: Okay. Thank you, Phil. 1



### I think one thing that just may be

-- and 2



### I'll take the prerogative because I have the mic in 3



### hand, is one of the things I hope that we hear from 4



### stakeholders when they consider this particular 5



### question is that we understand that there may 6



### need

-- somewhere, we need to find an accommodation 7



### in the middle between what's perfect and what's bad. 8



### You know, that there is an area where I 9



### think we can identify an appropriate criteria for 10



### pass/fail, which may not necessarily be the perfect 11



### answer but something that we feel will be

-- provide 12



### good data to give us a good indication that we think 13



### these respir ators will be able to be effectively 14



### used in the field. 15



### You know, again, I think when we looked at 16



### the data, I mean, ideally we could have a huge 17



### panel, 105 people, and we could do a lot of testing 18



### and feel good about a potential answer and the fit 19



### of the respirator and, you know, a higher fit 20



### factor. 21



### But, you know, at the end of the day, there 2276

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### also has to be a practical application with regard 1



### to, you know, one, what the manufacturer needs to do 2



### in terms of developing a product that they can 3



### market. And, two, what we can do as a certification 4



### body to be able to process new applications 5



### effectively. 6



### So with that, I'll look at the panel. And 7



### if we're all in agreement, we'll move on to the next 8



### slide. Okay. 9



### And, again, this slide gets into the 10



### testing procedure criteria. And one of the things I 11



### think I would like to defer, until we talk to the 12



### test method on corn oil, I think I would like to 13



### defer that until we get to the test method part of 14



### the discussion. 15



### But I think in general, we wanted to hear 16



### and get some feeling from stakeholders with regard 17



### to the technology that could be used for this type 18



### of evaluation. 19



### And I'll go to Colleen next. 20



### MS. MILLER: I honestly have given this 21



### question quite considerabl e thought. 2277

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### And I also have reviewed the history in 1



### trying to respect the history of the project. 2



### I'm assuming, based on your comments, that 3



### there isn't anything new that has happened in terms 4



### of the pass/fail criteria and the analysis of 5



### this

-- of inwa rd leakage requirements for these 6



### respirators. Ziqing kind of, in his presentation, 7



### showed data for all of these, you know, 20, 50, and 8



### 100. 9



### Based on my experience with our study, I 10



### think that, just in my opinion, that 100 is going to 11



### be a challenge to a chieve repeatedly, and I'm 12



### offering to consider other criteria. 13



### MR. COFFEY: I would agree with that, that 14



### the hundred with the CNC would be very difficult to 15



### do on a routine basis. 16



### MS. MILLER: Anyone on LiveMeeting? 17



### Now, the fun's going to start. 18



### SPE AKER: I don't know why you think that. 19



### Craig Colton, 3M. I don't have a comment, 20



### but I have a question. 21



### When you say CNC technology, what 2278

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### specifically is NIOSH referring to? 1



### Is this CNC using like the Pro+ in the N95 2



### mode, or using CNC without like th e particle 3



### classifier? 4



### MS. MILLER: Well, we actually, Craig, 5



### changed the order of the slides. 6



### Originally, we were going to talk about the 7



### test method first. And then when we saw

-- you 8



### know, talked about Ziqing's presentation, we kind of 9



### switched it aro und. So we do want to ask that 10



### question actually and talk about that. 11



### I think that's still something that needs 12



### to be discussed. 13



### SPEAKER: Yeah. So what

-- on the side, 14



### when you see CNC, what does that mean? 15



### Because that's going to affect the number 16



### then that you get, depending on whether you got the 17



### filter penetration or not. 18



### MS. MILLER: So you could answer it in 19



### terms of when you do your formal comments, you could 20



### answer it based on with a companion or without. 21



### SPEAKER: Okay. Thanks. 2279

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### MS. MILLER: Sure. That wasn't much fun. 1



### MR. SZALAJDA: I don't know. I think I 2



### could have answered that one by just saying yes. 3



### SPEAKER: It does mean more comments. 4



### MR. SZALAJDA: All right. I understand. 5



### Anything from LiveMeeting on this subject 6



### or this slide? No? 7



### All right. We'll move forward. 8



### The next discussion area is the test 9



### panels. And there are several slides that we have 10



### prepared that gives a little bit of an overview, at 11



### least in terms of why we went and developed a new 12



### panel as well as some panel information. 13



### I believe Colleen was go ing to cover those. 14



### MS. MILLER: I think we

-- I'll just go 15



### through it again briefly because we did kind of 16



### cover it in our other presentations. 17



### As you all know, we do currently use the 18



### LANL panels for certification. They are based on 19



### military data from t he '60s and '70s. 20



### There were concerns at NIOSH a few 21



### decades

-- a couple of decades ago about the 2280

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### applicability of them to the civilian work force and 1



### changes in the demography of the subjects. 2



### So Ziqing, of course, completed or 3



### initiated and completed his survey in 2003. And he 4



### developed the new panels, the NIOSH Bivariate panel 5



### and the principal component analysis panel. 6



### He had a peer review protocol for that in 7



### 2002, collected the data in 2003. It was peer 8



### reviewed and published in 2007. 9



### I call it th e bible. I carry it 10



### everywhere. 11



### And then, of course, that was reviewed by 12



### the IOM. And they concluded that both panels were 13



### an improvement over the existing LANL panels. 14



### And then, you know, again briefly, we have 15



### this

-- the LANL panels being used curre ntly for 16



### certification. 17



### In 2009, we proposed using the 35

- member 18



### Bivariate panel. And then we would screen the 19



### subjects with the PCA panel that was in the STP 20



### drafted to accompany the rule. 21



### And ISO currently is going to be 2281

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### using

-- or their standards are going to use the 1



### 25

- member PCA panel with three subjects per cell and 2



### one at random. 3



### So given all of that, we would like to hear 4



### your comments, if you're willing to share any, 5



### indicating what the advantages of using the NIOSH 6



### Bivariate panel in assessi ng the facepiece

- to

- face 7



### seal fit are, or if you would care to provide any 8



### disadvantages that we may also need to consider. 9



### If you have any experiences you would like 10



### to share regarding, for example, small size 11



### respirators and using any of the panels, we w ould 12



### like to hear that. 13



### MR. WASSELL: Terry Wassell. 14



### I would like to just comment, having just 15



### sort of become familiar with the idea of panels, not 16



### ten years ago, but say, I don't know, six months 17



### ago, and approaching it as a statistician, it's 18



### really puz zling to me what really is in a 19



### statistical context. 20



### And the best analogy I could come up with 21



### was that it's very much like survey sampling where 2282

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### you're sampling from strata. 1



### And I mentioned in my presentation the idea 2



### of the design effect. And it's just a way of 3



### comparing stratified samples to simple random 4



### samples. 5



### So it gives you a benchmark for comparison 6



### and a way to compare these types of stratified 7



### samples. 8



### Each of the ten cells is a strata. 9



### Now, you can sample equally from each of 10



### those cells, or you can sample proportionately from 11



### each of those cells. 12



### And in survey sampling, the goal is that if 13



### you are taking a sample from strata, you are 14



### reducing some source of variation. And your final 15



### estimates are more precise, less variability. 16



### And I t hink that that model is a good way 17



### to deal with these different types of what choice of 18



### panel are you going to use and what is the effect of 19



### it. 20



### And we have also discussed the idea of 21



### collapsing some of the cells into what we have used 2283

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### the term clouds and a different type of panel with 1



### fewer strata. So I just offer that as an 2



### observation. 3



### And it has helped me kind of solidify my 4



### understanding of the whole purpose and reason for 5



### panels. 6



### I think, originally, the purpose was you 7



### would like to test a respira tor in a way that 8



### represents the working population. So, you 9



### know

-- and hearing that idea first really kind of 10



### threw me off course. But I think from a statistical 11



### perspective, the idea of survey strata is a useful 12



### approach. 13



### MR. SZALAJDA: Anything else from our 14



### panel? 15



### Okay. Any comments from the local 16



### audience? 17



### LiveMeeting? 18



### All right. We'll move to the next slide, 19



### which asks what are the advantages or disadvantages 20



### of using the following panel, which include the LANL 21



### full face, the LANL half facepiece, and the 2284

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### principal component analysis. 1



### And I think maybe one thing to

-- I wanted 2



### to mention here, and then maybe from the 3



### manufacturer's perspective, we would be interested 4



### in hearing your insight in terms of looking at when 5



### you do make comment s, whether it's today or in the 6



### future, is what is the impact on how you do business 7



### with regard to establishing and maintaining a panel. 8



### You know, if you produce more than 9



### filtering facepieces and you do fit testing before 10



### you submit the device for NIOSH to evaluate, you 11



### have your own panel. 12



### I know one major manufacturer has done a 13



### significant amount of fit testing in their facility, 14



### and they have a panel. 15



### If NIOSH were to adopt and maintain, you 16



### know, different panels for different types of 17



### respirators, is that going to impact your business? 18



### How will that impact what you do and 19



### whether or not it makes sense for NIOSH to 20



### holistically look at implementing one panel across 21



### the board for all respirator devices. 2285

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### Now, I'll defer to Colleen. 1



### MS. MILLER: Yeah. I think the point is 2



### that we're trying to engage our stakeholders, who 3



### are not only engaged in this rulemaking activity but 4



### who also, you know, are producing other classes of 5



### respirators and perhaps aren't as engaged in using 6



### the NIOSH Bivariate panel an d the PCA panel. 7



### We want to hear from you as a program. 8



### MR. BERRY ANN: This is Roland. 9



### I would modify what Colleen said and say 10



### not only, you know, if you are engaging in using the 11



### new panels instead of the LANL panel, we would like 12



### to hear about that an d your experiences as well. 13



### And if we do it across the board, how that 14



### would impact your business. 15



### SPEAKER: Larry Green, Syntech, 16



### International. 17



### We manufacture PAPR respirators, so we 18



### don't

-- you know, we just use the LANL panel. 19



### MS. MILLER: Full fa cepiece or half

- mask 20



### facepiece? 21



### SPEAKER: Loose fitting, loose fitting PAPR 2286

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### respirators. 1



### MS. MILLER: All right. 2



### SPEAKER: Yeah. And we're a small company, 3



### but, you know, we always pull in people to form our 4



### panels. We use part of the people from inside 5



### and

-- but because of the different sizes, it 6



### becomes difficult for, you know, some of those 7



### sizes. 8



### Especially No. 1s become real difficult. 9



### I wouldn't expect that to change too much 10



### if we moved over to a PCA panel, you know, just have 11



### to search out diff erent people. 12



### Of course, that searching out the people is 13



### a bit, you know, a major effort. So it's

-- you 14



### know, we just try to do our best on it. 15



### But I think there's a lot of things in the 16



### panels that don't come out, and we get different 17



### types of faces. 18



### In California, we have a lot of what we 19



### classify as Asian faces, which are rounder faces 20



### versus a European face. As we look at 21



### different

-- those different things, those types of 2287

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### features have a significant impact on how things 1



### fit. 2



### It doesn't really show up in, you know, if 3



### we look at things on a pass/fail basis, we look at 4



### things in terms of raw numbers. And we're looking 5



### at the 99.99 percent deficiency rates and higher fit 6



### factors of 50,000, 100,000. 7



### So but even at that level, you know, we can 8



### see differences in how all those facial features 9



### affect the fit just in the drift of the numbers for, 10



### again, different types of faces. 11



### It's a lot different world than N95s. But 12



### things that are outside of what are shown in the 13



### panels definitely seem to affect the testing. 14



### MS. MILLER: Any comments from LiveMeeting? 15



### MR. SZALAJDA: All right. The next slide 16



### is, what panel or panels are c urrently used to 17



### develop new respirator models, including to modify 18



### existing products. 19



### You know, there are several different 20



### panels that are out there. 21



### Dr. Zhuang's research, which is documented 2288

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### with the PCA panel, and the Bivariate panel, and 1



### considerat ions in the international community, and 2



### then historically what's been done with the LANL 3



### panel, are there any comments from the panel with 4



### regard to answering this question? 5



### MS. MILLER: I think Larry kind of answered 6



### that question because he was more or less saying 7



### it's not a pass/fail criteria, more of a perhaps a 8



### product development or quality assurance test. 9



### SPEAKER: Yeah. 10



### MS. MILLER: Anyone else like to provide 11



### any information about how you do things? 12



### Okay. Would we like to go on to defined 13



### user groups, then? 14



### MR. SZALAJDA: Yes. How about anything 15



### from LiveMeeting? 16



### Okay. But I think as Colleen gets into the 17



### extension of using the panel to define user groups, 18



### do we have any comments on that particular question? 19



### MS. MILLER: I think overall, we would like 20



### to get a better sense of the significance of this 21



### issue, and whether or not there are any new thoughts 2289

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### about defined user groups since, you know, we last 1



### met in 2010. 2



### I think it is a challenge in terms of a 3



### certification requirement to set a pass/fail 4



### criteria that could anticipate and address every 5



### potential market that you might be considering. 6



### MR. SZALAJDA: With regard to this topic 7



### and looking at the

-- using the descriptions of the 8



### panel, are there any things I think, at least, in 9



### terms of what you're thinking in terms of providing 10



### comments? 11



### I think one of the things that we would be 12



### curious to hear more about are how you would apply 13



### or potentially apply the use of the panels to niche 14



### respirators. 15



### One of the things that we have heard a nd 16



### public comment was made with regard to that topic 17



### was, you know, that we need to have

-- be able to 18



### identify that criteria for how we would evaluate 19



### these types of products. 20



### But I could refer to Roland and ask him how 21



### many niche respirators NIOSH has a pproved in the 2290

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### last 20 years. And I think the answer is probably 1



### zero. 2



### And Roland shook his head yes. 3



### So I think one of the things that would be 4



### important for us to consider as part of the drafting 5



### the rule that if there is criteria, you know, for 6



### particu lar user group that we should consider and be 7



### able to address this part of the certification 8



### program, it would be important for us to hear that 9



### in the near term. 10



### MR. BERRY ANN: Yeah, this is Roland. 11



### I want to jump ahead and ask a question 12



### before we got to o far from Larry's comments where he 13



### was talking about difficulty in finding particular 14



### size people to fill the panel. 15



### And I was wondering if others are having 16



### problems filling the panel sizes. 17



### Do you have any thoughts about workarounds 18



### that you would like to mention or put forward that, 19



### you know, might be able to work around not being 20



### able to locate those ones that maybe they're named 21



### after their population base rather than their size. 2291

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### MR. SZALAJDA: Anything within the room 1



### that people would like to comme nt on that? 2



### SPEAKER: This is Larry Green, again. 3



### Larry Green, Syntech, International. 4



### You know, for some of the small sizes, we 5



### cheat, and we enlist younger people because in 6



### Southern California. That's all we have got. 7



### There's an Hispanic population. There's 8



### essentially nobody over the age of 12 that fits in 9



### anything under a size 4. 10



### And so, you know, it's

-- again, it's very, 11



### very difficult. I think sometimes you get into a 12



### lot of the ethic populations, that's where you could 13



### have other problems. 14



### And there's a lot of Asian populations with 15



### very distinct facial characteristics, and we get 16



### that in California. 17



### Making a loose fitting PAPR is not quite as 18



### bad for us as it is for a facepiece, where it has to 19



### fit on correctly onto those faces. 20



### But I thin k that, you know, that's 21



### something where when you're given the standard that 2292

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### you have to add that flexibility in there for those 1



### people, otherwise, you're not going to get 2



### respirators that fit those people. 3



### As were some of the hospitals that we sell 4



### to. They call them PAPRs for using it in hospitals 5



### because they have got Asian nurses and things like 6



### that and they just

-- they can't fit them on a 7



### standard respirator. 8



### So it's, you know, there's

-- all those 9



### flexibilities have to be built into it. 10



### Probably my thought is one of the most 11



### important issues is the type of instructions that 12



### you give to the end users in how these things fit. 13



### If you could find something here in the 14



### niche market, you just find something is

-- this is 15



### going to fit an Asian type face, a flat face, a long 16



### narrow European middle eastern type face, those 17



### things can be very, very helpful for the end users. 18



### Yet, it's

-- a big part of it is ends up 19



### falling onto having good user instructions, things 20



### like that. And with our loose fittings, w e have 21



### multiple sizes that we have tried to push the fit 2293

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### factors up, additional levels of protection on it, 1



### things like that, where you have

-- kind of have a 2



### hybrid where you find both some of the aspects of 3



### the tight fitting and some of the aspects of th e 4



### loose fitting to give kind of layers of protection. 5



### That's where we end up, you know, getting 6



### into the different facial configurations that affect 7



### how it fits. So we'll have a product that we put on 8



### somebody with a size ten or larger, very large 9



### faces, and trying to put it on somebody who is a 10



### size one and it doesn't do anything. 11



### And then you get on the other extreme, you 12



### get something that fits size 1 or 2, and you get to 13



### the larger sizes, it doesn't fit at all. 14



### I think those are the things that you ha ve 15



### to take into account as a lot of instruction as 16



### you're trying to get people to be successful in 17



### fitting their respirators quickly versus going 18



### through and trying 20 different types of respirator, 19



### which one fits, and that becomes a very difficult 20



### thing f or the end users. 21



### We have to try to keep the

-- what the end 2294

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### user has to do to know if it's going to fit properly 1



### down to just a couple of

-- 2



### MS. MILLER: Somebody on LiveMeeting needs 3



### to mute. All they can hear is you typing and 4



### breathing. 5



### MR. KATZ: He llo, this is Ted. 6



### As long as we're interrupting on that 7



### issue. He needs to be given a mic. It's completely 8



### unintelligible for everybody on the phone. I mean, 9



### it's totally a lost cause. 10



### So can something be done about the audio 11



### here? 12



### MR. SZALAJDA: Well, I think all we can do, 13



### Ted, is just encourage people to talk as loudly as 14



### they can into the microphone. 15



### Any other local comments on this subject? 16



### All right. We're going to move into the 17



### test method area. 18



### And in particular, talking about the 19



### condensatio n nuclei counter and the technology used 20



### for measuring fit. 21



### Colleen. 2295

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### MS. MILLER: Well, as we alluded to with 1



### Craig's comments earlier, we would like to ask you 2



### to consider commenting on the use of the CNC with or 3



### without the differential mobility analyzer , 4



### depending on the product being certified. 5



### If anyone has any comments they would like 6



### to share at this point, that would be lovely. If 7



### not, we look forward to seeing your comments in 8



### writing. 9



### And perhaps if anyone has any suggestions 10



### or comments about o ther detection methods that we 11



### should be considering, we would like to hear from 12



### you about that. 13



### And as I said, we would like to know how 14



### you feel about the same procedure for filtering 15



### facepieces and elastomerics. 16



### As you will see when we go on into the 17



### sl ides, we're going to talk about a couple of 18



### methods. 19



### Would anyone have any comments at this 20



### point? 21



### MR. SZALAJDA: Okay. Again, these are 2296

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### issues we would like you to be able to address in 1



### your written comments as we move forward. 2



### Anything from LiveMeeting? 3



### All right. One of the other aspects that 4



### we are considering is the possibility of being able 5



### to use corn oil testing as part of the evaluation of 6



### filtering facepiece respirators. 7



### And, again, it's a topic where we're 8



### looking for feedback fr om stakeholders for a variety 9



### of reasons. 10



### And I think the areas where we're looking 11



### for your feedback and your comments fall into these 12



### categories whether or not because is it going to be 13



### acceptable to test for a not

- for

- oil respirator in 14



### an oil environm ent, for one thing. And also, any 15



### issues associated with media degradation

-- or media 16



### degradation and test duration. 17



### Any comments on the corn oil perspective? 18



### MR. COFFEY: Chris Coffey. 19



### We didn't really talk about the corn oil 20



### test in any of our webina rs, but we did do 21



### approximately 80 subjects on the respirators in a 2297

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### corn oil chamber up here, just kind of like to do a 1



### little bit of background on that. 2



### The reason we did was we would be 3



### interested in seeing the difference

-- if there's 4



### any difference in the generated aerosol method with 5



### a fairly stable controlled concentration versus 6



### ambient, which could vary. 7



### And in Pittsburgh at the time the study was 8



### done, we only had the corn oil chamber. There was 9



### no sodium chloride chamber available to us. 10



### We do have that data on, like I say, on 80 11



### subjects. We're going to be looking at that data. 12



### But a preliminary review of the data

-- well, let me 13



### step back. We did do a filter penetration with 14



### sodium chloride, the certification test basically, 15



### before the sub jects went into the corn oil chamber 16



### and then right after they got done with that. 17



### And it appears from the preliminary, 18



### cursory, or looking at the data that we did not see 19



### a significant degradation in filter efficiency by 20



### doing two of the OSHA fit tests fo r about a total of 21



### 15, 20 minutes. 2298

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### So we were just interested in hearing your 1



### feedback on the use of the corn oil test for testing 2



### in N95s. 3



### MS. MILLER: Actually, too, I'll remind you 4



### that your ISEA comments on the last docket did 5



### recommend a size range i n the LRPL level, which we 6



### are currently using for our testing here. 7



### So does anyone care to discuss whether or 8



### not corn oil is considered to be an acceptable 9



### method or other concerns you might have about using 10



### that method? 11



### MR. SZALAJDA: Okay. If not, I guess than 12



### that's something we'll look for you to provide 13



### comments on before the end of the comment period. 14



### Anything from LiveMeeting on corn oil 15



### topic? 16



### No? 17



### All right. And then, the other questions 18



### related to should NIOSH allow the option of multiple 19



### inward leakage test methods. 20



### Comments on multiple methods? 21



### Anything from the panel? 2299

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### MS. MILLER: This is Colleen. 1



### I would just say that, you know, we 2



### understand your concerns about providing a detailed 3



### standard test procedure when we do publish a notice 4



### o f proposed rule making. 5



### We welcome this opportunity for you to 6



### share and discuss potential test methods and the 7



### procedures required in order for us to provide that 8



### detailed standard test procedure. 9



### Please feel free to provide those comments 10



### as well. 11



### MR. SZALAJDA: Anything from the audience 12



### on this topic? 13



### One from Terry, Terry Wassell. 14



### MR. WASSELL: Yeah. Terry Wassell. 15



### This is sort of a question, actually. 16



### Would multiple test methods be in parallel 17



### or in series? 18



### And it is possible to

-- is there a 19



### hypothetical example of

-- because I'm a little 20



### unclear, and it might be helpful to have some 21



### example. 22100

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### MR. COFFEY: I think, my personal opinion 1



### is, the question is that can we have multiple test 2



### methods available. 3



### So in saying CNC with ambien t, CNC with 4



### corn oil, or whatever, and the manufacturers and 5



### NIOSH would be able to choose whichever one was the 6



### most convenient to use at the time. 7



### We're not saying we're going to have a 8



### subject run ambient air solo and then run a corn oil 9



### like that. 10



### So it's going to be a pool of test methods 11



### that could be used to run inward leakage tests. 12



### Just like ISO has proposed in their standard, you 13



### can use corn oil, you can use sodium chloride, you 14



### can use sulfur hexafluoride. 15



### So that's what we will be questioning

. 16



### MR. SZALAJDA: Comments on this topic? 17



### Anything from LiveMeeting? 18



### Okay. And then the last area where we're 19



### looking for feedback with related to implementation 20



### strategies, in particular what strategies do you 21



### suggest to address the implementation of a standard 22101

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### for testing face seal, face leakage as part of a 1



### half

- mask air

- purifying respirator. 2



### And looking at it from a certification 3



### perspective. 4



### MS. MILLER: Chris, did you have a data 5



### sharing update? 6



### MR. COFFEY: Yes. We are working on the 7



### data shari ng requests, so that we would be able to 8



### share our inter

- panel variability study data with 9



### anyone who wants it. 10



### It goes through a number of reviews before 11



### it's approved and we can actually announce it. And 12



### we're hoping that the final approval will be comi ng, 13



### you know, in the next week or ten days or so, so 14



### that people can request our data and do their own 15



### analysis to help answer some of the questions that 16



### we're asking here. 17



### As soon as that is available, I guess, 18



### we'll put an announcement on our website and get it 19



### out to the public somehow, the data now available. 20



### MR. SZALAJDA: Yeah. The question came up 21



### after one of the webinars. 22102

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### I don't recall whether it was the July or 1



### the August one, whether or not the data that was 2



### used for the inter

- panel variabilit y study would be 3



### made available to the public. 4



### And that's what the

-- what Chris' comment 5



### on that we're working towards that end. 6



### Any comments from the panel on 7



### implementation strategies? 8



### MS. MILLER: Would anyone like to give us 9



### any indication as to wh ether or not you feel we're 10



### on the right track in terms of dealing with the 11



### issue of inter

- panel variability? 12



### Oh, it's temping. 13



### MR. SZALAJDA: Anything from LiveMeeting on 14



### this topic? 15



### Okay. Well, with that, you guys are lucky. 16



### We have run out of slide s. 17



### MS. MILLER: Well, we're not quite

-- 18



### MR. SZALAJDA: Oh, there's more things? 19



### SPEAKER: So you can hear me. 20



### This is Phil Eitzman with 3M. 21



### Just the reason that I would have the 22103

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### question about the simulated study, I figured out, 1



### was that only the title page is in the docket for 2



### that report. 3



### So I know what the title of that simulation 4



### study is, but there's

-- the content of that report, 5



### the RTI, is not

-- I couldn't see it on the docket. 6



### I could only see the first page. 7



### MS. MILLER: I think that happened when I 8



### sent it to Chris as well. 9



### So we'll check on that. 10



### Thank you. 11



### SPEAKER: You're welcome. 12



### MR. SZALAJDA: Thank you for bringing that 13



### to our attention. 14



### Are there any closing co mments from the 15



### panelists? 16



### MS. MILLER: One thing that Roland and I 17



### wanted to be sure to ask about was whether or not 18



### anyone had any comments about the timing of our 19



### implementation or a grandfathering strategy? 20



### You have had a few years to think about it. 21



### MR. SZALAJDA: And, again, I think that's 22104

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### something that we would encourage you to submit as 1



### part of your comments to the docket for us to 2



### consider in moving forward. 3



### MS. MILLER: And anything that we haven't 4



### considered that you feel that we should consid er. 5



### MR. SZALAJDA: All right. 6



### So at least in terms of

-- I'm sorry. 7



### MR. BERRY ANN: I'm sorry, Jon. 8



### This is Roland. 9



### In considering your comments about the 10



### length of the grandfathering, any conditions or 11



### specifications that you think would be appropriate 12



### as well, I think we would like to see. 13



### MR. SZALAJDA: Any comments from the 14



### LiveMeeting audience? 15



### I think, in general at least, to try to 16



### summarize moving forward from an organization 17



### perspective that this project is very important not 18



### only to NPPTL but al so NIOSH, CDC, and HHS. 19



### And we are very motivated seeing this rule 20



### come forward and be able to provide a performance 21



### requirement to help protect the users of these types 22105

INABNET REPORTING SERVICES (703) 331

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### of respirators. 1



### And I would, again, with the questions that 2



### were raised in the Federal Register as well as the 3



### discussion that was provided to today, these are 4



### issues that we are really looking for you to provide 5



### your feedback to us, whether

-- you know, and 6



### preferably through the docket so it becomes part of 7



### the record since we are trying to be as transparent 8



### as possible with regard to developing this 9



### performance requirement and implementing it through 10



### our certification practices. 11



### The information from the webinars that have 12



### been conducted, as well as the public meeting, and 13



### hopefully the re st of the RTI report will be 14



### available in Docket 250. 15



### The comment period is through October 1. 16



### I do not anticipate that we will extend the 17



### comment period since we are not technically in 18



### rulemaking; we're selecting comments. 19



### But, however, please be aware that we are 20



### on a path to get this moving forward again, and 21



### getting something out into the public domain within 22106

INABNET REPORTING SERVICES (703) 331

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### the next several months for moving these through the 1



### formal process. 2



### At least in terms of wrapping up the 3



### meeting here today, since you are in the facility, 4



### the shuttles will be available to take you back to 5



### the METL parking lot where you left your cars. All 6



### visitors have to get escorted. 7



### You technically need to be escorted while 8



### you're on site. So, you know, we'll make sure you 9



### get back down to the gate. 10



### Also, there's a box in the rear of the 11



### room, if you would like to recycle your name tag for 12



### future NPPTL meetings, that would be appreciated. 13



### So with that, I appreciate your attendance 14



### and we look forward to receivi ng your comments with 15



### regard to this very important rule. 16



### Thank you. 17107

INABNET REPORTING SERVICES (703) 331

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### CERTIFICATE OF REPORTER 1



### I, Joseph A. Inabnet, do hereby certify 2



### that the transcript of the foregoing proceedings was 3



### taken by me in Stenotype and thereafter reduced to 4



### typewriting under my supervision; that said 5



### transcript is a true record of the proceedings; that 6



### I am neither counsel for, related to, nor employed 7



### by any of the parties to the action in which these 8



### proceedings were taken; and further, that I am not a 9



### relative or employee o f any attorney or counsel 10



### employed by the parties thereto, nor financially or 11



### otherwise interested in the outcome of the action. 12

13

14



### __________________________ 15 

### Joseph A. Inabnet 16 

### Court Reporter 17 18