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1 NATIONAL INSTITUTE FOR 

2 OCCUPATIONAL SAFETY AND HEALTH

3 PERSONAL PROTECTIVE TECHNOLOGY PROGRAM 

4 PUBLIC MEETING

5 Development of Inward Leakage Standards for 

6 Half-Mask Air-Purifying Particulate Respirators

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8

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10

11 September 17, 2013

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18

19

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

21 Cochrans Mill Road, Pittsburgh, Pennsylvania. 

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1 MS. D'ALESSANDRO: Good afternoon, 

2 everyone. And welcome to the National Personal 

3 Protective Technology Laboratory public meeting on 

4 development of inward leakage standards for 

5 half-mask air-purifying particulate respirators.

6 The information for this meeting can be 

7 found in Docket No. 250.

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

9 the director of NPPTL.

10 The mission of NPPTL is to conduct the 

11 research and surveillance that leads to the 

12 standards and certification of personal protective 

13 equipment and conduct the outreach necessary to keep 

14 workers safe. 

15 Today's meeting will start with Jon 

16 Szalajda providing opening remarks. And Jon is the 

17 technology research branch chief, the acting 

18 technology research branch chief, who has led the 

19 development of these inward leakage requirements.

20 I will now turn it over to Jon to provide 

21 logistics information for you and the summary of 

22 where we will be going today.
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1 MR. SZALAJDA: Thank you, Maryann. 

2 And I hope everyone can hear me. And I 

3 think we're connected on LiveMeeting as well. 

4 As John had mentioned earlier in terms of 

5 conducting -- for the LiveMeeting audience in terms 

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

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

8 presentations. We'll take a break. 

9 And then we'll have hopefully a dialogue 

10 between stakeholders as well as the NIOSH panel 

11 representatives on different topics that were 

12 identified in the Federal Register notice that was 

13 published within the past few months. 

14 As far as the logistics for the 

15 housekeeping for the meeting site, the restrooms are 

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

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

18 next corridor down. 

19 For emergency exits if the fire alarm were 

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

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

22 well as the exit in the lobby. 
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1 And the meeting area for logistics purposes 

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

3 area which is known as the little parking lot.

4 Cell phones, if you have cell phones, if 

5 you could please either vibrate or mute them as a 

6 courtesy to the speaker and to the dialogue, we 

7 would appreciate that. 

8 And also, for the comment period, there is 

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

10 at the appropriate times when we want to take 

11 questions or get comments from the audience, if you 

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

13 your organization that you're representing clearly, 

14 and then also your comment. 

15 The meeting is being transcribed and will 

16 become part of our docket proceedings.

17 And, again, as Maryann had mentioned, the 

18 docket number is 250. And the presentations that 

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

20 transcript will be available in the docket.

21 In terms of how we're going to proceed 

22 through the meeting, we have three presentations 
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1 that different NIOSH researchers will be providing 

2 here within the next few minutes.

3 Dr. Coffey, who is our associate director 

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

5 has been detailed to NPPTL as supporting the 

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

7 respiratory protection research team leader.

8 I'm currently the acting branch chief for 

9 technology research. I think if you're familiar 

10 with our organization structure, in the past several 

11 years, I have worked in policy and standards. 

12 But while we go through our reorganization 

13 process, I'm responsible for the research branch. 

14 But I will continue to support the 

15 development of the proposed rule and the final rule 

16 for the inward leakage for filtering facepieces and 

17 particulate respirators until this project is 

18 complete.

19 Again, this is the -- the purpose of the 

20 meeting that was identified in the Federal Register 

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

22 looking to do is to get feedback from the 
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1 stakeholders prior to going into introducing a new 

2 proposal for what the performance requirements 

3 should be for inward leakage for filtering 

4 facepieces and half-mask.

5 And I wanted to at least talk a little bit 

6 about the history behind why we are here and where 

7 we're going. 

8 And one of the things that I have learned 

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

10 get into rule making and the finalization and 

11 establishing -- establishing rules, there's always 

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

13 when the rule is finalized. 

14 And for this particular topic, in 1995, 

15 there was not an inward leakage requirement 

16 established for filtering facepieces and particulate 

17 respirators, and that was primarily technology 

18 driven. And I believe the head of the respirator 

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

20 we'll have this done. 

21 Well, now here we are a few years later, 

22 but I do think that the time has come for being able 
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1 to establish the rule.

2 And, again, part of that was technology 

3 driven with regard to identifying how could a 

4 performance requirement be identified and applied in 

5 a repeatable fashion as part of the certification 

6 program for this type of respirator. 

7 So there was a significant amount of 

8 research that went into establishing the initial 

9 NPRM that was published in 2009. 

10 I think most people who have been involved 

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

12 anthropometric research in looking at trying to 

13 update the panel that was established for 

14 determining fit of the respirators. And other 

15 research was done in support of the proposed rule 

16 that was published in 2009. 

17 As far as the background, additional 

18 background, I think people who have been more 

19 involved in the field know that the existing 

20 certification policy and the use of panels is based 

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

22 The Air Force had conducted a study, and I 
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1 think many people are familiar with the LANL panel 

2 that was developed. And this currently was 

3 established by NIOSH as part of the certification 

4 testing program for respirators.

5 Also, along with the establishment of 42 

6 CFR, for every other class of respirators other than 

7 filtering facepieces and half-mask particulate 

8 respirators, there is a fit test requirement that is 

9 established as part of respiratory protection.

10 But for these types of respirators, when 

11 they were introduced in 1995, the focus was solely 

12 on testing filter penetration and resistance. There 

13 was no performance measurement of leakage through 

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

15 respirators, like exhalation valve leakage. 

16 And I think one of the things that we are 

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

18 performance of these types of respirators and treat 

19 filtering facepieces and half-masks the same as we 

20 treat any other type of respirator that we certify 

21 for the work force. 

22 So where we were in 2009 is that we had 
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1 proposed a regulation to establish a performance 

2 requirement for filtering facepieces and half-mask. 

3 And while the testing wasn't considered 

4 that this was going to be a substitute for fit 

5 testing or respiratory protection or respiratory 

6 program requirements in the field or training 

7 necessary to how to properly use the respirators, 

8 NIOSH felt that it was important to establish a 

9 minimum level, a minimum performance requirement 

10 associated with how well these types of respirators 

11 should fit to the individual in order to be able to 

12 increase the probably that people could get a fit 

13 that met their requirements in the field.

14 Also, with the information that was 

15 provided in the NPRM, we also introduced bringing 

16 forward the panels that were developed by Dr. Zhuang 

17 over the past decade incorporating the Principal 

18 Component Analysis as a screening tool and then 

19 introducing the NIOSH Bivariate panel as a means of 

20 identifying appropriate sizes for individuals as 

21 part of the fit test. 

22 And I don't want to go into a lot of the 
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1 details associated with the rule at the time because 

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

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

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

5 comments that we received from stakeholders with 

6 regard to the NPRM as it was initially crafted in 

7 2009.

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

9 the comments were, they are available in 

10 dockets -- NIOSH Dockets 36 and 137 that include the 

11 history behind the development of that one proposal. 

12 And a lot of the focus of what you're going 

13 to hear about today is the discussion of intra- and 

14 inter-panel variability. 

15 And over the past couple of months, 

16 Dr. Coffey has led some scientific webinars that 

17 have address some of the research that was conducted 

18 here at NPPTL over the last year and a half to try 

19 to address that question that was identified by our 

20 stakeholders as part of the initial rulemaking 

21 activity.

22 Along with the discussion of variability, 
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1 some of the things that we want to capture later in 

2 the day when we have our panel discussion are some 

3 of the other comments that we received with regard 

4 to the test method, panel size, and implementation 

5 type issues, things that were identified in our 

6 notification of this meeting that we wanted to get 

7 your feedback on, at least in terms of ideas that we 

8 should consider in moving forward.

9 One thing that we aren't going to discuss 

10 today but will be addressed as part of our plan 

11 moving forward is the economic impact.

12 We did receive some significant comments 

13 from stakeholders with regard to the potential 

14 impact of the rule.

15 And while we disagree philosophically with 

16 the magnitude of the comment or the magnitude of the 

17 potential costs that were identified as part of the 

18 public comments that were received, that we are 

19 moving forward and have contracted with ERG to help 

20 assist us in development of an economic analysis to 

21 determine what the impact of this rule will be on 

22 the marketplace moving forward.
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1 So and to kind of tie up where we're going, 

2 formally, the one point that we wanted to make to 

3 our stakeholders is that the proposal that was 

4 identified in 2009 is not going to be finalized. 

5 That at this point, we are going to move 

6 forward with development of a new notice of proposed 

7 rule. 

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

9 today is to give stakeholders the opportunity to 

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

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

12 either response to Dockets 36 or 137.

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

14 continue to share the results of what we did with 

15 the inter-panel variability study. But at this 

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

17 any new knowledge that our stakeholders may have 

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

19 the makeup of the panel, the test method, the 

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

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

22 should we consider with regard to the implementation 
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1 in moving forward with the proposed rule.

2 And with that, what I would like to do is 

3 introduce Dr. Chris Coffey, who is our Associate 

4 Director for Science at NPPTL. 

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

6 terms of moving forward for the presentations, there 

7 will be breaks for questions at the end of 

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

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

10 presentation.

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

12 we go forward. 

13 And then, after the presentations have been 

14 delivered, we'll take a ten, 15-minute break. And 

15 then we'll reconvene the panel session to address 

16 the questions that were provided in the Federal 

17 Register notice. 

18 When Dr. Coffey was our principal 

19 investigator for the inter-panel variability study 

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

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

22 like to say, with regard to how the testing was 
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1 conducted here in Pittsburgh. 

2 So with that, Chris.

3 MR. COFFEY: Thanks, Jon.

4 I would just like to give a brief synopsis 

5 of the methodology that we used in collecting the 

6 data for our inter-panel variability study. 

7 And first, just give you a definition of 

8 what we consider to be inter-panel variability is 

9 just basically the degree of agreement among various 

10 panels. 

11 And we are concerned about what we are 

12 calling grid variability, which is the assembly of 

13 one panel to another in various time frames, and 

14 then the same panel even running in between 

15 different laboratories. 

16 So as Jon mentioned, we had a number of 

17 public comments on inter-panel variability. That 

18 boils down to two basic ones. 

19 It causes a wide range of results among the 

20 various panels. And to decrease the inter-panel 

21 variability to an acceptable level, it was suggested 

22 that we increase the number of panel members up to 
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1 105. 

2 So we contracted with RTI to look at this 

3 issue of panel size. 

4 So our study of objectives basically were 

5 to determine the variability between different 

6 anthropometric panels, to experimentally confirm 

7 what RTI came up with from a mathematical 

8 standpoint, a theoretical standpoint. 

9 And then also, in the process for our 

10 certification program, investigate the ease of 

11 implementing the panels that were proposed, and we 

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

13 statistical analysis. 

14 So we used ten different N95 respirators 

15 from seven different manufacturers. They were 

16 evenly distributed between five filtering 

17 facepieces, which consisted of one size fits all, 

18 and two size respirator models and five elastomeric 

19 N95 respirators, each one having three sizes.

20 We measured the inward leakage using the 

21 PortaCount Pro+ 8038. We ensured that an ambient 

22 concentration was within the PortaCount range shown 
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1 here. 

2 Actually, ours was not anywhere near those 

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

4 third particles per cubic centimeter. 

5 Each respirator on each subject performed 

6 two replicates. We had them break the seal 

7 completely in between each replicate. And our 

8 panels consisted of both experienced people, who 

9 performed respirator testing up here in Pittsburgh,

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

11 the panel.

12 So the exercises we used were the normal 

13 eight exercises found in the OSHA fit test 

14 protocols. You're all familiar with those.

15 And we started out looking at an evenly 

16 distributed panel consisting of 40 subjects with, of 

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

18 So in order to gather enough data to do our 

19 analysis, our goal was to recruit 120 subjects, 

20 which we did eventually.

21 And for each respirator, we arbitrarily, 

22 just arbitrarily, assigned the following criteria: 
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1 For one size fits all, of course, everybody in cells 

2 1 through 10 got that size.

3 For the two size respirators, the filtering 

4 facepieces, people in cells 1 through 5 got the 

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

6 were issued the larger of the two sizes. 

7 And then for the three sizes, cells 1 

8 through 3 received the medium. People in cells 4 

9 through 7, the -- the small, 1 through 3, were the 

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

11 then of course the large for people in cells 8 

12 through 10. 

13 So we had some recruitment issues. We had 

14 difficulty in finding four people per cell, 

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

16 made the decision about halfway through the study to 

17 move it to Morgantown since we had a -- possibly a 

18 better subject pool down there to draw from. 

19 We also found some issues with the physical 

20 limits of each cell size. So we went about 

21 recruiting enough members to fill out three 

22 35-member population based panels, which was the 
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1 proposal in the 2009 notice of proposed rulemaking.

2 So then, the multiple panel distributions 

3 were analyzed, both were evenly distributed and 

4 population based panels. 

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

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

7 analysis that he conducted on our data. 

8 MR. WASSELL: Thank you.

9 Good afternoon. One thing you should not 

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

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

12 I'm Terry Wassell, research statistician. 

13 And I want to describe and present some of 

14 the preliminary results for the comparison of six 

15 different types of panels and to characterize the 

16 relative variability for these six different types 

17 of panels.

18 Okay. The methodology used here is 

19 nonparametric bootstrap sampling. 

20 In contrast to the earlier work that relied 

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

22 laboratory data collected on human test subjects, as 
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1 Chris has described.

2 This is an important distinction that 

3 separates these results from the previous work.

4 Nonparametric bootstrap is a data-based 

5 resampling, computer intensive procedure. This 

6 method was used to generate 500,000 bootstrap 

7 samples, each representing a panel.

8 I want to mention that there's an 

9 assumption of independence here so that the 

10 replicates on the same human test subject are 

11 considered independent. 

12 In this situation of bootstrapping 

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

14 bootstrap sampling -- bootstrap is sampling with 

15 replacement, and that is has been shown to be 

16 appropriate.

17 Sampling with replacement means the same 

18 observation could be included in the bootstrap 

19 sample more than once. Sampling with replacement is 

20 recommended as samples with two replicate 

21 measurements from the same test subject as possible.

22 Chris, Dr. Coffey, has described the data 
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1 collection procedures. The data set I worked with 

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

3 subjects with two replicate measurements each for 

4 ten respirator models. 

5 From each of these bootstrap samples, I 

6 calculate the mean of the data values, which gives 

7 me 500,000 mean values. 

8 Next, I measure the variability in terms of 

9 the coefficient of variation of these 500,000 mean 

10 values. 

11 The coefficient of variation is defined as 

12 the sample standard deviation divided by the mean. 

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

14 types of respirators included in the study. 

15 We used a second similar procedure to 

16 obtain samples for the 35-member population based 

17 panel, also the 30-member population based panel, 

18 and for the 25-member population based panel.

19 Also, we used the similar procedure to 

20 generate bootstrap samples for the 40-member equal 

21 distribution panel, the 30-member equal distribution 

22 panel, and the 20-member equal distribution panel.
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1 These are the six panels investigated here.

2 Each panel's information was collapsed from 

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

4 an aside, I also used -- tried using geometric mean, 

5 but the results were identical to using the usual 

6 sample mean. 

7 Just a word about the coefficient of 

8 variation as a statistic. 

9 Intuitively, you can think of it as the 

10 standard deviation adjusted for or standardized to 

11 the sample mean. 

12 In this respect, the coefficient of 

13 variation is better than just comparing standard 

14 deviation estimates. The coefficient of variation 

15 has no units, so its main advantage is for 

16 comparison to other values of the coefficient of 

17 variation.

18 Bootstrapping is a methodology. It has 

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

20 older related and similar techniques have been 

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

22 a very straightforward introduction by Efron and 
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1 Tibshirani.

2 Okay. Moving right to the results. 

3 Here are the results for comparing equal 

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

5 N=20.

6 For Model A, which is an FFR type, the 

7 40-member panel coefficient of variation estimate is 

8 0.241. For the 30-member panel, the coefficient of 

9 variation is 0.278. That number represents a 15.4 

10 percent increase.

11 For the 20-member panel, the coefficient of 

12 variation is 0.341, which represents a 41.3 percent 

13 increase as compared to the 40-member panel. 

14 You can quickly see that within each row or 

15 within each model of respirator investigated, that 

16 the percentage increase in the coefficient of 

17 variation are generally similar. In other words, 

18 those red numbers are fairly close.

19 Just a word of caution. The study is not 

20 intended to compare respirator models. For example, 

21 because Model E has a smaller coefficient of 

22 variation than Model F, it's not a valid comparison 
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1 of respirator performance.

2 Recall, the objective of this study is to 

3 determine variability among panels using the largest 

4 N=40, or four cells -- or four cells of four each as 

5 the reference.

6 We find that using a smaller sample of size 

7 N=30 results in 15.3 to 15.7 greater coefficent of 

8 variation for the equal distribution panels.

9 Likewise, the 20-member panel has 41.2 to 

10 42 percent greater variability than a 40-member 

11 panel. 

12 Okay. This table represents the results 

13 for the population based panels. The top row shows 

14 the results for Model A. 

15 For the 35-member panel, the coefficient of 

16 variation is 0.251. And for the 30-member panel, 

17 30-member population based panel, the coefficient of 

18 variation increases to 0.271, which is 7.8 percent 

19 increase. 

20 The 25-member panel coefficient of 

21 variation is 0.297, which is a 17.8 percent increase 

22 from the 35-member panel.
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1 For the population based panels, using the 

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

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

4 results in between 6.7 percent to 10.7 percent 

5 greater coefficient of variation.

6 And the 25-member panel has between 15.3 

7 percent to 21.6 greater variability as measured by 

8 the coefficent of variation that is relative to the 

9 35-member panel. 

10 When the sample sizes are the same, here 

11 when we are considering the 30-member panels, it's 

12 possible to compare the coefficient of variation 

13 estimates for the equal distribution panel to the 

14 population based panel. 

15 For eight out of ten of the models, the 

16 population based panels have a lower coefficient of 

17 variation. This can be explained because the 

18 population based panels take a larger sample from 

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

20 cells have a smaller area than shown in the 

21 graphics.

22 Compared to the equal distribution panels, 
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1 the population based panels take a smaller sample 

2 from more heterogeneous cells, that is cells 5 and 

3 6.

4 We would need to test more respirator 

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

6 eight out of ten yielded a binomial test p-value of 

7 0.11. 

8 Okay. To summarize the statistical 

9 results, we have compared six different types of 

10 panels with different numbers of members. 

11 We have compared three equal distribution 

12 panels to three of the population based panels. The 

13 comparisons are done within each of the ten 

14 respirator models in this study.

15 We found that panels with smaller numbers 

16 of subjects have greater variability, and this 

17 result is consistent for all of the respirator 

18 models.

19 In the cases of 30-member panels, the 

20 population based panels have smaller variability 

21 than the equal distribution panels, but the result 

22 is not statistically significant. 
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1 It makes sense because the population based 

2 panels take a larger number of observations from the 

3 more homogeneous cells, and fewer observations from 

4 the more heterogeneous cells compared to the equal 

5 distribution panels. 

6 Okay. We can actually use something called 

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

8 design to measure the efficiency of sampling by 

9 cells to compare the population based to the equal 

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

11 not actually investigated, combining cells into 

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

13 designs. 

14 All these could be compared by using the 

15 design effect. 

16 Basically, the design effect is comparison 

17 of the variance, sample variance, relative to the 

18 sample variance obtained from a simple random sample 

19 of the same data. 

20 Preliminary results indicate that for six 

21 out of ten models investigated, the population based 

22 panels are more efficient. So that work is still 
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1 ongoing.

2 In conclusion, our study shows that panel 

3 variability is different for the six panels 

4 investigated here. The next problem will be to 

5 define a certification test and requirement. 

6 Thank you for your attention. 

7 MR. SZALAJDA: Any questions so far about 

8 this material? 

9 Yes, please. 

10 SPEAKER: Phil Eitzman from 3M. I just 

11 have a quick question. 

12 When you talked about the simulated study, 

13 was that a previous NIOSH study when you were 

14 talking about the simulation? 

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

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

17 talking about something that -- other studies or 

18 something in the comments or something.

19 I wanted to make sure.

20 MR. SZALAJDA: Yeah. Phil, could you 

21 repeat that because I think the mic was off.

22 SPEAKER: Better? Okay.
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1 Repeating the question. The question was 

2 Dr. Wassell talked about previous simulated studies 

3 or studies based on simulations, and I just wanted 

4 to make sure I understood what study he was 

5 referring to. 

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

7 RTI report, which --

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

9 MR. WASSELL: -- it's on the web.

10 SPEAKER: Okay. Thank you.

11 All right.

12 MR. WASSELL: Anything else? 

13 MR. SZALAJDA: Ask if there's anything 

14 online. 

15 MR. WASSELL: Anything online, please?

16 Any questions from the online audience? 

17 Nothing.

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

19 questions later at any time.

20 Thank you very much. 

21 MR. ZHUANG: Okay, good afternoon.

22 My name is Ziqing Zhuang, and so, yeah, of 
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1 the first webinar on July 23, I sat down with 

2 Dr. Coffey and talked about some additional 

3 analysis. And so -- and I presented some of my 

4 results last month on August 20th, the second 

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

6 today. 

7 And so these additional analyses have a new 

8 study design, a new approach to the inter-panel 

9 variability issue. And so I think they are covering 

10 more -- relating to the comments or the questions 

11 regarding inter-panel variability.

12 And then also these additional analyses 

13 will help identify where the inter-panel variability 

14 is and then also quantify how much the inter-panel 

15 variability. And then also, more importantly, the 

16 analysis will demonstrate how to reduce the 

17 inter-panel variability.

18 And also at the second webinar, I have them 

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

20 inter-subject variability, and some of the study 

21 talking about inter-subject variability dated back 

22 to 1983 or so. And then we also, yeah, we all look 
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1 at we have differences, different respirator models. 

2 So we have inter-respirator variability and then 

3 lately we also identify inter-respirator sample 

4 variability. 

5 So when you take different respirator out 

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

7 as well.

8 So -- and for this particular inter-panel 

9 variability issue, after the first presentation, I 

10 still had some comment or questions. So I thought 

11 it needed some more clarification here. 

12 So what it is, it's like inter-panel 

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

14 inward leakage test results between two respirator 

15 fit test panels, like two geographical locations or 

16 at the same location.

17 So we had manufacturer throughout the 

18 country or the world, and they have -- they can 

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

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

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

22 not. 
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1 So how to measure the inter-panel 

2 variability should be the same or similar to how to 

3 describe the inward leakage test result. 

4 So at last month's webinar, I 

5 covered -- presented the passing rate, which is 

6 expressed as a frequency of number of subjects 

7 passed or percentage, and then also like passing or 

8 failing a respirator in inward leakage test. 

9 So but typically, like from scientific 

10 standpoint, we would compare mean, like -- and look 

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

12 their distribution and look at the standard 

13 deviation. 

14 And then most of the like fit factor or 

15 inward leakage data tend to be like normal 

16 distribution, so we transform them and calculate 

17 geometric mean and geometric standard deviation. 

18 And then there's another like way to look 

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

20 percentile or 95th percentile. And then 95th 

21 percentile is the one that the ISO TC94/SC 15, yeah, 

22 used to like -- for the inward -- total inward 
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1 leakage performance. 

2 But then the committee met over the 

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

4 still meeting today and tomorrow. 

5 So but then I attended the meeting. And, 

6 now, we have like move forward with like binomial 

7 approach, calling a number of subjects required to 

8 pass. 

9 So like getting all of the fit factor data 

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

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

12 inward leakage level or so, you fail that 

13 respirator. 

14 So now they changed the approach also. So 

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

16 country for comment or so it will be using the 

17 binomial approach.

18 And so then you also heard from Dr. Wassell 

19 about coefficient of variation. But typically when 

20 we do that, we will estimate like very -- like 

21 various component and see which one is like smaller, 

22 which one is like larger. And then with the -- in 
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1 the panel, coefficient of variation is large -- what 

2 is large or what is small. 

3 So then we also have like two approach. So 

4 what I presented last time was how I call 

5 deterministic. So we just like recruited like two 

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

7 then -- but today, I will also talk about 

8 probabilistic approach. 

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

10 we create like -- when we compare like three panel, 

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

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

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

14 that you see for my result today.

15 So here is a summary of what we found for 

16 the August webinar.

17 So passing rate was not significantly 

18 different among the three panels for all respirator 

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

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

21 And then using 26 or more subjects out of 

22 like 35 to pass the inward leakage test, all three 
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1 panels have very consistent passing or failing 

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

3 But then some inconsistency was observed 

4 for pass and fail level .02. And then pass/fail 

5 level is very important factor affecting inter-panel 

6 variability. So that's what we found. 

7 And then also -- so, yeah, we found out 

8 that like inter-panel variability exists, and then 

9 it's like rather small. 

10 And then the also the concern about the 

11 inter-panel variability and other types of 

12 variability can be alleviated by properly selecting 

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

14 .05. The example here.

15 And then also the panel size. Like in that 

16 case, like last time, we used 35. But here I'm 

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

18 and see what in fact we see from inter-panel 

19 variability data. 

20 And then also the minimum number of 

21 subjects required to pass is also another variable 

22 that we can look into. 
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1 And so just -- yeah. So this idea will 

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

3 analysis here.

4 And so when we talk about like the first 

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

6 the panel size.

7 Yeah, like NIOSH certification test methods 

8 for known particulate respirator use like ten 

9 subjects. And then the CBRN standard require at 

10 least like 25 subjects. 

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

12 or 38 if you have three size. 

13 And then the EN standard only require 10 

14 subjects. And then the 2009 proposed like rule 

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

16 earlier, ISO, they propose a 25 subject.

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

18 science about this, like sample size issue.

19 So we have two previous approach. Like one 

20 is the binomial distribution approach. And so 

21 Dr. Landsittel and myself and Roland BerryAnn and 

22 then also (unintelligible), we are coauthors for 
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1 that paper. And has been accepted and it is being 

2 like published very soon in JOEH.

3 And then also using the information from 

4 that paper, I prepare a presentation at Boston at 

5 the ISRP conference. 

6 And so using that approach, we use standard 

7 statistical calculation to assess results under 

8 different assumptions. So -- and then calculate 

9 probabilities using the binomial distribution. 

10 So like we assume the respirator is how 

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

12 percentage of the population. And then for 

13 different respirator with different fitting 

14 characteristic, we determine how likely it would be 

15 rejected if used certain sample size. 

16 And so that approach. And then the other 

17 approach is the random effects model approach, and 

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

19 use a closed-form normal approximation, and then 

20 sample size estimates for testing the proportion of 

21 users who will receive a specified level of 

22 protection, so...
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1 And then here I'm trying to use the third 

2 just to look at inter-panel variability and see what 

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

4 size, and do we still have similar inter-panel 

5 variability or not. 

6 So the objective of this portion of the 

7 analysis is to determine if panel size affect 

8 passing rate and also consistency of passing or 

9 failing of respirator in inward leakage test. 

10 So -- and, again, this is a deterministic 

11 approach. So from the 144 subjects, we just 

12 randomly assigned them to one of three test panels. 

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

14 three panels. So we only used 75 subjects.

15 So here is how we selected the subjects or 

16 assigned them. 

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

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

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

20 panel, and then 7 we use four here.

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

22 have created more panel, but just try to be 
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1 consistent with what we created for -- using the 35 

2 subject, so just to see similar pattern.

3 So a subject was considered to pass the 

4 inward leakage test if one of the two inward leakage 

5 variables was less than or equal to pass and fail 

6 level. And then also calculated passing rate for 

7 all 144 subjects and ten respirator combined using 

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

9 Just to give an initial estimate of how 

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

11 not a real like passing rate, but just some -- but 

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

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

14 least we have some data to get started with. 

15 And I know we can improve this number 

16 because like Chris Coffey talk about earlier, like 

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

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

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

20 will be better. Yeah, but just to 

21 use -- demonstrate a concept here.

22 And then calculated passing rate for each 
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1 panel or for all three, and then each respirator as 

2 well for the same level.

3 And then we also tested if passing rates 

4 were significantly different among the three 

5 respirator fit test panels.

6 And a respirator was considered to pass the 

7 inward leakage test if 19 or more subjects out of 

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

9 can determine if the passing or failing of the 

10 respirator inward leakage test was consistent with 

11 the three different respirator fit test panel or 

12 not.

13 So here are the result.

14 And this is the figure shown -- like show 

15 the inward leakage data for all ten models and by 

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

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

18 respirator models, so we have 500 data points. 

19 So you can see data equal this range from 

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

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

22 2. 
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1 So -- but then you look at like 90 percent 

2 also of the data will be less than 1 point something 

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

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

5 So if you test their mean, their mean may 

6 not be statistically significant, and -- but then 

7 when we look at one individual model, this is a good 

8 performance model with the lowest mean inward 

9 leakage and the respirator in the presentation here. 

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

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

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

13 or so. 

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

15 like variation among the three panels, so the three 

16 curves are very similar.

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

18 with the highest like mean inward leakage, for the 

19 three panel, they kind of scattered little bit. 

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

21 more like leakage, like -- let's say like here, you 

22 have more than 70 percent is less than .2. But then 
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1 like if you look at the 70 percent or so, it's like 

2 for the other two panels, they are less than .1.

3 So -- and you can see some variation among 

4 the three panels.

5 And so this is the table showing the 

6 passing rate for all 144 subjects and for the three 

7 level of pass and fail level.

8 So for respirator A, we have 66 subject 

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

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

11 percent.

12 So -- and then -- so for this column -- and 

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

14 like 125 or more than 80 percent. So -- and in our 

15 previous paper, we have categories. Like anything 

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

17 them effective respirator model. 

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

19 here. Also like some of these respirators could be 

20 impacted as well if you do the right test. 

21 And -- but just demonstrate a concept here. 

22 And so for the respirator in red, I -- they 
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1 are less than 60 percent. So they are -- we call 

2 ineffective. 

3 And then the one somewhere like between 60 

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

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

6 But as we change our pass and fail level to 

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

8 passing. That's about a 76 percent. 

9 And so now you can see like Respirator F 

10 and J, they become green. So they are effective now 

11 if we use that pass and fail level. 

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

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

14 they are still ineffective. And then -- but then 

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

16 in that category.

17 So but then when we change our -- the pass 

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

19 effective. They all have more than 80 percent 

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

21 and the other one -- the other two model is like 60 

22 and 69. 
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1 So that's -- and this is the concept that 

2 we will use throughout the analysis for the next 

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

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

5 the three panel now. 

6 So for Respirator A, only 12 subjects pass 

7 here, 11 and 10. So they all -- they are all less 

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

9 consistent result. 

10 But then when you look at Respirator F, so 

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

12 greater than 19. So, again, like all three panels, 

13 this respirator pass the test. So very consistent 

14 result.

15 So -- but then -- so in this case, like for 

16 this level, like the passing or failing are very 

17 consistent among the three panel. And then the 

18 other one is we did the chi-square test like just to 

19 check the number and see whether this number are 

20 statistically different, and this are how the p 

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

22 they are different. But if greater than .05, there 
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1 is no statistical differences there.

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

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

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

5 effective. So they all pass.

6 And then, the red respirator, they all fail 

7 three panel.

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

9 we have some inconsistency here. 

10 For A, they pass like panel A and -- fail 

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

12 failed the first two, but pass the second one -- or 

13 the third one.

14 And so you have some inconsistency here.

15 But then when the pass and fail level is 

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

17 level again. So you only have like all the 

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

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

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

21 case, I -- this respirator fail all three.

22 So you only see a little bit of 
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1 inconsistency. 

2 So passing rate was not significantly 

3 different among the three panels for all respirators 

4 combined or by respirator with exception of 

5 Respirator E. And then this is true for all 

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

7 And the variability is generally smaller 

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

9 results for panel size of 35.

10 But using 19 or more subjects to pass the 

11 inward leakage test, like all three panel has 

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

13 level. And then so some inconsistency was observed 

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

15 similar to those for sample size of 35.

16 So, yeah. So when panel size was decreased 

17 from 35 to 25 subjects passing, it was slightly 

18 affected. Not a whole lot. And consistency of 

19 passing or failing a respirator was not affected by 

20 this decrease in panel size. 

21 And further simulation analyses can be 

22 conducted to confirm these findings. So the next 
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1 portion of my analysis will be, instead of looking 

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

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

4 then we will calculate some probability.

5 So any questions for now? 

6 SPEAKER: Charles Okay (phonetic). 

7 I was curious if you were using the inward 

8 leakage level at the same thing at the confident 

9 interval. 

10 MR. ZHUANG: Confident interval?

11 SPEAKER: Yeah. Were you using the same 

12 concept?

13 MR. ZHUANG: No. No. Like, yeah, if you 

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

15 are -- like inward leakage datas are highly 

16 variable. 

17 So like from different subjects, different 

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

19 to like .5. So -- but then if you like -- basically 

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

21 here. 

22 So if you use that like criteria and see 
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1 how many of the subjects can get like there, like 

2 inward leakage even less than that. And we consider 

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

4 So it's not a confident interval.

5 SPEAKER: My next question. How did you 

6 determine your sample size? 

7 MR. ZHUANG: The sample size like from the 

8 other two approach, we have our calculation. 

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

10 just to see like do we have any like -- if we 

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

12 have or how much like more inconsistent result we 

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

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

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

16 certain formula to derive that sample size.

17 But the other approach we try like the 

18 binomial approach, we look at probability, and we 

19 also look at alpha/beta error. 

20 And then using that, trying to, like I 

21 said, we would like to have the alpha error like 

22 less than 20 percent. Like we try to like to reject 
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1 a good model like less than 20 percent. And then 

2 also we tried to like accept the effective model 

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

4 criteria to pick the sample size. 

5 MS. D'ALESSANDRO: Anything on LiveMeeting?

6 MR. ZHUANG: Yes. Anyone would like -- or 

7 have any question from LiveMeeting? 

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

9 power -- my presentation. 

10 So, again, we look at the same research 

11 question or research issue, but using a different 

12 approach, like which is probabilistic approach. 

13 So the objective was to estimate 

14 probability that two panels will pass or fail a 

15 respirator. And this will cover linking the 

16 question that like, yeah, some people would like to 

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

18 probability for respirator to pass the inward 

19 leakage test and then investigate the effect of pass 

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

21 the main three parameters to set a good like, like 

22 performance criteria.
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1 So from the 144 subjects, we use 

2 bootstrapping to generate two 35-subject panels.

3 And then in this case, they are not 

4 like -- there's no replacement. So when you -- from 

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

6 we did for the deterministic approach. But then you 

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

8 yeah, like -- so, and then a subject was considered 

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

10 inward leakage values is less than or equal to the 

11 pass and fail level again. And then also calculated 

12 the passing rate for each panel and each respirator 

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

14 test to see if passing rate was significantly 

15 different between the two respirator fit test 

16 panels.

17 And the respirator was considered to pass 

18 the inward leakage test if 26 or more subjects out 

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

20 passing or fail a respirator in inward leakage test 

21 was the same for the two panels. 

22 So typically, yeah, we will see if they all 
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1 passed, they all passed all panel or like they may 

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

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

4 So we have three outcomes.

5 And then we repeat that procedure until 

6 1,000 pair of 35-subject panels was completed.

7 So -- and then calibrated the probability 

8 that passing rates were not significantly different 

9 between the two respirator fit test panels for each 

10 respirator for the three pass and fail levels. 

11 And then also calculated the probability 

12 for the following outcomes, like the respirator 

13 passed both panels or failed both panels or passed 

14 one and failed the other.

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

16 created, we can also calculate probability for a 

17 respirator to pass the inward leakage test. And 

18 then also this procedure was repeated for like 

19 25-subject panel as well. So we had two set of 

20 results.

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

22 the probability that passing rates were not 
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1 significantly different between the two 35-subject 

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

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

4 like, when we do the chi-square test, like 949 pair, 

5 like it no show any difference. And the remaining 

6 like show differences.

7 So but then, if you look at all the 

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

9 of like not getting significant differences between 

10 the two panels is very high.

11 So the low is like 93.7 here for that 

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

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

14 like -- if we use that to look at our inter-panel 

15 variability, we do see, yeah, inter-panel 

16 variability here.

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

18 number of subjects and do the, yeah, the chi-square 

19 test. But here we look at whether the respirator 

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

21 you look at -- recall what I mentioned earlier, like 

22 the effective model, the passing panel is very high. 
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1 So here you are not looking at one hundred 

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

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

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

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

6 here. 

7 But then for the ineffective model, it's 

8 kind of almost -- yeah, it's 100 percent here. Like 

9 they all failed both panel. So only like for 

10 the -- somewhere like -- somewhat effective model, 

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

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

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

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

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

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

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

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

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

20 green color, they have very high probability of 

21 repeating the result.

22 And the, yeah, ineffective model is hundred 
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1 percent fail both panel, also. So -- and so just a 

2 little bit of like disagreement or inconsistency we 

3 can find. 

4 So the probability that passing rates were 

5 not significantly different between two 35-subject 

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

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

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

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

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

11 inconsistency was observed for .02 pass and fail 

12 level.

13 So this is the result for subject for panel 

14 size of 25 now.

15 So, again, like the minimum here, the 

16 smallest is like 98.3 (sic), and it also goes to 

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

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

19 little differences. 

20 And, again, like looking at the probability 

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

22 it's good like repeatability. And then for the 
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1 ineffective model, they all fail a very high 

2 percentage of the time. 

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

4 like ineffective model, and we have some like 

5 low -- like higher percentage, all probability of 

6 passing one panel and then fail the other panel. 

7 And then -- but for the effective model, they all 

8 have good repeatability.

9 And the same for .05 level also.

10 So the probability that passing grades were 

11 not significantly different between the two 

12 25-subject test panel was very high, ranged from 

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

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

15 pass/fail level .01 and .05.

16 And only some inconsistency was observed 

17 for .02 and the probability were very similar to 

18 sample size 35 subject for the panel.

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

20 the probability for the respirator to pass the 

21 inward leakage test for three different pass and 

22 fail level or two panel size as well. So this is 
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1 another table. 

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

3 say for Respirator F, like 9 -- 63 of the 1,000 

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

5 here. 

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

7 or so, like that passing probability is very small 

8 at this pass and fail level. And then J is the one 

9 somewhat effective, but it passed like 83 of the 

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

11 And so as we increase the pass and fail 

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

13 is like the 90 something percent passing probability 

14 and 100 percent here.

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

16 still very low. And they are somewhat effective. 

17 They pass at a certain -- like this one is like 

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

19 when we increase that pass and -- pass/fail level to 

20 .05, so more like passing like -- so all seven 

21 models passed like are effective, pass like at very 

22 high probability right now. And so like 99, 98 and 
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1 100 percent. 

2 And then the red -- the Model 

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

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

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

6 time. 

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

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

9 like a good model, if you increase your sample size 

10 or you decrease like -- for smaller sample size, it 

11 tend to give you a lower like passing probability. 

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

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

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

15 tend to increase your alpha error, which is like 

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

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

18 it's only about two percent differences. 

19 And then on the other hand, like for like 

20 somewhat effective model also, like the -- for 

21 sample size is smaller.

22 It tend to accept them or reject them like 
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1 less. So in this case, like you allow them to pass 

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

3 increase your sample size. You only allow this 

4 model to pass at like around 5.5 percent of the 

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

6 8.3 percent of the time. 

7 So you can see that how it affect between 

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

9 is the same.

10 So -- and then the other one I want to show 

11 here is that now, like instead of using 19 out of 25 

12 and 26 out of 35, we used 15 out of 25 to pass a 

13 respirator or 21 out of 35 to pass a respirator, and 

14 these are the current probability.

15 So when -- for .01, now, even the yeah, the 

16 ineffective motel, the so-called ineffective model, 

17 they pass like little bit now. So increase 8 

18 percent here, 3 percent.

19 But for the effective one is 100 percent. 

20 And then this one somewhat effective is like 60 

21 percent now. 

22 So but any of you look at the pass/fail 
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1 level of .02, they are all quite high now. So 

2 except the three ineffective model, like still less 

3 than 1 percent. But then the rest of the model is 

4 like 85, 84, 95, 82, or 100.

5 So they all passed, yeah, like have a high 

6 probability of passing now.

7 And for .05 level, it's almost like 100 

8 percent for those respirators. And even for the, 

9 yeah, for the ineffective, the so-called ineffective 

10 model -- like respirator, it also can pass about 

11 like 30 percent of the time now. And here is like 

12 90 percent and 60 percent for the other two models.

13 So this is like the same as like I show in 

14 the past.

15 This is the figure. We look at number of 

16 subjects required to pass, and then this is the 

17 percentage of the time a given respirator will fail.

18 And so you can see that 80 percent, 90 

19 percent effective like by using that 19 out of 25 or 

20 so, they will pass a high percentage of the time. 

21 And then the 60 or 50 percent, the 

22 only -- the rejection is so high, they only pass 
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1 about like a few percent of the time. So for 50 

2 percent or so, beta L is less than 5 percent. And 

3 but then, yeah, just like the previous table, if you 

4 shift this line to here, so now like the green line, 

5 those are effective, they will continue to pass like 

6 100 percent of the time or so. And then even the 

7 somewhat effective, like between 60 to 80, now they 

8 can also -- like they will be rejected a very like 

9 small percentage of the time. 

10 And but then, at the same time, you accept 

11 some of the ineffective models at a little bit 

12 higher percentage of the time. 

13 So that's the kind of trend when you change 

14 the like number of subjects required to pass like in 

15 the panel.

16 So probability for a respirator to pass the 

17 inward leakage test was not significantly different 

18 between 35-subject test panel and 25-subject test 

19 panel. And this was true for all pass and fail 

20 level of .01, .02, and .05. 

21 When number of subjects required to pass 

22 was decreased, probability for respirator to pass 
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1 the inward leakage test increased. 

2 Yeah, so pass and fail level affects the 

3 classification of the respirator effectiveness and 

4 then also the probability.

5 And so when panel size was decreased from 

6 35 to 25 subjects, that probability for repeating 

7 test results was very similar. And then when panel 

8 size was decreased from 35 to 25 subjects, the 

9 probability for respirator to pass the inward 

10 leakage test remained similar. So when the number 

11 of subjects required to pass was increased, then 

12 more respirators passed the inward leakage test.

13 So any questions? Again, what did you say.

14 SPEAKER: Lisa Brosseau. So that was a lot 

15 of data.

16 So here's my question. What's a good pass 

17 rate?

18 MR. ZHUANG: Okay. I think like today, we 

19 may not want to do that yet. But the data show that 

20 like if I'm -- yeah, to set the formal requirement, 

21 just using the data that we have right now, I think 

22 like .02 and 25 and maybe I would change the number 
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1 from 50 to 60 because ISO committee was able to 

2 accept the 60 out like 25 on Sunday. So I think 

3 that may be a good number.

4 SPEAKER: So what you're saying is that 

5 if -- what you're looking for as a passing fit 

6 factor is 50; right? 

7 MR. ZHUANG: Yeah, roughly, yes. If 

8 there's inward leakage like --

9 SPEAKER: I understand. But we can talk 

10 about fit factors for a minute because that means 

11 more to me than penetration. 

12 So you're looking for a fit factor of 50.

13 MR. ZHUANG: Yes. 

14 SPEAKER: You're saying it doesn't matter 

15 if it's 25 or 35 subjects.

16 MR. ZHUANG: Right. 

17 SPEAKER: And how many subjects do you 

18 think we should be looking for as a pass? 15?

19 MR. ZHUANG: This example show a 15.

20 SPEAKER: 15 out of --

21 MR. ZHUANG: 60 percent, basically 60 

22 percent.
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1 SPEAKER: Okay. 

2 MR. ZHUANG: But I think going up to 60 is 

3 not too bad also. Like 63 or 64 percent.

4 SPEAKER: Yeah. So the one question I have 

5 about the design of the original experiments is this 

6 sort of forcing people into cells. 

7 MR. ZHUANG: Yes.

8 SPEAKER: How much effect do you think that 

9 would have? Because it misclassifies some people. 

10 It gives them the wrong respirator.

11 What's the effect of that on your data 

12 analysis?

13 MR. ZHUANG: I would expect the results 

14 will improve the data here. 

15 Because like if you -- let's say with the 

16 two respirator size, and if some subject fail, and 

17 you let them test the other size and they pass. So 

18 that number will go up. 

19 So the number of subjects pass, I expect 

20 that number to go up. So I would expect to see less 

21 inter-panel variability also. 

22 And then for the three size, you know, they 
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1 are two more chance to pass. But know like the real 

2 test like if some of them are in the middle, they 

3 could go next or they could go up and maybe one or 

4 two. Either way. 

5 So but just, again, this is for all of us 

6 to consider and think about it. But to me, the 

7 trend is very similar, and it won't like increase 

8 the inter-panel variability. It can only help that 

9 like inter-panel variability will be smaller.

10 Yeah.

11 So but very good point, so...

12 Any questions from the LiveMeeting? 

13 Okay. If not, we are ready for break.

14 Fifteen minutes break. So thank you very 

15 much.

16 (A recess was taken.) 

17 MR. SZALAJDA: All right. What we would 

18 like to do at least for the balance of the meeting 

19 is to have a panel discussion. 

20 And we have people who have been involved 

21 with the process of developing the project and 

22 conducting our research and helping us get to where 
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1 we are today. 

2 And just for purposes of introduction, we 

3 have three of our presenters, Dr. Wassell on the 

4 end, Dr. Coffey, Colleen Miller, Roland BerryAnn, 

5 who is currently dual hatted as the deputy director 

6 for NPPTL as well as the evaluation branch chief, 

7 and Dr. Zhuang.

8 And what we'll be doing with regards 

9 to -- for the next hour or so is to go over the 

10 topics that were identified in the Federal Register 

11 notice. And they fell into -- the questions that we 

12 asked fell into these topics. 

13 And what we'll do is we'll go forward.

14 I'll introduce the topic. There will be 

15 some slides that Colleen will lead the presentation 

16 on with regard to the different criteria. There 

17 will be some background material that's also 

18 incorporated as part of the discussion, and then, we 

19 will elect to engage stakeholders in dialogue. 

20 If you have any particular -- what I would 

21 encourage you to do -- because we do not have a 

22 summary planned at the end of the discussion. If 
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1 you have comments on any particular area, pass/fail 

2 criteria, test panel, methods, other things, come up 

3 and, you know, identify yourself, your organization, 

4 and make your comment with regard to where your 

5 comment falls within this framework.

6 We'll handle the meeting internally first, 

7 then we'll go to LiveMeeting, and then we'll just 

8 move through each topic until we go through all the 

9 slides.

10 And so initially, we would like to -- you 

11 know, as a follow-on to Dr. Zhuang's presentation, 

12 we would like to talk about the pass/fail criteria. 

13 And basic -- the first basic question is, what panel 

14 size would be sufficient for conducting a 

15 facepiece-to face seal certification test. 

16 And I'll defer to Colleen first for the 

17 initial comments. 

18 MS. MILLER: Thanks, Jon.

19 I think there are a couple of points that I 

20 wanted to make about this question. 

21 First of all, to remind everyone in the 

22 notice of proposed rule that went out in 2009, and 
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1 some of the discussions around that, it was 

2 considered important by some of our researchers and 

3 contributors that we require at least one pass per 

4 cell as part of the pass/fail criteria. 

5 The comments that we received back from 

6 ISEA and others indicated that perhaps that wasn't 

7 the best methodology. So we are interested in 

8 getting more feedback on that.

9 And the other thing I think that's 

10 important to remember in terms of where we're coming 

11 from, as Ziqing made reference to, we do have a 

12 criteria right now for the other half-mask 

13 respirators that we certify in the isoamyl acetate 

14 test, where we do use the LANL half-facepiece panel. 

15 And we do require, you know, every -- a pass in 

16 every cell for all ten cells.

17 So in opening up this conversation, we 

18 would kind of like you to keep that in mind and hear 

19 what your thoughts are about whether or not one pass 

20 per cell is required or not.

21 Anyone like to contribute? 

22 MR. WASSELL: Well, I just I mean, I know 
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1 the difficulty in our study to recruit subjects in 

2 certain cells. 

3 So certain cells were all female, right? 

4 That's our requirement; right?

5 MS. MILLER: Right.

6 MR. WASSELL: And so in cell one, all the 

7 subjects were female. 

8 So I mean, we do have issues like that that 

9 pertain to this question. And we just, you know, 

10 I'm not really sure to require a pass in each cell, 

11 you know, thinking about it from a statistical 

12 perspective, that's a pretty strict criteria, I 

13 think. So we may make the whole certification 

14 process difficult. 

15 SPEAKER: Jim Johnson.

16 You know, I guess I would like to step back 

17 and say, to me, the value of running a human fit 

18 test protocol as part of certification is not to be 

19 absolute.

20 And to me, one fit in each -- or one pass 

21 in each cell gets a little more than saying look, 

22 you want to make sure that the respirator fits 
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1 people and can pass on a large number of them, and 

2 let the final decision be done in the field with the 

3 OSHA required fit test where it is significant and 

4 you have the variability rather than trying to force 

5 the certification process into a really difficult 

6 situation.

7 And from the -- anybody that would try and 

8 reproduce this, either in research or as a 

9 manufacturer, before they submit it, it gets even 

10 more difficult for them if they want to try and 

11 duplicate what you're going to do before they submit 

12 a respirator.

13 So I don't see it. 

14 I think it should be made as reasonable and 

15 as easy as possible to evaluate respirators fit on 

16 individuals, on people with variable face sizes, but 

17 not be absolute.

18 MS. MILLER: So perhaps the focus should be 

19 more on the boundaries of the panel, the outer 

20 boundaries complete rather than the interior 

21 separations into cells.

22 SPEAKER: And, you know, the fact that 
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1 you're going to say, what was it, a 60 percent pass? 

2 MS. MILLER: That was just something that 

3 Ziqing was giving us --

4 SPEAKER: Okay. But I mean, just saying, 

5 pick out a number, 60, 70 percent. It's not going 

6 to be 95 percent. 

7 So why would you make the cells 100 

8 percent? 

9 I mean, I think it just -- again, if that 

10 becomes an issue, it will be sorted out in the field 

11 with the person doing the fit testing.

12 I don't think it's part of certification.

13 MR. BERRY ANN: Well, let me ask a 

14 different question here.

15 I think it's along the same lines as where 

16 you were going.

17 You have got the panel divided up into ten 

18 cells, which are not equally distributed by the 

19 population. 

20 So if you require at least one pass in each 

21 of the -- in each cell, then aren't you giving 

22 disproportionate weight to some of them, the lesser 
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1 populated cells that one is a higher percentage of 

2 the population of that cell but a lower population 

3 of the overall? 

4 SPEAKER: I guess for me, Roland, my 

5 perspective is, it's such an improvement to have a 

6 requirement to fit test or do the certification with 

7 people on these air-purifying respirators that I 

8 think it becomes almost too difficult to go that 

9 way, or too demanding to come up with those numbers. 

10 And that becomes too restrictive.

11 MR. ZHUANG: Yes. 

12 I had a comment on that.

13 I guess I heard about like one cell, at 

14 least one cell in the past is that you worry that 

15 there may not be anyone to pass in that cell. 

16 But that's not true because like, again, I 

17 think fit factor tend to be more normally 

18 distributed. So when you get only two subjects and 

19 you don't get the two to pass or so, there should be 

20 still other people out there that can get a good 

21 fit. 

22 SPEAKER: Lynn Feiner, Honeywell.
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1 My concern with requiring a pass in every 

2 cell for that particular group is you may be 

3 designing a respirator for a very specific facial 

4 type or a facial size.

5 And I don't want to see a respirator so 

6 limited that it -- by actually requiring it to be 

7 all things to all groups.

8 And let the market decide, and let the 

9 people decide through the OSHA fit testing what's 

10 the right respirator for them.

11 MS. MILLER: Okay. Does anyone beyond what 

12 Ziqing presented, you know, his examples and his 

13 analysis of 25 and 35 subjects, was anyone willing 

14 to give a recommendation for a number of test 

15 subjects for the criteria? 

16 Not you Bob.

17 SPEAKER: Hi, Dan Glucksman, ISEA. 

18 And I don't have a direct response to your 

19 question but to say that, you know, we obviously saw 

20 the questions in the Federal Register, and we'll 

21 certainly be submitting comments to those. 

22 So I understand that concerning the -- not 
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1 disconnect, but the timing here, you're asking these 

2 questions before we have had a chance to sit down as 

3 a group. 

4 We figured we would wait until after this 

5 event to respond.

6 So the fact that people aren't responding 

7 right away is probably because there's a lot of wait 

8 and see going on wanting to hear and see the 

9 presentations here and then reconnect.

10 So I just wanted to put that out there, the 

11 reason you're hearing the crickets.

12 MS. MILLER: We expected it.

13 It's okay.

14 SPEAKER: Okay.

15 MS. MILLER: We're trying to improve the 

16 audio quality for our folks on the cheap seats.

17 MR. SZALAJDA: Are there any comments on 

18 the LiveMeeting or questions on the LiveMeeting? 

19 Yeah. I guess if you come up to the 

20 microphone, if you can just make sure that you're as 

21 loud as possible.

22 And I guess the one -- I did get one note. 
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1 If the panel members, before you speak, if you can 

2 identify yourselves for the transcript that would be 

3 appreciated.

4 So we'll move on to the next slide, which 

5 includes should the pass/fail criteria require an 

6 overall higher requirement to pass and allow for a 

7 percentage of failures. 

8 And should the pass/fail criteria require 

9 an overall lower requirement to pass and allow for 

10 no failures. 

11 And I'll defer to Colleen to open the 

12 discussion.

13 MS. MILLER: Well, I'm hoping that everyone 

14 will loosen up a little bit.

15 I think that part of that question was 

16 already addressed by some of the speakers on the 

17 previous question. 

18 But I mean, we're basically saying going 

19 back, you know, should we say fit factor of 100 and 

20 only like, you know, 12 out of 20 subjects just as 

21 an example we would need to pass, or should we lower 

22 the criteria of the fit factor to 50. 
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1 And, again, go back as we just discussed to 

2 ten out of ten or whatever, a hundred percent pass 

3 rate.

4 Does anyone have any other comments on the 

5 panel, try and get things going? 

6 In the audience? 

7 How about LiveMeeting?

8 MR. SZALAJDA: Okay. 

9 Well, I think as -- oh, thank you, Phil.

10 Phil Eitzman from 3M.

11 SPEAKER: Phil Eitzman from 3M. 

12 I just kind of second what Dan was saying. 

13 We will be submitting comments. And just 

14 wanted to get kind of all the information that you 

15 guys presented today before we did that. 

16 And want to make sure that we have kind of 

17 good complete answers, not just something that I 

18 thought up on the spot. 

19 So we're going to -- we will be doing that. 

20 So apologize for not interacting with you on this, 

21 but want to make sure that we have good answers and 

22 responses to your questions.
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1 MR. SZALAJDA: Okay. Thank you, Phil.

2 I think one thing that just may be -- and 

3 I'll take the prerogative because I have the mic in 

4 hand, is one of the things I hope that we hear from 

5 stakeholders when they consider this particular 

6 question is that we understand that there may 

7 need -- somewhere, we need to find an accommodation 

8 in the middle between what's perfect and what's bad. 

9 You know, that there is an area where I 

10 think we can identify an appropriate criteria for 

11 pass/fail, which may not necessarily be the perfect 

12 answer but something that we feel will be -- provide 

13 good data to give us a good indication that we think 

14 these respirators will be able to be effectively 

15 used in the field.

16 You know, again, I think when we looked at 

17 the data, I mean, ideally we could have a huge 

18 panel, 105 people, and we could do a lot of testing 

19 and feel good about a potential answer and the fit 

20 of the respirator and, you know, a higher fit 

21 factor. 

22 But, you know, at the end of the day, there 
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1 also has to be a practical application with regard 

2 to, you know, one, what the manufacturer needs to do 

3 in terms of developing a product that they can 

4 market. And, two, what we can do as a certification 

5 body to be able to process new applications 

6 effectively.

7 So with that, I'll look at the panel. And 

8 if we're all in agreement, we'll move on to the next 

9 slide. Okay.

10 And, again, this slide gets into the 

11 testing procedure criteria. And one of the things I 

12 think I would like to defer, until we talk to the 

13 test method on corn oil, I think I would like to 

14 defer that until we get to the test method part of 

15 the discussion. 

16 But I think in general, we wanted to hear 

17 and get some feeling from stakeholders with regard 

18 to the technology that could be used for this type 

19 of evaluation. 

20 And I'll go to Colleen next.

21 MS. MILLER: I honestly have given this 

22 question quite considerable thought. 
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1 And I also have reviewed the history in 

2 trying to respect the history of the project. 

3 I'm assuming, based on your comments, that 

4 there isn't anything new that has happened in terms 

5 of the pass/fail criteria and the analysis of 

6 this -- of inward leakage requirements for these 

7 respirators. Ziqing kind of, in his presentation, 

8 showed data for all of these, you know, 20, 50, and 

9 100. 

10 Based on my experience with our study, I 

11 think that, just in my opinion, that 100 is going to 

12 be a challenge to achieve repeatedly, and I'm 

13 offering to consider other criteria. 

14 MR. COFFEY: I would agree with that, that 

15 the hundred with the CNC would be very difficult to 

16 do on a routine basis. 

17 MS. MILLER: Anyone on LiveMeeting? 

18 Now, the fun's going to start.

19 SPEAKER: I don't know why you think that. 

20 Craig Colton, 3M. I don't have a comment, 

21 but I have a question.

22 When you say CNC technology, what 
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1 specifically is NIOSH referring to? 

2 Is this CNC using like the Pro+ in the N95 

3 mode, or using CNC without like the particle 

4 classifier? 

5 MS. MILLER: Well, we actually, Craig, 

6 changed the order of the slides.

7 Originally, we were going to talk about the 

8 test method first. And then when we saw -- you 

9 know, talked about Ziqing's presentation, we kind of 

10 switched it around. So we do want to ask that 

11 question actually and talk about that.

12 I think that's still something that needs 

13 to be discussed. 

14 SPEAKER: Yeah. So what -- on the side, 

15 when you see CNC, what does that mean? 

16 Because that's going to affect the number 

17 then that you get, depending on whether you got the 

18 filter penetration or not.

19 MS. MILLER: So you could answer it in 

20 terms of when you do your formal comments, you could 

21 answer it based on with a companion or without.

22 SPEAKER: Okay. Thanks.
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1 MS. MILLER: Sure. That wasn't much fun.

2 MR. SZALAJDA: I don't know. I think I 

3 could have answered that one by just saying yes.

4 SPEAKER: It does mean more comments.

5 MR. SZALAJDA: All right. I understand.

6 Anything from LiveMeeting on this subject 

7 or this slide? No? 

8 All right. We'll move forward. 

9 The next discussion area is the test 

10 panels. And there are several slides that we have 

11 prepared that gives a little bit of an overview, at 

12 least in terms of why we went and developed a new 

13 panel as well as some panel information. 

14 I believe Colleen was going to cover those.

15 MS. MILLER: I think we -- I'll just go 

16 through it again briefly because we did kind of 

17 cover it in our other presentations.

18 As you all know, we do currently use the 

19 LANL panels for certification. They are based on 

20 military data from the '60s and '70s.

21 There were concerns at NIOSH a few 

22 decades -- a couple of decades ago about the 
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1 applicability of them to the civilian work force and 

2 changes in the demography of the subjects.

3 So Ziqing, of course, completed or 

4 initiated and completed his survey in 2003. And he 

5 developed the new panels, the NIOSH Bivariate panel 

6 and the principal component analysis panel.

7 He had a peer review protocol for that in 

8 2002, collected the data in 2003. It was peer 

9 reviewed and published in 2007. 

10 I call it the bible. I carry it 

11 everywhere.

12 And then, of course, that was reviewed by 

13 the IOM. And they concluded that both panels were 

14 an improvement over the existing LANL panels. 

15 And then, you know, again briefly, we have 

16 this -- the LANL panels being used currently for 

17 certification. 

18 In 2009, we proposed using the 35-member 

19 Bivariate panel. And then we would screen the 

20 subjects with the PCA panel that was in the STP 

21 drafted to accompany the rule. 

22 And ISO currently is going to be 
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1 using -- or their standards are going to use the 

2 25-member PCA panel with three subjects per cell and 

3 one at random.

4 So given all of that, we would like to hear 

5 your comments, if you're willing to share any, 

6 indicating what the advantages of using the NIOSH 

7 Bivariate panel in assessing the facepiece-to-face 

8 seal fit are, or if you would care to provide any 

9 disadvantages that we may also need to consider.

10 If you have any experiences you would like 

11 to share regarding, for example, small size 

12 respirators and using any of the panels, we would 

13 like to hear that.

14 MR. WASSELL: Terry Wassell. 

15 I would like to just comment, having just 

16 sort of become familiar with the idea of panels, not 

17 ten years ago, but say, I don't know, six months 

18 ago, and approaching it as a statistician, it's 

19 really puzzling to me what really is in a 

20 statistical context.

21 And the best analogy I could come up with 

22 was that it's very much like survey sampling where 
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1 you're sampling from strata.

2 And I mentioned in my presentation the idea 

3 of the design effect. And it's just a way of 

4 comparing stratified samples to simple random 

5 samples. 

6 So it gives you a benchmark for comparison 

7 and a way to compare these types of stratified 

8 samples. 

9 Each of the ten cells is a strata. 

10 Now, you can sample equally from each of 

11 those cells, or you can sample proportionately from 

12 each of those cells. 

13 And in survey sampling, the goal is that if 

14 you are taking a sample from strata, you are 

15 reducing some source of variation. And your final 

16 estimates are more precise, less variability.

17 And I think that that model is a good way 

18 to deal with these different types of what choice of 

19 panel are you going to use and what is the effect of 

20 it.

21 And we have also discussed the idea of 

22 collapsing some of the cells into what we have used 
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1 the term clouds and a different type of panel with 

2 fewer strata. So I just offer that as an 

3 observation. 

4 And it has helped me kind of solidify my 

5 understanding of the whole purpose and reason for 

6 panels.

7 I think, originally, the purpose was you 

8 would like to test a respirator in a way that 

9 represents the working population. So, you 

10 know -- and hearing that idea first really kind of 

11 threw me off course. But I think from a statistical 

12 perspective, the idea of survey strata is a useful 

13 approach.

14 MR. SZALAJDA: Anything else from our 

15 panel? 

16 Okay. Any comments from the local 

17 audience? 

18 LiveMeeting? 

19 All right. We'll move to the next slide, 

20 which asks what are the advantages or disadvantages 

21 of using the following panel, which include the LANL 

22 full face, the LANL half facepiece, and the 
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1 principal component analysis. 

2 And I think maybe one thing to -- I wanted 

3 to mention here, and then maybe from the 

4 manufacturer's perspective, we would be interested 

5 in hearing your insight in terms of looking at when 

6 you do make comments, whether it's today or in the 

7 future, is what is the impact on how you do business 

8 with regard to establishing and maintaining a panel.

9 You know, if you produce more than 

10 filtering facepieces and you do fit testing before 

11 you submit the device for NIOSH to evaluate, you 

12 have your own panel. 

13 I know one major manufacturer has done a 

14 significant amount of fit testing in their facility, 

15 and they have a panel.

16 If NIOSH were to adopt and maintain, you 

17 know, different panels for different types of 

18 respirators, is that going to impact your business? 

19 How will that impact what you do and 

20 whether or not it makes sense for NIOSH to 

21 holistically look at implementing one panel across 

22 the board for all respirator devices.
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1 Now, I'll defer to Colleen.

2 MS. MILLER: Yeah. I think the point is 

3 that we're trying to engage our stakeholders, who 

4 are not only engaged in this rulemaking activity but 

5 who also, you know, are producing other classes of 

6 respirators and perhaps aren't as engaged in using 

7 the NIOSH Bivariate panel and the PCA panel. 

8 We want to hear from you as a program.

9 MR. BERRY ANN: This is Roland.

10 I would modify what Colleen said and say 

11 not only, you know, if you are engaging in using the 

12 new panels instead of the LANL panel, we would like 

13 to hear about that and your experiences as well.

14 And if we do it across the board, how that 

15 would impact your business. 

16 SPEAKER: Larry Green, Syntech, 

17 International. 

18 We manufacture PAPR respirators, so we 

19 don't -- you know, we just use the LANL panel.

20 MS. MILLER: Full facepiece or half-mask 

21 facepiece? 

22 SPEAKER: Loose fitting, loose fitting PAPR 
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1 respirators.

2 MS. MILLER: All right.

3 SPEAKER: Yeah. And we're a small company, 

4 but, you know, we always pull in people to form our 

5 panels. We use part of the people from inside

6 and -- but because of the different sizes, it 

7 becomes difficult for, you know, some of those 

8 sizes. 

9 Especially No. 1s become real difficult.

10 I wouldn't expect that to change too much 

11 if we moved over to a PCA panel, you know, just have 

12 to search out different people.

13 Of course, that searching out the people is 

14 a bit, you know, a major effort. So it's -- you 

15 know, we just try to do our best on it. 

16 But I think there's a lot of things in the 

17 panels that don't come out, and we get different 

18 types of faces. 

19 In California, we have a lot of what we 

20 classify as Asian faces, which are rounder faces 

21 versus a European face. As we look at 

22 different -- those different things, those types of 
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1 features have a significant impact on how things 

2 fit. 

3 It doesn't really show up in, you know, if 

4 we look at things on a pass/fail basis, we look at 

5 things in terms of raw numbers. And we're looking 

6 at the 99.99 percent deficiency rates and higher fit 

7 factors of 50,000, 100,000.

8 So but even at that level, you know, we can 

9 see differences in how all those facial features 

10 affect the fit just in the drift of the numbers for, 

11 again, different types of faces.

12 It's a lot different world than N95s. But 

13 things that are outside of what are shown in the 

14 panels definitely seem to affect the testing. 

15 MS. MILLER: Any comments from LiveMeeting? 

16 MR. SZALAJDA: All right. The next slide 

17 is, what panel or panels are currently used to 

18 develop new respirator models, including to modify 

19 existing products. 

20 You know, there are several different 

21 panels that are out there. 

22 Dr. Zhuang's research, which is documented 
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1 with the PCA panel, and the Bivariate panel, and 

2 considerations in the international community, and 

3 then historically what's been done with the LANL 

4 panel, are there any comments from the panel with 

5 regard to answering this question? 

6 MS. MILLER: I think Larry kind of answered 

7 that question because he was more or less saying 

8 it's not a pass/fail criteria, more of a perhaps a 

9 product development or quality assurance test.

10 SPEAKER: Yeah. 

11 MS. MILLER: Anyone else like to provide 

12 any information about how you do things? 

13 Okay. Would we like to go on to defined 

14 user groups, then? 

15 MR. SZALAJDA: Yes. How about anything 

16 from LiveMeeting? 

17 Okay. But I think as Colleen gets into the 

18 extension of using the panel to define user groups, 

19 do we have any comments on that particular question? 

20 MS. MILLER: I think overall, we would like 

21 to get a better sense of the significance of this 

22 issue, and whether or not there are any new thoughts 
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1 about defined user groups since, you know, we last 

2 met in 2010.

3 I think it is a challenge in terms of a 

4 certification requirement to set a pass/fail 

5 criteria that could anticipate and address every 

6 potential market that you might be considering. 

7 MR. SZALAJDA: With regard to this topic 

8 and looking at the -- using the descriptions of the 

9 panel, are there any things I think, at least, in 

10 terms of what you're thinking in terms of providing 

11 comments? 

12 I think one of the things that we would be 

13 curious to hear more about are how you would apply 

14 or potentially apply the use of the panels to niche 

15 respirators.

16 One of the things that we have heard and 

17 public comment was made with regard to that topic 

18 was, you know, that we need to have -- be able to 

19 identify that criteria for how we would evaluate 

20 these types of products. 

21 But I could refer to Roland and ask him how 

22 many niche respirators NIOSH has approved in the 
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1 last 20 years. And I think the answer is probably 

2 zero.

3 And Roland shook his head yes.

4 So I think one of the things that would be 

5 important for us to consider as part of the drafting 

6 the rule that if there is criteria, you know, for 

7 particular user group that we should consider and be 

8 able to address this part of the certification 

9 program, it would be important for us to hear that 

10 in the near term.

11 MR. BERRY ANN: Yeah, this is Roland.

12 I want to jump ahead and ask a question 

13 before we got too far from Larry's comments where he 

14 was talking about difficulty in finding particular 

15 size people to fill the panel.

16 And I was wondering if others are having 

17 problems filling the panel sizes.

18 Do you have any thoughts about workarounds 

19 that you would like to mention or put forward that, 

20 you know, might be able to work around not being 

21 able to locate those ones that maybe they're named 

22 after their population base rather than their size.
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1 MR. SZALAJDA: Anything within the room 

2 that people would like to comment on that?

3 SPEAKER: This is Larry Green, again. 

4 Larry Green, Syntech, International. 

5 You know, for some of the small sizes, we 

6 cheat, and we enlist younger people because in 

7 Southern California. That's all we have got.

8 There's an Hispanic population. There's 

9 essentially nobody over the age of 12 that fits in 

10 anything under a size 4.

11 And so, you know, it's -- again, it's very, 

12 very difficult. I think sometimes you get into a 

13 lot of the ethic populations, that's where you could 

14 have other problems.

15 And there's a lot of Asian populations with 

16 very distinct facial characteristics, and we get 

17 that in California.

18 Making a loose fitting PAPR is not quite as 

19 bad for us as it is for a facepiece, where it has to 

20 fit on correctly onto those faces. 

21 But I think that, you know, that's 

22 something where when you're given the standard that 
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1 you have to add that flexibility in there for those 

2 people, otherwise, you're not going to get 

3 respirators that fit those people. 

4 As were some of the hospitals that we sell 

5 to. They call them PAPRs for using it in hospitals 

6 because they have got Asian nurses and things like 

7 that and they just -- they can't fit them on a 

8 standard respirator.

9 So it's, you know, there's -- all those 

10 flexibilities have to be built into it. 

11 Probably my thought is one of the most 

12 important issues is the type of instructions that 

13 you give to the end users in how these things fit. 

14 If you could find something here in the 

15 niche market, you just find something is -- this is 

16 going to fit an Asian type face, a flat face, a long 

17 narrow European middle eastern type face, those 

18 things can be very, very helpful for the end users. 

19 Yet, it's -- a big part of it is ends up 

20 falling onto having good user instructions, things 

21 like that. And with our loose fittings, we have 

22 multiple sizes that we have tried to push the fit 
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1 factors up, additional levels of protection on it, 

2 things like that, where you have -- kind of have a 

3 hybrid where you find both some of the aspects of 

4 the tight fitting and some of the aspects of the 

5 loose fitting to give kind of layers of protection.

6 That's where we end up, you know, getting 

7 into the different facial configurations that affect 

8 how it fits. So we'll have a product that we put on 

9 somebody with a size ten or larger, very large 

10 faces, and trying to put it on somebody who is a 

11 size one and it doesn't do anything. 

12 And then you get on the other extreme, you 

13 get something that fits size 1 or 2, and you get to 

14 the larger sizes, it doesn't fit at all.

15 I think those are the things that you have 

16 to take into account as a lot of instruction as 

17 you're trying to get people to be successful in 

18 fitting their respirators quickly versus going 

19 through and trying 20 different types of respirator, 

20 which one fits, and that becomes a very difficult 

21 thing for the end users.

22 We have to try to keep the -- what the end 
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1 user has to do to know if it's going to fit properly 

2 down to just a couple of --

3 MS. MILLER: Somebody on LiveMeeting needs 

4 to mute. All they can hear is you typing and 

5 breathing.

6 MR. KATZ: Hello, this is Ted.

7 As long as we're interrupting on that 

8 issue. He needs to be given a mic. It's completely 

9 unintelligible for everybody on the phone. I mean, 

10 it's totally a lost cause.

11 So can something be done about the audio 

12 here?

13 MR. SZALAJDA: Well, I think all we can do, 

14 Ted, is just encourage people to talk as loudly as 

15 they can into the microphone.

16 Any other local comments on this subject? 

17 All right. We're going to move into the 

18 test method area. 

19 And in particular, talking about the 

20 condensation nuclei counter and the technology used 

21 for measuring fit.

22 Colleen.
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1 MS. MILLER: Well, as we alluded to with 

2 Craig's comments earlier, we would like to ask you 

3 to consider commenting on the use of the CNC with or 

4 without the differential mobility analyzer, 

5 depending on the product being certified. 

6 If anyone has any comments they would like 

7 to share at this point, that would be lovely. If 

8 not, we look forward to seeing your comments in 

9 writing.

10 And perhaps if anyone has any suggestions 

11 or comments about other detection methods that we 

12 should be considering, we would like to hear from 

13 you about that.

14 And as I said, we would like to know how 

15 you feel about the same procedure for filtering 

16 facepieces and elastomerics.

17 As you will see when we go on into the 

18 slides, we're going to talk about a couple of 

19 methods.

20 Would anyone have any comments at this 

21 point? 

22 MR. SZALAJDA: Okay. Again, these are 
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1 issues we would like you to be able to address in 

2 your written comments as we move forward.

3 Anything from LiveMeeting? 

4 All right. One of the other aspects that 

5 we are considering is the possibility of being able 

6 to use corn oil testing as part of the evaluation of 

7 filtering facepiece respirators. 

8 And, again, it's a topic where we're 

9 looking for feedback from stakeholders for a variety 

10 of reasons. 

11 And I think the areas where we're looking 

12 for your feedback and your comments fall into these 

13 categories whether or not because is it going to be 

14 acceptable to test for a not-for-oil respirator in 

15 an oil environment, for one thing. And also, any 

16 issues associated with media degradation -- or media 

17 degradation and test duration.

18 Any comments on the corn oil perspective? 

19 MR. COFFEY: Chris Coffey.

20 We didn't really talk about the corn oil 

21 test in any of our webinars, but we did do 

22 approximately 80 subjects on the respirators in a 
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1 corn oil chamber up here, just kind of like to do a 

2 little bit of background on that.

3 The reason we did was we would be 

4 interested in seeing the difference -- if there's 

5 any difference in the generated aerosol method with 

6 a fairly stable controlled concentration versus 

7 ambient, which could vary. 

8 And in Pittsburgh at the time the study was 

9 done, we only had the corn oil chamber. There was 

10 no sodium chloride chamber available to us. 

11 We do have that data on, like I say, on 80 

12 subjects. We're going to be looking at that data. 

13 But a preliminary review of the data -- well, let me 

14 step back. We did do a filter penetration with 

15 sodium chloride, the certification test basically, 

16 before the subjects went into the corn oil chamber 

17 and then right after they got done with that.

18 And it appears from the preliminary, 

19 cursory, or looking at the data that we did not see 

20 a significant degradation in filter efficiency by 

21 doing two of the OSHA fit tests for about a total of 

22 15, 20 minutes. 
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1 So we were just interested in hearing your 

2 feedback on the use of the corn oil test for testing 

3 in N95s.

4 MS. MILLER: Actually, too, I'll remind you 

5 that your ISEA comments on the last docket did 

6 recommend a size range in the LRPL level, which we 

7 are currently using for our testing here.

8 So does anyone care to discuss whether or 

9 not corn oil is considered to be an acceptable 

10 method or other concerns you might have about using 

11 that method? 

12 MR. SZALAJDA: Okay. If not, I guess than 

13 that's something we'll look for you to provide 

14 comments on before the end of the comment period.

15 Anything from LiveMeeting on corn oil 

16 topic? 

17 No? 

18 All right. And then, the other questions 

19 related to should NIOSH allow the option of multiple 

20 inward leakage test methods.

21 Comments on multiple methods? 

22 Anything from the panel?
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1 MS. MILLER: This is Colleen.

2 I would just say that, you know, we 

3 understand your concerns about providing a detailed 

4 standard test procedure when we do publish a notice 

5 of proposed rule making. 

6 We welcome this opportunity for you to 

7 share and discuss potential test methods and the 

8 procedures required in order for us to provide that 

9 detailed standard test procedure.

10 Please feel free to provide those comments 

11 as well.

12 MR. SZALAJDA: Anything from the audience 

13 on this topic? 

14 One from Terry, Terry Wassell.

15 MR. WASSELL: Yeah. Terry Wassell.

16 This is sort of a question, actually. 

17 Would multiple test methods be in parallel 

18 or in series? 

19 And it is possible to -- is there a 

20 hypothetical example of -- because I'm a little 

21 unclear, and it might be helpful to have some 

22 example.
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1 MR. COFFEY: I think, my personal opinion 

2 is, the question is that can we have multiple test 

3 methods available. 

4 So in saying CNC with ambient, CNC with 

5 corn oil, or whatever, and the manufacturers and 

6 NIOSH would be able to choose whichever one was the 

7 most convenient to use at the time. 

8 We're not saying we're going to have a 

9 subject run ambient air solo and then run a corn oil 

10 like that.

11 So it's going to be a pool of test methods 

12 that could be used to run inward leakage tests. 

13 Just like ISO has proposed in their standard, you 

14 can use corn oil, you can use sodium chloride, you 

15 can use sulfur hexafluoride.

16 So that's what we will be questioning. 

17 MR. SZALAJDA: Comments on this topic? 

18 Anything from LiveMeeting?

19 Okay. And then the last area where we're 

20 looking for feedback with related to implementation 

21 strategies, in particular what strategies do you 

22 suggest to address the implementation of a standard 
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1 for testing face seal, face leakage as part of a 

2 half-mask air-purifying respirator. 

3 And looking at it from a certification 

4 perspective. 

5 MS. MILLER: Chris, did you have a data 

6 sharing update? 

7 MR. COFFEY: Yes. We are working on the 

8 data sharing requests, so that we would be able to 

9 share our inter-panel variability study data with 

10 anyone who wants it.

11 It goes through a number of reviews before 

12 it's approved and we can actually announce it. And 

13 we're hoping that the final approval will be coming, 

14 you know, in the next week or ten days or so, so 

15 that people can request our data and do their own 

16 analysis to help answer some of the questions that 

17 we're asking here.

18 As soon as that is available, I guess, 

19 we'll put an announcement on our website and get it 

20 out to the public somehow, the data now available.

21 MR. SZALAJDA: Yeah. The question came up 

22 after one of the webinars. 
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1 I don't recall whether it was the July or 

2 the August one, whether or not the data that was 

3 used for the inter-panel variability study would be 

4 made available to the public. 

5 And that's what the -- what Chris' comment 

6 on that we're working towards that end. 

7 Any comments from the panel on 

8 implementation strategies?

9 MS. MILLER: Would anyone like to give us 

10 any indication as to whether or not you feel we're 

11 on the right track in terms of dealing with the 

12 issue of inter-panel variability? 

13 Oh, it's temping.

14 MR. SZALAJDA: Anything from LiveMeeting on 

15 this topic? 

16 Okay. Well, with that, you guys are lucky. 

17 We have run out of slides.

18 MS. MILLER: Well, we're not quite --

19 MR. SZALAJDA: Oh, there's more things? 

20 SPEAKER: So you can hear me.

21 This is Phil Eitzman with 3M. 

22 Just the reason that I would have the 
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1 question about the simulated study, I figured out, 

2 was that only the title page is in the docket for 

3 that report. 

4 So I know what the title of that simulation 

5 study is, but there's -- the content of that report, 

6 the RTI, is not -- I couldn't see it on the docket.

7 I could only see the first page.

8 MS. MILLER: I think that happened when I 

9 sent it to Chris as well.

10 So we'll check on that. 

11 Thank you.

12 SPEAKER: You're welcome.

13 MR. SZALAJDA: Thank you for bringing that 

14 to our attention.

15 Are there any closing comments from the 

16 panelists?

17 MS. MILLER: One thing that Roland and I 

18 wanted to be sure to ask about was whether or not 

19 anyone had any comments about the timing of our 

20 implementation or a grandfathering strategy? 

21 You have had a few years to think about it. 

22 MR. SZALAJDA: And, again, I think that's 
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1 something that we would encourage you to submit as 

2 part of your comments to the docket for us to 

3 consider in moving forward.

4 MS. MILLER: And anything that we haven't 

5 considered that you feel that we should consider.

6 MR. SZALAJDA: All right. 

7 So at least in terms of -- I'm sorry. 

8 MR. BERRY ANN: I'm sorry, Jon.

9 This is Roland.

10 In considering your comments about the 

11 length of the grandfathering, any conditions or 

12 specifications that you think would be appropriate 

13 as well, I think we would like to see.

14 MR. SZALAJDA: Any comments from the 

15 LiveMeeting audience?

16 I think, in general at least, to try to 

17 summarize moving forward from an organization 

18 perspective that this project is very important not 

19 only to NPPTL but also NIOSH, CDC, and HHS.

20 And we are very motivated seeing this rule 

21 come forward and be able to provide a performance 

22 requirement to help protect the users of these types 
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1 of respirators.

2 And I would, again, with the questions that 

3 were raised in the Federal Register as well as the 

4 discussion that was provided to today, these are 

5 issues that we are really looking for you to provide 

6 your feedback to us, whether -- you know, and 

7 preferably through the docket so it becomes part of 

8 the record since we are trying to be as transparent 

9 as possible with regard to developing this 

10 performance requirement and implementing it through 

11 our certification practices.

12 The information from the webinars that have 

13 been conducted, as well as the public meeting, and 

14 hopefully the rest of the RTI report will be 

15 available in Docket 250. 

16 The comment period is through October 1.

17 I do not anticipate that we will extend the 

18 comment period since we are not technically in 

19 rulemaking; we're selecting comments. 

20 But, however, please be aware that we are 

21 on a path to get this moving forward again, and 

22 getting something out into the public domain within 
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1 the next several months for moving these through the 

2 formal process.

3 At least in terms of wrapping up the 

4 meeting here today, since you are in the facility, 

5 the shuttles will be available to take you back to 

6 the METL parking lot where you left your cars. All 

7 visitors have to get escorted.

8 You technically need to be escorted while 

9 you're on site. So, you know, we'll make sure you 

10 get back down to the gate.

11 Also, there's a box in the rear of the 

12 room, if you would like to recycle your name tag for 

13 future NPPTL meetings, that would be appreciated.

14 So with that, I appreciate your attendance 

15 and we look forward to receiving your comments with 

16 regard to this very important rule.

17 Thank you. 
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1 CERTIFICATE OF REPORTER

2 I, Joseph A. Inabnet, do hereby certify 

3 that the transcript of the foregoing proceedings was 

4 taken by me in Stenotype and thereafter reduced to 

5 typewriting under my supervision; that said 

6 transcript is a true record of the proceedings; that 

7 I am neither counsel for, related to, nor employed 

8 by any of the parties to the action in which these 

9 proceedings were taken; and further, that I am not a 

10 relative or employee of any attorney or counsel 

11 employed by the parties thereto, nor financially or 

12 otherwise interested in the outcome of the action.

13

14

15 __________________________

16 Joseph A. Inabnet

17 Court Reporter 

18