12/20/10 Deposition Transcript- Rourke

Capital Reporting Company

Rourke, Daniel Lee 12-20-2010

(866) 448 - DEPOwww.CapitalReportingCompany.com

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IN THE UNITED STATES DISTRICT COURT

FOR THE DISTRICT OF MARYLAND

GREENBELT

- - - - - - - - - - - - - - - x

CHEVRON CORPORATION, :

Petitioner, :

vs. : Case No.

DANIEL LEE ROURKE, : 8:10-cv-02989-AW

Respondent. :

- - - - - - - - - - - - - - - x

Washington, D.C.

Monday, December 20, 2010

Videotaped Deposition of:

DANIEL LEE ROURKE

Called for oral examination by counsel for Petitioner,

pursuant to notice, at the law offices of Gibson,

Dunn & Crutcher, 1050 Connecticut Avenue, Northwest,

Washington, D.C., before Denise M. Brunet, RPR, of

Capital Reporting Company, a Notary Public in and for

the District of Columbia, beginning at 9:11 a.m., when

were present on behalf of the respective parties:




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1 A P P E A R A N C E S

2

3 On behalf of the Petitioner:

4 ANDREA E. NEUMAN, ESQUIRE

5 Gibson Dunn & Crutcher, LLP

6 3161 Michelson Drive

7 Irvine, California 92612

8 (949) 451-3800

9 [email protected]

10

11 MICHAEL M. LEE, ESQUIRE


13 333 South Grand Avenue

14 Los Angeles, California 90071

15 (213) 229-7000

16 [email protected]

17

18

19

20

21

22 (Appearances continued on the next page.)




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1 A P P E A R A N C E S C O N T'D

2

3 On behalf of the Petitioner (continued):

4 PETER E. SELEY, ESQUIRE


6 1050 Connecticut Avenue, Northwest

7 Washington, D.C. 20036

8 (202) 955-8500

9 [email protected]

10

11 On behalf of the Respondent:

12 ERIC WESTENBERGER, ESQUIRE

13 Patton Boggs, LLP

14 The Legal Center

15 One Riverfront Plaza

16 Newark, New Jersey 07102

17 (973) 848-5600

18 [email protected]

19

20 ALSO PRESENT: Michael A. Kelsh

21 Kenneth Satin

22 Daniel Holmstock, Videographer




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1 C O N T E N T S

2 EXAMINATION BY: PAGE

3 Counsel for Petitioner 7

4

5 DEPOSITION EXHIBITS: PAGE

6 1300 - Report of Daniel Rourke 16

7 1301 - Rourke-Native 000001 - 000009 50

8 1302 - Handwritten calculations 88

9 1303 - Rourke-Native 010096 127

10 1304 - Rourke-Native 010407 - 010412 128

11 1305 - Stratus-Native 043127 131

12 1306 - Cancer Mortality and oil production in the

13 Amazon Region of Ecuador 1990-2005 147

14 1307 - Outputs 181

15 1308 - DR00000037.xlsb, Chart 1 190

16 1309 - Rourke-Native 000579 207

17 1310 - Rourke-Native 007999 212

18 1311 - Slides of indigenous populations 215

19 1312 - Rourke-Native 008025 218

20 1313 - Subpoena 220

21

22 (Exhibits continued on the next page.)




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1 DEPOSITION EXHIBITS: PAGE

2 1314 - Rourke-Native 010088 - 010089 233

3 1315 - Rourke-Native 010092 237

4 1316 - Rourke-Native 010297 - 010298 241

5 1317 - Rourke-Native 010299 - 010300 244

6 1318 - Addendum of Report of Daniel Rourke 258

7 1319 - Declaration 277

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22 (*Exhibits attached to the original transcript.)




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1 P R O C E E D I N G S

2 THE VIDEOGRAPHER: This is tape number 1 of

3 the videotape deposition of Dr. Daniel Lee Rourke 09:09:39

4 taken in the matter she have corporation, petitioner, 09:09:43

5 versus Dr. Daniel Lee Rourke, respondent, pending 09:09:47

6 before for the United States District Court for the 09:09:52

7 District of Maryland, Greenbelt, action number 09:09:52

8 8:10cv02989-AW. 09:09:52

9 This deposition is being held at the law 09:09:56

10 offices of Gibson Dunn at 1050 Connecticut Avenue, 09:10:04

11 Northwest, in Washington, D.C., on December 20th, 09:10:09

12 2010, at approximately 9:11 a.m. 09:10:11

13 My name is Daniel Holmstock from the firm of 09:10:15

14 Capital Reporting Company, and I am the certified 09:10:17

15 legal video specialist. The court reporter this 09:10:20

16 morning is Denise Brunet, also in association with 09:10:22

17 Capital Reporting Company, located at 1821 Jefferson 09:10:26

18 Place, Northwest, in Washington, D.C. 09:10:29

19 For the record, will counsel please introduce 09:10:31

20 themselves and whom they represent. 09:10:33

21 MS. NEUMAN: Andrea Neuman, Gibson Dunn, on 09:10:35

22 behalf of Chevron Corporation. 09:10:39




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1 MR. LEE: Michael Lee, Gibson Dunn, Chevron. 09:10:43

2 MR. SELEY: Pete Seley, Gibson Dunn, Chevron. 09:10:43

3 MR. WESTENBERGER: Eric Westenberger, Patton 09:10:46

4 Boggs on behalf of the respondent and Ecuadorian 09:10:51

5 plaintiffs. 09:10:51

6 THE VIDEOGRAPHER: Also present, please 09:10:51

7 identify. 09:10:51

8 MR. KELSH: Michael Kelsh. 09:10:56

9 MR. SATIN: Kenneth Satin. 09:10:57

10 THE VIDEOGRAPHER: Will the court reporter

11 please swear or affirm in the witness.

12 WHEREUPON,

13 DANIEL LEE ROURKE,

14 called as a witness, and after having been first duly

15 sworn, was examined and testified as follows: 09:11:09

16 MR. WESTENBERGER: Just before we begin, I 09:11:09

17 understand that this deposition is being streamed 09:11:12

18 somewhere to someplace. Are there others observing or 09:11:13

19 present at this deposition? 09:11:19

20 MS. NEUMAN: Not that I know of. 09:11:20

21 MR. WESTENBERGER: Okay. 09:11:20

22 EXAMINATION BY COUNSEL FOR CHEVRON CORPORATION 09:11:20




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1 BY MS. NEUMAN: 09:11:20

2 Q Good morning, Dr. Rourke. 09:11:24

3 A Good morning. 09:11:25

4 Q Could you state your full name for the 09:11:26

5 record, please. 09:11:28

6 A My name is Daniel Lee Rourke. The last name 09:11:28

7 is R-O-U-R-K-E. 09:11:32

8 Q What's your date of birth? 09:11:34

9 A April 26th, 1942. 09:11:36

10 Q Where were you born? 09:11:39

11 A Charleston, Illinois. 09:11:41

12 Q Where do you currently reside? 09:11:42

13 A Rockville, Maryland. 09:11:46

14 Q Is there any reason you can think of that you 09:11:48

15 cannot not give your best testimony today? 09:11:53

16 A No, there is not. 09:11:54

17 Q Are you currently taking any medication that 09:11:55

18 would interfere with your ability to testify? 09:11:58

19 A No, I am not. 09:12:00

20 Q Have you ever been deposed before? 09:12:01

21 A Yes, I have. 09:12:04

22 Q On how occasions? 09:12:05




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1 A Five times. 09:12:06

2 Q Five times. When was the first time? 09:12:07

3 A I'm going to be hazy on the dates. It was 09:12:10

4 the Fuller-Austin bankruptcy, and I believe it was 09:12:15

5 around 1995. 09:12:19

6 Q What was the nature of your testimony, just 09:12:20

7 generally, no details? 09:12:25

8 A I was one of the folks who estimated the 09:12:26

9 number and value of future claims arising from 09:12:29

10 workplace exposure to asbestos. 09:12:32

11 Q Did you come up with a damage number? 09:12:34

12 A Yes, I did. 09:12:38

13 Q What was that number? 09:12:39

14 A I have no memory of that. 09:12:41

15 Q Was it millions or billions or something less 09:12:43

16 than both of those? 09:12:46

17 A More than millions, less than billions. 09:12:47

18 Q How many plaintiffs were involved in the '95 09:12:51

19 Fuller-Austin case? 09:12:56

20 A Plaintiffs? I'm sorry, I can't recall. 09:12:58

21 Q Were you counting up people or what were you 09:13:05

22 counting up? 09:13:09




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1 A Oh. I'm not quite understanding your 09:13:09

2 question. 09:13:13

3 Q When you calculated damages in the 09:13:13

4 Fuller-Austin case, were you calculating the number of 09:13:16

5 cases of mesothelioma or asbestosis? What were you 09:13:20

6 counting? 09:13:26

7 MR. WESTENBERGER: Objection to form. 09:13:26

8 THE WITNESS: We were estimating the total 09:13:27

9 number broken by down the injury -- two of the injury 09:13:31

10 categories that were mentioned, plus more. 09:13:34

11 BY MS. NEUMAN: 09:13:34

12 Q The total number of what? 09:13:37

13 A Mesotheliomas, lung cancers, other cancers, 09:13:39

14 asbestotics and pleural injuries. 09:13:44

15 Q And were you counting actual injuries or were 09:13:46

16 you estimating? 09:13:57

17 A The future claim number was an estimate, and 09:13:57

18 we attempted to count the number of pending claims 09:14:04

19 also. 09:14:07

20 Q Did you use the same model in the 09:14:07

21 Fuller-Austin case that you've used here? 09:14:15

22 A No, I did not. 09:14:17




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1 Q You used a different model? 09:14:18

2 A That is correct. 09:14:20

3 Q What was the next time you were deposed? 09:14:20

4 A Twice in the matter of W.R. Grace, once for 09:14:25

5 bodily injury and again for property damage. 09:14:36

6 Q What was your role in the W.R. Grace case? 09:14:41

7 A I provided estimates to W.R. Grace for what 09:14:46

8 was at that time referred to as the Sealed Air 09:14:58

9 transaction. 09:15:00

10 Q What is that? 09:15:00

11 A W.R. Grace sold part of their packaging 09:15:02

12 division to Sealed Air. 09:15:10

13 Q Uh-huh. 09:15:11

14 A And this -- I was deposed in a shareholder 09:15:16

15 suit against W.R. Grace. 09:15:19

16 Q In the Fuller-Austin case, were you 09:15:23

17 testifying for the plaintiffs or the defendants? 09:15:27

18 A For the defendant. And I was not the 09:15:28

19 testifying expert. I worked on the case. 09:15:32

20 Q But you were deposed in -- 09:15:34

21 A Yes, I was. 09:15:36

22 Q What law firm did you work with on the 09:15:38




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1 Fuller-Austin case? 09:15:43

2 A Anderson Kill & Olick. 09:15:44

3 Q What law firm did you work for on the 09:15:47

4 W.R. Grace case? 09:15:51

5 A I was represented by attorneys from a big 09:16:01

6 Chicago law firm, David Bernick's. I can't think of 09:16:09

7 the name of it now. I can't think of the name. 09:16:12

8 Q That's fine. When were you next deposed? 09:16:16

9 A 2009 in the Quigley bankruptcy. 09:16:22

10 Q Were you a testifying expert in the Quigley 09:16:35

11 bankruptcy? 09:16:41

12 A Yes, I was. 09:16:41

13 Q Who were you testifying on behalf of? 09:16:43

14 A The ad hoc claimants committee. 09:16:45

15 Q What was the nature of your testimony? 09:16:49

16 A To make estimates of the value and cash flow 09:16:50

17 of the plan as proposed by Pfizer. 09:16:58

18 Q The plan for what? 09:17:04

19 A The bankruptcy. 09:17:07

20 Q Was your testimony excluded in that case by 09:17:07

21 the judge? 09:17:13

22 A No, it was not. 09:17:13




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1 Q What lawyers did you work with in the Quigley 09:17:14

2 bankruptcy case? 09:17:31

3 A Brown Rudnick. 09:17:32

4 Q What was the fifth time you were deposed? 09:17:35

5 A It was again in the matter of W.R. Grace. It 09:17:43

6 was another shareholder suit. And I was defended by 09:17:51

7 Elli Levenstein of -- I still can't think of the law 09:18:02

8 firm -- Kirkland & Ellis. 09:18:07

9 Q Oh, yes. And when was this most recent 09:18:08

10 deposition? 09:18:17

11 A Also in 2009. 09:18:17

12 Q And the party you were testifying on behalf 09:18:19

13 of was W.R. Grace? 09:18:22

14 A Yes. 09:18:23

15 Q Have you ever testified at trial? 09:18:24

16 A As a fact witness, yes. 09:18:37

17 Q What type of case? 09:18:41

18 A It was in the U.S. court of claims. It was a 09:18:45

19 suit of the federal government against Coltec 09:18:50

20 Industries. 09:18:53

21 Q What was the nature of your factual 09:18:54

22 testimony? 09:18:56




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1 A Some years previously I had reviewed a 09:18:56

2 liability estimate prepared by Tillinghast-Towers 09:19:04

3 Perrin. At that time, as I recall, Coltec was trying 09:19:12

4 to set up an entity to deal with their 09:19:16

5 asbestos-related claims for a subsidiary they owned 09:19:21

6 name Garlock. 09:19:24

7 Q And were you just testifying as to what you 09:19:25

8 recalled from these documents you had seen? 09:19:33


10 Q Had the documents been lost or something? 09:19:36

11 Why were they having you testify about them? 09:19:40

12 MR. WESTENBERGER: Objection to the form of 09:19:43

13 the question. 09:19:45

14 THE WITNESS: No, the documents weren't lost, 09:19:45

15 but I was asked about the nature of my review of the 09:19:48

16 Tillinghast-Towers Perrin estimate. 09:19:52

17 BY MS. NEUMAN: 09:19:55

18 Q Have you ever testified at trial on any other 09:19:55

19 occasion? 09:20:01

20 A No, I have not. 09:20:01

21 Q Have you testified in any other type of 09:20:02

22 proceeding? 09:20:05




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1 A No, I have not. 09:20:05

2 Q How did you come to be involved in this case? 09:20:06

3 A I received a telephone call from Tom Vasquez 09:20:31

4 of ARPC saying that they had given my name to the 09:20:39

5 Weinberg Group as someone who had the necessary 09:20:47

6 expertise to do a particular task for them. 09:20:52

7 Q What is ARPC? 09:20:56

8 A Analysis Research Planning Corporation. That 09:21:00

9 is the firm that I was one of the founders of back in 09:21:05

10 1998. 09:21:07

11 Q Do you still work with Analysis Research 09:21:08

12 Planning Corporation? 09:21:15

13 A I retired in 1963 and, two years later, I 09:21:15

14 served as a consultant to them for a few matters 09:21:23

15 number. 09:21:52

16 MS. NEUMAN: I'm going to mark as 09:21:52

17 Exhibit 1300 the report of Dr. Rourke -- or Daniel 09:21:54

18 Rourke, Ph.D., September 12th, 2010. The title page 09:22:02

19 says, "Estimate of the number and cost of excess 09:22:06

20 cancer deaths associated with residents in the 09:22:08

21 oil-producing areas of the Sucumbios and Orellana 09:22:11

22 provinces in Ecuador. 09:22:14




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1 (Deposition Exhibit Number 1300 was marked 13:56:32

2 for identification.) 13:56:32


4 Q Is Exhibit 1300 the report that you submitted 09:22:32

5 in this matter, Dr. Rourke? 09:22:36

6 A I believe so. 09:22:40

7 Q Did you ever see the -- do read Spanish, sir? 09:23:19

8 A No, I do not. 09:23:24

9 Q Do you speak or read any languages other than 09:23:25

10 English? 09:23:29

11 A French, reading. 09:23:29

12 Q Have you reviewed a certified translation of 09:23:32

13 the Spanish version of your report that was submitted 09:23:36

14 in Ecuador? 09:23:39

15 A No, I have not. 09:23:39

16 Q The last two pages of Exhibit 1300 appear to 09:23:41

17 be your CV. Have you turned to those? 09:23:48

18 A Yes, I have turned to it. 09:23:52

19 Q Is that a true and accurate version of your 09:23:53

20 CV that you prepared? 09:23:57

21 A Yes, it is. 09:23:57

22 Q Can you describe for me your educational 09:24:00




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1 background, just starting with when and where you 09:24:05

2 graduated from high school? 09:24:07

3 A Oh. Received a bachelor's degree from UCLA 09:24:08

4 in 1965. I graduated cum laude and was a member of 09:24:16

5 Phi Beta Kappa. 09:24:24

6 Q What was your degree in? 09:24:25

7 A Psychology. In 1971, I received a Ph.D. in 09:24:26

8 experimental psychology from UCLA with specializations 09:24:38

9 in mathematical psychology, human cognitive processes 09:24:43

10 and perception. 09:24:50

11 Q What is experimental psychology? 09:24:50

12 A The application of experimentation and the 09:24:55

13 scientific method to human behavior. 09:25:01

14 Q And once you got your Ph.D. in experimental 09:25:04

15 psychology, did you begin treating patients? 09:25:10

16 A That's not the sort of psychologist I was. 09:25:11

17 That would be a clinical psychologist. 09:25:15

18 Q Okay. So you never treated patients as a 09:25:16

19 psychologist -- 09:25:20

20 A No. 09:25:20

21 Q -- correct? 09:25:20

22 After you obtained your Ph.D. in experimental 09:25:22




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1 psychology in 1971, what did you do next? 09:25:25

2 A I had a two-year post-doctoral fellowship at 09:25:28

3 the Rockefeller University in New York City in the 09:25:32

4 laboratory of Professor William K. Estes, continuing 09:25:35

5 my psychological research, serving as the in-house 09:25:39

6 statistician, and also setting up what became a 09:25:43

7 computer-based laboratory for human memory and 09:25:48

8 learning experiments. 09:25:54

9 In 1973 -- and at some point the dates are 09:25:59

10 going to be fuzzy, but I'll at least have the order 09:26:04

11 correct -- I took a faculty position at Wayne State 09:26:07

12 University in Detroit, Michigan, as an assistant 09:26:12

13 professor of psychology, teaching predominantly 09:26:15

14 undergraduate statistics, the first course in graduate 09:26:23

15 statistics which covered things like regression 09:26:28

16 analysis, a graduate course in multivariate analysis, 09:26:31

17 some seminars in human learning and mathematical 09:26:39

18 psychology. And I also taught the undergraduate 09:26:43

19 learning course and the undergraduate perception 09:26:47

20 course. 09:26:51

21 Q And how long were you at Wayne State 09:26:51

22 University? 09:26:53




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1 A From -- as assistant professor, from 1973 to 09:26:53

2 about 1976. 09:26:59

3 Q What was your next position? 09:27:01

4 A I joined the computing center at Wayne State 09:27:05

5 University for the following one-and-a-half to two 09:27:11

6 years as the person responsible for the statistical 09:27:15

7 software on the university's mainframe, the numerical 09:27:21

8 analysis software and the instruction in the uses of 09:27:26

9 this software. This was before the era of personal 09:27:30

10 computers, and everybody used the university mainframe 09:27:34

11 to do their statistical analyses. 09:27:39

12 Q What did you do after that year and a half? 09:27:41

13 A I -- excuse me. Would it be possible to get 09:27:45

14 some water? 09:27:50

15 Q Yes. 09:27:51

16 A Thank you very much. 09:27:54

17 Q Do you want to proceed or do you want to go 09:27:55

18 off the record while we wait? 09:27:58

19 A Could we go off the record just for a second 09:27:59

20 so -- 09:28:03

21 Q Sure. 09:28:03

22 A -- I can got a sip? I'm beginning to sound 09:28:04




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1 like Kermit the Frog. 09:28:06

2 MS. NEUMAN: We can go off the record. 09:28:07

3 THE VIDEOGRAPHER: The time is 9:28 a.m. 09:28:07

4 We're going off the record. 09:28:10

5 (Whereupon, a short recess was taken.) 09:28:11

6 THE VIDEOGRAPHER: The time is 9:32 a.m., and 09:28:11

7 we're back on the record. 09:31:18

8 THE WITNESS: I believe I'm up to going from 09:31:19

9 Wayne State -- the Wayne State computer -- mainframe 09:31:25

10 computer -- computing center over to Michigan, but can 09:31:33

11 I back up? Because I see I forgot the RAND 09:31:36

12 Corporation. 09:31:39

13 BY MS. NEUMAN: 09:31:39

14 Q Sure. 09:31:40

15 A When I was a graduate student, I had first a 09:31:40

16 part-time and then a full-time position with the RAND 09:31:44

17 Corporation. I worked in the social sciences 09:31:47

18 department, but the person I worked with was in the 09:31:50

19 math department, Norm Dalkey. And we were working on 09:31:55

20 something called the Delphi Method, and I was doing 09:32:04

21 the statistics for that and some of the experimental 09:32:06

22 design and some other things that I can't talk about. 09:32:09




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1 Q Okay. 09:32:12

2 A Around 1979, I decided to go back to school, 09:32:14

3 graduate school, and I went over to the University of 09:32:26

4 Michigan in the statistics department and, at the same 09:32:31

5 time, had a half-time job in something called the 09:32:34

6 statistical research laboratory. 09:32:38

7 Q Did you got a degree in statistics from the 09:32:39

8 University of Michigan? 09:32:42

9 A No, I did not. 09:32:43

10 Q Why not? 09:32:48

11 A A more lucrative opportunity came our way, 09:32:50

12 and I'll get to that in a moment. 09:32:56

13 In the statistical research laboratory, I did 09:33:00

14 statistical consulting about half-time and software 09:33:04

15 development on an interactive computer analysis system 09:33:07

16 called MIDAS, which was an acronym for the Michigan 09:33:12

17 data analysis system. 09:33:18

18 Around 1981 or 1982, a friend of mine from 09:33:22

19 graduate school days at UCLA got a consulting 09:33:28

20 assignment with IBM, and we began to do some of the 09:33:32

21 market research that actually led to the IBM personal 09:33:39

22 computer in September 1981. Among this, for example, 09:33:45




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1 picking the software that would be converted to run on 09:33:51

2 the IBM PC back around 1979. And we did PC sales 09:33:55

3 forecasting and market tracking from 1982 through 09:34:06

4 around 1988. 09:34:09

5 Q Okay. And then what did you do in '89? 09:34:11

6 A Around 1988, IBM decided to go in a somewhat 09:34:19

7 different direction with respect to the personal 09:34:26

8 computer, and we lost the funding for the PC sales 09:34:28

9 forecasting component. And I took a position with 09:34:33

10 Arbitron Ratings and became their senior statistician. 09:34:39

11 The responsibilities were the design of what 09:34:45

12 at that time was going to be the competitor with the 09:34:52

13 A.C. Nielson national rating system, the Nielson 09:34:59

14 television index, and to respond to questions from our 09:35:03

15 clients concerning the adequacy of the TV sample that 09:35:11

16 we were running for local markets at the time. 09:35:17

17 Q So is it fair to say that, although your 09:35:19

18 degrees are in psychology and experimental psychology, 09:35:23

19 most of your career has been spent as a statistician? 09:35:27


21 Q But you don't have any particular degrees in 09:35:31

22 statistics? 09:35:35




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1 A No, I do not. 09:35:35

2 Q So then where did you go after Arbitron? 09:35:37

3 A I joined what at that time was the Analysis 09:35:41

4 Research -- well, RPC, Research Planning Corporation, 09:35:44

5 but those were really the folks that I worked with for 09:35:50

6 the following 18 years, 1990 through -- well, 09:35:53

7 actually, the present day, come to think of it; longer 09:35:58

8 than 18 years. 09:36:01

9 Initially, we were a free-standing consulting 09:36:03

10 group, and did I not have an equity position then. 09:36:07

11 That went from 1990 through, let's say, 1993. We were 09:36:10

12 purchased by KPMG and became KPMG Resource Planning 09:36:17

13 Consultants. And I worked at KPMG as a senior manager 09:36:25

14 doing pretty much the same thing I did with RPC. 09:36:32

15 Q From '93 till? 09:36:35

16 A We bought ourselves out of KPMG in 1998 and 09:36:37

17 formed what is now ARPC. 09:36:42

18 Q I like the acronyms. 09:36:44

19 A We had to actually add the A because KPMG 09:36:48

20 wouldn't let us have RPC back. 09:36:56

21 Q Ah. So ARPC currently exists? 09:36:58

22 A Yes, it does. 09:37:03




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1 Q You're currently an equity shareholder? 09:37:03

2 A No, I am not. They bought me out when I 09:37:05

3 retired. 09:37:08

4 Q Okay. And remind me your year of retirement. 09:37:08

5 A 2003. 09:37:12

6 Q Since 2003, have you had full-time 09:37:17

7 employment? 09:37:22

8 A No, I have not. 09:37:22

9 Q From '93 to 2003 what was your title at 09:37:25

10 either KPMG or ARPC? 09:37:46

11 A I was a senior manager at KPMG. 09:37:49

12 Q In any particular department or just a senior 09:37:52

13 manager? 09:37:57

14 A Well, the department was KPMG Resource 09:37:57

15 Planning Consultants, except they... 09:38:04

16 Q KPMG is an accounting firm? 09:38:05

17 A Yes. 09:38:14

18 Q And they were helping their clients plan the 09:38:15

19 allocation of their resources, I take it? 09:38:17

20 MR. WESTENBERGER: Objection to form. 09:38:20

21 THE WITNESS: What I did was -- part of the 09:38:20

22 time was the same sort of thing that I had done when 09:38:27




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1 we were RPC. And the first day I walked in the door 09:38:31

2 at RPC -- actually, I think my second meeting was the 09:38:35

3 beginning of my career beginning to estimate future 09:38:40

4 claims arising from workplace exposure to asbestos. 09:38:47

5 That work continued, but it turns out that 09:38:53

6 accounting firms actually make use of survey samples 09:39:00

7 even though they're called audit samples, and I spent 09:39:06

8 about half of my time working on audit samples for 09:39:08

9 KPMG. 09:39:13

10 Then, when we formed ARPC, I was one of the 09:39:18

11 principals and I was a vice president. 09:39:25

12 BY MS. NEUMAN: 09:39:28

13 Q And when you were working for ARPC from '98 09:39:32

14 to 2003, what was the primary nature of your work? 09:39:37

15 A More asbestos claims forecasting. 09:39:42

16 Q Were these forecasts for companies so they 09:39:46

17 could plan for potential liability or for some other 09:39:56

18 purpose? 09:39:59

19 A It depended who the client was. We worked -- 09:40:00

20 do you want a roster of the clients? 09:40:05

21 Q Yeah. Who would you do this forecasting work 09:40:07

22 for? 09:40:11




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1 A We did it for the EaglePicher and the 09:40:11

2 EaglePicher bankruptcy for the corporation, and then 09:40:16

3 subsequently for the trust that was developed. We 09:40:19

4 worked for the UNR trust, for the Manville personal 09:40:23

5 injury settlement trust, for W.R. Grace, for the 09:40:28

6 corporation, for Babcock & Wilcox for the future claim 09:40:37

7 representative, for Fuller-Austin for the corporation 09:40:43

8 that owned Fuller-Austin, for the Manville personal 09:40:50

9 injury settlement trust, if I haven't mentioned them 09:40:58

10 already, for Owens Corning for both the 09:41:00

11 corporation and I guess the trust, for Celotex, the 09:41:05

12 trust, the Amatex trust -- that's a small one up in 09:41:09

13 Philadelphia you might not have heard of -- and Paycor 09:41:18

14 is another small one up in Philadelphia that you might 09:41:22

15 not have heard of either. 09:41:24

16 And there are probably some that I've 09:41:27

17 forgotten. 09:41:28

18 Q The model that you use to do these estimates 09:41:28

19 of future claims based on workplace exposure to 09:41:34

20 asbestos, is that the same model you used in this 09:41:39

21 case? 09:41:43

22 A No, it is not. 09:41:43




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27

1 Q When you were estimating future claims due to 09:41:45

2 workplace exposure to asbestos, were you estimating 09:41:50

3 the number of claims that were anticipated? 09:41:54

4 A Yes. 09:41:57

5 MR. WESTENBERGER: Object to the form. 09:41:58


7 Q When you were estimating future claims due to 09:42:00

8 workplace exposure to asbestos, were you putting 09:42:02

9 valuations on those claims? 09:42:06

10 A Sometimes yes and sometimes not. It depended 09:42:07

11 on what the client wanted. 09:42:14

12 Q When you would put a valuation on these 09:42:15

13 future asbestos claims, would the valuation vary 09:42:17

14 depending on disease type? 09:42:22

15 A Yes, it would. 09:42:24

16 Q Do you put a higher value on a mesothelioma 09:42:26

17 claim than you would on an asbestosis claim? Is that 09:42:30

18 fair to say? 09:42:34

19 A That's generally true, yes. 09:42:34

20 Q Can you tell me the values in these analyses 09:42:37

21 you would put on a mesothelioma claim? 09:42:41





© 2010

28


2 THE WITNESS: That would depend on who the 09:42:46

3 client was. 09:42:48


5 Q Why would it depend on that? 09:42:52

6 A If it were a trust, the trust would, more 09:42:53

7 often than not, have a document called a TDP, the 09:43:00

8 trust distribution plan, in which at least the range 09:43:03

9 of possible values for each of the compensable 09:43:07

10 injuries would be specified. 09:43:11

11 If it were for a bankruptcy situation and one 09:43:19

12 was evaluating the claims had it not been for the 09:43:25

13 bankruptcy, you'd use perhaps an average adjusted for 09:43:30

14 inflation or the result of some regression analysis 09:43:38

15 perhaps adjusted for inflation. 09:43:42

16 Q An average of what? 09:43:44

17 A The settlement values. And let me -- the 09:43:47

18 settlement values could be a result of settlements and 09:43:53

19 jury verdicts. 09:43:58

20 Q What's the highest value you put on a 09:43:58

21 mesothelioma claim in one of these models where you 09:44:12

22 were estimating future claims due to asbestos exposure 09:44:20




© 2010

29

1 in the workplace? 09:44:23



4 THE WITNESS: I couldn't answer without 09:44:26

5 having a chance to review documents that are no longer 09:44:28

6 in my possession. 09:44:32


8 Q Could you give me a range? 09:44:34

9 A This is solely from memory. It varies, 09:44:36

10 depending on the client. Do you want a TDP value or 09:44:48

11 an average? The question is not simple to answer. 09:44:55

12 I'm sorry. 09:45:00

13 Q I'm just asking for your best recollection of 09:45:00

14 the highest value that you put on a mesothelioma claim 09:45:03

15 in one of your models? 09:45:06

16 MR. WESTENBERGER: Objection. I think he 09:45:08

17 said he didn't recall that. 09:45:10

18 THE WITNESS: It would be purely based on my 09:45:11

19 faulty memory at this point. Somewhere less than 09:45:14

20 $5 million, I would suspect, from mesothelioma, even 09:45:17

21 though I believe the jury verdicts are now higher than 09:45:23

22 that. 09:45:27




© 2010

30


2 Q Would the settlement values in the trust vary 09:45:27

3 based on jurisdictions? 09:45:32

4 A It depends on the TDP of the trust. 09:45:33

5 Q What is a TDP? 09:45:38

6 A Trust distribution plan. These are generally 09:45:41

7 available on the websites for all these trusts, by the 09:45:47

8 way, that spell out what gets to go into a thing. 09:45:51

9 Some of the recent trusts allow the jurisdiction to be 09:45:54

10 taken into account and some do not. 09:45:59

11 Q The model that you used for your report in 09:46:08

12 this case, have you ever used it before or did you 09:46:10

13 create it for this case? 09:46:15

14 A It was created for this case. 09:46:16

15 Q Have you ever -- withdraw that. 09:46:18

16 Had the model that you created for this case 09:46:34

17 ever been independently validated by another 09:46:37

18 scientist? 09:46:39



21 THE WITNESS: No, it has not. 09:46:42

22 BY MS. NEUMAN: 09:46:43




© 2010

31

1 Q Have you published any work on the model that 09:46:46

2 you created for this case, disclosing to other 09:46:50

3 scientists/statisticians how it works? 09:46:54



6 THE WITNESS: No, I have not. 09:47:01


8 Q In the trusts, would the settlement values 09:47:17

9 vary by the age of the population exposed? 09:47:19



12 THE WITNESS: Again, it depends on the trust, 09:47:28

13 but the answer is -- in the recent trusts, the answer 09:47:32

14 is yes. 09:47:36

15 BY MS. NEUMAN: 09:47:36

16 Q Now, have you prior to this case ever done 09:47:36

17 any work in the petroleum industry? 09:47:48




21 BY MS. NEUMAN: 09:47:55

22 Q Have you ever been to an oil field? 09:47:55




© 2010

32

1 A No, I have not. 09:47:57

2 Q Have you ever previously worked for an oil 09:47:58

3 company? 09:48:08

4 A I don't believe so. 09:48:08

5 Q Have you ever previously opposed an oil 09:48:14

6 company in litigation? 09:48:18

7 A I take that back. In fact, I believe I did 09:48:19

8 work for an oil company, Sun Oil. 09:48:29

9 Q On what type of matter? 09:48:34

10 A Estimation of claims arising from Sun ship, 09:48:40

11 as a matter of fact. 09:48:47

12 Q Sun ship or Sun chip? 09:48:48

13 A Sun ship. 09:48:51

14 Q Really? I thought those were healthy. 09:48:52

15 A No, it turns out that Sun Oil had one of the 09:48:54

16 biggest shipyards during the Second World War that 09:48:58

17 continued until about 1951 or '2. 09:49:01

18 Q So this was asbestos-related work? 09:49:03

19 A Yes, it was. 09:49:05

20 Q Do you have any ill feelings or animus of any 09:49:08

21 kind towards Texaco? 09:49:16

22 A No. 09:49:17




© 2010

33

1 Q Do you have any ill feelings or animus of any 09:49:18

2 kind towards Chevron? 09:49:22

3 A No. 09:49:22

4 Q Are you an epidemiologist? 09:49:23

5 A No, I am not. 09:49:27

6 Q Is it fair to say you have no expertise in 09:49:30

7 the field of epidemiology? 09:49:32

8 A Other than to point out I've been working in 09:49:37

9 the asbestos, certainly, injury field and am 09:49:44

10 moderately well-known, I guess -- I don't know how 09:49:48

11 else to answer your question. 09:49:52

12 Q Well, you don't have a degree in 09:49:53

13 epidemiology, correct? 09:49:57

14 A No, I do not. 09:49:57

15 Q You don't have any formal training in 09:49:58

16 epidemiology; is that right? 09:50:01


18 Q You've never published in the field of 09:50:04

19 epidemiology, have you? 09:50:06


21 Q You've never taught in that field, correct? 09:50:08

22 A No, I haven't. 09:50:09




© 2010

34

1 Q Do you belong to any of the epidemiological 09:50:10

2 associations, like the American College of 09:50:15

3 Epidemiology? 09:50:17

4 A No, I do not. 09:50:18

5 Q Do you have any expertise with regard to 09:50:19

6 crude oil? 09:50:37

7 A No, I do not. 09:50:37

8 Q Do you have any expertise regarding the 09:50:39

9 components of crude oil? 09:50:42

10 A To what degree of specificity? 09:50:43

11 Q Do you know what chemical components comprise 09:50:52

12 crude oil? 09:50:58

13 A Only some of them. 09:50:58

14 Q Have you done any research on the health 09:50:59

15 effects of crude oil? 09:51:03

16 A No, I have not. 09:51:04

17 Q Have you done any research on the health 09:51:05

18 effects of any component of crude oil? 09:51:07

19 A Yes, I have. 09:51:12

20 Q Can you describe that research for me, 09:51:13

21 please? 09:51:16

22 A It was benzene. 09:51:16




© 2010

35

1 Q What was the research? 09:51:18

2 A Reviewing the sum of the epidemiological 09:51:22

3 literature relating benzene exposure to AML, acute 09:51:32

4 myelitic, I guess -- 09:51:39

5 Q Myelogenous? 09:51:39

6 A Right. Leukemias. 09:51:41

7 Q And for whom did you review the 09:51:44

8 epidemiological literature regarding an association 09:51:47

9 between benzene and AML? 09:51:51

10 A It was for a law firm named Zevnik Horton, 09:51:52

11 and they were defending some of their clients against 09:52:02

12 suits for leukemias brought by -- related to benzene 09:52:11

13 exposure. 09:52:17

14 Q And did you write a report based on this 09:52:17

15 review of the literature? 09:52:21

16 A Yes, I did. 09:52:22

17 Q Was that report filed with the court, to your 09:52:23

18 knowledge? 09:52:26

19 A No, it was not. 09:52:26

20 Q And what type of benzene exposure were you 09:52:27

21 looking at? 09:52:34

22 A It were -- was -- this is going back a few 09:52:34




© 2010

36

1 years -- a component of cleaning solvents, as a matter 09:52:43

2 of fact. I can't remember if it were cleaning 09:52:51

3 solvents for engine degreasers or for dry cleaning. 09:52:52

4 Q And were you giving opinions on whether or 09:52:58

5 not benzene in cleaning solvents caused leukemia or 09:53:04

6 something else? 09:53:08



9 THE WITNESS: I was attempting to make an 09:53:11

10 estimate of the number of claims that might arise. 09:53:13

11 BY MS. NEUMAN: 09:53:15

12 Q From exposure to the cleaning solvent? 09:53:18

13 A That's correct. 09:53:20

14 Q Did you just look at one epi study or all 09:53:23

15 valuable epi studies -- 09:53:28

16 MR. WESTENBERGER: Objection to the form. 09:53:30

17 BY MS. NEUMAN: 09:53:32

18 Q -- on the benzene? 09:53:34



21 THE WITNESS: Certainly more than one, but 09:53:37

22 less than all. 09:53:39




© 2010

37


2 Q In that case, how did you decide which 09:53:40

3 benzene studies to look at? 09:53:43

4 A I may have the name of this wrong, but I 09:53:44

5 believe Science Citation Index gives the number of 09:53:56

6 references to published articles, and I tended to look 09:54:00

7 at the ones that had the larger number of references, 09:54:09

8 citations, in other scientific literature. 09:54:12

9 Q You tended to look at the studies that were 09:54:15

10 more heavily cited by the other scientists; is that 09:54:19

11 right? 09:54:21


13 Q Why did you do that? 09:54:22

14 A My assumption was that these were the more 09:54:23

15 important articles. 09:54:28

16 Q Based on the fact that they appeared to have 09:54:28

17 been cited, if not relied on, by the other scientists 09:54:38

18 in the same field; is that right? 09:54:41


20 Q Do you know whether weathered crude contains 09:54:46

21 any benzene? 09:54:54

22 A No, I do not. 09:54:55




© 2010

38

1 Q Other than -- and your benzene research was 09:54:56

2 related to cleaning solvents, not crude oil, correct? 09:55:06


4 Q Have you ever published any articles on the 09:55:11

5 health effects of exposure to crude oil? 09:55:19

6 A No, I have not. 09:55:22

7 Q Are you -- do you consider yourself to be 09:55:24

8 thoroughly familiar with the epidemiological 09:55:28

9 literature on the health effects of crude oil? 09:55:32



12 THE WITNESS: I would have to say no. 09:55:37

13 BY MS. NEUMAN: 09:55:40

14 Q Are you aware that there is a body of 09:55:42

15 epidemiological literature that looks at the health 09:55:44

16 effects of crude oil? 09:55:47

17 A Yes, I am. 09:55:49

18 Q As I understand it, that's not a body of 09:55:51

19 literature that you reviewed in forming your opinions 09:56:03

20 in this case; is that right? 09:56:07






© 2010

39

1 THE WITNESS: That really isn't quite right 09:56:10

2 because I did, in fact, review articles that related 09:56:12

3 at least residents in the areas in which crude oil was 09:56:19

4 present in Ecuador to association with health effects. 09:56:23


6 Q How many such articles did you review? 09:56:30

7 A Five or six. 09:56:37

8 Q And these were all epi studies based in 09:56:39

9 Ecuador? 09:56:45

10 A Or reports, yes. 09:56:45

11 Q Did you produce all of these? 09:56:46

12 A Yes, I did. 09:56:51

13 Q Did you review any of the epidemiological 09:56:54

14 literature relating to the health effects of crude oil 09:57:06

15 outside of Ecuador? 09:57:10

16 A No, I did not. 09:57:10

17 Q The five or six reports that you reviewed 09:57:14

18 relating to the health effects of crude oil in 09:57:29

19 Ecuador, were those reports that you found yourself or 09:57:32

20 that were provided to you? 09:57:35

21 A I found them myself. 09:57:35

22 Q How did you find them? 09:57:36




© 2010

40

1 A Through the use of Google. 09:57:38

2 Q What did you search? 09:57:42

3 A I'm trying to recall. Various combinations 09:57:49

4 of Google searches involving Ecuador, cancer -- 09:58:03

5 "excess risk" might have been a phrase I used. And I 09:58:12

6 guess I was aware, from other things that I did, of 09:58:22

7 the International Journal of Epidemiology and a few of 09:58:24

8 the others. I probably went to their web sites also 09:58:28

9 and -- started recently and went back in time, looking 09:58:33

10 at the titles and abstracts. 09:58:38

11 Q Why did you limit your search to only looking 09:58:39

12 at epidemiology that involved Ecuador in particular? 09:58:43

13 A Because it seemed to me the most relevant to 09:58:46

14 the task at hand. 09:58:50

15 Q When you say "the task at hand," what were 09:58:51

16 you asked to do in this case? 09:59:08

17 A Estimate -- to estimate the number and cost 09:59:10

18 or value -- I mean the same by both words -- of the 09:59:18

19 number of excess cancers. 09:59:25

20 Q Now, when you Google the types of terms 09:59:27

21 you've listed, Google brings up Chevron's rebuttal 09:59:39

22 reports to the cancer claims in Ecuador. Are those 09:59:44




© 2010

41

1 some of the reports that you analyzed? 09:59:47

2 A I focused on the articles that appeared in 09:59:49

3 the peer-reviewed journals, so I don't know if I have 09:59:57

4 any Chevron rebuttals or not. 10:00:00

5 Q But if Chevron's -- if scientists whom 10:00:04

6 Chevron had hired to look at these issues' work 10:00:08

7 appeared in a peer-reviewed journal, you would have 10:00:11

8 looked at that report, correct? 10:00:16

9 A I should have at least found it. 10:00:19

10 Q And if you found it and if it pertained to 10:00:21

11 Ecuador, would you have reviewed it? 10:00:26

12 A I would have read it, I believe. 10:00:27

13 Q Would you have considered it in forming your 10:00:29

14 opinion? 10:00:37



17 THE WITNESS: Yes. 10:00:41

18 BY MS. NEUMAN: 10:00:47

19 Q Do you consider yourself an expert on cancer? 10:00:50



22 THE WITNESS: Generally, I'd have to say no, 10:00:55




© 2010

42

1 I'm not a physician. 10:01:05


3 Q You're not a medical doctor; is that right? 10:01:07


5 Q Have you ever conducted any research with 10:01:09

6 regard to cancer causation? 10:01:13

7 A Conducted research? No, I have not. 10:01:14

8 Q Have you ever published any articles or books 10:01:16

9 on the issue of cancer causation? 10:01:23

10 A No, I have not. 10:01:25

11 Q Do you consider yourself to be an expert in 10:01:26

12 the etiology of any cancer? 10:01:30

13 A When you say etiology, do you mean at the 10:01:31

14 cellular level? 10:01:43

15 Q I mean the -- have you ever studied the 10:01:44

16 causes of any cancer from a medical perspective? 10:01:47



19 THE WITNESS: Studied the causes? All right. 10:01:52

20 I have to interpret that as, yes, I have read quite a 10:01:59

21 bit of the literature concerning mesothelioma, 10:02:05

22 especially the dose response models that have been 10:02:11




© 2010

43

1 developed relating asbestos exposure to mesothelioma 10:02:15

2 incidence. 10:02:26


4 Q You understand the concept of dose response; 10:02:28

5 is that fair to say? 10:02:31

6 A Yes. 10:02:31

7 Q Is that a recognized concept in disease 10:02:32

8 causation? 10:02:37



11 THE WITNESS: It could include causation, but 10:02:40

12 not necessarily. 10:02:45

13 BY MS. NEUMAN: 10:02:46

14 Q Well, do -- from your review of the 10:03:01

15 literature of -- asbestos literature, is it generally 10:03:05

16 accepted that there's a dose response to exposure or 10:03:09

17 not? 10:03:17



20 THE WITNESS: I'm sorry. It's just not that 10:03:20

21 simple. Back when the EPA, under William Nicholson's 10:03:23

22 guidance, developed the dose response model that was 10:03:30




© 2010

44

1 used for a rulemaking for mesothelioma, it was 10:03:35

2 believed that the only cause for mesothelioma was 10:03:40

3 asbestos exposure. And that, given a relatively large 10:03:43

4 number of background cases that occur in the absence 10:03:50

5 of good documentation for asbestos exposure, suggests 10:03:55

6 that there may be other causes, in which case the dose 10:04:00

7 response model might not be enough to account for all 10:04:06

8 causation. 10:04:10


10 Q But the concept of dose response is an 10:04:13

11 accepted concept in disease causation? In other 10:04:17

12 words, the more you're exposed to, the more likely you 10:04:20

13 are to get whatever the thing you're being exposed to 10:04:24

14 causes, right? 10:04:26



17 THE WITNESS: I have to say yes. 10:04:28

18 BY MS. NEUMAN: 10:04:30

19 Q Is it accurate that you don't have any 10:04:35

20 experience in diagnosing or treating cancer? 10:04:43

21 A That is correct. I do not. 10:04:48

22 Q Other than the work that you did for the 10:04:50




© 2010

45

1 plaintiffs in this case, have you ever performed an 10:05:04

2 assessment of excess cancer death damages at an oil 10:05:07

3 exploration and production site? 10:05:11

4 A No, I have not. 10:05:13

5 Q Other than the work that you did for the 10:05:14

6 plaintiffs in this case, have you ever worked on a 10:05:24

7 case in Ecuador? 10:05:27

8 A No, I have not. 10:05:27

9 Q Have you ever worked on a case outside the 10:05:32

10 United States? 10:05:36

11 A I can't recall having done so. 10:05:36

12 Q Plaintiffs' counsel who you're working with 10:05:47

13 now from Patton Boggs, have you ever worked for that 10:06:02

14 firm before? 10:06:06

15 A I believe I did once back in 1991, but I have 10:06:07

16 no memory of what it was. 10:06:15

17 Q What it was that you might have done for 10:06:18

18 them? 10:06:23


20 Q Can you take -- you have still got 10:06:24

21 Exhibit 1300 in front of you, Dr. Rourke? 10:06:37

22 A Yes. 10:06:39




© 2010

46

1 Q Can you turn to page 1 for me, please. The 10:06:39

2 title of your report is, "Estimate of the number and 10:06:50

3 cost of excess cancer deaths associated with residents 10:06:54

4 in the oil-producing areas of Sucumbios and Orellana 10:06:57

5 provinces of Ecuador"; is that right? 10:07:01


7 Q Have you ever been to either of those 10:07:03

8 provinces? 10:07:06

9 A No, I have not. 10:07:07

10 Q Have you ever been to Ecuador? 10:07:08

11 A No, I have not. 10:07:09

12 Q When you say "associated with residents," how 10:07:10

13 did you determine there was any excess cancer deaths 10:07:17

14 associated with residents in oil-producing areas? 10:07:20

15 A I relied on one of the references to this 10:07:24

16 report that's in the list of references, the Hurtig 10:07:31

17 and San Sebastian ten paper. 10:07:40

18 Q For that association -- conclusion? 10:07:40


20 Q Did you rely exclusively on the San Sebastian 10:07:42

21 paper? 10:07:46

22 A Yes, I did. 10:07:46




© 2010

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1 Q Why did you rely exclusively on that paper? 10:07:47

2 A Because it seemed the most germane and 10:07:50

3 presented the evidence in the article in a way that I 10:07:54

4 could use. 10:07:59

5 Q Did you read all the published criticisms of 10:08:00

6 the San Sebastian paper before you relied on it? 10:08:03

7 A I attempted to. 10:08:05

8 Q When you say "associated," what do you mean? 10:08:08

9 A I am ruling out -- well, let me rephrase 10:08:18

10 that. I guess the issue here is causation, and I am 10:08:28

11 not inferring causation. 10:08:37

12 Q You're not inferring causation from this 10:08:38

13 association; is that right? 10:08:47


15 Q So you're not giving an opinion that living 10:08:49

16 in a oil-producing area causes you to get cancer; is 10:08:51

17 that right? 10:08:55


19 Q Your opinion -- well, let me withdraw that. 10:08:55

20 Okay. I understand that associated doesn't 10:09:06

21 mean caused. What does it mean the way you use it? 10:09:08

22 A It means correlation. 10:09:10




© 2010

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1 Q Can you define that for me? 10:09:12

2 A Define that? If you live -- although the 10:09:20

3 evidence -- well, "evidence" is perhaps not the right 10:09:36

4 word I want to use, but the Hurtig and Jamieson (sic) 10:09:38

5 article suggested that residents in the four cantons 10:09:43

6 they studied led to a somewhat greater risk of cancer. 10:09:50

7 But, again, "led to" does not mean causation. It's a 10:09:54

8 correlational thing where, if one thing -- if you 10:09:57

9 observe one thing, then you're more likely to observe 10:10:01

10 another thing. 10:10:05

11 The one thing they observed is residents in 10:10:06

12 the oil-producing region. The other thing they 10:10:09

13 observed is somewhat more cancers than would be 10:10:13

14 expected. 10:10:17

15 Q Is it accurate that Hurtig and San Sebastian 10:10:17

16 did not make any determination about why living in an 10:10:22

17 oil-producing canton might cause you to have an excess 10:10:30

18 cancer? 10:10:34



21 THE WITNESS: I didn't really focus on that 10:10:35

22 part of their paper, if they dealt with it at all. 10:10:41




© 2010

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2 Q So, for example, they don't say living in an 10:10:47

3 oil-producing canton causes excess cancer because 10:10:49

4 there's a denser population or there's more stress 10:10:53

5 or -- they don't give a reason; they just note the 10:10:57

6 association; isn't that fair to say? 10:10:59



9 THE WITNESS: I can't recall if their 10:11:02

10 discussion did or did not go into causation. 10:11:04

11 BY MS. NEUMAN: 10:11:07

12 Q According to Hurtig and San Sebastian, how 10:11:17

13 long does an area have to be oil-producing before this 10:11:20

14 association begins, shows up? 10:11:23

15 A They didn't specifically specify, but as I 10:11:26

16 recall, they looked at areas that had at least a 10:11:36

17 20-year history, if I'm not mistaken. 10:11:43

18 Q And didn't they, in fact, specify that the 10:11:45

19 exposed population was defined as a population that 10:11:49

20 had been living in areas which had been producing oil 10:11:52

21 for a minimum of 20 years? 10:11:58





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2 THE WITNESS: Without the article in front of 10:12:01

3 me, I can't say. 10:12:04

4 MS. NEUMAN: Let's mark as Exhibit 1301 the 10:12:05

5 article entitled, "Geographical differences in cancer 10:12:18

6 incidence in the Amazon basin of Ecuador in relation 10:12:21

7 to residents near oil fields," Anna-Karen Hurtig and 10:12:24

8 Miguel San Sebastian, in the International Journal of 10:12:30

9 Epidemiology, 2002, bearing the Bates number 10:12:34

10 Rourke-Native 1 through 6, and also attaching 10:12:38

11 commentary, "Epidemiology on the side of the angels," 10:12:53

12 by Jack Siemiatycki, Rourke-Native 7 through 9. 10:12:57



15 BY MS. NEUMAN: 10:13:15

16 Q Can you turn for me, Dr. Rourke, to page 10:13:15

17 Bates numbered Rourke-Native 3 of the San Sebastian 10:13:26

18 article. Do you see the paragraph that's entitled, 10:13:31

19 "Exposure status" on the left-hand column? 10:13:39

20 A Yes. 10:13:44

21 Q Do you see under "exposure status" where the 10:13:45

22 authors state, "The exposed population was defined as 10:13:53




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1 those living in a county where oil exploitation had 10:13:56

2 been ongoing for a minimum of 20 years to the date of 10:14:02

3 the study"? 10:14:05

4 Do you see that? 10:14:05

5 A Yes, I do. 10:14:05

6 Q Does that refresh your recollection that, 10:14:06

7 according to the San Sebastian study, the association 10:14:09

8 was only seen or documented in populations where oil 10:14:16

9 exploitation had been ongoing for a minimum of 20 10:14:24

10 years? 10:14:28



13 THE WITNESS: I'm sorry. Would you repeat 10:14:29

14 the question? 10:14:35

15 BY MS. NEUMAN: 10:14:36

16 Q Certainly. 10:14:37

17 A Thank you. 10:14:37

18 MS. NEUMAN: Actually, can you read the 10:14:40

19 question back to the witness? 10:14:41

20 (The reporter read the record as requested.) 11:16:28

21 THE WITNESS: That question was answered, but 10:14:42

22 you had a question following that one, if I'm not 10:15:08




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1 mistaken. 10:15:12


3 Q No, that's the pending question. 10:15:13

4 A Okay. In that case -- I'm sorry for zoning 10:15:14

5 out here. 10:15:22

6 Q That's okay. You said you couldn't recall 10:15:22

7 for sure if they said they only looked at areas where 10:15:25

8 there had been a minimum of 20 years of oil-producing 10:15:30

9 activity. 10:15:33

10 A That's true. 10:15:33

11 Q Okay. 10:15:34

12 A And this is where the 20 -- that I guess was 10:15:34

13 the number that I used -- came from, my memory of 10:15:37

14 this. I believe I said 20 years, did I not? 10:15:42

15 Q No, sir. 10:15:44

16 So we're in agreement that, according to 10:15:45

17 San Sebastian, the association with increased risk is 10:15:47

18 only documented as to persons living in a county where 10:15:54

19 oil exploitation has been ongoing for a minimum of 20 10:15:59

20 years, correct? 10:16:02






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1 THE WITNESS: The exploration has been going 10:16:04

2 on for 20 years, that is correct. 10:16:14


4 Q Before the risk is documented by 10:16:18

5 San Sebastian, correct? 10:16:19

6 A Well, in that -- that was their criteria for 10:16:20

7 the inclusion in this article, yes. 10:16:28

8 Q This is -- did you have other work of 10:16:29

9 San Sebastian that you relied on? 10:16:34

10 A No. 10:16:35

11 Q Okay. So the only San Sebastian work you 10:16:36

12 relied on was an article showing an association to an 10:16:38

13 increased risk of cancer for populations in counties 10:16:44

14 where oil exploitation has been going on for a minimum 10:16:49

15 of 20 years; is that right? 10:16:53


17 Q Did the San Sebastian article provide you any 10:16:55

18 information as to how extensive the oil operations had 10:17:02

19 to be during the 20-year period prior to the 10:17:04

20 determination that there was an increased risk? 10:17:10






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1 THE WITNESS: No, the article does not. 10:17:13


3 Q Can you turn to page 3 of your report for me. 10:17:22

4 Do you see the chart in the middle? 10:17:38

5 A Yes. 10:17:39

6 Q You have two different years there for last 10:17:40

7 year of entry of newly exposed persons. Do you see 10:17:44

8 that? 10:17:48

9 A Yes, I do. 10:17:48

10 Q Why is that? 10:17:49

11 A Well, 2009 would be to the present day, and I 10:17:50

12 believe 1990 is the year that operations were turned 10:18:08

13 over from -- I guess at the time it was Texaco -- 10:18:16

14 to -- and I could be totally wrong on this -- to the 10:18:24

15 Ecuadorian oil company, whatever that was. 10:18:28

16 Q Petroecuador? 10:18:32

17 A Okay. 10:18:33

18 Q Were these dates of last entry, 1990 and 10:18:34

19 2009, dates that you chose or dates that were given to 10:18:37

20 you? 10:18:40






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1 THE WITNESS: The 2009 arose, as I said, 10:18:41

2 because it's the date of the last full year. And the 10:18:48

3 1990 arose because of the handover of operations. 10:18:55


5 Q Did you decide to use the last year that 10:19:03

6 TexPet operated in Ecuador as a last year of entry or 10:19:08

7 is that something the lawyers asked you to do? 10:19:12



10 THE WITNESS: Without the lawyers having told 10:19:14

11 me that that was the last year, I wouldn't have known. 10:19:18

12 So I guess the lawyers said that was the year of 10:19:21

13 interest. 10:19:23

14 BY MS. NEUMAN: 10:19:23

15 Q And the 2009 -- did the lawyers ask you to do 10:19:24

16 your calculation to the present year? 10:19:28

17 A That was my choice. 10:19:30

18 Q Why did you choose to do that? 10:19:33

19 A Because that was the last full year. 10:19:35

20 Q Now, in this table on page 3, you also appear 10:19:40

21 to be looking at two different populations, one 10:19:46

22 population in the Concession area and one population 10:19:50




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1 within five kilometers of oil facilities; is that 10:19:54

2 right? 10:19:58


4 Q Why did you look at these two different 10:19:58

5 populations? 10:20:02

6 A The Concession area was -- my understanding 10:20:03

7 of the Concession area was that it was the area that 10:20:09

8 the nation of Ecuador said that the exploration and 10:20:15

9 production facilities could be established. And the 10:20:21

10 five kilometers was picked because it was one of 10:20:28

11 the -- well, about the midpoint of the various areas 10:20:33

12 that were evident from one of the maps that I 10:20:40

13 received. 10:20:47

14 Q What do you mean by various areas that were 10:20:47

15 evident? 10:20:52

16 A I turned over to you folks files that were 10:20:52

17 JPEGs of maps that were produced by a geographic 10:21:04

18 information system of some form. And at least two of 10:21:08

19 these report the areas of the Concession area and 10:21:13

20 then, within various distances from the oil production 10:21:21

21 facilities themselves, and these areas ultimately 10:21:24

22 served as the basis for my estimation of the number of 10:21:28




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1 people. 10:21:33

2 Q The -- did you have any scientific literature 10:21:35

3 indicating that people within the Concession area were 10:21:47

4 at more risk of cancer than any other group? 10:21:52



7 THE WITNESS: The Concession area is 10:21:57

8 predominantly inside the four counties studied by 10:22:02

9 Hurtig and San Sebastian. That was the basis of my 10:22:10

10 assumption. 10:22:13

11 BY MS. NEUMAN: 10:22:13

12 Q Did you have any studies indicating that 10:22:15

13 people inside the Concession area were at any more 10:22:18

14 risk of any disease than those outside the Concession 10:22:23

15 area, but living in the four cantons? 10:22:26



18 THE WITNESS: I believe, if I understand your 10:22:30

19 question correctly, the answer would be no. 10:22:36

20 BY MS. NEUMAN: 10:22:38

21 Q Did you have any literature indicating that 10:22:38

22 people living within five kilometers of oil facilities 10:22:40




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1 were at any higher risk of cancer than people living 10:22:45

2 anywhere else in the four cantons? 10:22:49



5 THE WITNESS: Since the Concession area and 10:22:52

6 all places smaller are within the area -- 10:23:03

7 predominantly within the area studied by Hurtig and 10:23:09

8 San Sebastian, what is being assumed here is that the 10:23:13

9 excess risk is uniform. 10:23:17

10 BY MS. NEUMAN: 10:23:19

11 Q Throughout the four cantons? 10:23:21

12 A Throughout the four cantons. 10:23:22

13 Q So there's no more risk of cancer if you live 10:23:24

14 within five kilometers of the oil-producing facilities 10:23:28

15 than if -- there is if you live at the furthest point 10:23:34

16 from those producing facilities in the cantons? 10:23:39



19 THE WITNESS: Given the way the computations 10:23:42

20 were done, the answer is yes. 10:23:45

21 BY MS. NEUMAN: 10:23:46

22 Q Why did you calculate excesses based on this 10:23:52




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1 five-kilometer mark? 10:23:58

2 A It was approximately halfway between the ten 10:23:59

3 kilometers and -- I think they had one kilometer in 10:24:06

4 the maps. Without the maps in front of me, I can't 10:24:10

5 recall what the actual distances were. 10:24:13

6 Q But what is the relevance of being within the 10:24:15

7 five kilometers? 10:24:18



10 THE WITNESS: Okay. Clearly, the larger the 10:24:23

11 distance from the production facilities, the greater 10:24:31

12 the number of people. And this was to illustrate the 10:24:33

13 effect of increasing the population considered at 10:24:41

14 risk. 10:24:43

15 BY MS. NEUMAN: 10:24:43

16 Q The more people you put at risk, the higher 10:24:44

17 number of people you're going to say had an associated 10:24:46

18 cancer; is that right? 10:24:51




22 BY MS. NEUMAN: 10:24:55




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1 Q So is this just intended to be a mathematical 10:25:00

2 example of that? 10:25:03

3 A As opposed to what? 10:25:03

4 Q Well, as I read your report, you seem to be 10:25:11

5 calculating -- well, let me withdraw that. 10:25:16

6 Did you understand yourself to be making a 10:25:19

7 damage estimate for which someone should actually be 10:25:21

8 liable? 10:25:24



11 THE WITNESS: When you say damage estimate, 10:25:25

12 do you mean a dollar amount? 10:25:29

13 BY MS. NEUMAN: 10:25:30

14 Q Yes, sir. A dollar amount that, in your 10:25:32

15 opinion, someone should have to pay? 10:25:34

16 MR. WESTENBERGER: You are asking 10:25:37

17 specifically about liability, correct, whether he made 10:25:38

18 an assessment about liability? 10:25:41

19 MS. NEUMAN: No, whether it's his 10:25:42

20 understanding that his report was being used as a 10:25:45

21 measure of liability. 10:25:48

22 THE WITNESS: My understanding was that it 10:25:49




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1 would be up to the judge to decide, A, if there were 10:25:55

2 excess cancers, and, B, if there were, how they should 10:26:00

3 be valued. 10:26:05


5 Q Your report simply calculates numbers of what 10:26:18

6 you've labeled as excess cancers based on certain 10:26:26

7 assumptions regarding population at risk, correct? 10:26:29



10 BY MS. NEUMAN: 10:26:38

11 Q And you're not offering the opinion that 10:26:38

12 anybody actually got cancer because they live near an 10:26:40

13 oil production facility? 10:26:45

14 A If you're saying there is causation in what 10:26:45

15 were offered as the conclusions of this, the answer is 10:27:00

16 no, I am not making any statement about causation. 10:27:03

17 Q So you're not saying any particular person 10:27:06

18 got cancer because they live near an oil production 10:27:08

19 facility? 10:27:11

20 A This is solely an estimate based on 10:27:11

21 assumptions of the number that may develop an excess 10:27:14

22 cancer. 10:27:20




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1 Q The maps that you used for your 10:27:21

2 five-kilometer population, you have that population 10:27:34

3 from 1967 forward, correct? 10:27:36

4 A The map lets me compute that number at a 10:27:38

5 fixed point in time. 10:27:48

6 Q The map lets you compute that area at a fixed 10:27:49

7 point in time, correct? 10:27:54

8 A And the population, yes. 10:27:55

9 Q And did you do any analysis as to whether the 10:27:56

10 map was valid in all the years in which you used it? 10:28:01



13 THE WITNESS: I considered the map valid and 10:28:08

14 the area that was listed as valid, and that would 10:28:14

15 necessarily be true over all years since the area 10:28:20

16 wouldn't change. 10:28:23

17 BY MS. NEUMAN: 10:28:23

18 Q So you assumed that oil production facilities 10:28:23

19 existed in the cantons starting in 1967, correct? 10:28:27


21 Q Is that a valid assumption? 10:28:33





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2 THE WITNESS: I believe so. 10:28:36


4 Q Turn to page 4 of your report for me. Under 10:28:53

5 the heading "The range of years covered by these 10:29:01

6 estimates," do you see where you state, "Exploration 10:29:04

7 for oil began prior to 1967 when commercially viable 10:29:09

8 reserves were found"? 10:29:14

9 A Yes. 10:29:15

10 Q And do you see your next statement, 10:29:15

11 "Commercial production began in 1972"? 10:29:18

12 A Yes. 10:29:19

13 Q Did you do any analysis of when they first 10:29:22

14 started building production stations in the four 10:29:26

15 cantons? 10:29:30

16 A No, I did not. 10:29:30

17 Q If they did not have any production stations 10:29:31

18 in the four cantons beginning in 1967, would you need 10:29:37

19 to adjust your model for that? 10:29:41



22 THE WITNESS: If that were the case, the 10:29:48




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1 answer would be yes. Not -- well, let me back up a 10:29:53

2 little bit. 10:29:56

3 The form of the model, no. But the 10:29:56

4 parameters that go into it, yes. 10:29:59


6 Q You go on to say in this paragraph, "Oil 10:30:04

7 production and expansion of the production facilities 10:30:08

8 continues to the present day." 10:30:10


10 A Yes. 10:30:12

11 Q Is it your understanding that since 1990 to 10:30:12

12 the present day it's Petroecuador that has been 10:30:17

13 expanding the oil production facilities in the 10:30:22

14 Concession area? 10:30:24

15 A That is what I have been informed. 10:30:25

16 Q What data did you analyze to determine that 10:30:28

17 oil production facilities in the Concession have been 10:30:32

18 continually expanding since 1972? 10:30:35

19 A There was an article that, again, I gave 10:30:36

20 you -- and I cannot recall the name of the person who 10:30:51

21 wrote it -- that gave a little bit of the history. 10:30:53

22 Q And you based this statement on that article? 10:30:56




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1 A Yes, I did. 10:31:01

2 Q In your model, did the continuing expansion 10:31:02

3 of the oil production facilities from 1990 to the 10:31:09

4 present create any additional risk of disease? 10:31:12



7 THE WITNESS: No, it did not. 10:31:20


9 Q In your model, is the risk of disease related 10:31:32

10 in any way to the amount of oil production facilities 10:31:35

11 in the four cantons at any point in time? 10:31:39

12 A No, it is not. 10:31:41

13 Q You indicate that you used INEC? 10:31:50

14 A I-N-E-C. 10:32:03

15 Q We can pronounce that however you like. What 10:32:04

16 do you want to go with? 10:32:09

17 A Okay. We can call it the Ecuadorian Census 10:32:10

18 Bureau. 10:32:14

19 Q Okay. You used Ecuadorian Census Bureau from 10:32:14

20 1990 and 2001; is that right? 10:32:17

21 A Among other data they provide, yes. 10:32:19

22 Q Was there any reason that you didn't use the 10:32:22




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1 data -- the census data for other years? 10:32:27

2 A It doesn't exist. 10:32:30

3 Q You don't think it exists in the INEC 10:32:33

4 database? 10:32:37

5 A It may exist as documents, but these 10:32:37

6 documents weren't available on the website. And 1990 10:32:40

7 was the first year -- well, Ecuador runs a ten-year 10:32:45

8 census, just like the United States. They don't 10:32:48

9 appear to have anything like -- oh, I'm teaching; I 10:32:53

10 shouldn't do that. 10:32:59

11 So they have 1990, 2001, and they're actually 10:33:01

12 in the middle of doing their 2010 census right now. 10:33:05

13 I'm sure if you discover -- if you go to the website, 10:33:09

14 you can get all the information about that. 10:33:13

15 They appear not to have something referred to 10:33:16

16 here as the current population survey that is what is 10:33:19

17 used predominantly to estimate the population between 10:33:23

18 the census years. 10:33:27

19 Q Did they have data prior to 1990? 10:33:29

20 A I didn't find any -- hold it. One of the 10:33:31

21 documents, as I recall, that I found on the website I 10:33:40

22 believe has some data prior to 1990. 10:33:48




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1 Q But you didn't use that data? 10:33:54

2 A Without reviewing my work, I can't say if I 10:33:59

3 did or not, actually, because it may have been used 10:34:04

4 for something. I hope I remember -- I'm relying on my 10:34:08

5 memory here, which is fallible. 10:34:13

6 Q If INEC had had census data for years other 10:34:16

7 than 1990 and 2001, would you have used that data? 10:34:30

8 A Yes. 10:34:33

9 Q Can you go to your table 3 for me, 10:34:33

10 Dr. Rourke. I -- this table confuses me. 10:34:51

11 A Table 3? 10:35:02

12 Q Yes, it's on page 8. 10:35:05

13 A Sorry. 10:35:06

14 Q Now, prior to 2001, when you're looking at 10:35:07

15 the population in the cantons, you extrapolate 10:35:16

16 backwards, correct? 10:35:30


18 Q And you assume a 5 percent population growth 10:35:31

19 rate that evenly occurs each year from 1967 to 2001; 10:35:36

20 is that right? 10:35:43

21 A That is correct. Assume. I calculated a 10:35:43

22 growth rate assuming that a multiplicative -- next 10:35:47




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1 year is a-hundred-and-X percent of this year. I did 10:35:52

2 assume that. 10:35:57

3 Q You assumed even growth rate as opposed to 10:35:57

4 some other pattern of the population growth rate in 10:36:01

5 the area? 10:36:03


7 Q And there could have, in fact, been a 10:36:04

8 different pattern, correct? 10:36:07


10 Q Now, when you got to 2001, you indicate that 10:36:10

11 you switched methods. 10:36:12

12 A Yes. 10:36:15

13 Q Why did you do that? 10:36:15

14 A The first column after the year is the growth 10:36:17

15 rate one, using the constant growth rate model to 10:36:25

16 interpolate between 1990 and 2001 and then backwards, 10:36:32

17 as you point out. 10:36:38

18 The second one is a result of some official 10:36:39

19 projections that appear on the INEC website of the 10:36:43

20 population growth in -- I'd have to actually look, but 10:36:49

21 I believe -- I'd have to look it up, what they were. 10:36:53

22 But there was the smallest geography I could get that 10:37:00




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1 was at least as large as the area of interest. And 10:37:04

2 they provide the figures year by year. 10:37:08

3 Q Now, what is the relationship between the 10:37:12

4 figures that appear in the second column with the 10:37:16

5 heading "'67 to '01" to the figures that appear in the 10:37:21

6 fourth column with the heading "within the Concession 10:37:26

7 area"? 10:37:29

8 A Okay. May I look in my report back a page or 10:37:29

9 two? 10:37:41

10 Q Oh, sure. Look at whatever you need to. 10:37:41

11 A All right. I'm using the series numbers 10:37:50

12 as -- what is sometimes referred to as an index 10:38:04

13 series. And if you look at the Concession area column 10:38:08

14 for 2001, I've got a number 133,432, if I'm reading 10:38:20

15 that right with my glasses, which need cleaning. And 10:38:26

16 that number is a result of multiplying the area of the 10:38:30

17 Concession area, which I can't recall what it is, by 10:38:33

18 my calculation of the average population. 10:38:37

19 Now, to go backwards, what I am doing is 10:38:42

20 using the first series column -- and the 127,071 10:38:48

21 number is a result of taking the ratio of 205 to 225 10:38:56

22 and multiplying that by the 133,432. 10:39:04




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1 And I know that sounds like it makes no 10:39:11

2 sense. The -- that's for 2000. For 1999, the 121,012 10:39:14

3 is a result of taking the ratio of the 195,430 in the 10:39:20

4 index column to the 215,488 and multiplying that again 10:39:26

5 by the 13432 (sic), and then carrying that computation 10:39:33

6 back. 10:39:37

7 Q So do you use the numbers in column 2 to 10:39:37

8 calculate the numbers in column 4? 10:39:43


10 Q And are they -- there's some sort of 10:39:45

11 relationship there, ratio or percentage or something, 10:39:49

12 right? 10:39:51

13 A It is assumed, yes. 10:39:51

14 Q So here's my question: When the numbers in 10:39:54

15 column 2 went down, how come there wasn't a 10:39:58

16 corresponding result in column 4? 10:40:04

17 A Column 2? 10:40:07

18 Q Well, let me show you what I'm thinking of. 10:40:10

19 Okay. In 2000 you say that the population is 205,214. 10:40:13


21 A What year was that again, please? 10:40:27

22 Q 2000. 10:40:32




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1 A Okay. Yeah -- 205,214, yes. 10:40:33

2 Q That's the population number? 10:40:40

3 A That is an estimate. 10:40:43

4 Q Right. Okay. It's an estimate of the 10:40:44

5 population in the cantons? 10:40:46

6 A In -- no, it is not. It is an estimate of 10:40:47

7 the number in Sucumbios and Orellana. 10:40:58

8 Q Okay. In those two cantons? 10:41:07

9 A Yes. Provinces, actually. Not cantons. 10:41:09

10 Provinces. Bigger. 10:41:14

11 Q Provinces. The 127,071 is the subset of the 10:41:14

12 205,214 that you estimate lived within the Concession 10:41:24

13 area, correct? 10:41:31

14 A Yes. 10:41:31

15 Q So when there were 205,000 people in those 10:41:34

16 two provinces, 127,000 of them, more or less, lived 10:41:38

17 within the Concession as best you could estimate? 10:41:42

18 A You're probably ascribing more meaning to it 10:41:46

19 than I would, but I guess the answer is yes. 10:41:49

20 Q Okay. So look down in 2004. Do you see how 10:41:51

21 you have 200,000 people estimated in the provinces? 10:41:57

22 A Actually, this is probably in the -- probably 10:42:03




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1 in the cantons because -- okay. Anything 2002 after 10:42:15

2 is based on the '01 to '09 index series. 10:42:22

3 Q Right. 10:42:27

4 A And anything up to 2000 we'll say is based on 10:42:28

5 the '67 to '01 index series. So I believe, if I'm not 10:42:34

6 mistaken, that the numbers increase -- do they go 10:42:40

7 down? I don't believe they do. They -- 10:42:44

8 Q They go down. You go from 215 to 180 and 10:42:45

9 then you don't get back up to -- 10:42:48

10 A Oh, but -- but -these are index series. 10:42:50

11 These are not -- these give the numbers -- all right. 10:42:52

12 It is my fault for not making this clear to you. I'm 10:42:59

13 sorry about that. 10:43:03

14 All I am using these for -- and perhaps I 10:43:05

15 should have rescaled it but I didn't -- as numbers 10:43:09

16 that are correlated with the change in population. 10:43:13

17 They do not represent subsets of my thing or bigger 10:43:17

18 areas or things like that in the extent you're 10:43:24

19 thinking -- well, let me tell you -- all right. 10:43:30

20 Where is -- let's take 2002 where I have 10:43:33

21 135,217. 10:43:38

22 Q Uh-huh. 10:43:38




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1 A That is the result of multiplying 13342 (sic) 10:43:40

2 times the ratio of 189,369, the 2002 number in the 10:43:45

3 second index series, divided by 189,489, the first 10:43:52

4 number in the second index series. 10:43:58

5 Q Let me ask you this: When you switch 10:43:59

6 methods, why does the population within the canton 10:44:02

7 become such a larger percentage of the indexed 10:44:07

8 population that you're using? 10:44:12



11 THE WITNESS: Okay. For -- I'd have to go 10:44:18

12 back and look at my geography, but the '67 to '01 are 10:44:20

13 the provinces, and the other one is, I believe, the 10:44:29

14 cantons of interest, but I'd have to look at my 10:44:34

15 original work to see. 10:44:38

16 BY MS. NEUMAN: 10:44:39

17 Q That's a bigger area, right, the cantons of 10:44:39

18 interest? 10:44:41

19 A No, they're smaller. 10:44:41

20 Q Than the provinces? 10:44:42

21 A Than the provinces, yeah. Geography there 10:44:44

22 is -- province is kind of like state, cantons is kind 10:44:48




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1 of like county, and parishes are kind of like 10:44:51

2 townships within counties. 10:44:55

3 Q Well, do you use the numbers in column 2 in a 10:44:55

4 different formula than you use the numbers in column 3 10:44:59

5 in, of table 3? 10:45:05

6 A I only use ratios, actually. They're there 10:45:06

7 for the -- being able to calculate ratios. 10:45:13

8 Q Right. But do you plug them into the same 10:45:15

9 formula to calculate the ratio or different formulas? 10:45:19

10 Because you seem to be getting very different answers; 10:45:22

11 that's why I'm asking. 10:45:25

12 A Well, the important thing is that it's this 10:45:26

13 column that matters. The answer is I use a different 10:45:29

14 index series, which is why there is nothing below 2001 10:45:31

15 for the '67 to '01 series and nothing above 2001 for 10:45:37

16 the '01 to '09 index series. 10:45:44

17 Q But in addition to changing the index, you 10:45:50

18 change your formula, correct? 10:45:54

19 A I have to, yes, because I'm switching to a 10:45:55

20 different index series. The denominator for all this, 10:45:58

21 incidentally, are the numbers in the 2001 row for the 10:46:02

22 multipliers. 10:46:05




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1 Q The calculation of people living within the 10:46:13

2 five-kilometer area, column 5 in table 3, is there any 10:46:20

3 cap on that or can as many people be shoved in there 10:46:26

4 as the math shoves in there? 10:46:29



7 THE WITNESS: Again, I have an estimate of 10:46:34

8 what that is for 2001, and again, the same index 10:46:39

9 series is used. 10:46:45

10 BY MS. NEUMAN: 10:46:46

11 Q No, my question is more of a practical one, 10:46:47

12 like -- 10:46:50

13 A Okay. 10:46:50

14 Q -- like, literally, no more people could fit 10:46:51

15 in the five kilometers. Does your model take that 10:46:52

16 into account? Or it just keeps saying a certain 10:46:55

17 percentage will live within the five kilometers no 10:46:59

18 matter how big the population gets? 10:47:02



21 THE WITNESS: Okay. I hate to make it more 10:47:05

22 complicated than it is, but if it turns out that 10:47:08




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1 you've got X percentage in 2001, I believe that same 10:47:12

2 percentage holds for all time -- prior years and 10:47:17

3 following years. 10:47:20


5 Q No matter how big the population might get? 10:47:21


7 Q There's no rate-limiting event in the model? 10:47:24

8 A Not for small areas, no. 10:47:26

9 Q Why didn't you use canton data from INEC for 10:47:28

10 your whole index series? 10:47:35

11 A They didn't provide it. I mean, the 10:47:36

12 projections -- they provided the information at the 10:47:51

13 canton level. 10:47:59

14 For the other one, the '67 to '01 -- oh, I -- 10:48:01

15 okay. Orellana is a new province and didn't exist in 10:48:07

16 1990. And it was evidently a result of separating 10:48:13

17 Napo, but I couldn't find what the cantons were that 10:48:16

18 became Orellana. 10:48:23

19 Q In the 1990 census data? 10:48:23

20 A In the 1990 census data. So what I relied on 10:48:27

21 were -- was two tables or -- two tables, I guess, in a 10:48:31

22 census report that gave the population in what 10:48:36




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1 would -- in 1990 for what would become Orellana. 10:48:44

2 Q Can you turn to page 10 of your report, 10:48:44

3 Exhibit 1300 for me, Doctor. Do you see the last 10:48:50

4 sentence on that page where you state, "The rates in 10:49:06

5 table 5 also demonstrates why it is necessary to trend 10:49:09

6 them over time. Generally, a 2001 rate is less than 10:49:12

7 its corresponding 1990 rate, sometimes substantially 10:49:16

8 so." 10:49:20


10 A Yes. 10:49:20

11 Q And you're referring there to cancer 10:49:21

12 mortality rates; is that right? 10:49:23

13 A Well, probably not exclusively. 10:49:24

14 What I am looking at is table 5 and comparing 10:49:43

15 the 1990 numbers to the 2001 numbers for total and for 10:49:47

16 all cancers. 10:49:57

17 Q And what you found, which I think you're 10:49:58

18 commenting on on the prior page, is that cancer 10:50:01

19 mortality trended down over the course of that 10:50:03

20 10-year -- 11-year period, correct? 10:50:07

21 A As did total mortality. 10:50:09

22 Q In your model, do your excess cancers trend 10:50:11




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1 down in that period or do they trend up? 10:50:19

2 A If -- other things being equal, if the total 10:50:23

3 cancer -- well, if the background cancer rate 10:50:37

4 declines, then other things being equal, the excess 10:50:41

5 cancer rate -- well, the excess cancers will also 10:50:44

6 decline. 10:50:48

7 Q When you calculate -- how are you defining 10:50:48

8 excess? 10:50:52

9 A Complicated. 10:50:52

10 Q Let me withdraw that. When you calculate 10:50:56

11 excess cancer, you're intending to exclude background 10:51:04

12 cancers from your calculation, correct? 10:51:08


14 Q You're intending to calculate the number of 10:51:10

15 cancers over and above background? 10:51:12


17 Q Do you have a definition for excess cancer 10:51:15

18 the way you use that term in your report? 10:51:28

19 A The number of would be precisely as you just 10:51:30

20 said prior to your question. The number of cancers 10:51:41

21 above what would be expected according to a background 10:51:45

22 rate. 10:51:48




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1 Q Does table 5 show cancer mortality trending 10:51:48

2 down while oil production is increasing in Ecuador? 10:51:56



5 THE WITNESS: If oil production is 10:52:03

6 increasing, then the answer to your question would be 10:52:08

7 yes. 10:52:10

8 MS. NEUMAN: We've been going over an hour. 10:52:10

9 Is this a good time for just a ten-minute break? 10:52:16

10 THE WITNESS: That would be fine. Thank you. 10:52:16

11 MS. NEUMAN: Let's go off the record. 10:52:19

12 THE VIDEOGRAPHER: The time is 10:53 a.m., 10:52:19

13 December 20th, 2010. Going off the record, concluding 10:52:23

14 video 1. 10:52:28


16 THE VIDEOGRAPHER: The time is 11:05 a.m., 10:52:29

17 December 20th, 2010. On the record with video 11:04:36

18 number 2. 11:04:40

19 BY MS. NEUMAN: 11:04:40

20 Q Do you still have your report in front of 11:04:40

21 you, Dr. Rourke? 11:04:46

22 A Yes, I do. 11:04:47




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1 Q Would you turn to page 4 for me, please. 11:04:48

2 Are you at page 4, Dr. Rourke? 11:05:08

3 A I believe so. You have no Bates number on 11:05:10

4 this, so -- yes, I am. 11:05:14

5 Q There's page numbers at the top. 11:05:17

6 A Yes. 11:05:19

7 Q And do you see the four numbered paragraphs 11:05:19

8 that you have there? 11:05:23

9 A Yes, I do. 11:05:23

10 Q That's the -- the information that you needed 11:05:25

11 to perform your computations; is that right? 11:05:29

12 A Yes. 11:05:31

13 Q Number 1 is an estimate of the number of 11:05:39

14 persons residing in the Concession area and within 11:05:44

15 five kilometers by year from '67 to 2009. Am I 11:05:47

16 reading that right? 11:05:54

17 A Yes, that is correct. 11:05:55

18 Q Then number 2 is the age distribution of 11:05:56

19 persons residing in the Concession area within the 11:05:58

20 five-kilometer area; is that right? 11:06:01


22 Q Then number 3 says estimates of age-specific 11:06:04




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1 total mortality rates and age-specific rates for all 11:06:10

2 malignant neoplasms. Is that within the Concession 11:06:14

3 area as well? 11:06:20

4 A That is correct. No, no, I'm sorry. That is 11:06:20

5 not correct. These rates are computed for all deaths 11:06:24

6 in Ecuador. So they're not solely within the 11:06:38

7 Concession area. 11:06:43

8 Q For the age-specific mortality rates, why did 11:06:44

9 you use all of Ecuador as opposed to a different 11:06:51

10 population? 11:06:55

11 A The death rates -- well, for the 1990 death 11:06:55

12 rates, I have the problem of not knowing where 11:07:14

13 Orellana is, because it didn't exist, I believe, until 11:07:23

14 1998. And I was also concerned that there might be 11:07:27

15 undercount of cancers in the less well-developed 11:07:43

16 sections of Ecuador. 11:07:48

17 Q What was the basis of that concern? The 11:07:49

18 scientific basis, I mean. 11:08:13



21 THE WITNESS: It was my assumption. 11:08:16

22 BY MS. NEUMAN: 11:08:23




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1 Q If you had had age-specific mortality rates 11:08:32

2 for the four cantons, would you have used those rather 11:08:35

3 than the ones for all deaths in Ecuador? 11:08:38

4 A If I had them available at two points in time 11:08:40

5 at least, the answer was perhaps. 11:08:55

6 Q Why do you say perhaps? 11:08:57

7 A I'd have to take a look at them. 11:09:03

8 Q As you sit here, can you think of any reason 11:09:05

9 why you wouldn't use the age-specific mortality rates 11:09:14

10 for the cantons if they were available to you? 11:09:16



13 THE WITNESS: I don't mean to hesitate for so 11:09:23

14 long, but I'm just thinking through the ramifications. 11:09:40

15 I guess what I -- I would like to see how the rates 11:09:49

16 compare to all of Ecuador's and how they compare over 11:09:53

17 time, how they vary over time. If they -- I can think 11:09:58

18 of no obvious reason why I would not use them if 11:10:05

19 they -- they looked reasonable. 11:10:12

20 BY MS. NEUMAN: 11:10:15

21 Q And what expertise would you use to determine 11:10:15

22 whether or not cancer rates in a given area looked 11:10:19




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1 reasonable? 11:10:22

2 MR. WESTENBERGER: Objection to form of the 11:10:22


4 THE WITNESS: All right. With cancers, what 11:10:23

5 usually happens is the older you get, the greater the 11:10:36

6 likelihood of a cancer, the greater the likelihood of 11:10:40

7 dying, actually, except for the very high age ranges 11:10:45

8 where, actually, sometimes the cancer rates drop. 11:10:48

9 If there were some reversal of this trend 11:10:52

10 where you had younger people more -- showing a higher 11:10:58

11 mortality rate, I would be concerned about that. That 11:11:03

12 usually just doesn't happen. 11:11:06

13 BY MS. NEUMAN: 11:11:07

14 Q And that would give you a concern about 11:11:10

15 reporting? 11:11:13

16 A Well, about using those rates. 11:11:13

17 Q Okay. Can you turn to page 13 of your report 11:11:16

18 for me, please, the table there where you have a 11:11:21

19 column labeled, "excess risk." Do you see that? 11:11:38

20 A Yes, I do. 11:11:40

21 Q Are those numbers you took from 11:11:41

22 San Sebastian's study? 11:11:43




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1 A They do not appear explicitly in the study, 11:11:46

2 but I derived them from two charts that they do show. 11:11:50

3 Q Can you identify the two charts in 11:11:54

4 Exhibit 1301 that you derived those numbers from? 11:11:58

5 A They are figures -- figure 2 on the bottom 11:12:01

6 right-hand corner of page -- Bates page 3, 11:12:11

7 Rourke-Native, Bates page 3. 11:12:17

8 Q These two charts? 11:12:19


10 Q And are the numbers that you label excess 11:12:21

11 risk the numbers that San Sebastian labels relative 11:12:25

12 risk? 11:12:30

13 A No, actually, because they do -- their 11:12:30

14 relative risk number is based on -- I forgot what 11:12:43

15 population they used, but a weighted average 11:12:49

16 population figure, and mine are age-specific. They 11:12:52

17 have no -- they have no age-specific relative risk 11:12:58

18 numbers in their report. 11:13:07

19 Q In table 6 were you intending to calculate a 11:13:08

20 relative risk number on an age-specific basis? 11:13:16


22 Q And so excess risk correlates to relative 11:13:20




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1 risk directly, conceptually? 11:13:28



4 THE WITNESS: Actually, I don't think you 11:13:33

5 could get from one to the other. Because the relative 11:13:40

6 risk you compute an age-corrected death rate for, 11:13:48

7 quote, the exposed condition, the age corrected death 11:13:54

8 rate for the unexposed condition, and then take the 11:13:58

9 ratio. 11:14:00

10 Here, the ratio has been done on an 11:14:02

11 age-by-age basis, and you can't get there from here, 11:14:05

12 I'm afraid. 11:14:09

13 BY MS. NEUMAN: 11:14:09

14 Q You can't get from San Sebastian's relative 11:14:13

15 risk to your excess risk; is that what you're saying? 11:14:17

16 A Not the number they report, I don't believe. 11:14:19

17 Q Is San Sebastian's article the scientific 11:14:25

18 basis for your age-specific excess risk or is there 11:14:32

19 some other scientific basis? 11:14:35

20 A It is the basis. 11:14:36

21 Q But you can't derive them from his report? 11:14:37

22 A No. I'm saying, mathematically, you can't 11:14:40




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1 take table 6 numbers and compute their total relative 11:14:44

2 risk number, whose value I forgot, because of the way 11:14:52

3 the relative risk is defined. 11:14:56

4 Q Well, in table 6, were you intending to 11:15:00

5 calculate a relative risk for, for example, the age 11:15:06

6 range of 50 to 54, or were you intending to calculate 11:15:11

7 something else? 11:15:14

8 A That's the -- that is the relative risk for 11:15:14

9 50 to 54. 11:15:17

10 Q The 1.65? 11:15:18


12 Q Okay. How do you use the age-specific total 11:15:23

13 mortality rate data in your calculations? 11:15:27

14 A There are three causes of death here, or 11:15:28

15 things are split up into three groups. One is the -- 11:15:43

16 well, the three ultimate groups would be excess 11:15:59

17 cancers, in which there may be none, baseline -- we'll 11:16:03

18 call them -- cancers, and all other deaths. 11:16:13

19 Q From things other than cancer? 11:16:18


21 Q Okay. And baseline, is that the same as 11:16:24

22 background? 11:16:28




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2 Q Okay. I'm with you so far. So how did you 11:16:30

3 use the total mortality age-specific rates in your 11:16:37

4 calculations? 11:16:40

5 A Okay. Focusing only on a single age, the 11:16:41

6 total mortality rate is either increased by the excess 11:16:54

7 cancers or not. That is used to compute the 11:17:05

8 probability of death according to a standard actuarial 11:17:13

9 formula, and then the number dying are split up into 11:17:17

10 the three groups: Excess cancers, if any -- and there 11:17:25

11 may be none because, note, for the 75 and up, the 11:17:36

12 excess risk is actually .75 which means that the -- 11:17:38

13 there are no excess cancers. And, in fact, in this 11:17:44

14 particular case, the background cancer rate is 11:17:47

15 reduced. 11:17:50

16 So we've got the excess -- all right. This 11:17:55

17 is -- I'm sorry; this is complicated. You split up 11:17:59

18 the total number dying in proportion to the risks, 11:18:03

19 mortality rates, into the three groups: All other 11:18:10

20 causes, baseline cancers, excess cancers. 11:18:15

21 Q Would you be able to write out, as a formula, 11:18:20

22 how you're using these cancer rates? I couldn't find 11:18:34




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1 it in your report and I'm having a little bit of 11:18:37

2 difficulty following what you're saying. 11:18:40

3 A I could try. 11:18:42

4 MS. NEUMAN: Let's mark as Exhibit 1302 a 11:18:59

5 piece of paper for Dr. Rourke to write this formula 11:19:04

6 on. 11:19:07

7 MR. WESTENBERGER: Well, I'm going to object 11:19:10

8 to Dr. Rourke writing out this formula. If this is 11:19:11

9 just used for -- to help you understand this, that's 11:19:16

10 fine, but it's not part of his report. 11:19:21



13 THE WITNESS: This is going to take a few 11:19:42

14 moments. 11:19:44

15 MS. NEUMAN: That's fine. 11:19:44

16 THE WITNESS: What do you want to see, how I 11:23:12

17 split the number dying up into the three groups? 11:23:13

18 BY MS. NEUMAN: 11:23:16

19 Q Well, I just wanted to understand what you 11:23:17

20 were doing with the cancer mortality rate, but if 11:23:21

21 that's not explainable, we can move on. 11:23:26

22 A No, no, no, no. There's all sorts of caveats 11:23:28




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1 here. At least I've got here the number dying. The 11:24:19

2 first formula gives the probability of death, and 11:24:27

3 that's a standard actuarial formula. 11:24:30

4 The second one shows the calculation -- 11:24:33

5 Q Before we get to the second one, probability 11:24:37

6 of death equals 1 -- 11:24:39

7 A -- minus the exponential. That's a 11:24:40

8 mathematical function. 11:24:44

9 Q Which stands for? 11:24:45

10 A Raising E, the magic number E, 2.17, 11:24:47

11 something or other, to a particular power. It's a 11:24:56

12 built-in mathematical function in all the scientific 11:24:59

13 programming languages. 11:25:03

14 Q Okay. 11:25:04

15 A Okay. And that's the negative of the 11:25:08

16 mortality rate. And I'm computing the mortality rate 11:25:10

17 as the total rate plus 1 minus my risk factor times 11:25:15

18 the cancer rate. 11:25:23

19 Q Okay. 11:25:31

20 A And the total number dying in a given year is 11:25:33

21 equal to the probability of death during that year 11:25:40

22 times the number still alive. And this is all 11:25:46




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1 conditional on a given age. I did not put in the age. 11:25:51

2 Q Right. In Exhibit 1302? 11:25:56

3 A Yeah. This is conditional on a fixed age 11:26:01

4 range. 11:26:04

5 Q Now, in this formula in Exhibit 1302 you say 11:26:04

6 that you subtract 1 from your excess risk number; is 11:26:08

7 that right? 11:26:12

8 A That's correct, so I do not want to 11:26:12

9 double-count the cancer rate along with the total 11:26:15

10 rate. Total rate includes cancer rate, background 11:26:19

11 cancer rate. 11:26:23

12 Q But in your report where you describe your 11:26:25

13 calculation, you don't subtract the 1? 11:26:27

14 A I don't even give you any formulas in the 11:26:29

15 report, I believe. 11:26:32

16 Q Well, you say, for example, someone between 11:26:32

17 the ages of 60 and 64 had a cancer mortality rate of 11:26:38

18 19.7 (sic) per 100,000 in 2001. Do you see that? 11:26:41

19 A Yes. 11:26:45

20 Q That's actually incorrect, though, isn't it, 11:26:46

21 based on your chart? It should be -- 11:26:48

22 A Wait a minute. Where are we talking about? 11:26:51




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1 Sorry. 11:26:55

2 MR. WESTENBERGER: Page 2. 11:26:55


4 Q So 60 to 64 is 190.7 mortality rate from 11:27:00

5 table 5? 11:27:06

6 A For 2001, yes. 11:27:06

7 Q Okay. And then you go on to say, "On the 11:27:14

8 other hand, if the person resides near an oil field, 11:27:17

9 that person's cancer mortality rate is 2.64." 11:27:20


11 A Yes. 11:27:25

12 Q Now, you switched age groups, right? You've 11:27:25

13 gone from 60 to 64 to 65 to 69? 11:27:29

14 A That's a typo. I'm sorry. I didn't mean 11:27:32

15 that. 11:27:34

16 Q So the 2.64 should have been 1.46; is that 11:27:34

17 right? 11:27:39

18 A Yes. That is correct. 11:27:39

19 Q So then you say you take the person's cancer 11:27:45

20 mortality rate and you multiply it by -- I mean, you 11:27:49

21 take their excess risk and you multiply it by the 11:27:56

22 mortality rate in your formula here? 11:28:00




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1 A That's the effective total mortality rate for 11:28:02

2 cancer for the 60 to 64 people. 11:28:08

3 Q The 503? 11:28:10

4 A It would be smaller, because you'd have to 11:28:11

5 substitute the 1.46 in there, but yes. 11:28:15

6 Q Okay. Well, let's just pretend we're looking 11:28:17

7 at 65 to 69. 11:28:20

8 A Okay. 11:28:21

9 Q Well, still the -- well, let's assume these 11:28:22

10 two numbers are right for some age group. How about 11:28:25

11 that? 11:28:28

12 A Okay. 11:28:28

13 Q So you'd multiply the excess risk number by 11:28:28

14 the cancer mortality, right, and you get a number per 11:28:32

15 hundred thousand? 11:28:39

16 A That's mortality rate, yes. 11:28:39

17 Q And why don't you subtract the 1 from the 11:28:43

18 2.64? 11:28:49

19 A Because I thought you were asking me for the 11:28:49

20 number of people dying. And bear in mind that cancers 11:28:54

21 is a component of total mortality. So what you're 11:28:59

22 after here is the total mortality, which includes 11:29:09




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1 baseline cancers, plus the increment, the excess part. 11:29:12

2 And that's where this minus 1 comes from. 11:29:19

3 Q So why don't you need to use it on this 11:29:22

4 example on page 13? 11:29:27

5 A Because what I'm giving here is the total -- 11:29:28

6 the cancer mortality, baseline plus total, baseline 11:29:32

7 plus excess. 11:29:37

8 Q And you don't think, to get baseline plus 11:29:37

9 excess, you need to subtract the 1? 11:29:49

10 A Not if you want to know the total cancer 11:29:50

11 mortality, no. But when you're after the total cause 11:29:53

12 of death, or the total mortality for cause of death, 11:30:06

13 you don't want to count it twice. 11:30:08

14 Q Right. Could you look at table 5 for me. If 11:30:10

15 we look at the 60 to 64 age range, the all cancer rate 11:30:47

16 in 1990 is 249; is that right? 11:30:58

17 A Yes. 11:31:01

18 Q And, in 2001, it's 190.7; is that right? 11:31:02


20 Q And we discussed earlier that's a downward 11:31:08

21 trend in the cancer rate? 11:31:13





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1 Q Did you do any analysis as to why the cancer 11:31:14

2 rates were trending down? 11:31:17

3 A No. 11:31:20

4 Q These rates, like the 190.7, that's per 11:31:20

5 hundred thousand people in Ecuador? 11:31:28

6 A Yes. 11:31:28

7 Q And these rates in table 5 apply to people 11:31:31

8 who live in areas far from an oil field; is that 11:31:38

9 correct? 11:31:41

10 A They're based on the entire country, so the 11:31:41

11 answer is yes. 11:31:45

12 Q So the cancer rates shown in table 5 are not 11:31:45

13 associated in any way with exposure to oil field 11:31:55

14 operations, correct? 11:31:59



17 THE WITNESS: The numerator is cancer deaths; 11:32:05

18 the denominator is the population. So to the extent 11:32:19

19 that cancers from the oil-producing regions are 11:32:26

20 included in the total, they in some sense are 11:32:29

21 included. 11:32:35

22 BY MS. NEUMAN: 11:32:35




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1 Q So the cancers, if there are any, caused by 11:32:40

2 living in the oil-producing regions, are included in 11:32:45

3 the totals in table 5? 11:32:49

4 A You're using the word "caused," and I did 11:32:50

5 not. 11:32:54

6 Q Okay. I'm sorry. So the cancers, if any, 11:32:54

7 assorted with living in the oil-producing regions are 11:33:01

8 included in the totals in table 5? 11:33:04

9 A They should be, yes. 11:33:06

10 Q So when you are determining an excess cancer, 11:33:09

11 you're looking at this total from table 5 and 11:33:12

12 determining what percentage is background and what 11:33:15

13 percentage is excess in a given canton, for example? 11:33:18

14 A Wait a minute. Would you please repeat the 11:33:23

15 question? 11:33:26

16 Q Yes, sir. 11:33:26

17 A Thank you. 11:33:27

18 Q When you're trying to figure out the excess 11:33:27

19 deaths associated with living in an oil-producing 11:33:33

20 area, are you taking the total cancer number and then 11:33:37

21 dividing it between background and excess? 11:33:43





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2 THE WITNESS: The cancer number in table 5 11:33:49

3 represents the background. 11:33:57


5 Q So it doesn't represent deaths associated 11:34:00

6 with living in an oil-producing area? 11:34:02

7 A Again, to the extent that the cancers from 11:34:05

8 the oil-producing regions are in the total cancer 11:34:09

9 count, going into the numerator of these rates, they 11:34:12

10 are in some sense there. 11:34:17

11 Q Well, I'm -- now I'm confused. Are those 11:34:18

12 cancers included in the total or they're not included 11:34:26

13 in the total? 11:34:31

14 A They're included -- the cancers from the 11:34:31

15 entire country are included in the calculation of 11:34:36

16 these rates. 11:34:38

17 Q So the totals that you're referring to as 11:34:39

18 excess are already included in these numbers? 11:34:46


20 THE WITNESS: Along with all the other 11:34:50

21 cancers in Ecuador. 11:35:00

22 BY MS. NEUMAN: 11:35:01




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1 Q So that's a yes? 11:35:03

2 A Yes. 11:35:04

3 Q And to calculate the estimated cancer rate 11:35:04

4 for an exposed population in the age 60 to 64, you 11:35:13

5 take the 1.97 from table 5 and you then multiply it by 11:35:20

6 your calculation of excess risk in table 6 for the 11:35:30

7 same age group; is that right? 11:35:33

8 A 0190.7, 60 to 64, times 1.46, yes. That's 11:35:34

9 the total cancer rate. 11:35:46

10 Q And the risk factors in table 6 are based 11:35:47

11 solely on age, not on, for example, the time the 11:35:50

12 population had spent living in an oil-producing 11:35:55

13 canton; is that right? 11:35:59

14 A You said solely on time? 11:35:59

15 Q Solely on age. 11:36:07

16 A Age. Yes, that is true. 11:36:08

17 Q When you add new arrivals to the area that 11:36:11

18 you're performing calculations on in each year, what 11:36:14

19 age do you assume the new arrivals are? 11:36:21

20 A I assumed that they have the age distribution 11:36:23

21 that I estimated for that year of entry. 11:36:30

22 Q What's the scientific basis for that 11:36:33




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1 assumption? 11:36:41

2 MR. WESTENBERGER: Objection. Form of the 11:36:41


4 THE WITNESS: What I'm assuming here is that 11:36:43

5 the age distribution of the newly arriving folks, 11:36:59

6 newly arriving people, have the same age distribution 11:37:04

7 as whatever I computed -- estimated for that year. 11:37:09

8 So -- 11:37:13


10 Q I understand. You're assuming that the 11:37:15

11 people who come -- 11:37:17

12 A Yeah, it's an assumption. 11:37:17

13 Q -- into the area happen to have the same age 11:37:18

14 distribution as the people already living there each 11:37:20

15 year. 11:37:22

16 A What I can't recall is -- right now is where 11:37:22

17 I got the age distribution estimates from. If they 11:37:35

18 are in that area, the answer is yes. 11:37:39

19 Q My question is not what did you assume, but 11:37:41

20 what's the scientific basis for making that 11:37:46






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2 THE WITNESS: I could think of no scientific 11:37:54

3 basis. 11:37:56


5 Q The age of the new arrivals each year, does 11:37:59

6 that have an impact on the cancer mortality 11:38:03

7 calculation? 11:38:07

8 A Of the new arrivals? 11:38:12

9 Q Yes, sir. 11:38:14

10 A Yes, because all the calculations are indexed 11:38:14

11 to age. 11:38:18

12 Q And in your calculations, there's no excess 11:38:19

13 risk until after the age of 50, right? 11:38:23


15 Q So if you assumed the increases in population 11:38:26

16 each year were due exclusively to births, as opposed 11:38:32

17 to people moving into the area, you would have a much 11:38:35

18 lower number of excess cancers, ultimately, correct? 11:38:40

19 A Without doing the calculation, I can't say. 11:38:43

20 It would be lower, but I can't say by how much. 11:38:47

21 Q But it would be lower? 11:38:49

22 A Yes. Let me correct that. This is one of 11:38:51




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1 those things that you really would almost have to do 11:39:12

2 the computations to see what would happen. 11:39:14

3 If you didn't truncate the calculations 11:39:21

4 assuming that there was no exposure past a certain 11:39:29

5 point, then what would happen is that things would be 11:39:36

6 delayed because they would have to become old enough. 11:39:41

7 So the answer is it's not simply as a reduction in the 11:39:46

8 number. It could very well be a delay of the same 11:39:52

9 number. 11:39:55

10 And again, without actually doing the 11:39:56

11 calculations, I can't say. 11:39:58

12 Q But because you did it every year, if every 11:39:59

13 year there were newborns, inevitably people would drop 11:40:03

14 off the back end of your calculations, right, because 11:40:05

15 they wouldn't get old enough? 11:40:09

16 A If you truncated the calculations at a 11:40:11

17 certain point, yes. 11:40:13

18 Q In your model, once you move into one of 11:40:14

19 these oil-producing cantons, you never leave unless 11:40:19

20 you die, right? 11:40:22


22 Q Is that -- what was the scientific basis for 11:40:25




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1 that assumption? 11:40:29



4 THE WITNESS: It was a reasonable assumption 11:40:34

5 given the data that I had available. 11:40:40


7 Q That people never leave? 11:40:42

8 A No, I realize that people leave, but I have 11:40:43

9 no way of estimating what the out-migration rate is. 11:40:48

10 Q So you ignored it; is that right? 11:40:52

11 A In effect, yes. 11:40:55

12 MR. WESTENBERGER: Objection. 11:40:56

13 BY MS. NEUMAN: 11:40:58

14 Q If you had people migrating out of the area, 11:41:08

15 would that decrease the number of excess cancers you 11:41:11

16 calculated? 11:41:14

17 A Again, one of those things that would have to 11:41:14

18 be calculated, but it would be speculating on my part. 11:41:27

19 It would depend on when they left. 11:41:34

20 Q Do you mean at what age or something else? 11:41:36

21 A What age. What age. 11:41:41

22 Q Your model looks at how long of a time span 11:41:42




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1 in total? 11:41:50

2 A This one in this report stops in -- well, let 11:41:50

3 me look at my table. Year of last entry is 2009, but 11:42:02

4 I calculate things to the last death. 11:42:22

5 Q When is the last death? I couldn't tell that 11:42:24

6 from looking at your report. What calendar year is 11:42:27

7 it? 11:42:30

8 A Around 2080. 11:42:30

9 Q 2080? 11:42:32

10 A Yeah. 11:42:33

11 Q How old are these people when they die? 11:42:34

12 A Some of them are very old. 11:42:36

13 Q Like over 100? 11:42:37

14 A No. I don't let people get over 100. 11:42:38

15 Q How old do you let them get? 11:42:42

16 A I think 99. 11:42:43

17 Q And if they die at 99, can they still be 11:42:44

18 counted as an excess cancer death? 11:42:52

19 A I suspect they end up being fractional at 11:42:55

20 that part. I actually believe I discuss this in the 11:42:59

21 report why the tail is so long. 11:43:04

22 Q Oh, yeah, we'll get to the whole meso thing. 11:43:06




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1 A Okay. 11:43:09

2 Q But I'm more interested in the 99-year-olds. 11:43:10

3 Do they get counted in the excess deaths if they die 11:43:13

4 of cancer of -- well, if you assume they die of cancer 11:43:17

5 at 99? 11:43:20

6 A I'm estimating what they die of, the numbers, 11:43:20

7 the relative numbers. And the answer is yes. Wait a 11:43:23

8 minute, no. Excess cancers at that age? My table 6 11:43:37

9 says no. 11:43:49

10 Q 75 and up? 11:43:49

11 A 75 and up, yeah, it's under. No, there 11:43:50

12 aren't any excess cancers past 74. 11:43:54

13 Q Speaking of which, you said earlier that the 11:43:56

14 .75 means background is reduced. Doesn't the .75 mean 11:44:00

15 that the risk of cancer among the exposed population 11:44:09

16 is lower than background? 11:44:12

17 A Yes. 11:44:13

18 Q So the people are getting less cancer than 11:44:14

19 they were elsewhere? 11:44:18

20 A That's what the data say. 11:44:19

21 Q Do you think the assumption that nobody left 11:44:21

22 these four areas from 1967 to approximately 2080 is 11:44:30




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1 valid? 11:44:36

2 MR. WESTENBERGER: Objection to form of the 11:44:36


4 THE WITNESS: It doesn't capture the reality 11:44:38

5 of the population dynamics, but it would depend on the 11:44:45

6 out-migration rate. I don't know what that is. 11:44:49


8 Q So it could be invalid depending on the 11:44:53

9 out-migration rate? 11:44:56



12 BY MS. NEUMAN: 11:44:58

13 Q Is that right? 11:44:59

14 A I would prefer to say it's less valid. 11:44:59

15 Q Did you do anything to test its validity? 11:45:01

16 A I had no data on the out-migration rate. 11:45:05

17 Q Did you try to get data? 11:45:12

18 A Yes. 11:45:18

19 Q And how did you do that? 11:45:19

20 A The INEC website. 11:45:21

21 Q Anything other than looking at that website? 11:45:26

22 A No. 11:45:30




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1 Q Had you had out-migration data, would you 11:45:31

2 have incorporated it into your model? 11:45:47

3 A Ideally, yes. 11:45:49

4 Q Did you check your population in 2020 against 11:45:57

5 your projected age distributions? 11:46:04



8 THE WITNESS: Check my population against the 11:46:09

9 projected age distribution? The population at that 11:46:18

10 time? 11:46:24

11 BY MS. NEUMAN: 11:46:24

12 Q In 2020. 11:46:26

13 A In 2020? Not that I recall, no. 11:46:27

14 Q Did you do anything to validate the 11:46:35

15 assumption that people moving into the area on which 11:46:39

16 you were performing calculations would have the same 11:46:45

17 age distribution as those already living there? 11:46:48

18 A No, I did not. 11:46:50

19 Q Would you tend to think, as a practical 11:46:55

20 matter, that people moving into a new area would be 11:46:59

21 younger? 11:47:02





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2 THE WITNESS: Without having the data, I 11:47:04

3 couldn't say. 11:47:08


5 Q You couldn't say one way or the other? 11:47:10

6 A But I'd be speculating. 11:47:12

7 Q Okay. So you couldn't say one way or the 11:47:14

8 other because you'd be speculating; is that right? 11:47:21


10 Q Is it fair to say your model currently 11:47:23

11 speculates that the age distribution just happens to 11:47:29

12 match the age distribution of the people who already 11:47:31

13 live there? 11:47:33



16 THE WITNESS: The age distribution of the 11:47:40

17 new -- of the new entrants -- the reason I'm 11:47:43

18 hesitating is that's kind of a complicated question. 11:48:01

19 Without thinking through the dynamics of the model, 11:48:12

20 I'm not sure. I'm not sure now. 11:48:16

21 BY MS. NEUMAN: 11:48:18

22 Q Well, having no data on the age distribution 11:48:18




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1 of the new entrants, you just speculated that it would 11:48:21

2 match the age distribution of the existing population, 11:48:26

3 correct? 11:48:28


5 THE WITNESS: I assumed it would match my 11:48:30

6 estimates of what the age distribution of the 11:48:34

7 population was. 11:48:37


9 Q And you made that assumption based on no 11:48:38

10 data? 11:48:40

11 A No, I did have some data. 11:48:40

12 Q Okay. What data was that? 11:48:43

13 A Again, they were some documents from the INEC 11:48:45

14 website that -- without looking at them, I can't 11:48:52

15 recall to what geographic specificity they were, but 11:49:00

16 the age distribution from the beginning of this to 11:49:04

17 2001 was based on the census data that we cited 11:49:15

18 earlier and perhaps something from another document. 11:49:21

19 I believe it's referred to in my report. 11:49:28

20 The age distribution from 2001 to 2009 11:49:30

21 definitely came from the document that I used to get 11:49:35

22 that index series that was mentioned earlier. They 11:49:41




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1 had that broken down by age. I can't recall now the 11:49:44

2 geographic specificity of it. 11:49:49

3 Q But that's just the people who already live 11:49:52

4 there? 11:49:55

5 A Well, in any given year, it would be the 11:49:55

6 people who live there the prior year and are still 11:50:01

7 there plus the new arrivals less the departures. 11:50:04

8 Q The population in your -- let me withdraw 11:50:09

9 that. 11:50:20

10 You project the age distribution for each 11:50:21

11 year, correct? 11:50:24

12 A Not exactly, no. The age distribution -- 11:50:26

13 well, which age -- 11:50:36

14 Q Well, I'll ask a more precise question. You 11:50:37

15 project the age distribution for the population in 11:50:40

16 2020; is that right? 11:50:43


18 Q Your model also assumes an age distribution 11:50:44

19 for that population in that same year, correct? 11:50:49

20 A Yes. Well -- 11:50:56

21 Q Did you compare those two? 11:50:57

22 A No. 11:50:59




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1 Q Could you have compared those two numbers? 11:51:12

2 A I'm actually trying to think here of how you 11:51:17

3 would do that. The answer is potentially the 11:51:20

4 information needed to do so I believe is there, but 11:51:28

5 I -- without looking at some documents, I can't say 11:51:31

6 for sure. 11:51:34

7 Q So the population in your model in 2020 could 11:51:34

8 be older than the population that you projected for 11:51:39

9 that same year, correct? 11:51:44



12 THE WITNESS: Without actually doing it, I 11:51:49

13 would be reluctant to say. 11:51:51

14 BY MS. NEUMAN: 11:51:52

15 Q Well, it could be older, it could be younger; 11:51:53

16 we don't know, correct? 11:51:56



19 THE WITNESS: Possibly, yes. 11:51:58

20 BY MS. NEUMAN: 11:52:00

21 Q Now, you indicated that there's no difference 11:52:04

22 in risk among exposed and unexposed populations before 11:52:10




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1 age 50, correct? 11:52:13

2 A That's what the data or the results in the 11:52:15

3 Hurtig and San Sebastian paper indicate. 11:52:23

4 Q And that's the assumption you used? 11:52:25

5 A Yes. 11:52:27

6 Q So in your model, a 49-year-old who lived 11:52:28

7 adjacent to an oil well for their entire life has no 11:52:34

8 greater risk of cancer incidence than someone of the 11:52:38

9 same age who lives in Quito, right? 11:52:41

10 A The way the model works, that is correct. 11:52:44

11 Q And if you're in the 65 to 69 age group, your 11:52:49

12 cancer risk is 2.64; am I reading that correctly? 11:53:02

13 A The excess risk factor, yes. 11:53:07

14 Q So that means that 65-year-olds living in one 11:53:10

15 of the oil-producing cantons have a 2.64 times greater 11:53:15

16 incidence of cancer, right? 11:53:20

17 A Yes. Yes. 11:53:21

18 Q And it doesn't matter, for the purposes of 11:53:28

19 your model, how long that 65-year-old has been living 11:53:34

20 in that canton, correct? 11:53:39

21 A No, it actually does matter. 11:53:40

22 Q Because of the latency -- 11:53:44




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1 A Yes. 11:53:47

2 Q -- calculation? 11:53:48

3 A Yes. 11:53:49

4 Q What is the scientific basis for your 11:53:49

5 altering the risk numbers for this latency period that 11:53:55

6 you use? 11:53:59



9 THE WITNESS: My experience in dealing with 11:54:02

10 models for asbestos-related cancers, along with my 11:54:09

11 reading of latencies for cancers of the blood. 11:54:16

12 BY MS. NEUMAN: 11:54:23

13 Q Are petroleum products related to the same 11:54:27

14 cancers as asbestos products? 11:54:30



17 THE WITNESS: No. 11:54:37

18 BY MS. NEUMAN: 11:54:46

19 Q Turn to page 17 of your report for me. You 11:54:50

20 say there, "By 1984, asbestos had been effectively 11:55:05

21 removed from the workplace." 11:55:09





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1 A Yes. 11:55:10

2 Q You don't cite anything for that. Is there a 11:55:10

3 citation? 11:55:13

4 A Some of the EPA documents would probably 11:55:13

5 indicate that. Most of the forecasting models 11:55:25

6 actually end in 1979 when the PEL limit or the 11:55:38

7 exposure limit was dropped to whatever it is now -- I 11:55:47

8 can't even recall at this point. I could probably 11:55:50

9 find some things for you. 11:55:57

10 Q Are you offering the opinion that exposure to 11:55:58

11 asbestos ended in 1984? 11:56:03

12 A Not entirely. 11:56:05

13 Q Other -- this idea that mesothelioma has a 11:56:07

14 75-year latency period, is that basically what you're 11:56:21

15 getting at in this paragraph? 11:56:27

16 A Oh, I never said a 75-year latency period. 11:56:28

17 Q Okay. What do you mean by the 75 years? 11:56:31

18 A The asbestos forecasting models generally 11:56:32

19 predict that the claims for asbestos-related injuries 11:56:42

20 will end around 2059. And all I was doing was taking 11:56:49

21 the difference between 1984, say, and 2059 to indicate 11:56:53

22 that that's close to a 75-year span before all the 11:57:01




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1 asbestos-related cancers would vanish. 11:57:06

2 Q And what, if anything, does that 75-year span 11:57:11

3 have to do with -- 11:57:15

4 A Only to indicate that the tail of the excess 11:57:16

5 cancer numbers is very long. 11:57:23

6 Q If they're mesothelioma; is that right? 11:57:27



9 THE WITNESS: And other cancers. 11:57:33

10 BY MS. NEUMAN: 11:57:37

11 Q What other cancer do you have this 75-year 11:57:38

12 span calculation for? 11:57:43

13 A Well, the last cancer death, I think, in my 11:57:43

14 calculations goes out pretty far. So I was just 11:57:54

15 trying to get across the notion that you shouldn't be 11:57:58

16 surprised for this kind of a long tail. 11:58:01

17 Q Well, you use a long tail in your model, 11:58:05

18 correct? 11:58:09

19 A No, it is a result of the model. I don't use 11:58:09

20 it. It's a result. 11:58:12

21 Q You made the model, yes? 11:58:13

22 A Yes, I did. 11:58:15




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1 Q And the model includes a long tail; is that 11:58:16

2 right? 11:58:19

3 A It is a result of the calculations that the 11:58:19

4 model does. 11:58:21

5 Q Well, it's a result of the formulas you put 11:58:22

6 in the model, right? 11:58:26



9 THE WITNESS: If you want to put it that way, 11:58:28

10 yes. 11:58:31

11 BY MS. NEUMAN: 11:58:31

12 Q And how long of a tail does your model 11:58:32

13 assume? 11:58:38

14 A It doesn't. It's a consequence. 11:58:38

15 Q A consequence of what? 11:58:44

16 A Of the model, not an antecedent of it. 11:58:46

17 Q Okay. Well, how is it a consequence of the 11:58:50

18 model? 11:58:52

19 A Because people will -- the last year new 11:58:52

20 arrivals in this particular version of the model was 11:58:56

21 2009, and some of them will be very young. 11:59:01

22 Q Okay. They arrive in 2009, they're very 11:59:05




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1 young, and then what does your model do with them? 11:59:12

2 A It uses the process that we -- that sort of 11:59:15

3 process. And bear in mind, there's 2008, 2007, 2006 11:59:23

4 also. And ageism -- is the term that's frequently 11:59:31

5 used -- reduces their number year by year by expected 11:59:37

6 number of deaths. And, again, this is the -- not 11:59:40

7 everybody is dead until very old ages. 11:59:47

8 Q So -- 11:59:47

9 A Small number of people, but -- 11:59:51

10 Q -- as long -- let me make sure I'm following 11:59:52

11 you here. So you add people into your model year 11:59:54

12 after year until you stop doing it, right? 12:00:01


14 Q And then you follow them until your model 12:00:04

15 says they're all dead? 12:00:08


17 Q And do you assume that no matter what year 12:00:10

18 they entered your model, a portion of the people, once 12:00:21

19 they get over the age of 50, suffer from these excess 12:00:26

20 cancers? 12:00:30

21 A The portion that suffer from the estimated 12:00:30

22 excess cancers is a result of the formulas that -- 12:00:41




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1 some of them you've seen there. 12:00:45

2 Q In your model, do you track latency for every 12:00:47

3 individual you add in a particular year? 12:00:51

4 A When you say individual, I don't add 12:00:53

5 individuals. I add -- 12:01:05

6 Q Numbers? 12:01:06

7 A -- a number of people of a given age. 12:01:08

8 Q Okay. And then do you -- 12:01:11

9 A And all those people that go in of -- that 12:01:14

10 year have that same latency curve which you referred 12:01:20

11 to earlier. 12:01:24

12 Q So you keep track of which 50-year-olds you 12:01:24

13 added in 2009, for example, from that point forward so 12:01:35

14 you can tell how many years they've lived in the 12:01:45

15 oil-producing area; is that right? 12:01:48



18 THE WITNESS: Yes, I believe that's a correct 12:01:52

19 statement. 12:01:57

20 BY MS. NEUMAN: 12:01:57

21 Q Now, on page 17 -- well, let me withdraw 12:01:59

22 that. 12:02:06




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1 Is there any other cancer, other than 12:02:06

2 mesothelioma, for which you had evidence relating to 12:02:10

3 this long tail concept? 12:02:16



6 THE WITNESS: The tail is a consequence of 12:02:21

7 the fact that you have an excess risk of cancer, 12:02:25

8 according to the Hurtig and San Sebastian article, 12:02:33

9 times a moderately substantial background rate of 12:02:36

10 cancer, and people enter this process at young ages. 12:02:41

11 Some people enter this process at young ages. You're 12:02:49

12 trying to turn this thing into an antecedent, and it's 12:02:54

13 a consequent. 12:02:58

14 BY MS. NEUMAN: 12:02:58

15 Q Well, if I enter your model in 2006 -- 12:02:59

16 A Yes. 12:03:05

17 Q -- can I be counted as an excess cancer death 12:03:06

18 down the road? 12:03:15



21 THE WITNESS: It would depend on how old you 12:03:17

22 were at the age of entry and when I stopped 12:03:21




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1 calculating the last death -- well, you're saying 12:03:30

2 you're going in at 2006? 12:03:35


4 Q Yes, sir. 12:03:38

5 A Okay. It would depend on your age at entry. 12:03:38

6 Q Now, San Sebastian says that his risk -- 12:03:42

7 excess risk calculations are for populations living in 12:03:50

8 a county where oil exploitation has been ongoing for a 12:03:55

9 minimum of 20 years. 12:04:01

10 In what year -- as of what calendar year had 12:04:02

11 oil exploitation been going on in Lago Agrio for a 12:04:06

12 minimum of 20 years? 12:04:10

13 A Around 1987 or '88, I would suspect. 12:04:11

14 Q Okay. Do you apply -- do you assume excess 12:04:18

15 risk exists in Lago Agrio in your model before 1987? 12:04:25

16 A Yes, I do. 12:04:28

17 Q On what scientific basis? 12:04:31

18 A It's an assumption of the calculations. 12:04:35

19 There is no scientific basis. 12:04:41

20 Q And is that true for all four cantons? 12:04:43

21 A That Hurtig and San Sebastian used? 12:04:46

22 Q That there's no scientific basis for assuming 12:04:52




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1 an excess risk for the time period from 1967 to 1987. 12:04:57



4 THE WITNESS: They -- that's too strong to 12:05:04

5 say there's no scientific basis. I think it's 12:05:19

6 somewhat more subtle than that. I guess there may be, 12:05:36

7 but I don't have any documentation of it. 12:06:00


9 Q You don't have any documentation of any 12:06:05

10 scientific basis? 12:06:07


12 Q The tort figure you used, the $7 million on 12:06:09

13 page 17, you got that from jury verdicts; do I 12:06:21

14 understand that correct? 12:06:28

15 A Could we refer to the table on page 18? 12:06:28

16 Q Well, I'm looking more on page 17 where you 12:06:34

17 have the U.S. tort system is an award arising from a 12:06:38

18 jury verdict -- 12:06:43

19 A Yes. 12:06:43

20 Q -- in favor of the plaintiff in a wrongful 12:06:45

21 death suit? 12:06:48

22 A Yes. 12:06:48




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1 Q And then you appear to have looked at 26 such 12:06:49

2 verdicts to get your two means, the 7 million and the 12:06:54

3 6 million; is that right? 12:07:02

4 A Yes. 12:07:02

5 Q In these jury verdict cases, those were all 12:07:02

6 cases where the deceased individual had been diagnosed 12:07:06

7 with and actually died of cancer, correct? 12:07:12

8 A That was the parameters of the search, yeah. 12:07:16

9 I did not do the search, though. 12:07:26

10 Q Who did the search? 12:07:27

11 A A gentleman named Chris Arthur. 12:07:29

12 Q Who is Mr. Arthur? 12:07:31

13 A He is an employee of the Weinberg Group. 12:07:32

14 Q Okay. So he provided you these numbers? 12:07:35

15 A Yes. 12:07:37

16 Q But it's your understanding that, in the 12:07:37

17 U.S., at least, the plaintiff -- the -- in a wrongful 12:07:41

18 death case, there's a deceased person? 12:07:49

19 A Yes. 12:07:50

20 Q And since he was looking at lung cancers and 12:07:50

21 leukemias, these were deceased persons who had been 12:07:55

22 diagnosed with and died from either lung cancer or 12:07:59




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1 leukemia; is that your understanding? 12:08:02





6 Q Did you ask Mr. Arthur to determine if there 12:08:07

7 are any verdicts in the U.S. awarded for a claim of 12:08:09

8 excess risk of cancer? 12:08:12

9 A No, I did not. 12:08:15

10 Q Do you know whether excess risk is 12:08:22

11 compensable under U.S. law? 12:08:26

12 A No, I don't. 12:08:30

13 Q Do you know whether excess risk is 12:08:33

14 compensable under Ecuadorian law? 12:08:35

15 A No, I don't. 12:08:37

16 Q Are you aware that there have been litigation 12:08:38

17 about whether the tort system should compensate for 12:08:44

18 claims of excess risk in which Nobel laureates have 12:08:47

19 weighed in with -- scientists have weighed in with 12:08:52

20 their opinions on whether or bit that would be 12:08:56

21 appropriate? Are you familiar with that? 12:08:57





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2 THE WITNESS: To the extent that some of that 12:09:01

3 has gotten into the asbestos arena, yes, a little bit. 12:09:03


5 Q And are you aware that, in the U.S., you 12:09:07

6 cannot get a damage award for a claim of excess risk? 12:09:11



9 THE WITNESS: If that's a true statement, I 12:09:23

10 am now. 12:09:25

11 BY MS. NEUMAN: 12:09:25

12 Q So if you were going to pick an award in the 12:09:26

13 U.S. that you would get for the excess risk deaths 12:09:31

14 that you're calculating, it would be zero, correct? 12:09:35

15 A No, I'm doing a calculation of an estimate of 12:09:39

16 the number of deaths times an estimate of what a U.S. 12:09:45

17 tort system value would be. 12:09:52

18 Q But in coming up with the U.S. tort system 12:09:53

19 value, you're not looking at hypothetical deaths, 12:09:58

20 you're looking at real deaths of real people, right? 12:10:01


22 Q But your calculations are not counting the 12:10:04




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1 real deaths of real people, correct? 12:10:10

2 A They're estimates. 12:10:11

3 Q You're hypothesizing how many people might 12:10:15

4 die based on a certain risk assumption, correct? 12:10:22

5 A Hypothesizing, that's a true statement, yes. 12:10:24

6 Q And you're doing that based on an excess -- 12:10:28

7 an exposure to an alleged excess risk, correct? 12:10:32

8 A Yes. 12:10:35

9 Q Did you get any data as to any kind of an 12:10:38

10 award in the U.S. based on the same scenario that your 12:10:43

11 model looks at, which is a hypothetical death based on 12:10:49

12 an excess risk assumption? 12:10:53

13 MR. WESTENBERGER: Objection. Asked and 12:10:53

14 answered. 12:10:55

15 THE WITNESS: I'm trying to understand the 12:10:55

16 gist of your question. Would you please repeat it? 12:11:05

17 BY MS. NEUMAN: 12:11:08

18 Q Sure. 12:11:08

19 A Thank you. 12:11:08

20 Q Did you get any data as to any kind of award 12:11:08

21 in the U.S. based on the same scenario as your model 12:11:13

22 looks at, a hypothetical death based on an excess risk 12:11:19




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2 MR. WESTENBERGER: Same objection. 12:11:22

3 THE WITNESS: Well, if your information that 12:11:23

4 you conveyed to me a moment ago was the case, then 12:11:30

5 there is no such thing, but I didn't look for it 12:11:34

6 either. 12:11:36


8 Q In these tort cases where people have gotten 12:11:42

9 these million-dollar awards, do you understand that 12:11:48

10 they had to prove that they had cancer and that it was 12:11:50

11 caused in some way by the defendant -- actually 12:11:54

12 caused? 12:11:56


14 Assumes facts not in evidence. 12:11:58

15 THE WITNESS: They had to demonstrate 12:11:59

16 exposure, and they had to present a compelling case to 12:12:11

17 a jury. 12:12:20

18 BY MS. NEUMAN: 12:12:20

19 Q That the exposure caused their disease, 12:12:20

20 correct? 12:12:22






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1 THE WITNESS: That's required, yes. 12:12:24


3 Q The $7 million, does it include any punitive 12:12:29

4 damages? 12:12:32

5 A They may. 12:12:32

6 Q Do you know one way or the other, sir? 12:12:35

7 A I don't know for sure. 12:12:39

8 Q The $7 million includes economic components; 12:12:39


10 A Yes, it would. 12:12:43

11 Q Like medical bills, lost wages, those sorts 12:12:44

12 of things? 12:12:48

13 A In the U.S., that would be the case, yes. 12:12:48

14 Q Did you do any analysis as to whether those 12:12:51

15 economic components would be comparable in Ecuador? 12:12:57

16 A No, I did not. 12:12:59

17 Q Do you have any knowledge as to whether, for 12:13:04

18 example, the average lost wage for a wrongful death 12:13:08

19 decedent in the U.S. would be at all similar to the 12:13:15

20 lost wages of a wrongful death decedent in Ecuador? 12:13:18

21 A I sort of answered this question before in 12:13:22

22 that, in some sense, this is going to be the judge's 12:13:25




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1 decision in Ecuador. This was to demonstrate, if the 12:13:27

2 judge were to decide that a U.S. tort system value is 12:13:31

3 not unreasonable, then this is what would happen. Or 12:13:36

4 this is what would be estimated to happen. 12:13:39

5 Q So you're not suggesting that the $7 million 12:13:42

6 is the appropriate measure for the hypothetical death 12:13:49

7 based on assumption of excess risk that you count up 12:13:56

8 in your model? 12:13:59

9 A That will ultimately be the judge's decision. 12:13:59

10 Q My question is, are you offering the opinion 12:14:09

11 that that is an appropriate valuation, or are you just 12:14:12

12 saying, if you want to say that these are similar, 12:14:16

13 then here's the number you could use? 12:14:22



16 THE WITNESS: Closer to the latter statement, 12:14:26

17 which I guess is the -- if you want to say statement. 12:14:30

18 BY MS. NEUMAN: 12:14:35

19 Q So you're not offering an opinion as to the 12:14:36

20 appropriate value for a hypothetical death based on an 12:14:39

21 excess risk calculation -- you're not offering an 12:14:44

22 expert opinion on that? 12:14:47




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1 A No, I'm not. 12:14:47

2 MS. NEUMAN: I'm going mark as Exhibit 1303 a 12:14:56

3 document produced from your files, Dr. Rourke. 12:15:21




7 Q And the second page of Exhibit 1303 is the 12:15:27

8 metadata for this document printed out from the 12:15:56

9 computer. 12:16:00

10 A Oh. May I ask a question? 12:16:03


12 A Do you happen to know what the original file 12:16:41

13 name of this was? The reason I'm asking is that this 12:16:44

14 is not a file name that I would have ever used. 12:16:48

15 Q The DR name? 12:16:57

16 A Yeah. 12:16:58

17 Q That's the Bates number, I think. 12:16:59

18 A Oh. 12:17:02

19 Q So the short answer to your question is no. 12:17:05

20 A Okay. 12:17:07

21 Q Can you tell me what this document is? 12:17:08

22 A I think -- oh, okay. Now, we're relying on 12:17:09




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1 my memory, such as it is, and I believe this is my 12:18:12

2 attempt to check Cabrera's arithmetic. 12:18:17

3 Q Cabrera, the expert in Ecuador -- in the 12:18:27

4 Ecuador matter? 12:18:34

5 A The court-appointed gentleman, yes. 12:18:35

6 Q And you determined that he, or whoever wrote 12:18:38

7 his report, made a conceptual error; is that right? 12:18:43

8 A There was an error in interpretation, yes. 12:18:48


10 Exhibit 1304 the annex to the Cabrera report that I 12:19:19

11 think you were looking at. See if you can tell me if 12:19:22

12 I'm right. 12:19:26



15 BY MS. NEUMAN: 12:19:27

16 Q Is this the annex you were analyzing in 12:19:27

17 Exhibit 1303? 12:20:43

18 A Yes, it is. 12:20:43

19 Q Exhibit 1304? This English copy that you 12:20:44

20 produced doesn't have a translation certificate 12:20:48

21 attached to it. Do you know if it's an English 12:20:51

22 original or a translation of the Spanish? 12:20:54




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1 A I don't know. 12:20:55

2 Q Now, on -- you mentioned you thought 12:20:57

3 Mr. Cabrera drafted this annex Q; is that right? 12:21:05

4 A He's the signatory. 12:21:07

5 Q Did anybody tell you that he drafted it? 12:21:10

6 A No one told me either he drafted it or he did 12:21:13

7 not, so... 12:21:21

8 Q Did anyone tell you that a David Mills of 12:21:22

9 Stratus Consulting in Denver did the calculations that 12:21:26

10 are in annex Q? 12:21:28


12 the question. Assumes facts not in evidence. 12:21:31


14 BY MS. NEUMAN: 12:21:34

15 Q Can you turn to Rourke-Native 10409 -- 12:21:38

16 A Yes. 12:21:41

17 Q -- where you wrote, "Wrong, 33 percent"? 12:21:43

18 A Yes. 12:21:46

19 Q Can you explain to me what you meant there? 12:21:46

20 A I believe that whoever wrote this document 12:21:49

21 misinterpreted the results of something else that we 12:21:54

22 may or may not get to, which was the survey that was 12:21:58




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1 done in which a question was asked, how many of these 12:22:03

2 people are dead, in effect? And it is my 12:22:08

3 interpretation that it was 33 percent. The person who 12:22:16

4 prepared this document took it as 67 percent. No -- 12:22:23

5 that's what it says, "Wrong, 33 percent." That's my 12:22:28

6 notes. 12:22:32

7 Q So your note is their 67 percent in annex Q 12:22:32

8 is wrong, and the 67 should have been 33? 12:22:37

9 A That's what I believe, based on my reading of 12:22:41

10 the other document. 12:22:43

11 Q Then, if we turn to the last page of 12:22:44

12 Exhibit 1304, there's a handwritten 1.435. Do you see 12:22:52

13 that? 12:22:59

14 A Yes. 12:22:59

15 Q That's your handwriting, Dr. Rourke? 12:23:09

16 A Yes. 12:23:12

17 Q And were you conveying by that that where, in 12:23:12

18 annex Q of the Cabrera report, there was an amount of 12:23:18

19 2,907,000,000 shown, the correct number should have 12:23:24

20 been 1,435,000,000; is that right? 12:23:30

21 A If that's the result of dividing 2 into 12:23:34

22 2,907,000,000, the answer is yes. 12:23:37




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1 Q Well, look at Exhibit 1303, which I think is 12:23:41

2 where you get your 1.403 -- I'm sorry, 435. 12:23:46

3 A Yeah. Yes. 12:23:51


5 Exhibit 1305 an e-mail from San Sebastian to David 12:24:13

6 Mills commenting on annex Q. 12:24:18




10 Q Have you met with Dr. Sebastian, Dr. Rourke? 12:24:21

11 A No, I have not. 12:24:35

12 Q Are you aware that he worked with the 12:24:36

13 plaintiffs in this matter? 12:24:38



16 THE WITNESS: No, I was not. 12:24:41

17 BY MS. NEUMAN: 12:24:43

18 Q Have you ever heard of an organization called 12:24:44

19 the Frente de Amazonia, the Front? 12:24:46

20 A My memory is fallible, but it is possible 12:24:49

21 that one of the documents I received may have been 12:24:57

22 authored by them. 12:25:00




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1 Q The Frente? 12:25:01

2 A Yeah. I can't recall for sure. 12:25:03

3 Q Were you aware, in doing your work, that the 12:25:05

4 Frente is the beneficiary of the potential judgment in 12:25:10

5 the Ecuador matter? 12:25:15



8 THE WITNESS: I actually didn't know who the 12:25:18

9 entity would be. I don't know. 12:25:21

10 BY MS. NEUMAN: 12:25:22

11 Q Were you aware that the Frente sponsored 12:25:24

12 Dr. Sebastian's research? 12:25:27



15 THE WITNESS: No I was not. 12:25:31

16 BY MS. NEUMAN: 12:25:33

17 Q Have you read Dr. Sebastian's thesis? 12:25:34

18 A His dissertation? 12:25:37

19 Q His thesis. 12:25:39

20 A No, I did not. 12:25:41

21 Q Can you take a minute -- oh. Have you seen 12:25:42

22 Exhibit 1305 before? Was it provided to you? 12:25:45




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1 A This I've never seen before. 12:25:48

2 Q Can you let me know when you've had a chance 12:25:50

3 to read it, sir. 12:25:53

4 Did you have a chance to read 1305? 12:27:35

5 A Yes, I have. 12:27:37

6 Q This relates to the survey you mentioned 12:27:38

7 earlier? 12:27:40

8 A Yes, it does. 12:27:40

9 Q Do you see that Dr. San Sebastian is 12:27:43

10 criticizing the way in which the survey was used in 12:27:49

11 the Cabrera report? 12:27:53

12 A Yes, I do. 12:27:54

13 Q Do you agree with his criticisms in 12:27:56

14 Exhibit 1305? 12:28:00

15 A I had concern about these points, yes. 12:28:00

16 Q Do you generally agree with what 12:28:09

17 Dr. Sebastian says in Exhibit 1305? 12:28:13

18 A Yes, I do. 12:28:15

19 MS. NEUMAN: It's 12:30. Do you want to take 12:28:21

20 our lunch break now? 12:28:23

21 MR. WESTENBERGER: Does that work for you? 12:28:24

22 THE WITNESS: Fine with me. 12:28:26




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1 MS. NEUMAN: Let's go off the record. 12:28:27

2 THE VIDEOGRAPHER: The time is 12:29 p.m. 12:28:30

3 Going off the record. Concluding video 2. 12:28:33

4 (Whereupon, at 12:29 p.m., a luncheon recess 12:46:19

5 was taken.) 12:46:19

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22




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1 AFTERNOON SESSION 12:28:38

2 (1:21 p.m.) 13:19:48

3 THE VIDEOGRAPHER: The time is 1:21 p.m., 13:19:48


5 number 3. 13:20:38

6 Whereupon,

7 DANIEL LEE ROURKE,

8 was called for continued examination, and having been

9 previously duly sworn, was examined and testified

10 further as follows:

11 EXAMINATION BY COUNSEL FOR CHEVRON CORPORATION 13:20:38

12 BY MS. NEUMAN: 13:20:38

13 Q Good afternoon, Dr. Rourke. 13:20:38

14 A Good afternoon. 13:20:42

15 Q Are we good to proceed? 13:20:44

16 A Yes, we are. 13:20:46

17 Q Oh, great. Do you still have Exhibit 1301 in 13:20:47

18 front of you, the San Sebastian study? 13:20:51

19 A Yes, I do. 13:20:55

20 Q Okay. This is an ecological study that you 13:20:56

21 relied on for cancer risk estimates, correct? 13:21:04

22 A Ecological? 13:21:06




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1 Q Yes, sir. 13:21:14

2 A Okay. Yes. 13:21:14

3 Q Did you analyze the entire study before 13:21:16

4 relying on it? 13:21:21



7 THE WITNESS: I focused on population and 13:21:24

8 methods, results and -- mainly those parts. 13:21:38


10 Q And you're just reading the headings of the 13:21:56

11 parts you focused on? 13:21:58

12 A Yes, I am. That's basically -- population 13:22:00

13 and methods is on page 2, middle of the right-hand 13:22:03

14 column. And the results, including figure 2, finishes 13:22:08

15 about halfway down the right-most column of page 3. 13:22:15

16 Q Okay. Did you read the entire paper? 13:22:24

17 A Yes, I did. 13:22:26

18 Q Okay. Would you agree that ecological 13:22:28

19 studies are generally considered a weak study design 13:22:35

20 in epidemiology? 13:22:39






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1 THE WITNESS: Weak in the sense that it 13:22:41

2 doesn't say anything definitive about causation. 13:22:48


4 Q Correct. 13:22:54

5 A Yes. 13:22:54

6 Q Have you reviewed any analysis of the error 13:22:55

7 rates in risk numbers derived from ecologic 13:23:09

8 epidemiological studies? 13:23:13

9 A Error rates? That's a broad term. Can you 13:23:17

10 be more specific? 13:23:23

11 Q Well, how often these ecologic studies turn 13:23:23

12 out to be wrong when they postulate an excess risk of 13:23:28

13 cancer. 13:23:31

14 A No, I did not. 13:23:31

15 Q Do you have a sense of how often these type 13:23:34

16 of studies turn out to be wrong? 13:23:36



19 THE WITNESS: No, I do not. 13:23:39

20 BY MS. NEUMAN: 13:23:41

21 Q Are you aware that, in epidemiology, ecologic 13:23:45

22 studies like the one Dr. San Sebastian did are 13:23:50




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1 generally considered exploratory and not relied upon 13:23:53

2 to prove causation? 13:23:58



5 THE WITNESS: Yes, I am. 13:24:01


7 Q Can you look at page 1025 -- and I'm going by 13:24:04

8 the journal page -- of Exhibit 1301 in the left-hand 13:24:09

9 column. Do you see where the authors state, "Because 13:24:23

10 they reflect group rather than individual 13:24:27

11 characteristics and exposures, ecologic studies must 13:24:29

12 be interpreted cautiously. The use of aggregated data 13:24:33

13 instead of joint distributions of exposure outcome and 13:24:37

14 covariates at the individual level may lead to severe 13:24:43

15 bias in ecologic analysis." 13:24:46


17 A Yes, I do. 13:24:49

18 Q And you read that before you formed your 13:24:51

19 opinions, correct? 13:24:53

20 A Yes, I did. 13:24:53

21 Q Do you understand that, in Exhibit 1303, the 13:24:54

22 San Sebastian study, the authors used aggregated data? 13:24:59




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1 A 1303? 13:25:03

2 Q I'm sorry, 130 -- 13:25:06

3 A -- 1? 13:25:09

4 Q -- 1. 13:25:10

5 A Yes, I am. 13:25:10

6 Q And did you further understand the authors to 13:25:11

7 state in their study that, by using aggregated data, 13:25:15

8 their study might have a severe bias? 13:25:21



11 THE WITNESS: Bias in what and -- when I'm 13:25:28

12 saying bias, I mean -- causation or the magnitude of 13:25:43

13 the risk factors? 13:25:48

14 BY MS. NEUMAN: 13:25:50

15 Q That it might be a biased study; its results 13:25:52

16 might be biased. 13:25:56




20 BY MS. NEUMAN: 13:26:01

21 Q What did you do in your analysis to account 13:26:01

22 for the potential severe bias in the San Sebastian 13:26:03




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1 risk numbers? 13:26:09



4 THE WITNESS: Firstly, severe prejudges any 13:26:13

5 magnitude of bias. And, secondly, no, I did not. 13:26:16


7 Q You did not do anything to account for bias? 13:26:22

8 A No, I did not. 13:26:24

9 Q Why not? 13:26:25

10 A It would be unclear to me how to do so, other 13:26:26

11 than vary the excess risk factors that I employed. 13:26:33

12 Q Is it scientifically accepted, when you have 13:26:38

13 a study that the authors say the results of which may 13:26:41

14 be severely biassed, to rely on those results without 13:26:46

15 taking that bias into account? 13:26:49



18 THE WITNESS: Without knowing the direction 13:26:53

19 of the bias, it's a little hard to do so if there is a 13:26:54

20 bias. There may not be. 13:26:58

21 BY MS. NEUMAN: 13:27:00

22 Q Or, according to the authors, there may be a 13:27:00




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1 severe bias, correct? 13:27:03


3 Q Do you think it's proper to use risk numbers 13:27:04

4 from a study in which there might be a severe bias -- 13:27:09

5 MR. WESTENBERGER: Objection to the form -- 13:27:12


7 Q -- uncorrected? 13:27:13



10 THE WITNESS: I'm sorry. Would you ask the 13:27:15

11 question again? 13:27:23

12 MS. NEUMAN: Could you read the question 13:27:23

13 back, please? 13:27:27


15 THE WITNESS: In the absence of any data to 13:27:28

16 indicate how to correct for the bias, all you could 13:27:59

17 fall on -- all you could fall back on would be 13:28:07

18 sensitivity analyses where you vary the factors around 13:28:11

19 the observed value. 13:28:18

20 BY MS. NEUMAN: 13:28:19

21 Q Did you do a sensitivity analysis? 13:28:19

22 A No, I did not. 13:28:21




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1 Q Could you have done one? 13:28:22

2 A Possibly, yes. 13:28:24

3 Q Would it have been more appropriate to do a 13:28:27

4 sensitivity analysis, given the potential bias in the 13:28:30

5 study? 13:28:33



8 THE WITNESS: Given the time available, I did 13:28:36

9 use the observed values. 13:28:47

10 BY MS. NEUMAN: 13:28:49

11 Q You mean the time available to do your work? 13:28:50

12 A Yes, that's correct. 13:28:52

13 Q Were you rushed in doing this work? 13:28:54



16 THE WITNESS: There was a relatively short 13:28:57

17 time frame in which the work was performed. 13:29:00

18 BY MS. NEUMAN: 13:29:02

19 Q And did that cause you to perform the work in 13:29:02

20 a different manner that you would have had you had 13:29:05

21 more time? 13:29:09

22 A I would have explored more options, possibly 13:29:09




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1 including a sensitivity analysis. 13:29:18

2 Q Now, you said that your only choice to deal 13:29:19

3 with the severe bias was to do a sensitivity analysis. 13:29:23

4 You could also choose not to use this study, correct? 13:29:27

5 A I am not aware of any data other than this 13:29:33

6 that reports the results in a manner that I could have 13:29:46

7 used. 13:29:49

8 Q I'm not sure what you mean by that. What do 13:29:49

9 you mean by any data other than this that reports the 13:30:03

10 results in a manner you could have used? 13:30:06

11 A The crucial thing here was the fact that they 13:30:08

12 presented the charts that showed the age-specific 13:30:12

13 effects, age-specific rates. And what tends to be 13:30:16

14 done is journal articles show age-corrected using some 13:30:22

15 standard population, and that data can't be used to 13:30:29

16 take into account the age distribution. 13:30:40

17 Q Okay. But if you concluded this study was -- 13:30:42

18 I'll withdraw that. 13:30:58

19 So just because it had age-specific data, you 13:30:59

20 felt it appropriate to use the San Sebastian study 13:31:01

21 without regard to the quality of the study? 13:31:05





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1 Mischaracterization of his testimony. 13:31:08

2 THE WITNESS: That's not quite right. Yes, 13:31:09

3 it did present the age-specific data, but it also 13:31:13

4 appeared in a peer-reviewed journal. Published in a 13:31:16

5 peer-reviewed journal. 13:31:21


7 Q A little further down the left-hand column of 13:31:22

8 page 1025, the authors state, "Errors in population 13:31:31

9 estimates, including differential migration patterns, 13:31:35

10 may bias estimates of risk. It is possible that 13:31:39

11 exposed counties have had a more rapidly increasing 13:31:43

12 population compared to non-exposed, providing a 13:31:47

13 relatively greater underestimate of population 13:31:50

14 denominators in these counties." 13:31:53


16 A Yes, I do. 13:31:56

17 Q If, as the authors of the article postulate, 13:31:57

18 exposed counties had a more rapidly increasing 13:32:03

19 population, that would have led to an overestimate of 13:32:06

20 risk by San Sebastian, correct? 13:32:09






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1 THE WITNESS: Make sure I have that 13:32:14

2 correctly. 13:32:16

3 Could that be repeated again. 13:32:16

4 MS. NEUMAN: Certainly. 13:32:19


6 THE WITNESS: Actually, no. 13:32:41


8 Q Why do you say no? 13:32:49

9 A Because the -- their mortality rates are 13:32:49

10 number of cancers at a given age range divided by 13:32:54

11 their estimate of the population over that time span. 13:32:58

12 And what you're suggesting would be that they had a 13:33:02

13 smaller population than should actually go into the 13:33:06

14 denominator, if I'm getting the order of that right. 13:33:11

15 Q The author say, "Errors in population 13:33:18


17 might bias the estimates of risk." 13:33:23


19 A Yes, I two. 13:33:24

20 Q What did you understand them to mean? 13:33:25

21 A That if their denominators are incorrect, 13:33:27

22 then their estimates must necessarily be incorrect. 13:33:32




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1 Q What are their denominators? 13:33:34

2 A For mortality rates, it is an estimate of the 13:33:37

3 population of the people at a given age in the same 13:33:39

4 geographic area. 13:33:45

5 Q What errors in their population -- what types 13:33:46

6 of errors in their population estimates would result 13:33:51

7 in their risk estimates being too high? 13:33:54

8 A If they underestimated the size of the 13:33:57

9 population going in the denominator. 13:34:01

10 Q For the exposed or unexposed or both? 13:34:04

11 A Potentially, both. But they seem to be 13:34:06

12 saying that they're focusing on the exposed 13:34:11

13 population. 13:34:15

14 Q So if they underestimated the size of the 13:34:15

15 exposed population -- "they" being San Sebastian -- 13:34:23

16 then their risk numbers would be artificially high; is 13:34:28

17 that right? 13:34:33

18 A Assuming that the numerators, the number of 13:34:33

19 cancers, remains the same, yes. 13:34:35

20 MS. NEUMAN: I'll mark as Exhibit 1306, 13:34:52

21 "Cancer mortality and oil production in the Amazon 13:35:03

22 region of Ecuador, 1990-2002," by Michael Kelsh, Libby 13:35:06




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1 Morimoto, Edmund Lau, published in the International 13:35:13

2 Occupational Environmental Health Journal in 2009. 13:35:17




6 Q Have you seen this study that we have marked 13:35:22

7 as Exhibit 1306 before, Dr. Rourke? 13:35:50

8 A Yes, I have. 13:35:52

9 Q Did you review this study during the course 13:35:53

10 of forming your opinions in this matter? 13:35:56

11 A Yes, I did. 13:36:00

12 Q This study reaches different conclusions than 13:36:00

13 those reached by San Sebastian and his co-authors, 13:36:07

14 correct? 13:36:11

15 A Yes, it does. 13:36:11

16 Q This study concludes that there is no 13:36:13

17 increased risk of cancer due to living in an 13:36:18

18 oil-producing canton, correct? 13:36:22

19 A That's the bottom line result, yes. 13:36:23

20 Q If you had relied on the Kelsh study, rather 13:36:27

21 than the San Sebastian study, would you have concluded 13:36:31

22 zero excess cancers in your calculations? 13:36:35




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2 Q What scientific basis did you use for 13:36:37

3 selecting the San Sebastian study rather than the 13:36:42

4 Kelsh study as the basis for your model? 13:36:45



7 THE WITNESS: Well, it's been almost three 13:36:49

8 months since I had a chance to review this, read this. 13:37:08

9 May I? 13:37:13

10 BY MS. NEUMAN: 13:37:13


12 A Thank you. 13:37:14

13 I would have to read the whole thing again, 13:38:26

14 but were I to use the results here, you're totally 13:41:06

15 correct; it would have been zero excess cancers. But 13:41:11

16 I can't recall why I decided not to right now, without 13:41:14

17 reading the whole thing. 13:41:17

18 Q So as you -- without taking the time to 13:41:17

19 review the entire Kelsh article -- 13:41:22


21 Q -- you can't articulate why you relied on the 13:41:24

22 San Sebastian study rather than the Kelsh study? 13:41:29




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1 A To the exclusion of this one? 13:41:31

2 Q Yes. 13:41:33


4 Q Now, the Kelsh study was not in your 13:41:34

5 production. Do you know why that is? 13:41:37

6 A Should have been. 13:41:38

7 Q Do you know if there's other things you 13:41:41

8 reviewed that weren't produced for some reason? 13:41:44

9 A I'm just thinking. I'm surprised this 13:41:46

10 wasn't, because I thought I had a PDF of it, and I 13:42:00

11 apologize, but I did have a PDF of it. 13:42:05

12 Q So it wasn't kept in some other location 13:42:08

13 or -- there's nothing you could think of that would 13:42:13

14 make it unique? 13:42:16

15 A I have two laptops upon which I did the work, 13:42:16

16 and I tend to back up one to the other and the other 13:42:20

17 to one, so at any point in time they're supposed to 13:42:24

18 have the same stuff on it. But sometimes, I mean, 13:42:28

19 that doesn't happen. Evidently, this got missed, and 13:42:30

20 I apologize -- 13:42:36

21 Q That's okay. 13:42:36

22 A -- but there's nothing else. 13:42:36




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1 Q Did you prepare your production off just one 13:42:37

2 laptop? 13:42:42

3 A I thought both, actually. 13:42:42

4 Q Oh, okay. We'll sort through it. Can you 13:42:42

5 look at table 3 in the Kelsh study for me? It's on 13:42:47

6 page 385. 13:42:58

7 A Yes. 13:42:59

8 Q Do you see it down there? 13:42:59

9 A Yes, I do. 13:43:02

10 Q If you look at table 3, you'll see that it 13:43:03

11 shows a more rapid growth in the exposed -- you know, 13:43:09

12 quote, exposed cantons compared to the unexposed 13:43:14

13 cantons based on San Sebastian's classifications using 13:43:17

14 the INEC data. Do you see that? 13:43:21

15 A Yes, I do. 13:43:24

16 Q That's the INEC data that you used, too, 13:43:26

17 right? 13:43:29

18 A I had '90 and 2001, and I had Orellana and 13:43:29

19 Sucumbios. I had a larger geographic area. 13:43:35

20 Q But same data that you relied on, generally? 13:43:40

21 A Yes. 13:43:43

22 Q Given that the INEC data establishes rapid 13:43:44




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1 population growth in the exposed area as compared to 13:43:48

2 the unexposed area, isn't it true that San Sebastian 13:43:50

3 overestimates the cancer risk? 13:43:54



6 THE WITNESS: To answer this question, I need 13:43:58

7 to look at what they did to get their population 13:44:17

8 number. 13:44:22


10 Q "They" being San Sebastian? 13:44:23

11 A San Sebastian. 13:44:24

12 Q So you're going to look at Exhibit 1301? 13:44:25

13 A That is correct. They use the population for 13:44:29

14 1992. May I read? 13:44:49


16 A "Population data from the counties" -- 13:44:52

17 Q Where are you reading from, though? 13:44:55

18 A I am reading the population data section on 13:44:56

19 page 1023 of the Hurtig and San Sebastian article. 13:44:59

20 Q Okay. Got you. 13:45:05

21 A "Population data from the counties of the 13:45:06

22 four provinces by gender and five-year age strata for 13:45:09




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1 the year 1990 were used." 13:45:12

2 Q I think it says 1992. 13:45:15

3 A '92, excuse me. "These were projections of 13:45:17

4 the National Institute of Statistics and Census based 13:45:21

5 on the 1990 national census." 13:45:24

6 What I don't see here is how they did their 13:45:28

7 projection, and they appear to be only based on the 13:45:31

8 1990 census, which is only two years earlier than the 13:45:35

9 date at which they got their cancer counts. So it's a 13:45:45

10 bit of an apples and oranges comparison here. 13:45:56

11 Q Well, San Sebastian says, at 1225 that we 13:45:58

12 looked at a minute ago, that, "Errors in population 13:46:06


14 might bias estimates of risk. It is possible that the 13:46:13

15 exposed counties had a more rapidly increasing 13:46:15

16 population compared to non-exposed." 13:46:18


18 A Yes, I do. 13:46:22

19 Q He says it's possible, and then Kelsh shows 13:46:23

20 that it was, in fact, true, correct? 13:46:28






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1 THE WITNESS: Okay. What is unclear from the 13:46:31

2 paper is the cancer counts extend from 1985 to 1998 in 13:46:41

3 the Hurtig and San Sebastian paper, and how they then 13:46:56

4 dealt with that in the denominators, the population 13:47:02

5 estimates, I can't tell from this article. 13:47:07


7 Q But you can tell, can't you, that they 13:47:13

8 acknowledge that if the exposed counties had a more 13:47:16

9 rapidly increasing population compared to the 13:47:21

10 non-exposed, providing a relatively greater 13:47:24

11 underestimated population denominators for these 13:47:27

12 counties (sic)? 13:47:32

13 A As, again, I stated earlier, if this is true, 13:47:32

14 then they would have tended to overestimate the cancer 13:47:39

15 rates, cancer incidence rates. 13:47:47

16 Q If there was more rapid population growth in 13:47:49

17 the exposed area? 13:47:53

18 A This is getting confusing for all of us. 13:47:53

19 Than they estimated, yes. They would underestimate 13:48:09

20 the population, the denominator and, therefore, 13:48:12

21 overestimate the mortality rate. 13:48:17

22 Q And when we look at Kelsh that looked at the 13:48:18




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1 data in table 3, we see that, as San Sebastian argued 13:48:22

2 might be the case -- but they didn't look at it -- 13:48:29

3 that there was more rapid population growth in the 13:48:32

4 exposed area compared to the unexposed area, correct, 13:48:36

5 based on INEC data? 13:48:39

6 A Well, that's true. 13:48:41

7 Q And so doesn't that confirm that San 13:48:42

8 Sebastian overestimated the risk? 13:48:46

9 A Without knowing exactly how San Sebastian and 13:48:47

10 Hurtig got to their population denominators, I really 13:48:50

11 can't say. 13:48:56

12 Q Well, when you read the Kelsh study and saw 13:48:56

13 that it created a case with data from INEC for the 13:48:58

14 fact that San Sebastian had overestimated the risk, 13:49:03

15 what did you do to take that into account in your 13:49:06

16 analysis? 13:49:09

17 A I did nothing. I used the San Sebastian and 13:49:09

18 Hurtig results as presented in their paper. 13:49:19

19 Q Why wouldn't you address the data shown in 13:49:21

20 the Kelsh study that the population growth in the, 13:49:30

21 quote, exposed area, was more rapid in (sic) the 13:49:38

22 unexposed? 13:49:42




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3 THE WITNESS: To do that, I would have needed 13:49:44

4 the counts in the numerator also, the number of 13:49:49

5 cancers, and I didn't have those. 13:49:53


7 Q Well, why didn't the data presented in the 13:49:55

8 Kelsh article about population growth cause you to 13:50:00

9 reject the San Sebastian risk numbers? 13:50:03

10 A Again, I'd have to read the whole article 13:50:05

11 carefully again to try to remember why I didn't. 13:50:10

12 Q Would you agree that the San Sebastian 13:50:15

13 article acknowledges that it didn't take into account 13:50:19

14 differentials in population growth? 13:50:22



17 THE WITNESS: I can't see if there's enough 13:50:29

18 description in their method section to determine if 13:50:36

19 that's true or not. 13:50:38

20 BY MS. NEUMAN: 13:50:39

21 Q You don't think they make that clear on 13:50:43

22 page 1025? 13:50:45




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1 A Not entirely, no. 13:50:45

2 Q Well, prior to forming your opinions and 13:50:48

3 adopting the San Sebastian risk numbers, did you 13:50:55

4 determine whether or not San Sebastian took into 13:50:57

5 account differentials in population growth? 13:51:01

6 A I used the article as it stands because it 13:51:02

7 appeared in a peer-reviewed journal. 13:51:13

8 Q Dr. Kelsh's article appears in a 13:51:15

9 peer-reviewed journal, correct? 13:51:19

10 A Yes, it does. 13:51:20

11 Q You could have used Dr. Kelsh's article, as 13:51:21

12 it stands, for the same reason, correct? 13:51:25

13 A Yes, I could. 13:51:27

14 Q Do you have any scientific basis for 13:51:28

15 rejecting Dr. Kelsh's and his co-authors' findings 13:51:31

16 that there was no increased risk in the four cantons? 13:51:37

17 MR. WESTENBERGER: Objection. The witness 13:51:44

18 has testified he would have to review the entire 13:51:46

19 article. 13:51:48

20 THE WITNESS: I would have included something 13:51:49

21 like this as a sensitivity analysis. The lower bound 13:51:54

22 would be zero and the -- zero excess risk, or an 13:52:03




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1 excess risk factor of 1, which would be no excess 13:52:08

2 risk. 13:52:12


4 Q You would have -- 13:52:13

5 A Included that in the sensitivity. 13:52:15

6 Q Rather than using the study? 13:52:21

7 A Exclusively, yes. 13:52:22

8 Q Why? 13:52:25

9 A I found the Hurtig and San Sebastian one more 13:52:26

10 compelling. And, again, I literally would have to 13:52:37

11 read the whole thing over again to find out what it 13:52:42

12 was that I was concerned about in the Kelsh article. 13:52:45

13 Q And how long would it take you to do that? 13:52:51

14 A 15 minutes. Maybe a little longer. 13:52:53

15 Q Okay. Let's go off the record and you can 13:52:58

16 read it. 13:53:01

17 MR. WESTENBERGER: I don't think we're going 13:53:01

18 to go off the record to do it. We'll go on the 13:53:03

19 record -- he can stay on the record and do it. 13:53:06

20 MS. NEUMAN: That's fine. 13:53:07

21 BY MS. NEUMAN: 13:53:10

22 Q One more question before you start reading. 13:53:18




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1 Did you review the Kelsh article in preparing for your 13:53:19

2 deposition? 13:53:22

3 A No, I did not. 13:53:23

4 Q Okay. 14:01:07

5 A Okay. There's two things about this that 14:01:08

6 I've noticed, and I think I now know why I didn't do 14:01:09

7 it. I also believe I got the same death count files 14:01:14

8 from INEC, and they didn't use ICD-10 from 1990 to 14:01:18

9 1996. They used ICD-9. So I don't know how -- how 14:01:24

10 this was done. 14:01:30

11 BY MS. NEUMAN: 14:01:30

12 Q Okay. So -- so the record is clear, who is 14:01:32

13 "they"? 14:01:36

14 A "They" would be -- sorry about that -- Kelsh, 14:01:36

15 Morimoto and Lau. 14:01:40

16 Q Okay. And in Exhibit 1306, their paper, what 14:01:43

17 are you -- can you direct me to the part where you're 14:01:48

18 talking about they used ICD-9 instead of ICD-10? 14:01:53

19 A Okay. Mortality -- this is on page 383 of 14:01:59

20 the article, "Mortality data and cause of death 14:02:08

21 classification." 14:02:13

22 Q 383 okay. 14:02:17




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1 A "Mortality data for the years 1990 to 2005 14:02:18

2 were reported at the canton level stratified by age -- 14:02:23

3 sex and age group, five-year age group intervals 14:02:28

4 beginning at age five." 14:02:32

5 Skipping to the -- well, third sentence -- 14:02:34

6 "The coded underlying cause of death classifications 14:02:42

7 were supplied by INEC and were based on the 14:02:45

8 international classification of diseases, 10th 14:02:49

9 revision, ICD-10." 14:02:52

10 Q Okay. 14:02:54

11 A My data that I got from the same source don't 14:02:55

12 use ICD-10 until 1997. So I had questions about their 14:02:59

13 data. 14:03:07

14 Q Because they said ICD-10? 14:03:07


16 Q And did you check their data against what you 14:03:20

17 have? 14:03:23

18 A I don't have their data. I have the data 14:03:23

19 that I downloaded from the INEC website. 14:03:26

20 Q And could you have looked at the INEC data 14:03:29

21 that they used? 14:03:35

22 A I presumed that's what I had. 14:03:36




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1 Q And when you compared your INEC data to their 14:03:41

2 reported INEC data, did it differ in any way? 14:03:45

3 A They didn't use ICD-10, international 14:03:47

4 classification of disease, until 1997, according to 14:03:51

5 the files I got. 14:03:59

6 Q Yes, but did you compare what they reported? 14:04:00

7 A I saw that and I said, something is not right 14:04:07

8 with this, and went no further. 14:04:12

9 Q Is there a way to convert ICD-9 data to 14:04:13

10 ICD-10? 14:04:19

11 A In the general, yes. In detail, no. 14:04:19

12 Q Now, this issue, this ICD-10, that only 14:04:25

13 relates to the cancer classifications, right? 14:04:33


15 Q Okay. 14:04:37

16 A That's how you determine if it's a cancer or 14:04:38

17 something else. 14:04:40

18 Q In table 3, we were just looking at the 14:04:40

19 population growth rate between two different cantons, 14:04:46

20 which shows that the so-called exposed cantons grew at 14:04:50

21 a faster rate than the unexposed cantons -- did you 14:04:55

22 see any issues with that data? 14:04:58




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1 MR. WESTENBERGER: Objection. The question 14:05:00

2 you asked I think that started this was for him to 14:05:02

3 review this to tell you why it was that he did not 14:05:05

4 rely on this report. He has answered those questions. 14:05:07

5 Now you're going from one thing to another. He 14:05:10

6 explained to why he didn't rely on -- 14:05:13

7 MS. NEUMAN: I can ask another question. 14:05:13

8 MR. WESTENBERGER: Well, you've been asking 14:05:16

9 the same questions over and over now. 14:05:17

10 MS. NEUMAN: I don't think so. 14:05:19

11 MR. WESTENBERGER: I do. 14:05:21

12 THE WITNESS: Again, we're back to the if -- 14:05:22

13 I've got to get the proper order of this. If Hurtig 14:05:28

14 and San Sebastian have too small a denominator, it 14:05:34

15 will have too large a mortality rate. 14:05:39

16 One way of having too small a denominator is 14:05:45

17 to not use a higher growth rate when, in fact, a 14:05:53

18 higher growth rate is the case. But, again, I didn't 14:06:02

19 have enough detail about precisely how Hurtig and 14:06:06

20 San Sebastian got their population numbers, so I 14:06:12

21 really can't say one way or the other. 14:06:16

22 BY MS. NEUMAN: 14:06:18




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1 Q Okay. My question was just Kelsh's 14:06:20

2 table 3 -- 14:06:22

3 A Yes. 14:06:23

4 Q -- the data it reports and the conclusion it 14:06:23

5 reaches, do you have any reason to reject that? 14:06:26

6 A No, I don't. 14:06:29

7 Q Okay. In the San Sebastian study, do you 14:06:31

8 have a problem with the fact that San Sebastian uses 14:06:37

9 ICD-9 for post-1997 data after ICD-10 was available? 14:06:41


11 question. 14:06:49

12 You can answer. 14:06:49

13 THE WITNESS: They got their data from a 14:06:49

14 different source than Kelsh, Morimoto and Lau. They 14:06:51

15 used -- may I read from the paper? This is the Hurtig 14:07:03

16 and San Sebastian paper, page 1023, cancer data. They 14:07:08

17 used -- going to the third census -- "All cases 14:07:24

18 diagnosed in Quito are registered in the National 14:07:28

19 Cancer Registry. This registry is used for the 14:07:34

20 purposes of our study in all" -- so the cases that 14:07:36

21 were referred from these areas on the cancer registry 14:07:42

22 to Quito were used. 14:07:46




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1 So it's not quite the same data source for 14:07:48

2 the numerator. 14:07:52


4 Q It's different than the INEC? 14:07:54

5 A Yes. 14:07:56

6 Q If you had no reason to reject the data or 14:08:01

7 conclusions in Kelsh table 3 about the population 14:08:04

8 growth differentials, why didn't you reach the 14:08:09

9 conclusion that San Sebastian's risks were 14:08:12

10 overestimated? 14:08:16

11 A Because, again, I didn't know what 14:08:16

12 San Sebastian used for the denominators of their 14:08:19

13 mortality rates. 14:08:26

14 Q Did you try to find out? 14:08:27



17 THE WITNESS: No, I did not. 14:08:34

18 BY MS. NEUMAN: 14:08:36

19 Q Any reason other than that you didn't know 14:08:46

20 what San Sebastian used for the denominators of their 14:08:49

21 mortality rates? 14:08:53





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2 THE WITNESS: What precisely is the question? 14:08:56


4 Q Why, based on table 3 of the Kelsh report, 14:09:03

5 which shows the differentials in the population growth 14:09:08

6 between the unexposed and exposed areas as defined by 14:09:11

7 San Sebastian, you didn't conclude that San Sebastian 14:09:15

8 had overstated risk? 14:09:19

9 A Again, I did not know what San Sebastian 14:09:20

10 precisely used in the denominator that he needed -- 14:09:28

11 that they needed to calculate the mortality rates. 14:09:32

12 Q Any reason other than that? 14:09:33

13 A No, I guess not. 14:09:35

14 Q Did you do a qualitative assessment of the 14:09:41

15 relative strength of the San Sebastian study versus 14:09:45

16 the Kelsh study in terms of their epidemiological 14:09:49

17 findings? 14:09:53



20 THE WITNESS: What invalidated the Kelsh 14:09:55

21 study to me was their claim that they used ICD-10 from 14:10:02

22 1990 to 1996 when the data that I received from INEC 14:10:06




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1 used ICD-9. So I don't know what data they used. I 14:10:13

2 basically stopped at that point. 14:10:17


4 Q It used ICD-9 for all those years? 14:10:21

5 A Okay. Who's "they" again? 14:10:24

6 Q INEC used ICD-9 for all the years of the 14:10:25

7 Kelsh study? 14:10:29

8 A That's what the files indicate, yes. ICD-10 14:10:30

9 doesn't appear till 1997. 14:10:34

10 Q If epidemiologists other than Dr. Kelsh had 14:10:40

11 criticized San Sebastian's studies, would you have 14:10:49

12 taken those criticisms into account? 14:10:52



15 THE WITNESS: It depends on the nature of the 14:10:55

16 criticism and my opportunity to review those articles. 14:10:58

17 BY MS. NEUMAN: 14:11:02

18 Q Do you know who Dr. Siemiatycki is? 14:11:03

19 A Yes, I do. 14:11:06

20 Q Who is he? 14:11:08

21 A He was evidently one of the reviewers of the 14:11:08

22 Hurtig and San Sebastian article, and he wrote a 14:11:15




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1 critique of it. 14:11:20

2 Q You're aware that he's a well-known 14:11:21

3 epidemiologist from the University of Quebec, 14:11:25

4 Dr. Siemiatycki? 14:11:29

5 A University of -- 14:11:29

6 MR. WESTENBERGER: Object to -- 14:11:30


8 Q Quebec. 14:11:30



11 THE WITNESS: I didn't know what his 14:11:33

12 institutional affiliation was. 14:11:34

13 BY MS. NEUMAN: 14:11:36

14 Q Are you aware that Dr. Siemiatycki has 14:11:36

15 conducted numerous epidemiologic studies on cancer 14:11:39

16 risk? 14:11:42



19 THE WITNESS: Yes, I am. 14:11:43

20 BY MS. NEUMAN: 14:11:47

21 Q Do you have any reason to question the 14:11:48

22 quality of Dr. Siemiatycki's work? 14:11:49




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1 A No. 14:11:53

2 Q The Siemiatycki commentary on the 14:11:53

3 San Sebastian study is something you reviewed, 14:11:57

4 correct? 14:12:00

5 A I read his review commentary and the response 14:12:00

6 from Hurtig and San Sebastian. 14:12:06

7 Q So Dr. Siemiatycki, at page 1207 (sic) of 14:12:09

8 Exhibit 1301, states, referring to the San Sebastian 14:12:18

9 study, "This study's limitations are clear and are 14:12:22

10 partly acknowledged by the authors. Stripped to its 14:12:27

11 essence, it is a geographical correlation study with 14:12:31

12 an N of 2, a real possibility of bias in the 14:12:34

13 ascertainment of outcomes between the two study areas, 14:12:38

14 a real possibility of confounding by a plethora of 14:12:42

15 ethnic and social factors, and the crudest measures of 14:12:44

16 exposure." 14:12:49


18 A Yes, I do. 14:12:50

19 Q Do you agree with Dr. Siemiatycki's 14:12:51

20 conclusion that the San Sebastian study has, "a real 14:12:53

21 possibility of bias in the ascertainment of the 14:12:56

22 outcomes between the two study areas"? 14:12:59




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3 THE WITNESS: We've partially address the 14:13:02

4 issue of bias before, and bias can be either you're 14:13:07

5 too low or too high. 14:13:12

6 The N of 2 -- they're comparing two areas, 14:13:14

7 which you consider two points, very low dose versus 14:13:21

8 whatever dose they have, and I agree with that. 14:13:25

9 But I want to point out that their exposed 14:13:30

10 region is -- well, generally contains the regions that 14:13:35

11 I -- the regions that I used for my estimates. 14:13:41

12 MS. NEUMAN: I'm going to move to strike as 14:13:47

13 non-responsive. 14:13:50

14 BY MS. NEUMAN: 14:13:50

15 Q My question was simply, do you agree with 14:13:51

16 Dr. Siemiatycki's conclusion that the San Sebastian 14:13:54

17 study has a real possibility of bias in the 14:13:57

18 ascertainment of the outcome? 14:13:59


20 answered. You may not like his answer, but he 14:14:02

21 answered the question. 14:14:06

22 MS. NEUMAN: Would you please object as to 14:14:06




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1 form. 14:14:09

2 THE WITNESS: Okay. Yes, I -- 14:14:09

3 MR. WESTENBERGER: I'll object as I see fit, 14:14:09

4 and he answered your question. 14:14:13



7 Q Do you agree with Dr. Siemiatycki's 14:14:18

8 conclusion that the San Sebastian study has a real 14:14:20

9 possibility of confounding by a plethora of ethnic and 14:14:23

10 social factors? 14:14:27

11 A This is bordering between going from an 14:14:40

12 observed effect, offering no explanation for it, to 14:14:48

13 one in which you want to begin to parse out the 14:14:53

14 causes. 14:15:00

15 And, yes, it is possible that there are 14:15:01

16 differences in -- ethnic differences, lifestyle 14:15:07

17 differences, whatever you want to come up with, 14:15:14

18 between these two areas. But I was focused more on 14:15:16

19 the effect rather than dealing with possible 14:15:29

20 explanations of why the effect occurred. That is to 14:15:34

21 say, I did not attempt to attribute cause. 14:15:38

22 Q But you did focus on whether or not there was 14:15:42




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1 an effect and a number by which to measure it, 14:15:55

2 correct? 14:15:58

3 A That is true. 14:15:58

4 Q Okay. So if the risk numbers calculated by 14:16:00

5 San Sebastian are incorrect, then your entire analysis 14:16:10

6 is incorrect; is that right? 14:16:12

7 A If the numbers are different, then my results 14:16:14

8 would necessarily be different. 14:16:21

9 Q What's a confounding factor, as you 14:16:22

10 understand it? 14:16:26

11 A Can I make up an example? 14:16:26

12 Q Sure. 14:16:30

13 A Let's say that there are more cancers because 14:16:31

14 of some genetic defect that is peculiar to some 14:16:45

15 race -- I hate to use the word "race," excuse me -- 14:16:57

16 some tribe or something like that, that's genetically 14:17:01

17 linked to a group of people, and these people happen 14:17:04

18 to reside in the exposed area where other people 14:17:07

19 without this trait, genetic, don't have it. Then here 14:17:12

20 you would have a case of a confounder, an explanation, 14:17:20

21 a cause, explaining why you get more cancers and a 14:17:25

22 higher cancer rate in one area rather than the other, 14:17:32




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1 not due to proximity to the oil wells, but the fact 14:17:34

2 that these folks, for whatever reason, have a genetic 14:17:40

3 predisposition to cancer. That would be a confounder. 14:17:45

4 Q So, in your example, if those people lived in 14:17:49

5 Dr. San Sebastian's exposed area, he would still 14:17:59

6 report an increased risk? 14:18:03

7 A Yes, he would. 14:18:04

8 Q And that increase would have nothing to do 14:18:05

9 with the fact that there's oil-producing operations 14:18:08

10 where these people live? 14:18:10

11 A That's correct. If those folks happened to 14:18:11

12 live someplace else and you compared that region with 14:18:14

13 whatever the control region was, you would observe the 14:18:18

14 same kind of thing. 14:18:21

15 Q And for your purposes, was it irrelevant 14:18:22

16 whether or not the increase in risk was actually 14:18:33

17 related to living in the oil-producing area? 14:18:36



20 THE WITNESS: The work is based on the 14:18:41

21 assumption. This is something we could never know 14:18:44

22 without doing the genetic testing. So the work is 14:18:52




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1 based on the results of this paper, the Hurtig and 14:18:55

2 San Sebastian paper. 14:19:01


4 Q I understand that. My question is, for the 14:19:02

5 purposes of your analysis, was it irrelevant to you 14:19:05

6 whether or not the risk was -- let me withdraw that. 14:19:09

7 Assuming there was an increase in risk as 14:19:14

8 reported by San Sebastian, was it irrelevant to you 14:19:17

9 whether that risk was caused by living near an 14:19:21

10 oil-producing facility? 14:19:26



13 THE WITNESS: I use the title "associated 14:19:29

14 with living," and I didn't offer any explanation as to 14:19:36

15 why. 14:19:42

16 BY MS. NEUMAN: 14:19:42

17 Q I'm still unclear on whether it's relevant or 14:19:46

18 irrelevant. 14:19:48

19 A Well, from your point of view, I guess it 14:19:49

20 would be irrelevant if it turned out to be due to not 14:19:52

21 the fact that they lived in this area -- which, again, 14:19:56

22 is expressing an explanation as to cause -- as opposed 14:20:00




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1 to some genetic predisposition of the people who 14:20:04

2 happen to live there, for whatever reason. 14:20:10

3 Q So is the reason that you didn't reject the 14:20:13

4 San Sebastian study after reviewing Dr. Siemiatycki's 14:20:30

5 criticisms that you felt all of his criticisms related 14:20:34

6 to whether or not the study proved causation? 14:20:38



9 THE WITNESS: I interpreted his critiques, by 14:20:47

10 and large, to be issues related to causation, yes. 14:20:52

11 BY MS. NEUMAN: 14:20:55

12 Q Did you interpret them to be issues related 14:20:56

13 to association? 14:20:58

14 A No. 14:20:58

15 Q Why not? 14:21:02

16 A Well, because they were related to causation. 14:21:03

17 An association can happen without causation precisely 14:21:12

18 for my explanation of this confounding effect. 14:21:18

19 Q Okay. Dr. Siemiatycki goes on to say, "While 14:21:21

20 the overall cancer incident (sic) was ostensibly 14:21:37

21 higher in the exposed area, the cancer site 14:21:40

22 distributions did not exhibit a pattern that would 14:21:43




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1 obviously throw suspicion on actiological agents 14:21:46

2 coming from the oil industry pollution." 14:21:50

3 Did you see that when you read his paper? 14:21:52

4 A Yes, I did. 14:21:53

5 Q Did you have any reason to agree or disagree 14:21:55

6 with that? 14:21:59

7 A Let me read his thing again, because when you 14:21:59

8 say "pattern," that could mean geographically or kind 14:22:06

9 of cancer or things like that. 14:22:10

10 Excuse me. Where is that sentence of his? 14:22:18

11 Q Oh. It's right after the sentence I read on 14:22:20

12 page 1. 14:22:30

13 A Okay. 14:22:31

14 Q Right-hand column, left full sentence. 14:22:32

15 A All right. He's talking about cancer of the 14:22:39

16 cervix in females being the big one, and it was his 14:23:05

17 concern, I take it, that he would have expected a 14:23:11

18 pattern similar between the males and the females. 14:23:19

19 That's one hypothesis about what he's saying there. 14:23:23

20 And I didn't find that compelling. 14:23:27

21 Q Well, don't you think what he was saying was 14:23:29

22 that if oil-producing facilities were causing cancer, 14:23:36




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1 you would expect to see the kinds of cancer that the 14:23:39

2 chemicals associated with those operations are known 14:23:43

3 to cause? 14:23:46


5 the question. Asked and answered. 14:23:48

6 THE WITNESS: It is possible -- well, first 14:23:49

7 of all, you can only have cancer of the cervix in 14:23:56

8 females. 14:24:00


10 Q I'm with you there. 14:24:01

11 A So you wouldn't see it in males. Now, 14:24:01

12 there's issues about what the cause is. And so it's 14:24:06

13 conceivable that, for whatever reason -- now, I'm 14:24:09

14 getting into causation, which I endeavored not to do 14:24:15

15 in the original -- my work, but I guess I surmised it 14:24:20

16 is possible that hydrocarbons or benzene or whatever 14:24:25

17 is the possible carcinogen here could target some 14:24:34

18 areas of a human female differentially. 14:24:40

19 Q Are you aware of the literature that over 14:24:45

20 90 percent of cervical cancer is -- 14:24:50

21 A Is caused by -- 14:24:50

22 Q -- caused by the human papillomavirus? 14:24:54




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1 A Yeah, human papillomavirus, yes. 14:24:57

2 Q Do you have any reason not to credit that 14:24:57

3 notion. 14:25:00

4 A Eh. 14:25:00

5 Q Is that a yes or a no? I'm not even sure how 14:25:04

6 she wrote that down. 14:25:07

7 A Yeah, I'm sure she did. At the time, I just 14:25:09

8 didn't find that argument compelling. 14:25:15

9 Q Is it your testimony that population 14:25:16

10 migration would not be a variable affecting 14:25:20

11 association? 14:25:24

12 A I can't say that. 14:25:24

13 Q Well, is it your testimony that differential 14:25:29

14 selection would not be a variable affecting an 14:25:36

15 association? 14:25:39

16 A Since an association is a demonstration of a 14:25:39

17 link -- or, rather, a correspondence between event A 14:25:45

18 and event B, I would not rule out -- well, your first 14:25:49

19 example and your second example. 14:25:53

20 Q Now, in the next page of Dr. -- 14:25:54

21 MR. WESTENBERGER: I need to take a very 14:25:58

22 short break. I just got a call. It's an emergency. 14:26:01




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1 MS. NEUMAN: That's fine. We'll go off the 14:26:03

2 record. 14:26:06

3 THE VIDEOGRAPHER: The time is 2:26 p.m. 14:26:06

4 going off the record, completing video 3. 14:26:12


6 THE VIDEOGRAPHER: The time is 2:34 p.m. on 14:26:20


8 number 4. 14:33:33


10 Q On the next page of the Siemiatycki 14:33:33

11 commentary, page 1028, Dr. Siemiatycki wrote, "Given 14:33:43

12 the limitations of the study design and the lack of 14:33:52

13 clear strong results, this study provides no more than 14:33:56

14 a hint that there may be a cancer problem in the area 14:33:59

15 around oil fields." 14:34:02


17 A Yes. 14:34:03

18 Q Did this comment by Dr. Siemiatycki give you 14:34:07

19 any concerns about relying on the San Sebastian study 14:34:10

20 to make a billion-dollar damage estimate? 14:34:15






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1 THE WITNESS: The billion dollars is what 14:34:20

2 happens if you use the $7 million figure that I used. 14:34:26

3 But, again, that's going to be the judge's call. The 14:34:31

4 real focus, I think, was more the estimation of the 14:34:38

5 potential -- well, the estimate of the number of 14:34:41

6 possible excess cancers. 14:34:44


8 Q Well, given what Dr. Siemiatycki has to say 14:34:51

9 about the limitations of the study design and the lack 14:34:55

10 of clear results, that doesn't give you any pause 14:34:58

11 about using San Sebastian's risk numbers? 14:35:00



14 THE WITNESS: This is an estimate and may be 14:35:06

15 greater, may be lesser. 14:35:12

16 BY MS. NEUMAN: 14:35:13

17 Q What's the margin of error in your estimates? 14:35:13

18 A I don't know. 14:35:15

19 Q You didn't determine that? 14:35:16

20 A Actually, it's probably not possible to 14:35:17

21 determine one without knowing the distributions of 14:35:20

22 everything that went into the calculation. 14:35:27




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1 Q So are you saying that you have no way of 14:35:36

2 validating your model and determining its margin of 14:35:39

3 error? 14:35:44

4 A Okay. Now, there's two different things 14:35:44

5 here. One is the margin of error and the other one is 14:35:48

6 the validation. The validation will be -- will occur 14:35:52

7 in 2080 or whenever. 14:35:59

8 The -- excuse my voice. The confidence 14:36:04

9 interval is -- which I take it is what you're getting 14:36:10

10 at here -- there is no such thing for this model 14:36:14

11 because in order to estimate a confidence interval, 14:36:29

12 you have to have some calibration series, series of 14:36:34

13 numbers; in this case, real counts that, by and large, 14:36:40

14 have not yet been determined. 14:36:45

15 Q What real counts are you referring to? 14:36:48

16 A The number of excess cancer deaths that have 14:36:53

17 actually occurred in the Amazon regions, the 14:37:00

18 Concession area or whatever. 14:37:06

19 Q How come that can't be determined? 14:37:08

20 A That's going to be the judge's decision, 14:37:11

21 based on the material given to him. 14:37:21

22 Q I guess -- how is the judge going to decide 14:37:24




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1 whether there's any excess deaths or not on his own? 14:37:28



4 THE WITNESS: By reading all the materials 14:37:33

5 that both sides presents. I mean, you said it 14:37:36

6 yourself earlier: Excess deaths is the number that 14:37:41

7 arose as a result of living in this area over and 14:37:52

8 above what you would expect. And those numbers aren't 14:38:00

9 really known. 14:38:07

10 BY MS. NEUMAN: 14:38:10

11 Q Well, could the real number of excess deaths 14:38:14

12 due to living in the four cantons be zero? 14:38:18



15 THE WITNESS: Yes, it could be. And it could 14:38:26

16 also be larger than any number that I presented. 14:38:28

17 BY MS. NEUMAN: 14:38:31

18 Q And why can't you determine whether there's 14:38:33

19 excess deaths for years that have already passed? 14:38:36

20 A Because the data are incomplete, to some 14:38:52

21 degree, and no determination has been made as to 14:39:05

22 whether the death is a wrongful death. 14:39:14




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1 Q Well, there are real counts of cancer in the 14:39:18

2 four cantons by year through 2005 in the INEC 14:39:28

3 database, right? 14:39:34

4 A Actually, through 2009 now. 14:39:35

5 Q Okay. So there's real counts of cancer for 14:39:37

6 the four cantons through 2009 in the INEC database, 14:39:43

7 right? 14:39:48

8 A Yes. 14:39:48

9 Q So you could compare your projections of 14:39:51

10 total cancers to the total cancers that really 14:39:56

11 occurred in the cantons, correct? 14:39:59

12 A Yes, I could. 14:40:00

13 Q Did you do that? 14:40:05

14 A No, I didn't. 14:40:07

15 Q If you have more excess cancers, what you 14:40:08

16 call excess cancers, than there are total cancers in 14:40:16

17 the cantons, would you agree that your model is 14:40:20

18 flawed? 14:40:26



21 THE WITNESS: Firstly, the comparison I would 14:40:28

22 make would be my estimate of the total number of 14:40:32




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1 cancers against what occurred in these areas, but let 14:40:36

2 me back up just a slight bit. 14:40:44

3 I did make a projection for the four cantons, 14:40:49

4 so that would probably be something to compare things 14:40:57

5 to. So it would be a comparison of my estimate of the 14:41:03

6 total of cancers over some time period to what the 14:41:06

7 INEC death counts indicated. And if they deviated 14:41:18

8 substantially, I would suspect that it wasn't so much 14:41:24

9 the model that was wrong as some of the numeric 14:41:32

10 assumptions that go into the calculations that weren't 14:41:37

11 quite right. 14:41:40

12 BY MS. NEUMAN: 14:41:40

13 Q But the answers would be wrong? 14:41:42

14 A The answers, yes, would be wrong. 14:41:42

15 Q Did you do any comparisons like that to real 14:41:44

16 data to determine if the answers you were getting were 14:41:50

17 right or wrong? 14:41:53

18 A No, I did not. 14:41:54

19 MS. NEUMAN: I'm going mark as Exhibit 1307 a 14:41:55

20 printout from one of your data files, DR37.xlsb. 14:42:10






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2 Q This is a printout from your outputs 14:42:24

3 worksheet, Dr. Rourke? 14:43:10


5 Q And the column CL represents the grouping of 14:43:11

6 the four cantons with people included up until 2010; 14:43:21


8 A 2009, yes. 14:43:28

9 Q 2009. Oh, I see. Okay. If you focus on 14:43:29

10 rows 26 to 41 of the CL column, those represent the 14:43:41

11 years 1990 through 2005? 14:43:47

12 A Uh-huh. 14:43:49

13 Q How many expected cancers does your model 14:43:50

14 predict in that time frame? 14:43:58

15 A Which range of years? 14:44:00

16 Q '90 through '05. 14:44:01

17 A By year? 14:44:04

18 Q Well, I got a total of 687. 14:44:19

19 A From 1990 through 2005 for what column? 14:44:23

20 Q The four canton -- CL, the CL column. 14:44:34

21 A Okay. But -- oh, CL. Okay. 14:44:39

22 May I write on what your total was for that 14:45:00




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1 period? 14:45:03

2 Q Yes. You're just going to write it on 14:45:03

3 Exhibit 1307? 14:45:08

4 A Yes. 14:45:08

5 Q Yes. 14:45:09

6 A And this is from 1990 through 2009? 14:45:10

7 Q Yes. I got 687. Do you want a calculator? 14:45:15

8 A I'll take your word for it. 14:45:18

9 Q Can you look at table 5 of the Kelsh study, 14:45:21

10 which is Exhibit 1306. Do you see that, for the 14:45:42

11 period 1990 through 2005, based on the reported data, 14:46:09

12 they report a total of 484 cancers for the same time 14:46:15

13 period? 14:46:20

14 A I see that figure. 14:46:20

15 Q And these are, as reported in the study, 14:46:22

16 observed cancers that they're counting in the data, 14:46:31

17 correct? 14:46:34

18 A But we're back to the problem I had with the 14:46:34

19 article originally, is I don't see how they were able 14:46:38

20 to use ICD-10 for 1990 to 1996. 14:46:40

21 Q How many cancer classifications changed 14:46:45

22 between ICD-9 and ICD-10? 14:46:49




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1 A The whole thing changed. The whole system 14:46:51

2 changed. 14:46:57

3 Q Every disease got a new classification? 14:46:58

4 A A new code, yes. And cancers appeared in 14:47:04

5 ICD-10 that were not considered exactly cancers. 14:47:08

6 Mesothelioma, for example, finally became a line item 14:47:13

7 in the causes of death. And that was not true prior 14:47:17

8 to ICD-10. 14:47:23

9 Q Well, assuming -- this question you had about 14:47:24

10 why they used ICD-10, did you ever contact anybody? 14:47:39

11 A No. 14:47:43

12 Q And you rejected the study completely based 14:47:43

13 on that one issue? 14:47:48


15 Mischaracterization of his testimony. 14:47:52

16 THE WITNESS: Predominantly, yes. 14:47:52

17 BY MS. NEUMAN: 14:47:56

18 Q And changing from ICD-9 to ICD-10 would 14:48:01

19 increase the number of cancers, right? 14:48:16

20 A In that there were things that were -- I 14:48:17

21 don't know what, if any, things were dropped out. 14:48:29

22 Mesothelioma was certainly added as a cause. I don't 14:48:32




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1 know if anything else was removed. 14:48:37

2 And the other issue is, did they -- "they" 14:48:39

3 being Kelsh -- use all the ICD-10 codes beginning with 14:48:50

4 the letter C to indicate a malignancy? I don't -- I'd 14:48:56

5 have to take a look and see what they said. 14:49:01

6 Q Well, assuming the 484 is what's reported in 14:49:03

7 the INEC data for this time period, whereas your model 14:49:10

8 is predicting 687, would you agree that there is an 14:49:16

9 issue with your model's ability to accurately predict 14:49:26

10 the number of cancers that are occurring? 14:49:29



13 Mischaracterization of his prior testimony. 14:49:35

14 THE WITNESS: If the 484 is close to 14:49:37

15 accurate, then your answer is yes. 14:49:46

16 BY MS. NEUMAN: 14:49:48

17 Q And if the number of cancers your model is 14:49:55

18 predicting is incorrect, then your excess cancer 14:50:02

19 estimates would be incorrect as well; is that true? 14:50:09



22 THE WITNESS: Well -- all right. What -- all 14:50:14




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1 right. Assuming, which I do not necessarily agree 14:50:18

2 with, but assuming that the 687 is higher than what 14:50:21

3 actually occurred, then it could be due to excess risk 14:50:25

4 factors that are too great or/and mortality rates for 14:50:30

5 cancers that are too large or/and population estimates 14:50:39

6 that are too large, and perhaps -- basically all the 14:50:44

7 numeric assumptions going into the model, or the age 14:50:54

8 distribution isn't quite right. 14:51:00


10 Q But there's something wrong? 14:51:05

11 A If there -- if that number is right, yes. 14:51:05

12 Q The 687 in Exhibit 1307, is that your 14:51:11

13 estimate of the total cancers for that time period or 14:51:15

14 just the excess cancers? 14:51:17

15 A This would be excess cancers. 14:51:18

16 Q The 687? 14:51:21

17 A Yes. 14:51:24

18 Q So if the 484 is correct, you're estimating 14:51:25

19 more excess cancers than there were total cancers; is 14:51:36

20 that right? 14:51:39

21 A Yes. 14:51:39

22 Q And how many total cancers would you be 14:51:40




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1 predicting for this same time frame? 14:51:44

2 A I don't know. 14:51:46

3 Q Significantly more than the 687? 14:51:48



6 THE WITNESS: I was trying to do the mental 14:51:55

7 arithmetic, but yes, more. 14:52:05


9 Q If the 484 is correct, would you agree that 14:52:10

10 your estimate of excess cancers of 687 is 14:52:22

11 significantly in error? 14:52:28



14 THE WITNESS: Again, if the 484 is correct, 14:52:33

15 the answer would have to be yes. 14:52:35

16 BY MS. NEUMAN: 14:52:36

17 Q If the 484 is correct, can you estimate the 14:52:38

18 margin of error we're talking about? 14:52:43

19 A Well, on a percentage basis, with a 14:52:49

20 calculator, probably, but -- no. I appreciate that. 14:52:51

21 Q I'm happy to provide a calculator. 14:52:56

22 I just don't have a sense of it. That's why 14:52:59




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1 I'm asking. 14:53:00

2 A All right. If that were the total, what I'd 14:53:01

3 have to be able to figure out, which I can't do from 14:53:06

4 this spreadsheet, is what the total cancer number was, 14:53:11

5 and then take some percentage number difference. But 14:53:13

6 I can't do that. I don't have the information I need 14:53:20

7 to do it. 14:53:21

8 Q If the 484 is right, would you agree that you 14:53:22

9 need to redo your model in some way? 14:53:26



12 THE WITNESS: I would certainly think you'd 14:53:31

13 have to revisit the assumptions on which the 14:53:40

14 calculations were based, yes. 14:53:43

15 BY MS. NEUMAN: 14:53:44

16 Q If the 484 is shown to be correct, would you 14:53:45

17 want to withdraw your opinions and rework them? 14:53:47


19 the question. Are you purporting to show him how 14:53:52

20 they're correct or not correct or valid or invalid? 14:54:01

21 MS. NEUMAN: I have no idea what you mean. 14:54:04

22 I'm just asking about the 484. 14:54:06




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1 MR. WESTENBERGER: Well, you're saying if 14:54:08

2 they were shown to be correct. It's an unfair 14:54:08

3 question. How are you going to show him that they're 14:54:08

4 correct? 14:54:11

5 MS. NEUMAN: The 484? Oh, he can look in the 14:54:11

6 INEC data. He already testified he could have looked 14:54:13

7 at it. 14:54:16

8 MR. WESTENBERGER: But are you purporting to 14:54:16

9 show him how it's correct here today? 14:54:19

10 MS. NEUMAN: I believe it is accurately 14:54:19

11 reported in the peer-reviewed study, which reports it. 14:54:19

12 MR. WESTENBERGER: No, I understand what you 14:54:25

13 believe, but you're asking him if it's shown to him to 14:54:26

14 be correct. 14:54:28

15 MS. NEUMAN: Can you read the question back 14:54:28

16 to the witness? 14:54:31


18 THE WITNESS: If that number were shown to be 14:54:33

19 correct, I would like the opportunity to revise my 14:55:11

20 work. 14:55:15

21 MS. NEUMAN: I'm going to mark as 1308 a 14:55:21

22 graph from your files, Dr. Rourke. It doesn't have a 14:55:26




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1 title, but on the left-hand axis it's labeled, "number 14:55:31

2 of excess cancer deaths per year," and on the bottom, 14:55:36

3 it's labeled, "year of death." 14:55:40




7 Q Is this a graph you created, Dr. Rourke? 14:55:41

8 A Yes. 14:56:00

9 Q What is it showing? 14:56:02

10 A What I don't -- well, it shows the number of 14:56:06

11 excess cancer deaths by year from '67 through 2077 -- 14:56:22

12 or a little later actually, I guess -- in the 14:56:28

13 Concession area within five kilometers of the 14:56:32

14 oil-producing facilities and within two kilometers of 14:56:34

15 the oil-producing facilities. 14:56:38

16 What I can't tell from this is if 2009 or 14:56:41

17 1990 or something else was the last year of entry. 14:56:46

18 Q Now, when you say it shows excess cancers 14:56:49

19 deaths, these are your hypothetical deaths based on an 14:56:54

20 assumption of increased risk, correct? 14:56:57

21 A Yes. 14:56:59

22 Q And the number of deaths that you calculate 14:57:00




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1 peaks in 2037, more or less? 14:57:22

2 A Somewhere -- probably the way Excel works, 14:57:26

3 it's probably 2034, '35, something like that. 14:57:32

4 Q Is it accurate that the vast majority of the 14:57:36

5 hypothetical deaths that your model predicts occur in 14:57:41

6 the future? 14:57:45

7 A Oh, yes. 14:57:45

8 Q Would you give me a rough percentage of the 14:57:47

9 amount that occur in the future? 14:57:50



12 THE WITNESS: May I refer to my document? 14:57:55

13 Then I think maybe we could figure it out from there. 14:58:02

14 BY MS. NEUMAN: 14:58:04

15 Q Certainly. Your report? 14:58:05

16 A Better than -- well, it depends on the last 14:58:08

17 year of entry. Yeah, that's true. 14:58:36

18 Assuming 2009 -- is that permissible? 14:58:41

19 Q That's fine. 14:58:45

20 A Okay. What's that? About -- mental 14:58:45

21 arithmetic is poor, but approximately 90 percent, I 14:58:49

22 guess, are futures and -- no, let me take that back. 14:58:55




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1 It's total. So call it 700 less -- oh, something like 14:58:59

2 90 percent, 80-some-odd percent. 14:59:12

3 Q If your model was overpredicting excess 14:59:14

4 deaths for the years for which there is data 14:59:22

5 available, would that cause an even larger error in 14:59:26

6 the later years? 14:59:34



9 THE WITNESS: Are you saying that when the 14:59:38

10 total counts are done, would there -- in an absolute 14:59:43

11 sense, that is to say, total counts clearly, yes. On 14:59:51

12 a percentage basis, perhaps not. 14:59:56

13 BY MS. NEUMAN: 14:59:58

14 Q Now, you have your report in front of you, 15:00:06

15 right? 15:00:09

16 A Yes. 15:00:09

17 Q Can you look back at table 5 for me. There 15:00:10

18 are age-specific mortality rates for all causes of 15:00:43

19 mortality in Ecuador; is that right? 15:00:45


21 Q And you use country-wide mortality rates to 15:00:52

22 determine the rates for the Amazon -- or Sucumbios and 15:00:57




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1 Orellana; is that right? 15:01:04


3 Q Would it have been more accurate to use the 15:01:05

4 cancer mortality rates that are actually reported for 15:01:10

5 Sucumbios and Orellana than to use the national rates 15:01:14

6 for your analysis? 15:01:18

7 A I think we had this question before. I was 15:01:18

8 concerned about getting to the -- well, the geography 15:01:30

9 of the 1990, I didn't know what -- Sucumbios didn't 15:01:34

10 exist. I'm not even sure if cantons existed, 15:01:41

11 actually, at that point in time. 15:01:45

12 Are you suggesting using the cantons or the 15:01:46

13 provinces. 15:01:50

14 Q Well, either one. More specific regional 15:01:51

15 data. 15:01:55

16 A Possibly, yes. 15:02:06

17 Q Do you know if the rates of cancer mortality 15:02:09

18 based on INEC data are higher or lower in the Amazon 15:02:14

19 region as compared to the rest of Ecuador? 15:02:18

20 A I can't recall if I saw anything indicating 15:02:23

21 that one way or the other. 15:02:29

22 Q If you assume that the cancer mortality rates 15:02:31




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1 in the Amazon region are lower than all of Ecuador, 15:02:37

2 did your use of the all Ecuador rates increase your 15:02:42

3 estimate of excess cancers? 15:02:47

4 A If that were the case, the answer would be 15:02:48

5 yes. 15:02:50

6 Q Can you turn to page 13 of your report for 15:02:50

7 me. You state, "It is very likely that the 15:03:04

8 mortality-based excess risk factors would be nearly 15:03:20

9 equal to incidence-based ones." 15:03:22


11 A Yes, I do. 15:03:25

12 Q What's the scientific basis for that 15:03:26

13 conclusion? 15:03:30



16 THE WITNESS: I believe I went on to attempt 15:03:32

17 to make an explanation of why I thought that might be 15:03:39

18 the case. If it turns out -- all right. Incidence 15:03:42

19 means a cancer arising -- usually it is a result of a 15:03:52

20 death and -- but incidence includes living cancers, 15:03:59

21 too. If it turns out that, between these two regions, 15:04:05

22 the exposed and non-exposed, that the proportion of 15:04:16




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1 dead people in the numerator of a mortality rate, is 15:04:22

2 the same between the exposed and non-exposed -- so 15:04:27

3 let's say it's 90 percent deceased, 10 percent living 15:04:34

4 for the exposed and 90 percent deceased, 10 percent 15:04:39

5 living for the unexposed -- then the mortality rate 15:04:44

6 would be the same whether it be based on incidence 15:04:48

7 or -- not the mortality rate. The mortality rates 15:04:54

8 would be lower, but the excess risk factors would be 15:04:57

9 the same. 15:04:59

10 The excess risk factor is the ratio at a 15:05:02

11 given age of the exposed to the unexposed mortality 15:05:08

12 rate. It's algebra, and it's messy. 15:05:11

13 BY MS. NEUMAN: 15:05:17

14 Q Okay. Well, did you analyze any data that 15:05:18

15 you had available to confirm whether the 15:05:22

16 mortality-based excess risk factors would be nearly 15:05:31

17 equal to incidence-based ones? 15:05:33

18 A No, I did not. 15:05:35

19 Q Would you look at page 15 of your report for 15:05:44

20 me. This graph you have, the impact of exposure 15:05:46

21 versus years of exposures. 15:06:05

22 A Yes. 15:06:06




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1 Q Did you have any exposure information 15:06:07

2 available to you? 15:06:10

3 A No. This is assumption-based. 15:06:10

4 Q And you show risk increasing with years of 15:06:13

5 exposure up to year ten, right? 15:06:24

6 A This was intended to represent, firstly, at 15:06:30

7 least a three-year delay before any risk begins to 15:06:35

8 accumulate, and then the ramp-up from that point up to 15:06:39

9 the tenth year. And, in fact, the introduction of 15:06:46

10 this, as compared to not doing anything like this, has 15:06:50

11 the impact of lowering the excess risk estimate. 15:06:59

12 Q The amounts by which you increase the risk 15:07:02

13 each year, what's the scientific basis for determining 15:07:17

14 those amounts? 15:07:21



17 THE WITNESS: The ten-year figure is -- this 15:07:24

18 is largely an assumption. 15:07:37

19 BY MS. NEUMAN: 15:07:39

20 Q Is it completely an assumption? 15:07:40

21 A By and large, yes. 15:07:44

22 Q Is it accurate that you had no scientific 15:07:46




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1 basis for determining that the risk began in 15:07:57

2 year three and reached its maximum level in year ten? 15:08:02



5 THE WITNESS: I believe I stated in the 15:08:07

6 report that cancers of the blood don't usually begin 15:08:14

7 to appear until the third or so year after exposure. 15:08:18


9 Q But you're assuming the exposure causes the 15:08:22

10 cancer, right? 15:08:23

11 A I'm trying to model what happens in the real 15:08:24

12 world here in that I did not use the full excess risk 15:08:31

13 factor, but gradually increased it over the seven, I 15:08:38

14 believe, year period shown here in this picture. 15:08:43

15 Q But in this model, you're assuming, A, that 15:08:45

16 there's exposure, correct, to a cancer-causing agent? 15:08:51

17 A In the model, no, I'm still not doing that. 15:08:59

18 But I am trying to mirror reality to some degree. I 15:09:02

19 don't mean to dance around here, but -- 15:09:08

20 Q Well, in the model, are you trying to 15:09:22

21 emulate -- well, withdraw that. 15:09:40

22 Doesn't this graph show that you are assuming 15:09:45




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1 there's an exposure that creates a risk of cancer? 15:09:50



4 THE WITNESS: Yes, I guess I am. 15:09:56


6 Q And also as part of your modeling, you assume 15:10:05

7 how much that exposure creates a risk of cancer, 15:10:10

8 correct? 15:10:13

9 A That's what the graph shows, yes. 15:10:13

10 Q And isn't it also true that there's no 15:10:20

11 scientific data on which your assumptions that there 15:10:28

12 was exposure to a carcinogen are based? 15:10:34



15 THE WITNESS: I'm sorry. I've got to hear 15:10:40

16 that one again. 15:10:42

17 BY MS. NEUMAN: 15:10:43

18 Q Isn't it also true that there's no scientific 15:10:44

19 data on which your assumption that there was an 15:10:46

20 exposure to a carcinogen is based? 15:10:49

21 MR. WESTENBERGER: Same objection. 15:10:53

22 THE WITNESS: I'm sorry. I'm just not 15:10:55




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1 grasping what the question is. 15:11:03


3 Q Okay. I'll rephrase it. You're assuming 15:11:05

4 that, when people move into these cantons, they're 15:11:07

5 exposed to something that gives them a risk of cancer 15:11:13

6 and that, the longer they live there up until year 15:11:17

7 ten, the more risk they have based on that exposure, 15:11:20

8 correct? 15:11:23

9 A Yes, that is what the graph attempts to 15:11:23

10 model, yes. 15:11:30

11 Q And that's part of the way you did your 15:11:30

12 calculations, correct? 15:11:33


14 Q Is there any data on which you based that 15:11:34


16 A Well, yes, there is. It so happens that the 15:11:40

17 cancers of the blood tend to show up relatively 15:12:00

18 quickly, whereas cancers of the more solid tissues 15:12:04

19 take longer to develop. So that is where this chart 15:12:10

20 is coming from. 15:12:14

21 Q Well, where is the data that supports the 15:12:15

22 assumption of exposure to something that causes cancer 15:12:20




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1 of the blood, I guess is what you're saying? 15:12:32

2 A Yes. And I have none. 15:12:34

3 Q Okay. Now, I think you were kind of implying 15:12:38

4 a minute ago that, by using this exposure assumption, 15:12:45

5 you ended up with less excess deaths. Did I get that 15:12:53

6 right? 15:12:57

7 A Yeah, that's true. 15:12:57

8 Q Is it also true that, by ignoring the 20-year 15:12:59

9 limitation in the San Sebastian study, that you put in 15:13:02

10 far more excess deaths that would have been in your 15:13:08

11 model had you taken that 20-year minimum into account? 15:13:10



14 THE WITNESS: I can't say. What I'm trying 15:13:20

15 to do is to work through in my head what -- they 15:13:36

16 picked four areas because there had been oil 15:13:42

17 facilities there for 20 years or longer, which gets 15:13:48

18 back to around 1960-whatever it was -- '67. 15:14:00

19 BY MS. NEUMAN: 15:14:05

20 Q Probably '72, but -- 15:14:05

21 A '72. It would depend on how quickly the oil 15:14:07

22 facilities were installed. Where were they? 15:14:16




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1 Q Well, San Sebastian doesn't give you risk 15:14:24

2 numbers for a community in which there have only been 15:14:30

3 oil-producing facilities for ten years, correct? 15:14:36

4 A No. 15:14:43

5 Q And San Sebastian doesn't give you risk 15:14:44

6 numbers for a community in which there have only been 15:14:47

7 oil-producing facilities for five years, correct? 15:14:51

8 A No. 15:14:52

9 Q Assuming that oil production began in these 15:14:53

10 cantons in 1974, which I think you actually say in 15:14:59

11 your report -- 15:15:03

12 A Oh, okay. 15:15:03

13 Q '72. 15:15:04

14 A Okay. 15:15:08

15 Q I'm sorry. Bad with the numbers, but they're 15:15:08

16 fixing me over here -- I'll start over. 15:15:12

17 Assuming that you correctly state in your 15:15:14

18 report that the oil production began in 1972 in these 15:15:15

19 cantons, San Sebastian doesn't provide you with excess 15:15:20

20 risk estimates until at least 1992, correct? 15:15:26


22 Q By ignoring that and starting your model in 15:15:35




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1 1967 using San Sebastian's risk estimates, you added a 15:15:40

2 lot of hypothetical deaths to your totals, correct? 15:15:47


4 THE WITNESS: Are you saying the people have 15:15:52

5 to be exposed for 20 years? 15:16:10


7 Q No. I'm saying San Sebastian says that his 15:16:16

8 exposed population are people living in a community 15:16:20

9 where there have been oil facilities for a minimum of 15:16:23

10 20 years. 15:16:28

11 A Uh-huh. 15:16:28

12 Q So if you're going the apply his risk 15:16:29

13 numbers, you would need to apply it to the population 15:16:32

14 that he defines, correct? 15:16:35

15 A I think this gets to the geographic 15:16:36

16 distribution of the oil facilities. 15:16:45

17 Q Why? 15:16:50

18 A Well, because what matters is where the 15:16:52

19 people were back in 1967, say -- or whenever I began 15:16:57

20 this -- with respect to the oil-producing facilities. 15:17:05

21 Q How do you know that's what matters? 15:17:06

22 A Because I think you're confusing duration of 15:17:09




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1 exposure with how long the oil has been there. 15:17:13

2 Q Well, I'm just going with San Sebastian. 15:17:18

3 Isn't that what you were doing? 15:17:23

4 A Yes, I am. 15:17:24

5 Q So if you're not going assume cause, if you 15:17:25

6 don't opine on cause, San Sebastian says his risk 15:17:28

7 numbers are for populations that have lived in an 15:17:31

8 oil-producing area for 20 years -- 15:17:36

9 A No. 15:17:36

10 Q -- for whatever reason. 15:17:37

11 A No, that is not what he says. 15:17:38

12 Q For people -- yeah, it is. Let's look. 15:17:39

13 A No. For people who have -- who are living in 15:17:42

14 a place in which the production facilities have 15:17:45

15 existed for 20 years. 15:17:49

16 Q Okay. And you took his risk numbers and you 15:17:50

17 applied them to people who had not been living in a 15:17:53

18 community where oil production had existed for 20 15:17:59

19 years, correct? 15:18:01

20 A No. There's a misunderstanding here, and 15:18:01

21 that has to do with the duration. Look, if 15:18:06

22 everybody -- let's say the oil-producing facilities 15:18:12




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1 had been there for a thousand years -- it's a stupid 15:18:14

2 number, but it's just to make an argument. What 15:18:18

3 matters is, when did the people show up after the oil 15:18:21

4 production facility was there? I'm not saying that 15:18:27

5 the people had to have been exposed for 20 years. 15:18:30

6 Q How do you know, based on science, that what 15:18:34

7 matters is when the people show up as opposed to the 15:18:54

8 fact that they show up in a place where there's been 15:18:57

9 oil production and the impacts of that production for 15:19:01

10 20 years? 15:19:05



13 THE WITNESS: The way this works is, for the 15:19:10

14 first three years, those people have no excess risk. 15:19:16

15 And it increases linearly from the fourth year to the 15:19:23

16 tenth when it reaches whatever the maximum excess risk 15:19:28

17 is for that. And that applies to each of the age 15:19:34

18 groups and all the newly arrived people at that point 15:19:42

19 in time. 15:19:47

20 BY MS. NEUMAN: 15:19:48

21 Q But you take San Sebastian's risk numbers and 15:19:50

22 you apply them to populations that are in areas where 15:19:53




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1 oil production has not been ongoing for a minimum of 15:19:59

2 20 years, correct? 15:20:02

3 A In that this begins at an earlier point in 15:20:03

4 time, yes. 15:20:15

5 Q What's the scientific basis for taking those 15:20:16

6 risk numbers that he says he calculates looking at 15:20:21

7 populations living in a county where oil exploitation 15:20:28

8 had been ongoing for a minimum of 20 years, and 15:20:32

9 applying them to other populations that don't have 15:20:34

10 that criteria? 15:20:37



13 THE WITNESS: I'm trying to translate this 15:20:46

14 into terms that -- as I understand them and -- well, 15:20:54

15 let's take these people. So the production facilities 15:21:03

16 have been there for 20 years. 15:21:08

17 BY MS. NEUMAN: 15:21:09

18 Q Not in your model. 15:21:10

19 A Well, I mean in San Sebastian's -- 15:21:11

20 Q Okay. 15:21:13

21 A Okay. Some of these people will have arrived 15:21:16

22 this year, some of them the previous year, some of 15:21:21




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1 them previous years, so it will be distributed all 15:21:26

2 over differing durations of exposure. 15:21:30

3 Q Assuming there's an exposure. 15:21:33

4 A Assuming -- all right. Assuming that there's 15:21:37

5 an impact of exposure duration. 15:21:40

6 I can't work out in my head without actually 15:21:48

7 doing some algebra, which would be kind of 15:22:09

8 complicated -- figure out what -- where this would be 15:22:13

9 going. I don't know. 15:22:17

10 Q Well, my question was simply, how can you 15:22:18

11 take San Sebastian's risk numbers and apply them to a 15:22:25

12 population for which he did not determine any risk 15:22:31

13 numbers? 15:22:34



16 THE WITNESS: I took it as the best available 15:22:39

17 evidence and an assumption. 15:22:42

18 BY MS. NEUMAN: 15:22:43

19 Q So you didn't have any basis for saying that 15:22:45

20 people who lived in the cantons in 1967 were at any 15:22:51

21 increased risk, correct? 15:22:57





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2 THE WITNESS: This would get to the -- how 15:23:01

3 the oil production facilities were developed. That I 15:23:12

4 didn't know. 15:23:15

5 MS. NEUMAN: Okay. Well, let's mark as 1309 15:23:18

6 a document you produced, Rourke-Native 579, "History 15:23:27

7 of Texaco's operations in Ecuador Oriente region, 1964 15:23:30

8 to 1990." 15:23:35

9 It's from annex F of the Cabrera report that 15:23:41

10 you produced. 15:23:47



13 BY MS. NEUMAN: 15:24:06

14 Q Do you recognize this document that you 15:24:07

15 produced, Dr. Rourke? 15:24:09

16 A What was the name of appendix F? This is it? 15:24:10

17 This is the whole appendix F? 15:24:24

18 Q Oh, no, I'm sure it's not just one page. "A 15:24:26

19 brief history of Texaco's operations in Ecuador's 15:24:45

20 Oriente region, 1964 to 1990." 15:24:50

21 A I don't remember reading this, actually. 15:24:53

22 Q Okay. This table 9 catalogs production 15:25:00




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1 stations built and operated by Texaco until June 1990. 15:25:07


3 A Yes, I do. 15:25:11

4 Q And do you see, if you look at the right-hand 15:25:20

5 column, start of operations, that the earliest 15:25:24

6 operations began in 1972? 15:25:26

7 A Yes, I do. 15:25:32

8 Q The earliest oil operations didn't begin 15:25:35

9 until 1972. Why does your model start in 1967? 15:25:41

10 A Well, had I seen this, I would have started 15:25:48

11 later. 15:26:13

12 Q When you would have started, sir? 15:26:13

13 A '72. 15:26:15

14 Q Why would you start your model as soon as the 15:26:21

15 oil production operations started rather than after 15:26:25

16 they had been ongoing for 20 years? 15:26:29

17 A Okay. There are really two issues here. The 15:26:31

18 first one, related to this 20-year thing, I think 15:26:50

19 addresses issues of, were the excess risk factors too 15:26:55

20 large? 15:27:02

21 And the second one is, on this one, when the 15:27:02

22 thing should start, and frankly, what should be the 15:27:06




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1 geographic areas, how should the geography grow? 15:27:10

2 There are kind of two issues there. 15:27:19

3 Q Okay. 15:27:21

4 A Okay? 15:27:21

5 Q Do you want to take the first one first? 15:27:22

6 A I'm back to the thing where I don't believe 15:27:24

7 this duration thing matters as far as the excess risk 15:27:29

8 factors are concerned. 15:27:32

9 Q What are you referring to as the duration 15:27:33

10 thing? 15:27:40

11 A The 20-year. 15:27:40

12 Q And on what basis do you conclude it doesn't 15:27:41

13 matter if it was a criteria of San Sebastian's study? 15:27:45

14 MR. WESTENBERGER: Objection. He's answered 15:27:48

15 this question numerous times. You may not like his 15:27:50

16 answer, but he's answered it. 15:27:55

17 THE WITNESS: Because it has little to do 15:27:56

18 with the duration of exposure issue. 15:28:06

19 BY MS. NEUMAN: 15:28:08

20 Q But you don't have any exposure information, 15:28:13

21 right? Correct? 15:28:15

22 A Well, other than living in a particular 15:28:15




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1 region, that is correct. 15:28:24

2 Q And it's actually other than living in a 15:28:25

3 particular region that has had certain types of 15:28:29

4 operations for a certain amount of time, correct? 15:28:31



7 THE WITNESS: That would be necessarily true, 15:28:36

8 yes. 15:28:40


10 Q Does your model assume that, if San Sebastian 15:29:03

11 had studied people only one year after oil operations 15:29:06

12 had begun -- well, withdraw that. 15:29:14

13 Does your model assume that, if San Sebastian 15:29:18

14 had studied people ten years after oil operations had 15:29:20

15 begun, i.e., in 1982, he would have seen the same 15:29:24

16 increased risk numbers that he saw studying people who 15:29:30

17 lived in areas that had an oil operations for a 15:29:35

18 minimum of 20 years? 15:29:38

19 A I think substantially the same. 15:29:39

20 Q That's what you're assuming? 15:29:45

21 A Yes. 15:29:46

22 Q Does your latency curve assume continual 15:29:47




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1 exposure over ten years or just a delay of ten years 15:29:54

2 after the first exposure? 15:29:58

3 A Continual exposure. 15:29:58

4 Q So they have to stay there and be exposed? 15:30:03

5 A Yes. 15:30:08

6 Q Well, but they all stay, right, unless they 15:30:09

7 die? 15:30:13

8 A The way the model works, yes. 15:30:13

9 Q So the model assumes people have continuous 15:30:15

10 exposure whether they really do or not? 15:30:20


12 answered. Argumentative. 15:30:26

13 THE WITNESS: It is assumed that the only way 15:30:27

14 people exit this is through death. 15:30:29

15 BY MS. NEUMAN: 15:30:31

16 Q In your model after 2009, do people still 15:30:31

17 have exposure? 15:30:47

18 A I don't know. I stop putting people in -- 15:30:47

19 the answer is yes. 15:31:14

20 Q They're still getting exposed? 15:31:15

21 A Yes, they are. 15:31:17





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1 document that Dr. Rourke produced, a map of the 15:32:31

2 Concession area. 15:32:36




6 Q Do you still have your report there, 15:32:37

7 Dr. Rourke? 15:33:07

8 A Yes, I do. 15:33:07

9 Q Okay. In table 3, you estimate the 15:33:08

10 population in the former Concession from '67 through 15:33:16

11 2009, correct? 15:33:24

12 A Yes, I do. 15:33:25

13 Q And in the absence of data for all those 15:33:28

14 years, you took the census data from '90 and '01 and 15:33:32

15 determined how much the population had increased 15:33:40

16 during that span of 10 years -- or 11 years, correct? 15:33:42

17 A I'm sorry, from 1990 through what? Yes, I 15:33:45

18 used the multiplicative growth rate approach. 15:33:57

19 Q Assuming even growth in each year in between 15:34:02

20 the two data points you had? 15:34:08

21 A Yes, of about 5 percent a year, I believe. 15:34:09

22 Q Did you check that assumption against any 15:34:12




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1 available data? 15:34:15

2 A I don't believe there was any available data. 15:34:15

3 Q On the in-between years? 15:34:26

4 A On the in-between years. They don't do a 15:34:31

5 count of which I'm aware. 15:34:37

6 Q After you calculated this annual growth rate 15:34:41

7 to get you from '90 to 2001, you then extrapolated 15:34:46

8 backwards in time all the way to 1967 assuming the 15:34:53

9 same growth rate applied, correct? 15:34:57


11 Q Did you do anything to test the validity of 15:34:59

12 your assumption that the cantons had a constant growth 15:35:07

13 rate from '67 through '01 of 5 percent every year? 15:35:13

14 A I could not find any documentation. 15:35:16

15 Q You couldn't find any documentation that 15:35:20

16 commented on the populations in those areas at that 15:35:26

17 time? 15:35:28


19 Q Did you request any that was available from 15:35:29

20 plaintiffs' counsel? 15:35:33

21 A I can't recall. 15:35:33

22 Q Did plaintiffs' counsel provide you with any 15:35:37




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1 of the expert reports that have been filed in the Lago 15:35:43

2 matter that discussed population issues? 15:35:48

3 A To the extent the Cabrera report had 15:35:50

4 something, the answer I guess would be yes, but I 15:36:02

5 don't recall any others. 15:36:06

6 Q They didn't provide you with any of the ones 15:36:06

7 prepared by anyone other than Mr. Cabrera? 15:36:10


9 Q Were you aware, at the time you were making 15:36:17

10 your assumptions about steady population growth, that 15:36:25

11 the plaintiffs have claimed that TexPet's operations 15:36:29

12 in Ecuador decimated the indigenous population in the 15:36:35

13 Concession area? 15:36:39



16 THE WITNESS: I was unaware of that -- 15:36:45

17 MS. NEUMAN: I'm going to mark -- 15:36:45

18 THE WITNESS: -- statement. Excuse me, I was 15:36:53

19 unaware of that statement. 15:36:54

20 BY MS. NEUMAN: 15:36:55

21 Q In your cancer calculations, do you make any 15:36:56

22 distinction between the indigenous population and the 15:37:00




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1 Colonos? 15:37:03

2 A No, I do not. 15:37:04


4 series of slides showing the indigenous populations in 15:37:22

5 the region over time. 15:37:29




9 Q Have you seen this or similar data on the 15:37:36

10 growth of indigenous populations in the Oriente, 15:38:06

11 Dr. Rourke? 15:38:14

12 A No, I've never seen this before. 15:38:14

13 Q Looking at page 3 of Exhibit 1311, the 15:38:16

14 Huaorani population -- do you see that one? 15:38:28

15 A Yes, I do. 15:38:35

16 Q Do you see that it started out very low in 15:38:35

17 the early 1900s and then, from about the mid-1960s 15:38:41

18 through 2000s, steadily increased? 15:38:50

19 A Yes. 15:38:56

20 Q And, in your view, is that increase even year 15:38:57

21 to year or less smooth? 15:39:01

22 MR. WESTENBERGER: Obviously, we reserve all 15:39:14




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1 rights to objections on this document, authenticity, 15:39:15

2 et cetera. 15:39:19

3 THE WITNESS: Well, there's a few things 15:39:20

4 about this I'm a little puzzled by. You've got two 15:39:28

5 points for -- call it 1970. It goes down, goes up. 15:39:32

6 Without understanding the provenance of this, all I 15:39:44

7 can do is describe what it does. It goes up, but 15:39:49

8 certainly in not no nice even thing like that 15:39:53

9 (indicating). I'm sorry about that. A smoothly 15:39:56

10 increasing curve. 15:40:04

11 BY MS. NEUMAN: 15:40:05

12 Q Were you asked to focus in any way on the 15:40:13

13 indigenous populations in the area and whether there 15:40:16

14 were any excess cancers among those populations? 15:40:20

15 A No, I was not. 15:40:25

16 Q And would you be able to estimate, based on 15:40:28

17 your work, how many, if any, indigenous persons are 15:40:39

18 included in your excess cancer estimate? 15:40:43

19 A There is a census document that I think says 15:40:49

20 something about the population, the indigenous 15:41:00

21 population, but I'm not sure when, and I can't recall 15:41:04

22 the figures from that. Again, I don't know where 15:41:12




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1 these numbers came from. 15:41:14

2 So if it were possible to split the 15:41:18

3 population out, along with where they lived, the 15:41:22

4 answer would probably be yes. But it wasn't the focus 15:41:28

5 of the work. 15:41:35

6 Q And it's not something you've done up until 15:41:35

7 this point in time? 15:41:38


9 Q Okay. We're done with Exhibit 1311. 15:41:39

10 Can you look back at Exhibit 1309 for me? 15:41:55

11 A Yes. 15:42:03

12 Q You said there was a first issue created by 15:42:04

13 this document that related to geography, and I 15:42:13

14 neglected to ask you what you meant by that. 15:42:18

15 A Going forward in time from 1972, not all of 15:42:21

16 these stations are represented -- and I guess there's 15:42:31

17 other issues here, too. The station is a fixed 15:42:35

18 location in which you've got a complex of oil 15:42:42

19 production facilities. And they show up, I guess, on 15:42:44

20 this map here. But you also have pipelines and things 15:42:51

21 like that, and that's not discussed here. 15:42:53

22 But the upshot of all this is that the 15:42:55




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1 geographic extent appears to be increasing over time 15:43:07

2 from 1972 even though it looks like a lot of these 15:43:11

3 come online about '72 or '73. They're added to it. 15:43:15

4 Q Okay. 15:43:21

5 A So the upshot would be a revision in the 15:43:25

6 population figures. 15:43:28

7 Q A revision downward? 15:43:29

8 A Yes. 15:43:34

9 Q And do you know how much that would change 15:43:36

10 your results? 15:43:39

11 A Well, it would certainly drop them a little 15:43:40

12 bit, but again, without doing the computation, I 15:43:42

13 couldn't say how much. 15:43:45


15 Exhibit 1312 another map that you produced, 15:43:50

16 Rourke-Native 008025. 15:43:59



19 BY MS. NEUMAN: 15:44:01

20 Q Do you have Exhibit 1312, Dr. Rourke? 15:44:01

21 A Yes, I do. 15:44:43

22 Q And is this the map that you used -- the GIS 15:44:44




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1 thing that you referred to earlier -- to figure out if 15:44:50

2 people were in five kilometers of an oil facility? 15:44:52

3 A Yes, even though the area figures are very 15:44:56

4 hard to read on this one because it's so small, but 15:45:01

5 yes. 15:45:04

6 Q Before you used this map, did you determine 15:45:05

7 when all these facilities went online? 15:45:12

8 A No, I did not. 15:45:14

9 Q In order for your calculations about people 15:45:16

10 who were within five kilometers of an oil production 15:45:23

11 facility to be accurate, wouldn't you need to date 15:45:26

12 when these different facilities went online? 15:45:32




16 BY MS. NEUMAN: 15:45:55

17 Q Why didn't you do that? 15:45:58

18 A I didn't have the dates, startup dates, 15:45:59

19 available to me, or at least I didn't think I did. 15:46:09

20 Q Well, did you ask for them? 15:46:11

21 A I looked for them. 15:46:12

22 Q Where did you look for them? 15:46:15




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1 A Through some of the Cabrera report, but 15:46:20

2 managed to miss F. 15:46:28

3 Q Did you ask plaintiffs' counsel to provide 15:46:30

4 them to you? 15:46:32

5 A No, I did not. 15:46:32

6 Q Any reason why not? 15:46:33

7 A No. 15:46:34

8 Q I'm done with 1312. 15:46:41

9 MS. NEUMAN: I'm going to mark as 1313 the 15:46:56

10 subpoena served on Dr. Rourke in this matter. 15:47:10



13 BY MS. NEUMAN: 15:47:11

14 Q Have you seen a copy of Exhibit 1313 before, 15:47:11

15 Dr. Rourke? 15:47:35

16 A This is the first time I've seen it. 15:47:36

17 Q You weren't provided a copy of the subpoena? 15:47:38

18 A This, again, is the first time I've seen it. 15:47:40

19 Q Okay. If you turn past the first page, on 15:47:43

20 page 13 of the attachment, you'll see documents to be 15:47:54

21 produced. 15:47:57

22 A Where do you want me to begin now? Page 15:48:00




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1 what? 15:48:25

2 Q 13. It looks like this (indicating). 15:48:25

3 A Okay. 15:48:30

4 Q Have you seen that list of documents before? 15:48:37

5 A No, I haven't. 15:48:39

6 Q Nobody asked you to review such a list before 15:48:40

7 you produced your documents? 15:48:45

8 A I sent you all the paper I had, along with 15:48:46

9 all the computer files, evidently except for the one 15:48:55

10 paper. 15:48:59

11 Q When you say "me," who did you actually send 15:48:59

12 all your stuff to? 15:49:03

13 A To -- to Patton Boggs. 15:49:03

14 Q And who at Patton Boggs did you send all your 15:49:05

15 things to? 15:49:09

16 A A gentleman named Eric Daleo -- I believe 15:49:09

17 Eric Daleo. 15:49:15

18 Q You sent Mr. Daleo a hundred percent of the 15:49:16

19 materials you had related in any way to the Ecuador 15:49:21

20 matter; is that right? 15:49:25

21 A Yes. 15:49:26

22 Q And if someone did a review of your documents 15:49:26




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1 to determine what would be responsive to produce to 15:49:28

2 us, that was someone other than yourself, correct? 15:49:31

3 A Yes. 15:49:33

4 Q Do you know what was produced to us versus 15:49:34

5 what was sent to Mr. Daleo? 15:49:44

6 A Not exactly. 15:49:45

7 Q Why do you say not exactly? 15:49:49

8 A Well, because I've seen some of the outputs 15:49:52

9 from spreadsheets I prepared, but I don't know what 15:49:55

10 you haven't received yet. 15:49:58

11 Q Can you tell me what you did to prepare for 15:50:00

12 your deposition today? 15:50:07

13 A I read my report over about four times. I 15:50:08

14 met with Patton Boggs' attorneys last Friday, and 15:50:18

15 there was a telephone conversation yesterday 15:50:30

16 afternoon. 15:50:36

17 Q What Patton Boggs' attorneys did you meet 15:50:36

18 with last Friday? 15:50:42

19 A Mr. Westenberger, John Zefutie. That was it. 15:50:42

20 Q How long was that meeting, more or less? 15:50:51

21 A It went from approximately 9:00 a.m. to 3:30. 15:50:54

22 Q What did you do during that meeting? 15:50:58




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1 MR. WESTENBERGER: Objection to the extent it 15:51:03

2 requires the witness to reveal privileged 15:51:04

3 communications or information. 15:51:09

4 MS. NEUMAN: Okay. But he's a testifying 15:51:12

5 expert. 15:51:13

6 MR. WESTENBERGER: Okay. But I'm still 15:51:13

7 asserting that, if we had discussions, that they're 15:51:15

8 privileged. 15:51:18

9 MS. NEUMAN: Are you instructing him not to 15:51:19

10 answer? 15:51:22

11 MR. WESTENBERGER: I'm going to advise him 15:51:22

12 not to answer if it requires him to reveal 15:51:24

13 information. 15:51:28

14 MS. NEUMAN: Could you read that question to 15:51:28

15 the witness, please? 15:51:30


17 MR. WESTENBERGER: My same objection stands. 15:51:31

18 I'm advising the witness not to answer to the extent 15:51:47

19 it requires him to reveal privileged information or 15:51:47

20 privileged communications. 15:51:53

21 THE WITNESS: We discussed -- 15:51:53

22 MR. WESTENBERGER: Don't get into any 15:51:56




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1 substance of any discussions. I'm advising you not to 15:52:01

2 answer because these are -- it's privileged 15:52:04

3 communications. 15:52:05

4 THE WITNESS: Okay. On the advice of my 15:52:05

5 counsel, I'm not going to answer. 15:52:10


7 Q You said there was a meeting and that was 15:52:21

8 followed by a phone call on a different date? 15:52:23

9 A Yesterday afternoon. 15:52:25

10 Q Who was the phone call with? 15:52:26

11 A John Zefutie and Adlai Small. 15:52:27

12 Q And how long was the phone call? 15:52:32

13 A Maybe an hour. 15:52:35

14 Q What was the purpose of the call? 15:52:38


16 requires you to reveal privileged communications or 15:52:43

17 information. 15:52:46

18 If you want to say it's in preparation for 15:52:47

19 the deposition, I think we've already established 15:52:49

20 that, that's fine. 15:52:51

21 THE WITNESS: On the advice of my counsel, I 15:52:52

22 respectfully decline to answer that question. 15:52:57




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2 Q Have you had any conversations with 15:53:01

3 Mr. Westenberger during your deposition today? 15:53:03

4 A We chitchatted at lunch. 15:53:06

5 Q Have you had any substantive conversations 15:53:10

6 about your testimony? 15:53:13

7 A No, we did not. 15:53:13

8 Q Is there anything you wanted to do to prepare 15:53:15

9 for your deposition that you were unable to do? 15:53:28

10 A No, there wasn't. 15:53:32

11 Q Did anyone direct you what you should or 15:53:33

12 should not say in your deposition today? 15:53:38

13 MR. WESTENBERGER: Objection. Again, 15:53:40

14 privileged communications. 15:53:42

15 You can answer the question. 15:53:44

16 THE WITNESS: No, they did not. 15:53:48

17 BY MS. NEUMAN: 15:53:51

18 Q When did you first hear of the Ecuador 15:53:56

19 litigation? 15:53:58

20 A Sometime in early August. 15:53:58

21 Q Had you read about it prior to that time? 15:54:07

22 A Actually, I didn't even know about it. 15:54:16




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1 Q And you first heard about it from a phone 15:54:19

2 call you mentioned earlier? 15:54:27

3 A With Tom Vasquez. 15:54:28

4 Q What happened after your phone call with 15:54:33

5 Mr. Vasquez in terms of your involvement in the 15:54:36

6 Ecuador litigation? 15:54:38

7 A I subsequently received a phone call from Ted 15:54:39

8 Dunkelberger -- D-U-N-K-E-L-B-E-R-G-E-R -- asking that 15:54:44

9 I send him my CV and discussing some of my expertise. 15:54:53

10 Q Did you send Mr. Dunkelberger your CV? 15:54:59

11 A Yes, I did. 15:55:03

12 Q Were you ultimately hired by the Weinberg 15:55:04

13 Group to work on this matter? 15:55:09

14 A Yes, I was. 15:55:10

15 Q Do you have a written contract with the 15:55:12

16 Weinberg Group? 15:55:15

17 A There is an agreement between us, yes. 15:55:15

18 Q Is it in writing? 15:55:18

19 A Yes, it is. 15:55:20

20 Q Do you know who signed that agreement? 15:55:21

21 A Certainly I did. I believe, for the Weinberg 15:55:28

22 Group, it was Matthew Weinberg, but I'm not sure. I 15:55:38




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1 can't recall. 15:55:41

2 Q When you gave all your documents over to 15:55:41

3 counsel, did it include your contract with the 15:55:45


5 A I believe so, yes. 15:55:48

6 MS. NEUMAN: I request that that be produced. 15:55:51


8 Q What is the nature of your compensation for 15:55:54

9 your work in the Ecuador case? 15:56:02

10 A I believe it's $325 an hour. 15:56:04

11 Q Do you have any interest in the outcome of 15:56:06

12 the case? 15:56:13

13 A Financial, no. 15:56:13

14 Q When you were first retained by the Weinberg 15:56:17

15 Group, who talked to you about what your role in the 15:56:25

16 case would be? 15:56:28

17 A Ted Dunkelberger. 15:56:28

18 Q What did he tell you? 15:56:30

19 A That it was to make an estimate of the number 15:56:31

20 of excess cancers, substantially the title of my 15:56:44

21 report. 15:56:52

22 Q The number of excess cancers associated with 15:56:52




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1 living in the area? 15:56:59

2 A Yes. 15:56:59

3 Q Were you ever asked to try and estimate the 15:57:00

4 number of excess cancers caused by TexPet's oil 15:57:03

5 operations? 15:57:09

6 A No. 15:57:10

7 Q Did Mr. Dunkelberger tell you that your 15:57:10

8 assignment was to estimate the number of excess 15:57:24

9 cancers for people living in the oil-producing regions 15:57:27

10 before or after you had the San Sebastian study? 15:57:33

11 A The geographies that I used -- he never 15:57:35

12 specified the geography. The geographies that were 15:57:58

13 used in my report are mine. 15:58:01

14 Q And by "geographies" you mean -- 15:58:03

15 A Concession area, five kilometers, four 15:58:08

16 cantons. 15:58:11

17 Q Okay. But did he tell you you were going to 15:58:11

18 estimate these hypothetical excess cancer deaths for 15:58:19

19 people living in oil-producing areas in Ecuador before 15:58:25

20 or after you had the San Sebastian report? 15:58:31

21 A I don't think we ever used the word -- we 15:58:33

22 specified any geography till I got to work on things. 15:58:41




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1 I can't quite recall. 15:58:51

2 Q Did you ever ask whether plaintiffs had any 15:58:51

3 experts who were opining on causation? 15:58:58

4 A No, I didn't. 15:58:59

5 Q I take it they never provided any expert 15:59:00

6 opinions to you on causation on which you could rely 15:59:03

7 or do any sort of cancer analysis? 15:59:07

8 A The only documents directly from the 15:59:09

9 plaintiffs was the Cabrera report. 15:59:18

10 Q That's the only document they gave you? 15:59:19

11 A That was -- there were a couple of paper 15:59:24

12 reports I believe I received, but I didn't make any 15:59:28

13 use of those, nor did I, come to think of it, the 15:59:37

14 Cabrera report. 15:59:41

15 Q Who provided with you the Cabrera report? 15:59:42

16 A The Weinberg Group did. 15:59:44

17 Q Like a particular person? 15:59:46

18 A I don't know what the provenance was. I 15:59:50

19 received a CD probably the -- can't remember the 15:59:53

20 date -- mid-August, and it was delivered by messenger. 16:00:02

21 Q Did they tell you anything about the Cabrera 16:00:07

22 report when they gave it to you, the Weinberg Group? 16:00:11




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3 THE WITNESS: Only that it was a report 16:00:18

4 prepared at the request of the judge. 16:00:24


6 Q Did you ultimately rely on the Cabrera report 16:00:26

7 in any way? 16:00:37

8 A No, I did not. 16:00:37

9 Q Other than Ted -- Dunkelberger? 16:00:39

10 A Dunkelberger. 16:00:43

11 Q Who else at the Weinberg Group did you 16:00:44

12 interact with? 16:00:47

13 A Kerry Roche -- K-E-R-R-Y, R-O-C-H-E, Chris 16:00:48

14 Arthur, Dr. Carlos Picone -- P-I-C-O-N-E, I believe, a 16:00:58

15 woman whose first name was Marla, but I can't remember 16:01:13

16 her last name, and this is -- and a person who was 16:01:15

17 familiar with toxicology and epidemiology whose name 16:01:29

18 escapes me who ended up doing very little, as I 16:01:34

19 recall. 16:01:38

20 Q Who was Marla? What was her job? 16:01:38


22 THE WITNESS: I believe -- and I could be 16:01:49




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1 totally wrong about this, because she wasn't relevant 16:01:54

2 to what I was doing -- I believe her role was to 16:01:57

3 assist Dr. Picone in preparing his report, but I could 16:02:00

4 be totally wrong about that. 16:02:04


6 Q Do you know what her area of expertise was, 16:02:06

7 if any? 16:02:09

8 A No, I don't. 16:02:09

9 Q What was Chris -- 16:02:09

10 A Arthur. 16:02:09

11 Q -- Arthur's role? 16:02:14

12 A He was the person who was responsible for web 16:02:14

13 searches, and he seemed to be the fellow who received 16:02:27

14 computer files from Ecuador. 16:02:34

15 Q Did he conduct web searches for you? 16:02:36

16 A He did the MELE (phonetic) search that you 16:02:42

17 referred to earlier. 16:02:45

18 Q The toxic tort verdicts? 16:02:46

19 A Yeah. 16:02:48

20 Q Any others? 16:02:49

21 A No. 16:02:50

22 Q What was -- 16:02:50




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1 A Well, now, wait a minute. I can't say that 16:02:52

2 that was true or not. I don't recall. 16:02:58

3 Q What was Kerry Roche's role? 16:03:01

4 A Kerry Roche is an epidemiologist -- or a 16:03:05

5 biostatistician -- actually, I couldn't ascertain 16:03:09

6 which. And she was making sure that I got things done 16:03:13

7 on time. 16:03:19

8 Q She was the taskmaster? 16:03:20

9 A Something like that. 16:03:24

10 Q Did you discuss with Ms. Roche, since she was 16:03:24

11 an epidemiologist, the relative strengths and 16:03:31

12 weaknesses of the Kelsh versus San Sebastian studies? 16:03:36

13 A No, I did not. 16:03:39

14 Q Is there any reason you didn't? 16:03:40

15 A The time that we were working on this was 16:03:41

16 pretty busy. So I did not discuss this with her. 16:03:44

17 Q Did you seek any advice from Ms. Roche as an 16:03:49

18 epidemiologist that in any way related to your work? 16:03:51

19 A No. And I'm not even sure about her 16:03:54

20 epidemiology credentials. I'm not sure what her 16:03:58

21 degree is in. 16:04:01

22 Q Other than giving you deadlines, apparently, 16:04:02




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1 did she substantively interact with you on your work? 16:04:06

2 A I did two things for her. One is very early 16:04:12

3 on -- and I believe this was is the first week -- I 16:04:16

4 prepared an outline because they wanted an outline. 16:04:19

5 And the second thing is she drafted some notes or 16:04:25

6 comments and wanted my reaction to them. And I edited 16:04:31

7 to some degree what she wrote, adding comments, 16:04:39

8 something like that. 16:04:43

9 Q And were her comments on what subject? 16:04:43

10 A I believe you probably have the thing. I 16:04:47

11 can't recall right now. 16:04:52

12 Q Okay. 16:04:52

13 MS. NEUMAN: I'm going to mark Exhibit 1314, 16:04:53

14 Rourke-Native 010088 through 89, with metadata 16:05:04

15 attached, to see if this is the outline that you just 16:05:11

16 mentioned that you prepared, Dr. Rourke. 16:05:16



19 MS. NEUMAN: Actually, before you look at 16:05:17

20 that, I'd like to go off the record for five minutes. 16:05:21


22 December 20th, 2010. Going off the record. 16:05:27




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1 Completing video 4. 16:05:29



4 December 20th, 2010. On the record with video 5. 16:14:46


6 Q Do you still have Exhibit 1314 in front of 16:14:50

7 you, Dr. Rourke? 16:14:55

8 A Yes, I do. 16:14:56

9 Q Is that the outline that you created for your 16:14:57

10 report? 16:15:04

11 A Yes, it is. 16:15:04

12 Q Now, on the outline you say cancer deaths 16:15:08

13 related to residents in oil-producing areas, but in 16:15:14

14 your report you say associated with. Why did you make 16:15:18

15 that change? 16:15:21

16 A They were intended to be synonyms. 16:15:22

17 Q Okay. To whom did you provide this outline? 16:15:24

18 A To Kerry Roche. 16:15:30

19 Q Did she discuss it with you substantively? 16:15:31

20 A All she said was, thank you for getting it in 16:15:35

21 on time. 16:15:38

22 Q She never got back to you on the -- 16:15:39




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1 A No. 16:15:39

2 Q -- changes she thought you should make to it 16:15:42

3 or anything like that? 16:15:44

4 A No. 16:15:45

5 Q In section 7 where you have estimates of the 16:15:45

6 value of a life -- do you see that? 16:15:49

7 A Yes. 16:15:51

8 Q You have 7.2, tort system estimates, and then 16:15:52

9 7.2.2, Ecuador. Do you see that? 16:15:58

10 A Yes. 16:15:59

11 Q Was it your original intent to include the 16:16:00

12 amounts of tort judgments from Ecuador? 16:16:04

13 A If such data existed, yes. 16:16:11

14 Q Did you request such data from the Weinberg 16:16:14

15 Group? 16:16:20

16 A I cannot recall. The data request was 16:16:20

17 never -- all right. There was an expedition or a trip 16:16:32

18 to Ecuador, and I don't recall if Ted Dunkelberger 16:16:36

19 went or just the attorneys from Patton Boggs. And -- 16:16:39

20 well, again, I don't recall if I put tort system in my 16:16:50

21 data request. 16:16:54

22 Q Do I understand you to say that you were able 16:16:55




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1 to make a data request of people who were going to 16:17:01

2 Ecuador? 16:17:04

3 A Yes, and I believe you have that. 16:17:04

4 Q The data request itself? 16:17:06

5 A Yes. 16:17:08

6 Q Okay. Now, ultimately, you did not include a 16:17:09

7 section on Ecuadorian tort awards in your report, 16:17:18

8 correct? 16:17:22


10 Q Would you anticipate that such awards would 16:17:23

11 be lower or higher than the ones you included from the 16:17:28

12 U.S. in your report? 16:17:31


14 THE WITNESS: Without any knowledge of how 16:17:32

15 the Ecuadorian court system works or even if they have 16:17:36

16 a notion overall, I couldn't say. 16:17:40

17 BY MS. NEUMAN: 16:17:43

18 Q Is the only reason you didn't include the 16:17:46

19 Ecuadorian figures in your report because you didn't 16:17:48

20 have the data? 16:17:50

21 A The -- generally, yes. 16:17:51





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1 Exhibit 1315 Rourke-Native 010092, with metadata 16:18:22

2 attached. 16:18:34




6 Q Dr. Rourke, is Exhibit 1315 the document you 16:18:35

7 were referring to earlier that Ms. Roche created that 16:18:57

8 you then commented on? 16:19:00

9 A Yes. 16:19:04

10 Q And what was your understanding about why she 16:19:05

11 created this document? 16:19:12



14 THE WITNESS: I don't know why she did, 16:19:18

15 actually. 16:19:19

16 BY MS. NEUMAN: 16:19:19

17 Q What did she tell you when she asked you to 16:19:19

18 comment on it? 16:19:29

19 A She thought that these were points that might 16:19:30

20 be important and wanted my reaction to them. 16:19:34

21 Q That they were points that might be important 16:19:37

22 for your work? 16:19:40




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1 A Yes. 16:19:41

2 Q Ms. Roche wrote, "Determine cancer endpoints 16:19:44

3 of interest, theoretically, those related to exposure 16:19:56

4 to TPH." 16:19:59


6 A Yes. 16:20:01

7 Q Did you discuss with her why she thought it 16:20:01

8 was important to determine the cancer endpoints 16:20:05

9 theoretically related to exposure of TPH? 16:20:09

10 A I don't know why she thought it was 16:20:11

11 important, but I didn't think that trying to parcel 16:20:33

12 out some cancers with respect to others was a 16:20:38

13 profitable exercise. 16:20:42

14 Q When you say, "If we don't care about the 16:20:43

15 type of cancer, then I doubt we will be able to assign 16:20:47

16 causation to TPH compared, say, to heavy metal 16:20:51

17 compounds," what did you mean by that? 16:20:58

18 A Well, first of all, at that time, I didn't 16:21:01

19 think we could do -- it would be -- we could do 16:21:05

20 anything with causation. Secondly, how you identify 16:21:09

21 cancers associated with TPH versus other kinds of 16:21:17

22 things, I didn't think we could deal with, given the 16:21:21




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1 data that we had. 16:21:26

2 Q Did Ms. Roche agree with that? 16:21:26

3 A Ultimately -- well, I don't know. She 16:21:30

4 never -- no, I don't know. 16:21:34

5 Q By using all cancer numbers, did you include, 16:21:35

6 in your theoretical excess cancers, cancers that 16:21:39

7 petroleum products can't cause, for example? 16:21:44



10 THE WITNESS: Since it was all cancers, if 16:21:50

11 you could identify cancers that petroleum doesn't 16:21:53

12 cause, then, because I used all cancers, they were 16:21:57

13 included. 16:22:00

14 BY MS. NEUMAN: 16:22:00

15 Q In the third bullet point it says, "What are 16:22:11

16 the dates of interest, 1990 to the present, what about 16:22:13

17 future, given expected latency?" 16:22:16

18 Then you write, "This is complex. Because 16:22:22

19 the environmental contamination is still present, 16:22:23

20 there are people who have yet to manifest a cancer. 16:22:25

21 Whether to include future cancers is a decision that 16:22:28

22 the attorneys will have to make, but I would attempt 16:22:31




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1 to compute that number." 16:22:34


3 A Yes. 16:22:35

4 Q Did the attorneys tell you to include future 16:22:36

5 cancers after you wrote this? 16:22:40

6 A As a matter of fact, no. I did compute the 16:22:42

7 futures and presented the -- a draft of the paper to 16:22:48

8 them with them in there. 16:22:53

9 Q With the futures already in there? 16:22:54

10 A Yes. 16:22:56

11 Q In the last bullet point you state, 16:22:56

12 "Determine value of cases of cancer and deaths due to 16:23:10

13 cancer. Does U.S. tort system value cancers and 16:23:14

14 deaths due to cancers differently?" 16:23:17

15 You write, "Yes, but it depends on lots of 16:23:20

16 factors. Is there any wrongful death data values in 16:23:22

17 the Ecuadorian civil justice system? These would be 16:23:26

18 more appropriate than U.S. values." 16:23:29


20 A Yes, I do. 16:23:32

21 Q Why did you say that the Ecuadorian values 16:23:33

22 would be more appropriate than the U.S. values? 16:23:36




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1 A That struck me as being true at the time. 16:23:37

2 Q Because the people are Ecuadorian? 16:23:39

3 A Yes. 16:23:42


5 Exhibit 1316 an invoice that you sent to the Weinberg 16:24:01

6 Group on August 27th, 2010. 16:24:06




10 Q Is Exhibit 1316 a document that you prepared, 16:24:08

11 Dr. Rourke? 16:24:42

12 A Yes, it is. 16:24:42

13 Q Does it accurately reflect the time you spent 16:24:45

14 on the matter between August 20th and August 27th of 16:24:48

15 2010? 16:24:53

16 A Yes, it does. 16:24:53

17 Q And do the descriptions also accurately 16:24:54

18 reflect what you were doing during that time? 16:24:59

19 A Yes, they do. 16:25:00

20 Q On August 27th, you have an entry that reads, 16:25:01

21 "Team status meeting." 16:25:11





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1 A Yes. 16:25:13

2 Q Who was at the team status meeting? 16:25:14

3 A Ted Dunkelberger, Kerry Roche, Chris 16:25:17

4 Arthur -- possibly Dr. Picone may have come in late, 16:25:25

5 but my memory is a little hazy -- myself and the other 16:25:35

6 person that I mentioned earlier whose name I still 16:25:41

7 cannot recall. I never even had his business card, 16:25:45

8 come to think of it. 16:25:48

9 Q Were any of the other experts at this 16:25:49

10 meeting? 16:25:52

11 A No, not that I recall. 16:25:52

12 Q Other than Dr. Picone, did you interact with 16:26:00

13 any of the other plaintiffs' experts that you're aware 16:26:06

14 of? 16:26:08

15 A I gave an estimate of the number of people in 16:26:08

16 the Concession area to Marla, I believe. And I 16:26:19

17 thought that Doug -- God, I can't remember his last 16:26:29

18 name either -- not Doug Adams. 16:26:35

19 Q Allen? 16:26:37

20 A Doug Allen, thank you. 16:26:39

21 -- would be the person who got the geographic 16:26:42

22 information system boundary files. So I asked him, 16:26:46




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1 did you get the GIS boundary files? 16:26:51

2 Q Get them from where? 16:26:55

3 A From anywhere. 16:26:57

4 Q Oh, I see. 16:26:58

5 A GIS bound -- there's no good example here. 16:27:00

6 Do you see the different colored regions? 16:27:11

7 Q Yes, sir. 16:27:12

8 A The boundary file is what lets you draw the 16:27:13

9 differing color regions that basically gives where a 16:27:17

10 particular geography is. And they mesh together so 16:27:21

11 you can load them all up and, lo and behold, get a 16:27:23

12 map. 16:27:28

13 I needed them for the lowest possible 16:27:28

14 geography I could get. And then, actually, that's 16:27:31

15 discussed in the report. 16:27:37

16 Q Okay. 16:27:38

17 A I thought that Doug Allen would be the person 16:27:39

18 who got them. 16:27:42

19 Q Other than Mr. Allen and Dr. Picone, did you 16:27:43

20 interact with any of the other experts? 16:27:47

21 A No, I didn't. 16:27:48

22 Q Did you do your work in the offices of the 16:27:50




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2 A Predominantly in the offices of the Weinberg 16:27:57

3 Group, but on weekends I worked at home. 16:28:02

4 Q Why were you working in the Weinberg Group 16:28:04

5 offices? 16:28:08

6 A Ted Dunkelberger was the project manager and 16:28:08

7 he was -- preferred my presence there as opposed to 16:28:16

8 working at home. 16:28:22

9 Q Did he say why? 16:28:22

10 A He thought I might be generally useful. 16:28:24

11 Q For the Ecuador case or something else? 16:28:27

12 A For other things, too. 16:28:33

13 Q Were you working on other things during this 16:28:35

14 time other than -- 16:28:39

15 A No. No. 16:28:40

16 MS. NEUMAN: I'm going mark as Exhibit 1317 16:28:40

17 your invoice dated September 17th, 2010 to the 16:28:47

18 Weinberg Group. 16:28:51



21 BY MS. NEUMAN: 16:28:52

22 Q Did you prepare Exhibit 1317, Dr. Rourke? 16:28:52




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1 A Yes, I did. 16:29:07

2 Q Does it accurately reflect the time that you 16:29:10

3 spent on this matter between August 28th and 16:29:15

4 September 15th? 16:29:20

5 A Yes, I did. 16:29:21

6 Q Or yes, it does? 16:29:22

7 A Yes, it does. I'm sorry. 16:29:24

8 Q And the descriptions are accurate as well? 16:29:26

9 A Yes. 16:29:28

10 Q You say that you began writing your report on 16:29:29

11 the 10th and finished completing it on the 12th, 16:29:45

12 correct? 16:29:48

13 A Yes, that's true. 16:29:48

14 Q And then the very next day you started on an 16:29:51

15 addendum. Do you see that? 16:29:55

16 A Yes. 16:29:56

17 Q Why did you do one report and then a separate 16:29:56

18 addendum? 16:30:06

19 A The 12th was the last day that the -- a 16:30:06

20 report, to my understanding, could be complete and 16:30:13

21 gotten to the translators. It had to be done by then. 16:30:17

22 Now, it turned out that there was a little 16:30:26




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1 extra time to do work that I thought was called for 16:30:29

2 and mentioned in the report dated the 12th. 16:30:37

3 Q Now, Mr. Cabrera's original report on excess 16:30:40

4 cancer deaths, which you looked at and found the math 16:31:05

5 therein, originally went in at over -- a little over 16:31:10

6 2 billion, and then he too did an addendum to increase 16:31:16

7 his number to 9 billion. Were you familiar with that? 16:31:19



10 THE WITNESS: I don't -- I may have read a 16:31:24

11 supplemental, but I don't recall it. 16:31:33

12 BY MS. NEUMAN: 16:31:34

13 Q And then the -- did you talk to any of the 16:31:36

14 plaintiffs' attorneys about doing this addendum? 16:31:40


16 answered. 16:31:45

17 THE WITNESS: I felt that, by the time I 16:31:45

18 completed the work on the 12th, it made sense to add 16:31:49

19 the four cantons and to consider the impact of 16:31:53

20 remediation. And they indicated there's time to do 16:31:57

21 it, evidently, if you can get it done quickly. 16:32:02

22 BY MS. NEUMAN: 16:32:05




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1 Q And why did you want to add the four cantons? 16:32:06

2 A Because they were the areas that were used by 16:32:08

3 Hurtig and San Sebastian. 16:32:12

4 Q Why did you want to add remediation? 16:32:13

5 A Because people -- no new entrants were 16:32:17

6 entered into the calculation past 2009, but any new 16:32:27

7 arrivals past that point are going to be living, to 16:32:33

8 some extent, in the oil-producing areas, and even if 16:32:37

9 remediation is begun immediately, there still is the 16:32:42

10 chance for more cancers. So I wanted to explore a 16:32:47

11 rather aggressive schedule for remediation and see 16:32:53

12 what impact that would have. 16:32:58

13 Q What makes you think your remediation 16:32:59

14 schedule was aggressive? 16:33:03

15 A Because the area is pretty big and it's going 16:33:04

16 to take a lot of work. 16:33:07

17 Q Are you aware that the remediation began in 16:33:08

18 1995? 16:33:11

19 A I have -- 16:33:11



22 THE WITNESS: I have heard that the quality 16:33:14




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1 of this work is in some dispute. 16:33:17


3 Q Were you or were you not aware that 16:33:21

4 remediation began in 1995? 16:33:24



7 THE WITNESS: The answer is yes. 16:33:29


9 Q Were you aware that Petroecuador has done 16:33:31

10 extensive remediation in the area since the early 16:33:35

11 2000s? 16:33:39

12 A No. 16:33:39

13 Q For purposes of your analysis, did you ignore 16:33:42

14 all of the remediation done by both TexPet and 16:33:46

15 Petroecuador to the present? 16:33:50



18 THE WITNESS: I was under the impression that 16:33:54

19 there was still some dispute about the quality of the 16:33:57

20 work that was done and whether, in fact, it really did 16:34:00

21 remediate. 16:34:04

22 BY MS. NEUMAN: 16:34:04




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1 Q Now, why does remediation matter if you're 16:34:05

2 not making a causation assumption? 16:34:08

3 A Well, if you remediate, then it is as though 16:34:10

4 you were no longer living in an oil-producing region, 16:34:32

5 so... 16:34:35

6 Q But that assumes that the reason there's an 16:34:35

7 excess cancer in an oil-producing region is because 16:34:41

8 it's an oil-producing region, right? That's a 16:34:43

9 causation assumption. 16:34:47



12 BY MS. NEUMAN: 16:34:50

13 Q Isn't that true? 16:34:50

14 A Yes. 16:34:51

15 Q So in your remediation analysis you were, by 16:34:51

16 definition, making a causation assumption, correct? 16:34:56

17 A Well, I was reducing the excess risk 16:34:58

18 associated with living in the oil-producing areas. 16:35:02

19 Q Based on what scientific evidence were you 16:35:05

20 doing that? 16:35:12






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1 THE WITNESS: I guess it is as though the 16:35:15

2 person moved from an exposed to an unexposed region. 16:35:17


4 Q In your addendum, don't you actually increase 16:35:22

5 the number of excess cancers by extending the exposure 16:35:25

6 period with your remediation assumptions? 16:35:29

7 A No, I don't extend the exposure period. What 16:35:31

8 I do is add people past 2009. 16:35:35

9 Q Okay. You create more exposed people? 16:35:42


11 Q And you made the number go up? 16:35:44



14 BY MS. NEUMAN: 16:35:49

15 Q The number of hypothetical excess cancers 16:35:50

16 went up, correct? 16:35:52

17 A Yes. 16:35:53

18 Q By adding this remediation idea; is that 16:35:53

19 right? 16:35:57

20 A And extending the period in which new people 16:35:57

21 arrive, yes. 16:36:00

22 Q Did you have any scientific basis on which to 16:36:00




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1 ignore all the remediation that had taken place to the 16:36:04

2 present? 16:36:08





7 Q In your analysis, do you presume that excess 16:36:23

8 risk is associated with the presence of unremediated 16:36:27

9 oil pits? 16:36:39

10 A That would be true, yes. 16:36:39

11 Q So there's no risk associated with just the 16:36:40

12 oil operations being there; it's just the pits? 16:36:44


14 mischaracterizes testimony. 16:36:52

15 THE WITNESS: Now we're getting back into 16:36:53

16 causation, I suspect, but it is as though the oil 16:36:56

17 weren't there. 16:37:00

18 BY MS. NEUMAN: 16:37:00

19 Q As though the operations weren't there or 16:37:02

20 just the oil? 16:37:04

21 A No, as though the oil weren't there. The 16:37:05

22 operations are there, but it is as though the person 16:37:09




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1 is not living in an oil-producing area. 16:37:11

2 Q Well, when they remediate, they don't 16:37:12

3 eliminate the operations, right? 16:37:17

4 A No, they don't. 16:37:19

5 Q So you do still live in a oil-producing area. 16:37:19


7 Q So what makes the risk go to zero, just the 16:37:23

8 fact that there's no pits, or something else? 16:37:29

9 A That the contamination goes away. 16:37:32

10 Q Did you have any evidence -- well, withdraw 16:37:34

11 that. So if people weren't exposed to contamination, 16:37:37

12 then, in your opinion, their risk would be zero, 16:37:41

13 correct? 16:37:44

14 A I guess so, yes. 16:37:44

15 Q When, in your expert opinion, did the cantons 16:38:08

16 which you were looking at first become contaminated? 16:38:14

17 A Well, the original version of the model has 16:38:16

18 it beginning in 1967. 16:38:23

19 Q Do you think that's accurate? 16:38:25

20 A If the table that you showed me later is 16:38:28

21 correct, then that is not quite accurate. 16:38:34

22 Q Do you have, as you sit here today, a basis 16:38:36




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1 to form an expert opinion on when the contamination 16:38:43

2 started in any of the cantons? 16:38:47



5 THE WITNESS: Were that table from the 16:38:52

6 Cabrera report correct, that would serve as the basis 16:38:57

7 of the start of that, I suspect. 16:39:00


9 Q Do you assume that whenever oil-producing 16:39:03

10 activities began is when contamination began? 16:39:09

11 A Yes. Well, the risk increased. I've tried 16:39:11

12 to steer clear of the causation issue. 16:39:17

13 Q How do you know the risk increased -- the 16:39:20

14 risk of what? 16:39:25

15 A Excess cancers. 16:39:25

16 Q How do you know the risk of excess cancer 16:39:27

17 increased as soon as oil operations increased? What 16:39:30

18 science do you have for that? 16:39:33



21 THE WITNESS: It's an assumption built into 16:39:36

22 the model and can be changed. 16:39:37




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2 Q Have you done anything to validate that 16:39:39


4 A No. 16:39:45

5 Q What cancers does your model assume are 16:39:52

6 related to -- or associated with oil contamination? 16:40:07

7 A All cancers. 16:40:10

8 Q Did you do anything to validate that 16:40:13


10 A No. 16:40:15

11 Q Is it generally accepted scientific 16:40:15

12 methodology to have a model in which you have not 16:40:20

13 validated the assumption? 16:40:22



16 THE WITNESS: Some assumptions, if the data 16:40:27

17 do not exist, cannot be validated and have to remain 16:40:32

18 as assumptions that can be varied by a person who has 16:40:36

19 a differing point of view. 16:40:40

20 BY MS. NEUMAN: 16:40:45

21 Q My question was more, in the field of 16:40:49

22 statistics, is it scientifically accepted to use a 16:40:51




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1 model without validating the assumptions? 16:40:57



4 THE WITNESS: To the extent that you can 16:41:05

5 validate the assumptions, you should. 16:41:11


7 Q And you had some assumptions in your model 16:41:15

8 that you could have validated, but you did not, 16:41:18

9 correct? 16:41:21

10 A I'm not entirely sure of that. 16:41:21

11 Q You don't know one way or the other? 16:41:25

12 A I'd have to look at the data that supports 16:41:27

13 the validation, and it just -- I can't give you a 16:41:31

14 simple yes or no answer to that question. 16:41:36

15 Q Would you be comfortable with anyone 16:41:38

16 currently using your model to assess billions of 16:41:42

17 dollars in liability? 16:41:46



20 THE WITNESS: I have to ask, compared to 16:41:49

21 what? 16:42:03

22 BY MS. NEUMAN: 16:42:03




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1 Q Well, not compared to anything. If you were 16:42:08

2 going to have to pay money based on your model -- 16:42:11

3 well, I'll withdraw that. 16:42:19

4 In its current unvalidated state, do you 16:42:21

5 think it's appropriate for your model to be used to 16:42:24

6 estimate billions of dollars in damages that anyone 16:42:29

7 would have to pay? 16:42:32



10 THE WITNESS: Well, I respectfully disagree 16:42:35

11 with the unvalidated state and, to some extent, this 16:42:44

12 is going to be the judge's decision, not mine. 16:42:51

13 BY MS. NEUMAN: 16:42:54

14 Q You're offering an expert opinion, correct? 16:42:56


16 THE WITNESS: I am offering numbers based on 16:42:59

17 my best estimate of certain parameters and 16:43:03

18 assumptions. 16:43:09

19 BY MS. NEUMAN: 16:43:09

20 Q And do you -- in your opinion, should the 16:43:12

21 judge accept those assumptions or should he question 16:43:18

22 those assumptions? 16:43:21




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2 the question. You're asking whether or not the 16:43:23

3 judge -- he thinks the judge -- 16:43:31

4 MS. NEUMAN: I object to the speaking 16:43:32

5 objections. 16:43:35

6 MR. WESTENBERGER: Well, you can object to 16:43:35

7 the speaking objections, but are you asking him to 16:43:36

8 decide what the judge should or not should not do in 16:43:37

9 this case? These are arguments for lawyers. 16:43:40

10 MS. NEUMAN: He's the one that's bringing up 16:43:42

11 the judge. 16:43:45

12 THE WITNESS: I would like the judge to 16:43:46

13 consider this. 16:43:47

14 BY MS. NEUMAN: 16:43:48

15 Q Your report? 16:43:49

16 A Yes. 16:43:49

17 Q Now, you said that you took issue with the 16:43:49

18 fact that it wasn't validated. What did you do to 16:43:52

19 validate the results of your model? 16:43:55



22 THE WITNESS: I believe -- all right. I 16:43:59




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1 believe the assumptions that went into these 16:44:28

2 calculations were reasonable assumptions, given the 16:44:30

3 data available to me. 16:44:34


5 Q Okay. But once you got the results, what, if 16:44:38

6 anything, did you do to validate them? 16:44:41

7 A I did nothing. 16:44:43


9 Exhibit 1318 the addendum dated September 15th, 2010, 16:44:53

10 estimate of the number and costs of excess deaths 16:44:58

11 associated with residents in the oil-producing areas 16:45:02

12 of the Sucumbios and Orellana provinces of Ecuador. 16:45:04



15 BY MS. NEUMAN: 16:45:07

16 Q Is this the addendum that you prepared, 16:45:07

17 Dr. Rourke? 16:45:34

18 A Yes, it is. 16:45:34

19 Q Was this addendum prepared using the same 16:45:35

20 model that we've been discussing today in connection 16:45:39

21 with your original report? 16:45:44

22 A Not quite. In between the 12th and the 15th, 16:45:44




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1 I believe beginning on the 13th, I had to do two 16:45:55

2 things, one of which was add a mechanism for 16:46:03

3 expressing remediation. That required me to add some 16:46:07

4 programming to the model. 16:46:12

5 Q And how did you decide how much risk would be 16:46:13

6 decreased by each year of remediation? 16:46:19

7 A That was an assumption that is variable, and 16:46:22

8 I picked one to be -- make the exposition reasonably 16:46:34

9 clear, and that was that the remediation begins 16:46:40

10 basically in 2010, or now, and takes ten years to 16:46:43

11 complete. 16:46:47

12 Q My question was, how did you decide how much 16:46:47

13 the risk would be decreased by each year of 16:46:59

14 remediation? 16:47:03


16 answered. 16:47:05

17 THE WITNESS: It's determined basically by 16:47:05

18 how long it takes for the remediation to occur. 16:47:14

19 BY MS. NEUMAN: 16:47:17

20 Q Well, does the risk decrease gradually over 16:47:20

21 the ten years or all at once? 16:47:22

22 A It decreases gradually. 16:47:24




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1 Q Okay. So how did you determine how much to 16:47:25

2 decrease it year by year? 16:47:28

3 A It's a straight line function. 16:47:29

4 Q And what data was that based on? 16:47:31

5 A It was based on an assumption that I made. 16:47:33

6 Q And did you have any scientific to support 16:47:36

7 that assumption? 16:47:39

8 A No, I did not. This is a what-if analysis. 16:47:39

9 Q Did anyone ask you to do a similar analysis 16:47:43

10 based on any remediation that had actually occurred in 16:47:46

11 the former Concession area? 16:47:50

12 A No, they did not. 16:47:51

13 Q Were you provided with any documents in which 16:47:53

14 the plaintiffs' other experts discuss the confined 16:48:03

15 nature of the contamination? 16:48:09

16 A What do you mean by other experts? 16:48:10

17 Q The plaintiffs originally used Stratus 16:48:17

18 Consulting in Denver as their experts. Are you 16:48:23

19 familiar with Stratus? 16:48:25

20 A Again, I never heard of them till this case. 16:48:26

21 Q Did you ever interact with them on your work 16:48:28

22 in this case? 16:48:31




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1 A No, I did not. 16:48:32

2 Q Provided -- they worked on the case for -- 16:48:33

3 provided materials, wrote things for Mr. Cabrera. Did 16:48:36

4 you ever discuss that with anybody? 16:48:40

5 MR. WESTENBERGER: Is that your testimony, 16:48:41

6 Counsel? 16:48:42

7 MS. NEUMAN: It's in the record. 16:48:43

8 THE WITNESS: No, I did not. 16:48:44


10 Q Were you provided with any of Stratus' 16:48:50

11 analysis of in what locations any contamination was 16:48:52

12 located? 16:49:00

13 A I can't say for sure. If it was included in 16:49:00

14 the Cabrera report and if I got the full Cabrera 16:49:08

15 report, I suspect it would be in there. But I didn't 16:49:12

16 pay much attention to that. 16:49:15

17 MS. NEUMAN: I'm going to show the witness a 16:49:18

18 document previously marked as Exhibit 1026. It's an 16:49:19

19 e-mail from Doug Beltman to Lorena Gamboa. 16:49:24

20 BY MS. NEUMAN: 16:49:24

21 Q I'll represent to you, Dr. Rourke, that Doug 16:49:43

22 Beltman is with Stratus Consulting and was working for 16:49:45




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1 the plaintiffs in this matter. 16:49:48

2 Towards the bottom of Exhibit 1026, in March 16:49:52

3 of '08 he states, "I do not think that contamination 16:49:57

4 sufficient to impact the ecology extends very far 16:50:02

5 beyond the pads, pits and spills at the wells. There 16:50:06

6 simply isn't a migration pathway." 16:50:09


8 A I'm sorry. Where was that again? 16:50:12

9 Q It's right at the bottom -- 16:50:14

10 A Of the first page. 16:50:16

11 Q -- the last sentence. Yes, sir. 16:50:16

12 MR. WESTENBERGER: Did you ask him if he ever 16:50:18

13 saw this document before? 16:50:25

14 THE WITNESS: I've never seen this document 16:50:26

15 before. 16:50:28

16 MR. WESTENBERGER: Why don't you establish 16:50:28

17 some foundation. You're asking about documents he's 16:50:30

18 never seen, people he's never talked to you. You've 16:50:32

19 established that very clearly. 16:50:35

20 BY MS. NEUMAN: 16:50:36

21 Q Do you see where Mr. Beltman says, "I do not 16:50:37

22 think that contamination sufficient to impact the 16:50:40




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1 ecology extends very far beyond pads, pits and spills 16:50:42

2 at the wells. There simply isn't a migration 16:50:48

3 pathway"? 16:50:51

4 A Yes. 16:50:51

5 Q Were you provided with information about the 16:50:51

6 locations at which any contamination had been 16:50:55

7 documented in the former Concession area? 16:50:59


9 answered. 16:51:03

10 THE WITNESS: Again, to the extent that this 16:51:03

11 information was in the Cabrera report, I may have been 16:51:06

12 provided with it, but I don't recall looking at it. 16:51:15

13 BY MS. NEUMAN: 16:51:17

14 Q You didn't use, in your analysis, the 16:51:18

15 locations of any contamination to determine allegedly 16:51:20

16 exposed populations, correct? 16:51:26



19 THE WITNESS: Back to where we were at the 16:51:29

20 start of this, I used the Concession area, the four 16:51:38

21 cantons, and areas of -- an area five kilometers from 16:51:42

22 the oil-producing facilities. 16:51:50




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1 So in terms of a point or a relatively 16:51:52

2 compact geographic area, no, I did not. 16:51:56


4 Q And you didn't use, for example, 16:52:00

5 five kilometers around identified areas of 16:52:02

6 contamination? 16:52:06

7 A No, I did not. 16:52:06

8 Q In your other work outside of the Ecuador 16:52:09

9 case, have you ever based excess risk numbers on a 16:52:17

10 single ecologic study? 16:52:23

11 A No, I have not. 16:52:26

12 Q In your work outside of the Ecuador case, 16:52:28

13 have you always had more data than a single ecologic 16:52:38

14 study for your risk numbers? 16:52:53



17 THE WITNESS: For the asbestos work, there's 16:52:56

18 a dose response model for both mesothelioma and lung 16:52:58

19 cancer. For the other injuries, there is no such 16:53:02

20 thing. There's none for -- well, that's not true. 16:53:11

21 There may be a dose response model for asbestotics. I 16:53:16

22 believe I saw one in a Swedish journal somewhere. But 16:53:21




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1 it turned out to be proportional to the same thing 16:53:24

2 that the lung cancer risk was related to. 16:53:27

3 For some benzene work, it only used 16:53:35

4 projection of incidence rates for AML. So I've never 16:53:41

5 used a -- what did you refer to it -- as an ecological 16:53:51

6 study? 16:53:55


8 Q Yes, sir. 16:53:56

9 A In my other work, no, I have not. 16:53:56

10 Q Have you only used these dose response 16:53:58

11 models? 16:54:01

12 A For the asbestos work, yes. Now, it doesn't 16:54:01

13 work for other cancers or the non-malignancies. You 16:54:11

14 have to use something else. 16:54:16

15 Q What do you use for those, or do you not do 16:54:16

16 those? 16:54:20

17 A The way it's usually done -- well, the way is 16:54:20

18 to take a multiplier based on claims or filings or 16:54:25

19 diagnoses of the disease in question, such as 16:54:33

20 asbestosis, to lung cancer -- and usually that is a 16:54:37

21 ratio over some known period of time; usually that 16:54:45

22 ratio is much bigger than 1 -- and to multiply that by 16:54:48




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1 your lung cancer estimate. 16:54:54

2 Q And who establishes those? 16:54:56

3 MR. WESTENBERGER: Objection to -- 16:55:04

4 THE WITNESS: Who establishes -- 16:55:04

5 MR. WESTENBERGER: -- the form of the 16:55:06


7 THE WITNESS: I'm sorry. 16:55:07

8 MR. WESTENBERGER: That's okay. 16:55:09

9 THE WITNESS: Who establishes what? 16:55:09

10 BY MS. NEUMAN: 16:55:11

11 Q The numbers that you use for those ratios. 16:55:12

12 A If I were doing the work, I would. 16:55:15

13 Q In the Quigley -- In Re: Quigley company 16:55:17

14 case -- did the court find that you used an 16:55:36

15 unreasonable assumption that rendered your figures 16:55:44

16 inaccurate? 16:55:46

17 A I believe the judge misread my report, 16:55:46

18 actually. 16:55:52

19 Q But did -- was that in his ruling? 16:55:52

20 A That's what appears in his ruling. You've 16:55:57

21 got his ruling there, don't you? 16:55:59

22 Q Yes. 16:56:01




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1 A Okay. That's what he said. 16:56:01

2 Q You're not offering the opinion that Chevron 16:56:03

3 took any action that actually caused anybody to get 16:56:22

4 cancer, correct? 16:56:25

5 A No, I'm not. 16:56:26

6 Q And you're not offering the opinion that 16:56:30

7 TexPet took any action that actually caused anybody to 16:56:33

8 get cancer, correct? 16:56:36

9 A No, I'm not. 16:56:37

10 Q And if asked how many deaths in your opinion 16:56:39

11 the defendant in the Ecuador case caused, would your 16:56:58

12 answer be that you don't have an opinion on that? 16:57:05

13 A You use the word "caused" and, therefore, I 16:57:07

14 would have to say I don't have an opinion on that. 16:57:11

15 Q And regardless of which population you're 16:57:12

16 looking at, the five kilometers, the Concession area 16:57:17

17 or the four cantons, all of your excess death numbers 16:57:21

18 are hypothetical deaths based on a risk assumption, 16:57:25

19 correct? 16:57:29

20 A Yes. That's true. 16:57:29

21 Q You previously testified that, in 1990, 16:57:31

22 operations were turned over to Petroecuador, right? 16:57:44




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1 A Did I say that in my report or is that 16:57:47

2 something -- 16:57:51

3 Q You said it today. I don't remember if it's 16:57:53

4 in the report. You said you chose 1990 because you 16:57:55

5 understood that's -- 16:57:55

6 A Oh, that's right -- 16:57:55

7 Q -- when Petroecuador took over? 16:58:00

8 A -- because that's when I understood that, 16:58:02

9 yes, operations were turned over to somebody else. 16:58:06

10 Q So the individuals entering the oil-producing 16:58:08

11 area after 1990 are being exposed to Petroecuador 16:58:15

12 operations, correct? 16:58:20



15 THE WITNESS: Splitting hairs. From your 16:58:23

16 definition, yes. 16:58:35

17 BY MS. NEUMAN: 16:58:36

18 Q Well, did you make -- in calculating these 16:58:38

19 excess cancers, did you make any determination whether 16:58:41

20 the risk was created by TexPet or Petroecuador or some 16:58:51

21 other oil company operating in these cantons? 16:58:55

22 A No, I did not. 16:58:57




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1 Q You mentioned earlier that you would like for 16:58:59

2 the judge to use your report. 16:59:16


4 mischaracterizes his testimony. 16:59:20


6 Q Is that right or wrong? 16:59:22

7 MR. WESTENBERGER: I think he said -- 16:59:23

8 THE WITNESS: I would like -- 16:59:23

9 MR. WESTENBERGER: -- consider it. 16:59:25

10 THE WITNESS: I would like him to consider 16:59:25

11 it. 16:59:26

12 BY MS. NEUMAN: 16:59:26

13 Q And would you like to address the issues 16:59:28

14 we've discussed here today before he considers it? 16:59:33



17 THE WITNESS: If -- I would prefer that he 16:59:37

18 takes it as it stands unless I -- I'm not going to 16:59:50

19 back away from the methodology. I might back away a 16:59:59

20 little bit from some of the assumptions that would 17:00:03

21 have a small-ish effect, I imagine. But I would be 17:00:05

22 foolish if I said -- but you can always do things 17:00:12




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1 better. So I guess I would be foolish to say no. 17:00:16

2 That makes no sense. I'm sorry. 17:00:23


4 Q I'm going to admit I wasn't following that. 17:00:25

5 A No, it wasn't. 17:00:25

6 Q Well, you said that you didn't have a 17:00:28

7 scientific basis for starting the model in '67; is 17:00:30

8 that fair? 17:00:35

9 A If that table is correct that you showed me 17:00:35

10 and I had an opportunity to review whatever document 17:00:41

11 would support this in a period of time, I'd like to 17:00:45

12 revisit that issue, yes. 17:00:50

13 Q And you also said that you didn't have a 17:00:51

14 scientific basis for applying San Sebastian's 17:00:54

15 increased risk numbers to areas that had not been oil 17:00:58

16 producing for 20 years, correct? 17:01:02


18 the -- 17:01:04

19 THE WITNESS: No, I did not say that, I 17:01:04

20 believe. I don't believe I said that. 17:01:07

21 BY MS. NEUMAN: 17:01:08

22 Q Okay. Well, what's your scientific basis for 17:01:09




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1 using his risk numbers on a population other than the 17:01:10

2 one he defines? 17:01:14

3 A Because -- we're back to an issue that we 17:01:15

4 discussed earlier. That has to do with -- just 17:01:18

5 because the oil-production facility was there for the 17:01:24

6 20 years doesn't mean the people were. And what I'm 17:01:28

7 having a hard time explaining is that I believe the 17:01:34

8 risk factors that they developed are relatively 17:01:44

9 insensitive. If they had used ten years -- 17:01:48

10 Q Okay. But my question is, what's your 17:01:50

11 scientific basis -- 17:01:52

12 MR. WESTENBERGER: Objection. This is -- 17:01:53

13 BY MS. NEUMAN: 17:01:53

14 Q -- for assuming -- 17:01:53

15 MR. WESTENBERGER: You've gone over this 17:01:55

16 multiple times. 17:01:55

17 BY MS. NEUMAN: 17:01:56

18 Q -- that the risk factors are -- what did you 17:01:57

19 call them? -- insensitive? 17:01:59

20 A Insensitive. 17:01:59

21 MR. WESTENBERGER: Objection. This has been 17:02:01

22 asked and answered and covered multiple times today. 17:02:03




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1 I'm going to ask you to move on. The doctor has 17:02:07

2 answered this question multiple times. 17:02:12


4 Q You can answer the question. 17:02:14

5 MR. WESTENBERGER: He already answered the 17:02:16

6 question multiple times. The record will show that 17:02:17

7 very clearly. You've asked that question at least ten 17:02:20

8 times. 17:02:23

9 MS. NEUMAN: You cannot find this question in 17:02:24

10 this transcript. 17:02:28

11 MR. WESTENBERGER: Oh, the one that you just 17:02:28

12 changed the last word to? 17:02:30

13 MS. NEUMAN: Can you read the question book 17:02:31

14 to the witness. I don't want to waste time. 17:02:35




18 BY MS. NEUMAN: 17:02:37

19 Q On what scientific basis did you conclude 17:03:01

20 that San Sebastian's risk factors are insensitive? 17:03:03

21 MR. WESTENBERGER: Same objections. 17:03:05

22 THE WITNESS: I have no evidence-based 17:03:08




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1 support for that assumption. 17:03:16


3 Q And what is it that you are assuming the risk 17:03:19

4 factors are insensitive to? 17:03:20

5 A It is my hypothesis that if you compared 10 17:03:25

6 years to 20, the difference between the risk factors, 17:03:33

7 10 years to 20 years for an oil-producing facility -- 17:03:38

8 I mean, a region in which oil production facilities 17:03:44

9 exist -- that you would not get a large difference 17:03:50

10 between the results that appear in the Hurtig and 17:03:52

11 San Sebastian paper compared to what if it were 10 17:03:56

12 years as opposed to 20. 17:03:59

13 Q But you don't have any data to support that 17:04:01


15 A No, I do not. 17:04:05


17 answered. 17:04:07

18 BY MS. NEUMAN: 17:04:07

19 Q Are you also making the assumption as to 17:04:09

20 years less than 10? 17:04:13

21 A Yes, I am. 17:04:14

22 Q And you have no basis for making that 17:04:16




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1 assumption either, correct? 17:04:24



4 THE WITNESS: Again, no evidence-based -- no 17:04:28

5 data. 17:04:35


7 Q How many people did San Sebastian's study 17:04:39

8 look at that had been diagnosed with cancer? 17:04:42



11 THE WITNESS: May I look at the report? 17:04:48

12 BY MS. NEUMAN: 17:04:51

13 Q Oh, certainly. 17:04:51

14 A This is a faint copy, but it looks like 185 17:04:52

15 men and 288 women had cancer. 17:05:33

16 Q So a little less than 500? 17:05:41

17 A Yes. 17:05:50

18 Q How many of those had a cancer for which 17:05:50

19 San Sebastian included (sic) there was a statistically 17:05:54

20 significant increased risk? 17:05:58

21 A All right. This is going to take a few 17:06:00

22 moments for me to count the ones where the confidence 17:06:18




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1 level does not include 1. And again -- well -- and 17:06:22

2 this is specific cancers as opposed to total? 17:06:38

3 Q Yes, sir. The cancers he actually 17:06:42

4 identified. 17:06:47

5 A Okay. If my count is correct, there were 17:06:47

6 one, two, three -- four for men and two for women. 17:07:56

7 Q Types of cancer? 17:08:00

8 A Yes. When you go down specific type. 17:08:03

9 Q And how many cases of cancer were there? 17:08:05

10 A At this point, I'd need to borrow your 17:08:14

11 calculator. 17:08:28

12 Q It's not mine, but I think it's quality 17:08:28

13 there. 17:08:32

14 A Boy, these things have gotten cheap. 17:08:32

15 I'm going to give an aggregate total rather 17:09:02

16 than separating it by men and women. Is that 17:09:05

17 permissible? 17:09:07

18 Q Yes, sir. 17:09:08

19 A 175, but I can't guarantee that's right. 17:09:09

20 Q Do you know whether any investigation was 17:09:58

21 done to determine the actual cause of any of those 175 17:10:01

22 people's deaths? 17:10:06




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1 A No, I don't believe so. 17:10:07

2 Q Were more than half of those deaths women 17:10:09

3 with cervical cancer? 17:10:17


5 Q If the excess cervical cancers are excluded 17:10:19

6 from the San Sebastian analysis, the risk numbers 17:10:30

7 would go down, correct? 17:10:37



10 THE WITNESS: That's an arithmetic 17:10:41

11 consequence, yes. 17:10:43

12 BY MS. NEUMAN: 17:10:44

13 Q Do you have any reason to believe that 17:10:46

14 cervical cancer is caused by exposure to petroleum 17:10:48

15 products? 17:10:52



18 THE WITNESS: I attempted to steer clear of 17:10:54

19 issues of causation. 17:10:58

20 BY MS. NEUMAN: 17:11:00

21 Q Okay. Well, what if the judge determines 17:11:04

22 that he doesn't think cervical cancer is caused by 17:11:07




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1 exposure to petroleum products, is there a way in 17:11:11

2 which he would adjust your numbers to take that into 17:11:16

3 account or would you have to rerun your model? 17:11:20


5 the question. Once again, now you're asking him what 17:11:23

6 the judge should or should not do or can or cannot do. 17:11:25

7 MS. NEUMAN: It's a hypothetical. 17:11:29

8 THE WITNESS: Okay. If the judge decided not 17:11:31

9 to include that sort of cancer, then -- I don't think 17:11:33

10 you could get there by a simple arithmetic 17:11:36

11 calculation. You'd have to actually rerun the model. 17:11:40

12 Too many moving parts. 17:11:46

13 MS. NEUMAN: I'm going to mark as 1319 your 17:11:47

14 declaration that you submitted in this matter, 17:12:15

15 Dr. Rourke. 17:12:18



18 BY MS. NEUMAN: 17:12:18

19 Q Is that your signature on page 3 of 17:12:18

20 Exhibit 1319? 17:12:42

21 A Yes, it is. 17:12:42

22 Q This is a declaration that you provided in 17:12:43




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1 this matter; is that right? 17:12:47

2 A Yes, it is. 17:12:48

3 Q Now, you used something called a statistical 17:12:49

4 machine to prepare your calculations. Is that 17:12:52

5 accurate? 17:12:54



8 THE WITNESS: That is what I've heard it's 17:12:56

9 called. 17:12:59

10 BY MS. NEUMAN: 17:13:00

11 Q What do you call it? I'm happy to call it 17:13:00

12 whatever you like. 17:13:03

13 A A computer program. 17:13:03

14 Q Okay. Is your -- the computer program that 17:13:05

15 you used in this case based on any commercially 17:13:10

16 available software? 17:13:14

17 A No, it is not. 17:13:14

18 Q Is it built on any software platform? 17:13:16

19 A I don't know exactly what you mean by that. 17:13:19

20 It is a computer program that runs on an IBM -- well, 17:13:28

21 a personal computer, an IBM-style as opposed to 17:13:35

22 Macintosh personal computer. 17:13:40




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1 Q Did you write the code for the computer 17:13:41

2 program? 17:13:43

3 A Yes, I did. 17:13:43

4 Q Does it have a name? 17:13:44

5 A I believe I called it Ecuador. 17:13:45

6 Q Ecuador. Okay. Does the program incorporate 17:13:48

7 any computer code that you did not write? 17:13:57

8 A No, it doesn't. 17:13:58

9 Q Did you create the - I'm going to call it 17:14:03

10 Ecuador, too. Did you create the Ecuador program 17:14:08

11 specifically for this case? 17:14:12

12 A Yes, I did. 17:14:13

13 Q Is it accurate that I could use the Ecuador 17:14:14

14 program to perform calculations for this case, but I 17:14:19

15 can't use it to perform calculations for some other 17:14:23

16 case that you've worked on? 17:14:26

17 A It was written for this case. 17:14:27

18 Q Are there any competitors of yours working on 17:14:30

19 this case? 17:14:35

20 A I don't know. 17:14:35

21 Q Have you ever created a computer program to 17:14:37

22 do your analysis for any other case? 17:14:44




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1 A Practically always. 17:14:46

2 Q Do the programs you create for each case you 17:14:52

3 work on vary from case to case? 17:14:56



6 THE WITNESS: There are two programs that are 17:15:02

7 used on the asbestos cases, and the similarity of task 17:15:08

8 is such that all you need do is to change the input 17:15:15

9 values to apply it to a different -- from one client 17:15:20

10 to another. 17:15:26

11 BY MS. NEUMAN: 17:15:27

12 Q Did you use any of the code from the asbestos 17:15:29

13 programs to do the Ecuador program? 17:15:31

14 A No, I did not. 17:15:33

15 Q Have you ever sought any intellectual 17:15:35

16 property protection for the Ecuador program? 17:15:45



19 THE WITNESS: I indicated to my attorneys 17:15:50

20 that I wanted some sort of non-disclosure arrangement. 17:15:56

21 BY MS. NEUMAN: 17:15:59

22 Q In the litigation, you mean? 17:16:03




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1 A In this case. 17:16:04

2 Q Well, did you ever seek to have the program 17:16:05

3 patented or copyrighted, anything like that? 17:16:08

4 A I didn't -- 17:16:11


6 THE WITNESS: I didn't put a copyright on the 17:16:13

7 program. 17:16:16


9 Q The program, is it -- what does it do? Runs 17:16:17

10 your whole calculation? 17:16:21

11 A The inputs consist of -- what -- the excess 17:16:22

12 risk numbers, the all cause and all cancers death 17:16:31

13 rates, the population distribution percents, and I may 17:16:39

14 be missing -- some years when certain things start and 17:16:46

15 when certain things stop, and then, finally, 17:16:50

16 year-by-year population totals for the geographic area 17:16:55

17 of interest. 17:17:00

18 Q Those are the inputs to the program? 17:17:00

19 A Yes. 17:17:03

20 Q And then what are the outputs from the 17:17:03

21 program? 17:17:07

22 A Year-by-year total number of deaths, total 17:17:07




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1 number of cancers, total number of excess cancers, and 17:17:12

2 probably a couple of other things that I can't think 17:17:18

3 of right now. 17:17:20

4 Q So the program calculates the total number of 17:17:21

5 theoretical excess cancers which you then multiply by 17:17:36

6 the $7 million to get your various monetary estimates 17:17:42

7 in your reports, correct? 17:17:48

8 A The totals, yes, are multiplied by the 17:17:49

9 7 million, that's correct. 17:17:54

10 Q To accurately recreate your work, would you 17:17:55

11 need to use the Ecuador program? 17:17:59

12 MR. WESTENBERGER: Object to the form of the 17:18:00

13 question. 17:18:01

14 THE WITNESS: Someone who was conversant with 17:18:01

15 life table methodologies and followed my description 17:18:15

16 of the report might be able to reproduce the results. 17:18:20

17 BY MS. NEUMAN: 17:18:25

18 Q But they couldn't see precisely what you did 17:18:29

19 without having the program, right? 17:18:31


21 Q Did you discuss your use of the Ecuador 17:18:32

22 program in this matter with anyone? 17:18:40




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1 A No, I did not. 17:18:43

2 Q Do you consider the Ecuador program to be a 17:18:45

3 trade secret? 17:18:50



6 THE WITNESS: Are you using the term in a 17:18:55

7 legal sense? 17:18:58


9 Q Yes, sir. 17:19:00

10 A I don't know exactly, then, what a trade 17:19:00

11 secret is. It's something that I'm particularly good 17:19:05

12 at, and it's how I make my living. So that was my 17:19:13

13 concern about disclosure. 17:19:20

14 Q You're particularly good at -- when you say 17:19:25

15 "something," what are you referring to? 17:19:31

16 A Computer programming complicated things. 17:19:32

17 Q The programs that you use in the asbestos 17:19:35

18 cases, have they ever been disclosed? 17:19:44

19 A As a matter of fact, under a non-disclosure 17:19:47

20 agreement, I believe so. 17:19:50

21 Q Have they been given to the other side? 17:19:51

22 A I believe so. 17:19:55




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1 Q Have they ever been disclosed without a 17:19:55

2 non-disclosure agreement? 17:20:00

3 A I don't know, but I doubt it. 17:20:00

4 Q Have they ever been filed with any court? 17:20:04

5 A The source code? 17:20:07

6 Q Your program, like an electronic copy. 17:20:09

7 A No. 17:20:14


9 THE WITNESS: They have not. 17:20:15

10 MS. NEUMAN: It's 5:20. We can go off the 17:20:16

11 record for a couple of minutes. 17:20:37

12 THE VIDEOGRAPHER: The time is 5:21 p.m. We 17:20:38

13 are going off the record. 17:20:42


15 THE VIDEOGRAPHER: The time is 5:31 p.m., and 17:20:43

16 we're back on the record. 17:30:26

17 BY MS. NEUMAN: 17:30:27

18 Q Dr. Rourke, are we good to proceed? 17:30:27

19 A Yes. 17:30:33

20 Q Oh, good. In your production that you gave 17:30:33

21 to your counsel, did you include your e-mail 17:30:41

22 correspondence that related to the case? 17:30:44




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1 A No, I did not. 17:30:46

2 Q Why not? 17:30:49

3 A I routinely delete my e-mails after I've 17:30:51

4 dealt with them. 17:30:55

5 Q And they're permanently deleted? 17:30:56


7 Q Do you still have your sent mails? 17:31:01

8 A No, that goes too. 17:31:04

9 Q Your whole thing is empty? 17:31:07


11 Q And you delete all your e-mail on a daily 17:31:08

12 basis? 17:31:13

13 A Yes. 17:31:13

14 Q In your asbestos work where you're estimating 17:31:13

15 future diseases related to asbestos, are you always 17:31:27

16 making estimates for diseases that are known to be 17:31:32

17 associated with asbestos exposure? 17:31:35



20 THE WITNESS: The reason I'm hesitating is 17:31:39

21 that the lung -- well, let's just take lung cancer as 17:31:52

22 an example. Lung cancer is certainly related to 17:31:59




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1 asbestos exposure. Other things being equal, the more 17:32:04

2 asbestos exposure, the greater likelihood of a 17:32:08

3 subsequent lung cancer. But it's also related to 17:32:11

4 cigarette smoking: Other things being equal, the more 17:32:15

5 cigarettes you smoke, the likelier you are to contract 17:32:19

6 lung cancer. 17:32:21

7 And there are probably other things that 17:32:22

8 cause it, too. So the causation is not exact. 17:32:24


10 Q But you don't include in your asbestos models 17:32:32

11 cancers that aren't in any way related to asbestos, 17:32:35

12 correct? 17:32:38



15 THE WITNESS: There is -- depending on the 17:32:41

16 client, there is sometimes a dispute about what should 17:32:45

17 be considered an asbestos-related other cancer. 17:32:50

18 BY MS. NEUMAN: 17:32:53

19 Q Do you ever do your asbestos models on all 17:32:56

20 cancers? 17:33:00

21 A No. 17:33:01

22 Q Have you ever met Steven Donziger? 17:33:01




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1 A Once. 17:33:04

2 Q When was that? 17:33:05

3 A Friday, September 10th. Maybe twice; maybe 17:33:06

4 it was the 9th and the 10th. 17:33:12

5 Q Where did you meet Mr. Donziger? 17:33:15

6 A At the offices of the Weinberg Group here in 17:33:17

7 D.C. 17:33:20

8 Q For how long did you meet with him on 17:33:20

9 the 9th? 17:33:26

10 A A half-hour, if that long. 17:33:26

11 Q Who else was present? 17:33:30

12 A Eric Westenberger, Adlai Small, Ted 17:33:31

13 Dunkelberger was in and out of the room. And I can't 17:33:38

14 recall if Ilaan Maazel was there on Thursday or Friday 17:33:46

15 or both. 17:33:51

16 Q What was the purpose of the meeting with 17:33:52

17 Mr. Donziger on the 9th? 17:33:56

18 A It was for me to present my initial results. 17:33:57

19 Q Did he -- did you have conversations with him 17:34:01

20 about your results? 17:34:06

21 A Yes. 17:34:07

22 Q What were the substance of those 17:34:08




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1 conversations? 17:34:12

2 A Here they are, here's how they were arrived 17:34:12

3 at. 17:34:18

4 Q Did he ask any questions or -- 17:34:18

5 A A few. Not very many, but a few. I can't 17:34:23

6 recall what the substance was. 17:34:27

7 Q Were there any changes made to your analysis 17:34:27

8 after the 9th? 17:34:32

9 A I'm not even sure I had the computations done 17:34:32

10 till Friday, actually. 17:34:44

11 Q Friday being the 10th? 17:34:46

12 A The 10th, yes. I don't think I had anything 17:34:47

13 to present the 9th other than to say, here's what I've 17:34:54

14 done so far and here's how I'm going to proceed. 17:34:59

15 And the 10th was the first time any of the 17:35:03

16 estimates, I believe, were presented. 17:35:09

17 Q When you presented on the 10th, was it the 17:35:10

18 same group that was present on the 9th or did the 17:35:15

19 group change? 17:35:18

20 A Mr. Westenberger was there. Adlai Small was 17:35:19

21 there. I can't recall if Donziger was there or not, 17:35:23

22 actually. Ilaan Maazel probably was there. I'm 17:35:27




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1 sorry, I have no notes on this, so I can't recall 17:35:35

2 exactly. 17:35:37

3 Q That's okay. After you presented on 17:35:37

4 the 10th, were changed made to your analysis? 17:35:42

5 A The work was not yet complete. The numbers 17:35:44

6 were substantially complete, but the first document 17:35:49

7 wasn't done -- first draft, as it were, wasn't done 17:35:52

8 till the 9th of -- the night of the 10th. 17:35:57

9 Q Did the lawyers who participated in the 17:36:00

10 meetings on the 9th and 10th make any comments on your 17:36:05

11 work that caused you to make any substantive changes? 17:36:09

12 A No, they did not. 17:36:12

13 Q According to your invoice, you began writing 17:36:14

14 your report on the 9th -- I mean the 10th. That was 17:36:24

15 after your numbers were complete? 17:36:28

16 A Yes. It was a very long day. 17:36:30

17 Q In your addendum at page 7 you have a 17:36:34

18 $69 billion amount. Is that the largest amount you've 17:36:44

19 ever calculated for an excess cancer claim? 17:36:48

20 A Yes, I believe so. 17:36:51

21 Q And you did this work in approximately three 17:37:01

22 weeks? 17:37:05




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2 Q Have you ever created a computer program from 17:37:06

3 scratch to model theoretical excess cancers and 17:37:12

4 applied values to them in the span of three weeks 17:37:21

5 before? 17:37:24

6 A Yes. 17:37:24

7 Q On what matter? 17:37:26

8 A Probably the first time it was used was 17:37:29

9 EaglePicher, the asbestos forecasting model. 17:37:38

10 Q You developed that in less than three weeks? 17:37:42

11 A Yes, I did. 17:37:44

12 Q And what year was that? 17:37:45

13 A 1991, perhaps. 17:37:46

14 Q And were changes made to it after that 17:37:50

15 three-week development period? 17:37:54

16 A The computational core was basically 17:37:55

17 established. There may have been additions to the 17:38:00

18 output, and some refinements, but it was substantially 17:38:06

19 complete then. 17:38:09

20 Q Now, in some of the documents the experts 17:38:09

21 produced it was discussed that there was originally 17:38:13

22 going to be one report of which your report was going 17:38:16




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1 to be a part. Did you have that understanding at some 17:38:20

2 point in time? 17:38:22

3 A That was my understanding of what was going 17:38:22

4 to be the case initially. 17:38:26

5 Q And why did that change? 17:38:26

6 A I don't know. 17:38:30

7 Q No one explained that to you? 17:38:30

8 A No, they didn't. 17:38:33

9 Q On page 7 of your addendum -- do you have 17:38:33

10 that in front of you? 17:38:40

11 A Somewhere here. Yes. 17:38:41

12 Q The 69.7 billion, that's the highest number 17:38:44

13 you've calculated in all your different calculations? 17:38:54


15 answered. 17:38:59


17 BY MS. NEUMAN: 17:39:00

18 Q And that assumes exposures through 2019, 17:39:02

19 correct? 17:39:07

20 A Yes. 17:39:07

21 Q And what is the last -- the year that the 17:39:10

22 last excess death occurs? 17:39:15




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1 A Without looking at some output, towards 2079, 17:39:17

2 2080, somewhere in there. 17:39:25

3 Q And this 69 billion is for -- let me withdraw 17:39:27

4 that. 17:39:44

5 You get to the 69 billion by having your 17:39:44

6 Ecuador program estimate the hypothetical number of 17:39:48

7 excess cancers based on a risk assumption and then 17:39:54

8 multiplying that output by $7 million; is that right? 17:39:57


10 Q Have you been made aware of any Ecuadorian 17:40:01

11 laws in connection with your work? 17:40:17

12 A No, I have not. 17:40:18

13 Q Have you been provided any Ecuadorian 17:40:20

14 regulations in connection with your work? 17:40:23

15 A No, I have not. 17:40:24

16 Q Do you have any personal relationship to any 17:40:25

17 plaintiff? 17:40:28

18 A No, I do not. 17:40:28

19 Q Do you know who the plaintiffs are? Have you 17:40:30

20 ever met them? 17:40:33

21 A No, I don't. 17:40:33

22 Q Do you have any personal relationship with 17:40:35




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1 any plaintiffs' counsel? 17:40:39

2 A No, I do not. 17:40:41

3 Q Have you ever been made aware of the 17:40:43

4 Ecuadorian standards for being an expert witness? 17:40:54





9 Q Do you know what it means when you sign your 17:41:03

10 report under Ecuadorian law, what you're representing? 17:41:05



13 THE WITNESS: No, I do not. 17:41:11

14 BY MS. NEUMAN: 17:41:55

15 Q Are you familiar with IARC, Dr. Rourke? 17:41:55

16 A International something research and cancer. 17:42:00

17 Q Have you looked at the IARC classification 17:42:04

18 for crude oil? 17:42:10

19 A Crude oil. No, I haven't. 17:42:10

20 Q Do you know if it's classified as not being 17:42:13

21 carcinogenic to humans? 17:42:17

22 MR. WESTENBERGER: Objection. If he 17:42:18




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1 testified he hasn't looked at it, I don't think he's 17:42:21

2 going to know the answer to that question. 17:42:23

3 THE WITNESS: Counsel's statement is correct. 17:42:25

4 No, I didn't know that. 17:42:27


6 Q Does that impact your opinions in any way? 17:42:28

7 A Since the whole thrust of this was to try to 17:42:29

8 avoid issues of causation, in some sense it's 17:42:34

9 irrelevant. 17:42:39

10 MS. NEUMAN: Okay. We are at the seven-hour 17:42:41

11 mark. I'm not going to close the deposition because 17:42:44

12 we still haven't seen the program and we just got the 17:42:49

13 subcontract agreement by e-mail, so of course I 17:42:52

14 haven't had time to review it. But I know 17:42:55

15 Mr. Westenberger has a train, and I have a plane and 17:42:59

16 somebody probably has an automobile. 17:43:03

17 So I am ready to go off the record if 17:43:05

18 plaintiffs' counsel is ready to go off the record. 17:43:08

19 MR. WESTENBERGER: I'm ready to go off the 17:43:11

20 record. Obviously, we consider the deposition to be 17:43:13

21 closed. I was told -- I don't know if it's 17:43:13

22 accurate -- that the subcontract was previously 17:43:14




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1 produced. 17:43:15

2 MS. NEUMAN: We'll look into it. 17:43:16

3 Thank you for your time, Dr. Rourke. 17:43:19

4 THE WITNESS: You're welcome. 17:43:21


6 December 20th, 2010. Going off the record. 17:43:23

7 Completing today's videotape session. 17:43:26

8 (Whereupon, at 5:44 p.m. the deposition of

9 DANIEL LEE ROURKE was adjourned.)

10

11

12

13

14

15

16

17

18

19

20

21

22




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1 CERTIFICATE OF NOTARY PUBLIC

2 I, Denise M. Brunet, the officer before whom

3 the foregoing deposition was taken, do hereby certify

4 that the witness whose testimony appears in the

5 foregoing deposition was duly sworn by me; that the

6 testimony of said witness was taken by me

7 stenographically and thereafter reduced to print by

8 means of computer-assisted transcription by me to the

9 best of my ability; that I am neither counsel for,

10 related to, nor employed by any of the parties to this

11 litigation and have no interest, financial or

12 otherwise, in the outcome of this matter.

13

14

15

16

17 ______________________________

18 Denise M. Brunet

19 Notary Public in and for the

20 District of Columbia

21 My commission expires:

22 November 30, 2012




© 2010

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1 ACKNOWLEDGEMENT OF DEPONENT

2

3

4 I, DANIEL LEE ROURKE, do hereby acknowledge I

5 have read and examined the foregoing pages of

6 testimony, and the same is a true, correct and

7 complete transcription of the testimony given by me,

8 and any changes and/or corrections, if any, appear in

9 the attached errata sheet signed by me.

10

11

12

13

14

15

16

17

18

19

20

21 ________________ ____________________________

22 Date DANIEL LEE ROURKE




© 2010

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1 Eric Westenberger, Esquire

2 Patton Boggs, LLP

3 The Legal Center, One Riverfront Plaza

4 Newark, New Jersey 07102

5

6 IN RE: Chevron Corporation vs. Daniel Lee Rourke

7

8 Dear Mr. Westenberger:

9 Enclosed please find your copy of the

10 deposition of DANIEL LEE ROURKE, along with the

11 original signature page. As agreed, you will be

12 responsible for contacting the witness regarding

13 signature.

14 Within 30 days of receipt, please forward

15 errata sheet and original signed signature page to

16 counsel for Petitioner, Andrea E. Neuman, Esquire.

17 If you have any questions, please do not

18 hesitate to call. Thank you.

19 Yours,

20

21 Denise M. Brunet, RPR, Reporter/Notary

22 cc: Andrea E. Neuman, Esquire




© 2010

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1 Capital Reporting Company

2 1821 Jefferson Place, Northwest

3 3rd Floor

4 Washington, D.C. 20036

5 (202) 857-3376

6 E R R A T A S H E E T

7 Case Name: Chevron Corporation vs. Daniel Lee Rourke

8 Witness Name: Daniel Lee Rourke

9 Deposition Date: December 20, 2010

10 Page No. Line No. Change/Reason for Change

11

12

13

14

15

16

17

18

19

20

21 _____________________ ____________

22 Signature Date



Page 1


© 2010

$$325 228:10

$5 29:20

$69 290:18

$7 119:12 125:3,8126:5 178:2283:6 293:8

0000001 4:7

000009 4:7

000579 4:16

007999 4:17

008025 4:19219:16

01 69:5 72:2,573:12 74:15,1676:14 213:14214:13

010088 5:2 234:14

010089 5:2

010092 5:3 238:1

010096 4:9

010297 5:4

010298 5:4

010299 5:5

010300 5:5

010407 4:10

010412 4:10

0190.7 97:8

043127 4:11

05 183:16

07102 3:16 299:4

08 263:3

09 72:2 74:16

11 4:15 6:2 46:1

50:10 79:1480:13 89:6,1790:6,13 92:1793:2,9 139:3,4157:1 174:12266:22 276:1

1,435,000,000130:20

1.403 131:2

1.435 130:12

1.46 91:16 92:597:8

1.65 86:10

1.97 97:5

1:21 135:2,3

10 77:2 196:3,4213:16274:5,7,11,20

10:53 79:12

100 102:13,14

100,000 90:18

1023 151:19162:16

1025 138:7 144:8155:22

1026 262:18 263:2

1028 177:11

10409 129:15

1050 1:18 3:6 6:10

10th 159:8 246:11288:3,4289:11,12,15,17290:4,8,10,14

10-year 77:20

11 213:16

11:05 79:16

11-year 77:20

12:29 134:2,4

12:30 133:19

1207 167:7

121,012 70:2

1225 152:11

127 4:9

127,000 71:16

127,071 69:2071:11

128 4:10

12th 15:18246:11,19247:2,18 259:22

13 83:17 93:4195:6 221:20222:2

130 139:2

1300 4:6 15:1716:1,4,16 45:2177:3

1301 4:7 50:4,1384:4 135:17138:8 151:12167:8

1302 4:8 88:4,1190:2,5

1303 4:9 127:2,4,7128:17 131:1138:21 139:1

1304 4:10128:10,13,19130:12

1305 4:11 131:5,7132:22133:4,14,17

1306 4:12 146:20147:3,7 158:16184:10

1307 4:14182:19,21 184:3187:12

1308 4:15 190:21191:4

1309 4:16 208:5,11218:10

131 4:11

1310 4:17 212:22213:3

1311 4:18216:3,6,13 218:9

1312 4:19219:15,17,20221:8

1313 4:20221:9,11,14

1314 5:2 234:13,17235:6

1315 5:3 238:1,3,6

1316 5:4242:5,7,10

1317 5:5245:16,19,22

1318 5:6 259:9,13

1319 5:7278:13,16,20

133,432 69:14,22

13342 73:1

13432 70:5

135,217 72:21



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13th 260:1

147 4:13

15 157:14 196:19

15th 246:4259:9,22

16 4:6

17 111:19 116:21119:13,16

175 276:19,21

17th 245:17

18 23:6,8 119:15

180 72:8

181 4:14

1821 6:17 300:2

185 275:14

189,369 73:2

189,489 73:3

19.7 90:18

190 4:15

190.7 91:4 93:1894:4

1900s 216:17

1942 8:9

195,430 70:3

1951 32:17

1960-whatever201:18

1963 15:13

1964 208:7,20

1965 17:4

1967 62:3,1963:7,18 67:19103:22 119:1203:1,19 207:20

209:9 214:8253:18

1970 217:5

1971 17:7 18:1

1972 63:11 64:18202:18 209:6,9218:15 219:2

1973 18:9 19:1

1974 202:10

1976 19:2

1979 21:2 22:2112:6

1981 21:18,22

1982 21:18 22:3211:15

1984 111:20112:11,21

1985 153:2

1987 118:13,15119:1

1988 22:4,6

1990 23:6,1154:12,18 55:364:11 65:3,2066:6,11,19,2267:7 68:1676:16,19,2077:1,7,15 81:1193:16 152:1,5,8158:8 159:1164:22183:11,19184:6,11,20191:17 194:9208:8,20 209:1213:17 240:16268:21 269:4,11

1990-2002 146:22

1990-2005 4:13

1991 45:15 291:13

1992 151:14 152:2202:20

1993 23:11

1995 9:5 248:18249:4

1996 158:9 164:22184:20

1997 159:12 160:4165:9

1998 15:10 23:1681:14 153:2

1999 70:2

22 32:17 70:7,15,17

74:3 79:18 80:1884:5 91:2 130:21134:3 136:13,14167:12 168:6247:6

2,907,000,000130:19,22

2.17 89:10

2.64 91:9,16 92:18110:12,15

2:26 177:3

2:34 177:6

20 1:13 49:2151:2,952:8,12,14,1953:2,15 118:9,12201:17 203:5,10204:8,15,18205:5,10206:2,8,16209:16 211:18

271:16 272:6274:6,7,12 300:9

200,000 71:21

2000 70:2,19,2272:4

2000s 216:18249:11

2001 65:20 66:1167:7,14,1968:10,16 69:1474:14,15,21 75:876:1 77:6,1590:18 91:6 93:18107:17,20150:18 214:7

2002 50:9 72:1,2073:2

2003 24:5,6,925:14

20036 3:7 300:4

2004 71:20

2005 159:1 181:2183:11,19184:11

2006 115:3 117:15118:2

2007 115:3

2008 115:3

2009 12:9 13:1154:11,19 55:1,1580:15 102:3107:20114:21,22116:13 147:2181:4,6 183:8,9184:6 191:16192:18 212:16213:11 248:6251:8



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2010 1:13 6:1215:18 66:1279:13,17 135:4177:7 183:6234:22 235:4242:6,15 245:17259:9 260:10296:6 300:9

2012 297:22

2019 292:18

202 3:8 300:5

2020 105:4,12,13108:16 109:7

2034 192:3

2037 192:1

205 69:21

205,000 71:15

205,214 70:1971:1,12

2059 112:20,21

207 4:16

2077 191:11

2079 293:1

2080 102:8,9103:22 179:7293:2

20th 6:11 79:13,17135:4 177:7234:22 235:4242:14 296:6

20-year 49:1753:19 201:8,11209:18 210:11

212 4:17

213 2:15

215 4:18 72:8

215,488 70:4

218 4:19

220 4:20

225 69:21

229-7000 2:15

233 5:2

237 5:3

241 5:4

244 5:5

249 93:16

258 5:6

26 120:1 183:10

26th 8:9

277 5:7

27th 242:6,14,20

288 275:15

28th 246:3

33 50:17 54:3 55:20

67:9,11 74:4,575:2 80:2284:6,7 135:5136:15 150:5,10154:1 160:18162:2 163:7164:4 177:4213:9 216:13278:19

3:30 223:21

30 297:22 299:14

3161 2:6

33 129:17130:3,5,8

333 2:13

35 192:3

383 158:19,22

385 150:6

3rd 300:3

44 63:4 70:8,16

80:1,2 177:8235:1

4:06 234:21

4:15 235:3

41 183:10

435 131:2

451-3800 2:8

484 184:12186:6,14 187:18188:9,14,17189:8,16,22190:5

49-year-old 110:6

55 67:18 75:2

77:5,14 79:191:5 93:1494:7,1295:3,8,11 96:297:5 184:9193:17 213:21214:13 235:4

5:20 285:10

5:21 285:12

5:31 285:15

5:44 296:5,8

50 4:7 86:6,999:13 110:1115:19

500 275:16

503 92:3

50-year-olds116:12

54 86:6,9

579 208:6

66 50:10 84:19

86:1,4 97:6,10103:8 120:3

60 90:17 91:4,1392:2 93:1597:4,8

64 90:17 91:4,1392:2 93:1597:4,8

65 91:13 92:7110:11

65-year-old110:19

65-year-olds110:14

67 69:5 72:5 73:1274:15 76:1480:15 130:4,7,8191:11 201:18213:10 214:13271:7

687 183:18 184:7186:8187:2,12,16188:3,10

69 91:13 92:7110:11 293:3,5

69.7 292:12

7



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7 4:3 50:12 120:2236:5 283:9290:17 292:9

7.2 236:8

7.2.2 236:9

700 193:1

72 201:20,21202:13 209:13219:3

73 219:3

74 103:12

75 87:11,12103:10,11,14112:17

75-year112:14,16,22113:2,11

88 67:12

8:10cv02989-AW6:8

8:10-cv-02989-AW 1:9

80-some-odd193:2

848-5600 3:17

857-3376 300:5

88 4:8 118:13

89 22:5 234:14

99 50:12 208:22

247:7

9:00 223:21

9:11 1:21 6:12

9:28 20:3

9:32 20:6

90 150:18 175:20183:16 192:21193:2 196:3,4213:14 214:7

90071 2:14

92 152:3

92612 2:7

93 23:15 24:9

949 2:8

95 9:18

955-8500 3:8

973 3:17

98 25:13

99 102:16,17 103:5

99-year-olds 103:2

9th 288:4,9,17289:8,13,18290:8,10,14

AA.C 22:13

a.m 1:21 6:1220:3,6 79:12,16223:21

ability 8:18 186:9297:9

able 74:7 87:21184:19 189:3217:16 236:22239:15 283:16

absence 44:4141:15 213:13

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accept 257:21

accepted 43:1644:11 140:12255:11,22

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accounting 24:1625:6

accumulate 197:8

accurate 16:1944:19 48:15186:15 192:4194:3 197:22220:11 246:8253:19,21 279:5280:13 295:22

accurately 186:9190:10242:13,17 246:2283:10

acknowledge153:8 298:4

acknowledged167:10

ACKNOWLEDGEMENT 298:1

acknowledges155:13

acronym 21:16

acronyms 23:18

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actiological 174:1

action 6:7 268:3,7

activities 254:10

activity 52:9

actual 10:15 59:5276:21

actually 21:2123:7,19 25:2,651:18 60:7 61:1266:11 67:3 68:2071:9,22 74:683:7,8 84:1385:4 87:12 90:20100:10 102:20109:2,12 110:21112:6 120:7124:11 132:8145:6,13 150:3171:16 178:20179:17 181:4187:3 191:12194:4,11 202:10207:6 208:21211:2 222:11226:22 233:5234:19 238:15244:14 251:4261:10 267:18268:3,7 276:3278:11289:10,22

actuarial 87:889:3

acute 35:3

ad 12:14

Adams 243:18

add 23:19 97:17



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115:11 116:3,4,5247:18 248:1,4251:8 260:2,3

added 116:13185:22 203:1219:3

addendum 5:6246:15,18247:6,14 251:4259:9,16,19290:17 292:9

adding 234:7251:18

addition 74:17

additional 65:4

additions 291:17

address 154:19168:3 270:13

addresses 209:19

adequacy 22:15

adjacent 110:7

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adjust 63:19 278:2

adjusted 28:13,15

Adlai 225:11288:12 289:20

admit 271:4

adopting 156:3

advice 225:4,21233:17

advise 224:11

advising 224:18225:1

affecting176:10,14

affiliation 166:12

affirm 7:11

afraid 85:12

afternoon135:1,13,14223:16 225:9

against 11:1513:19 35:11105:4,8 159:16182:1 213:22

age 31:9 80:1883:7 85:7 86:587:5 90:1,391:12 92:1093:1597:4,7,11,15,16,19,2098:5,6,13,1799:5,11,13101:20,21 103:8105:5,9,17106:11,12,16,22107:2,6,16,20108:1,10,12,13,15,18 110:1,9,11115:19 116:7117:22 118:5143:16 145:10146:3 151:22159:2,3,4 187:7196:11 205:17

age-by-age 85:11

age-corrected 85:6143:14

ageism 115:4

agent 198:16

agents 174:1

ages 90:17 115:7117:10,11

age-specific 80:2281:1,8 82:1,9

84:16,17,2085:18 86:12 87:3143:12,13,19144:3 193:18

aggregate 276:15

aggregated138:12,22 139:7

aggressive248:11,14

ago 124:4 152:12201:4

agreed 299:11

agreement 52:16227:17,20284:20 285:2295:13

Agrio 118:11,15

Ah 23:21

a-hundred-and-X68:1

Air 11:8,12

algebra 196:12207:7

alive 89:22

alleged 123:7

allegedly 264:15

Allen 243:19,20244:17,19

allocation 24:19

allow 30:9

already 26:1096:18 98:14105:17 106:12108:3 180:19190:6 225:19241:9 273:5

altering 111:5

am 6:14 8:19 24:233:5,9 38:1747:9,10 61:1669:19 72:1477:14 80:4,15110:12 122:10136:12 138:5139:5 143:5151:18 166:19198:18 199:4204:4 257:16274:21 295:17297:9

Amatex 26:12

Amazon 4:13 50:6146:21 179:17193:22 194:18195:1

Amazonia 131:19

American 34:2

AML 35:3,9 266:4

among 21:2265:21 103:15109:22 217:14

amount 60:12,1465:10 130:18192:9 211:4290:18

amounts197:12,14236:12

analyses 19:1127:20 141:18

analysis 15:8,1118:16 19:821:15,17 23:328:14 62:9 63:1394:1 125:14137:6 138:15139:21 141:21



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142:4 143:1,3154:16 156:21170:5 172:5194:6 230:7249:13 250:15252:7 261:8,9262:11 264:14277:6 280:22289:7 290:4

analyze 64:16136:3 196:14

analyzed 41:1

analyzing 128:16

and/or 298:8

Anderson 12:2

Andrea 2:4 6:21299:16,22

[email protected] 2:9

Angeles 2:14

angels 50:11

animus 32:20 33:1

Anna-Karen 50:7

annex 128:10,16129:3,10130:7,18 131:6208:9

annual 214:6

answer 29:4,1131:13 33:1157:19 58:2061:15 64:1 71:1974:13 79:6 82:594:11 98:18100:7 103:7109:3 127:19130:22 151:6162:12 168:20186:15 188:15

195:4 210:16212:19 215:4218:4224:10,12,18225:2,5,22226:15 249:7256:14 268:12273:4 295:2

answered 51:21123:14 125:21161:4 168:20,21169:4 175:5178:13 186:12206:12 207:15210:14,16212:12 247:16249:6 256:3260:16 264:9272:22 273:2,5274:17 292:15

answers 74:10182:13,14,16

antecedent 114:16117:12

anticipate 237:10

anticipated 27:3

anybody 61:12129:5 185:10262:4 268:3,7

anyone 129:8215:7 226:11256:15 257:6261:9 283:22

anything 66:972:1,4 104:15,21105:14 112:2113:2 137:2140:7 186:1194:20 197:10214:11 226:8230:21 236:3

239:20 255:2,8257:1 259:6282:3 289:12

anywhere 58:2244:3

apologize149:11,20

apparently 233:22

appear 16:1655:20 66:9,1568:19 69:4,584:1 120:1 152:7165:9 198:7274:10 298:8

Appearances 2:22

appeared 37:1641:2,7 144:4156:7 185:4

appears 156:8219:1 267:20297:4

appendix208:16,17

apples 152:10

application 17:12

applied 204:17214:9 291:4

applies 205:17

apply 94:7 118:14203:12,13205:22 207:11281:9

applying 206:9271:14

appreciate 188:20

approach 213:18

appropriate121:21

126:6,11,20142:3 143:20241:18,22 257:5

approximately6:12 59:2 103:22192:21 223:21290:21

April 8:9

Arbitron 22:1023:2

area 47:16 49:1355:22 56:6,7,1957:3,7,13,1558:5,6,762:6,14,15 64:1468:569:1,7,13,16,1771:13 73:17 75:280:14,19,2081:3,7 82:2295:20 96:6 97:1798:13,18 99:17101:14105:15,20116:15 146:4150:19 151:1,2153:17 154:4,21170:18,22171:5,17 172:21173:21 177:14179:18 180:7191:13 204:8213:2 215:13217:13 220:3229:1,15 232:6243:16 248:15249:10 253:1,5261:11264:7,20,21265:2 268:16269:11 282:16

areas 15:21 39:3



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46:4,14 49:16,2052:756:11,14,19,2172:18 76:8 94:8103:22 162:21164:6 167:13,22168:6 169:18175:18 182:1201:16 205:22210:1 211:17214:16 229:19235:13 248:2,8250:18 259:11264:21 265:5271:15

arena 122:3

aren't 103:12180:8 287:11

argued 154:1

argument 176:8205:2

Argumentative212:12

arguments 258:9

arise 36:10

arising 9:9 25:432:10 119:17195:19

arithmetic 128:2188:7 192:21277:10 278:10

arose 55:1,3 180:7

ARPC 15:4,723:17,21 24:1025:10,13

arrangement281:20

arrivals 97:17,1999:5,8 108:7

114:20 248:7

arrive 114:22251:21

arrived 205:18206:21 289:2

arriving 98:5,6

Arthur 120:11,12121:6 231:14232:10 243:4

Arthur's 232:11

article 47:3 48:550:2,5,1853:7,12,17 54:164:19,22 85:17117:8 144:17148:19 151:19153:5155:8,10,13156:6,8,11,19157:12 158:1,20165:22 184:19

articles 37:6,1538:4 39:2,6 41:242:8 143:14165:16

articulate 148:21

artificially 146:16

asbestos 9:1025:4,15 26:2027:2,8,13 28:2233:9 43:1,1544:3,5 111:14,20112:11,18 122:3265:17 266:12281:7,12 284:17286:14,15,17287:1,2,10,11,19291:9

asbestosis 10:5

27:17 266:20

asbestos-related14:5 32:18111:10 112:19113:1 287:17

asbestotics 10:14265:21

ascertain 233:5

ascertainment167:13,21168:18

ascribing 71:18

asserting 224:7

assess 256:16

assessment 45:260:18 164:14

assign 239:15

assignment 21:20229:8

assist 232:3

assistant 18:1219:1

associated 15:2046:3,12,1447:8,20 59:1794:13 95:19 96:5172:13 175:2228:22 235:14239:21 250:18252:8,11 255:6259:11 286:17

association 6:1635:8 39:4 46:1847:13 49:6,1451:7 52:17 53:12173:13,17176:11,15,16

associations 34:2

assorted 95:7

assume 67:18,2168:2 92:9 97:1998:19 103:4114:13 115:17118:14 194:22199:6 204:5211:10,13,22254:9 255:5

assumed 58:862:18 68:3 70:1397:20 99:15107:5 212:13

assumes 108:18124:14 129:12212:9 250:6292:18

assuming 67:2298:4,10 100:4118:22 146:18172:7 185:9186:6 187:1,2192:18198:9,15,22200:3 202:9,17207:3,4 211:20213:19 214:8272:14 274:3

assumption 37:1457:10 62:2181:21 98:1,12,21101:1,4 103:21105:15 107:9110:4 118:18123:4,12 124:1126:7 171:21191:20197:18,20199:19200:15,22 201:4207:17 213:22214:12



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250:2,9,16254:21255:3,9,13 260:7261:5,7 267:15268:18274:1,14,19275:1 293:7

assumption-based197:3

assumptions61:7,21 182:10187:7 189:13199:11 215:10251:6 255:16,18256:1,5,7257:18,21,22259:1,2 270:20

attached 5:22128:21 234:15238:2 298:9

attaching 50:10

attachment221:20

attempt 128:2169:21 195:16240:22

attempted 10:1847:7 277:18

attempting 36:9

attempts 200:9

attention 262:16

attorneys 12:5223:14,17236:19 240:22241:4 247:14281:19

attribute 169:21

audit 25:7,8

August 226:20

242:6,14,20246:3

authenticity 217:1

author 145:15

authored 131:22

authors 50:22138:9,22 139:6140:13,22144:8,17 167:10

automobile 295:16

available 30:766:6 82:4,10101:5 142:8,11162:9 193:5196:15 197:2207:16214:1,2,19220:19 259:3279:16

Avenue 1:18 2:133:6 6:10

average 28:13,1629:11 69:1884:15 125:18

avoid 295:8

award 119:17122:6,12123:10,20

awarded 121:7

awards 124:9237:7,10

aware 38:14 40:6121:16 122:5131:12 132:3,11137:21 143:5166:2,14 175:19214:5 215:9243:13 248:17249:3,9 293:10

294:3

away 253:9 270:19

axis 191:1

BBabcock 26:6

bachelor's 17:3

background 17:144:478:3,11,15,2186:22 87:1490:10 95:12,2196:3 103:14,16117:9

backwards 67:1668:16 69:19214:8

Bad 202:15

bankruptcy 9:412:9,11,19 13:226:2 28:11,13

based 26:19 29:1830:3 35:14 37:1639:8 58:2261:6,20 64:2272:2,4 84:1490:21 94:1097:10 107:9,17123:4,6,10,11,21,22 126:7,20130:9 150:13152:4,7 154:5159:7 164:4171:20 172:1179:21 184:11185:12 189:14191:19 194:18196:6 199:12,20200:7,14 205:6217:16 250:19257:2,16

261:4,5,10 265:9266:18 268:18279:15 293:7

baseline 86:17,2187:20 93:1,6,8

basically 112:14136:12 165:2187:6 244:9260:10,17291:16

basin 50:6

basis 56:22 57:981:17,18 84:2085:11,18,19,2097:22 98:20 99:3100:22 111:4118:17,19,22119:5,10 148:2,4156:14 188:19193:12 195:12197:13 198:1206:5 207:19210:12 251:22253:22 254:6271:7,14,22272:11 273:19274:22 286:12

Bates 50:9,17 80:384:6,7 127:17

bear 92:20 115:3

bearing 50:9

became 18:6 22:1023:12 76:18185:6

become 73:7 77:1100:6 253:16

begin 7:16 17:15169:13 198:6209:8 221:22

beginning 1:21



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19:22 25:3 63:18107:16 159:4186:3 253:18260:1

begins 49:14 197:7206:3 260:9

begun 211:12,15248:9

behalf 1:22 2:33:3,11 6:22 7:412:13 13:12

behavior 17:13

behold 244:11

believe 9:4 16:620:8 29:2132:4,7 37:541:12 45:1552:14 54:1257:18 63:2 66:2268:21 72:5,773:13 76:1 80:381:13 85:1690:15 102:20107:19 109:4116:18 128:1129:20 130:9158:7 190:10,13195:16 198:5,14210:6 213:21214:2 222:16227:21 228:5,10230:12231:14,22 232:2234:3,10 237:3243:16 258:22259:1 260:1265:22 267:17271:20 272:7277:1,13 280:5284:20,22289:16 290:20

believed 44:2

belong 34:1

Beltman262:19,22263:21

beneficiary 132:4

benzene 34:2235:3,9,12,2036:5,18 37:3,2138:1 175:16266:3

Bernick's 12:6

best 8:15 29:1371:17 207:16257:17 297:9

Beta 17:5

better 192:16271:1

beyond 263:5264:1

bias 138:15139:8,11,12,22140:5,7,15,19,20141:1,4,16 142:4143:3 144:10145:17 152:14167:12,21168:4,17

biased 139:15,16

biassed 140:14

bigger 71:10 72:1773:17 266:22

biggest 32:16

billion 178:1247:6,7 290:18292:12 293:3,5

billion-dollar177:20

billions 9:15,17256:16 257:6

bills 125:11

biostatistician233:5

birth 8:8

births 99:16

bit 42:21 64:2,2188:1 121:20122:3 152:10182:2 219:12270:20

blood 111:11198:6 200:17201:1

bodily 11:5

body 38:14,18

Boggs 3:13 7:445:13 222:13,14223:14,17236:19 299:2

book 273:13

books 42:8

bordering 169:11

born 8:10

borrow 276:10

bottom 84:5147:19 191:2263:2,9

bought 23:16 24:2

bound 156:21244:5

boundary 243:22244:1,8

Boy 276:14

break 79:9 133:20

176:22

brief 208:19

bringing 258:10

brings 40:21

broad 137:9

broken 10:9 108:1

brought 35:12

Brown 13:3

Brunet 1:19 6:16297:2,18 299:21

building 63:14

built 209:1 254:21279:18

built-in 89:12

bullet 240:15241:11

Bureau 65:18,19

business 243:7

busy 233:16

CCabrera 128:3,10

129:3 130:18133:11 208:9215:3,7 221:1230:9,14,15,21231:6 254:6262:3,14 264:11

Cabrera's 128:2247:3

calculate 58:2270:8 74:7,978:7,10,14 84:1986:5,6 97:3102:4 164:11191:22

calculated 10:3



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© 2010

67:21 101:16,18170:4 214:6290:19 292:13

calculates 61:5206:6 283:4

calculating 10:460:5 118:1122:14 269:18

calculation 55:1669:18 75:1 78:1289:4 90:13 96:1597:6 99:7,19111:2 113:12122:15 126:21178:22 248:6278:11 282:10

calculations 4:886:13 87:4 97:1899:10,12100:3,11,14,16105:16 113:14114:3 118:7,18122:22 129:9147:22 182:10189:14 200:12215:21 220:9259:2 279:4280:14,15292:13

calculator 184:7188:20,21276:11

calendar 102:6118:10

calibration 179:12

California 2:7,14

cancer 4:12 15:2040:4,22 41:1942:6,9,12,1644:20 45:246:3,13 47:16

48:6,18 49:350:5 53:13 57:458:1,13 59:1861:12,18,2277:11,1878:3,5,11,1779:1 82:2283:6,8 86:1987:14,22 88:2089:1890:9,10,11,1791:9,19 92:2,1493:6,10,15,2194:1,12,1795:10,20 96:2,897:3,9 99:6102:18103:4,15,18110:8,12,16113:5,11,13117:1,7,10,17120:7,22 121:8124:10 135:21137:13 146:21147:17 151:3152:9153:2,14,15160:13,16162:16,19,21166:15 170:22171:3 173:20,21174:9,15,22175:1,7,20177:14 179:16181:1,5 184:21186:18 189:4191:2,11194:4,17,22195:19 198:10199:1,7 200:5,22215:21 217:18229:18 230:7235:12239:2,8,15

240:5,20241:12,13 247:4250:7 254:16265:19 266:2,20267:1 268:4,8275:8,15,18276:7,9277:3,14,22278:9 286:21,22287:3,6,17290:19 294:16

cancer-causing198:16

cancers 10:1340:19 48:1361:2,6 77:16,2278:5,12,15,2081:15 83:486:17,1887:7,10,13,2092:20 93:1 94:1995:1,696:7,12,14,2199:18 101:15103:8,12111:10,11,14113:1,9115:20,22120:20 145:10146:19 147:22148:15 155:5170:13,21 178:6181:10,15,16182:1,6 183:13184:12,16185:4,5,19186:10,17187:5,13,14,15,19,22 188:10191:18 195:3,20198:6 200:17,18217:14228:20,22

229:4,9239:12,21240:6,10,11,12,21 241:5,13,14248:10 251:5,15254:15 255:5,7266:13 269:19276:2,3 277:5282:12 283:1,5287:11,20 291:3293:7

canton 48:17 49:373:6 76:9,1395:13 97:13110:20 147:18159:2 183:20

cantons 48:5 57:1558:2,11,12,1662:19 63:15,1865:11 67:1571:5,8,9 72:173:14,17,2276:17 82:2,10100:19 110:15118:20150:12,13156:16160:19,20,21180:12181:2,6,11,17182:3 183:6194:10,12 200:4202:10,19207:20 214:12229:16 247:19248:1 253:15254:2 264:21268:17 269:21

cap 75:3

Capital 1:206:14,17 300:1

capture 104:4



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© 2010

carcinogen 175:17199:12,20

carcinogenic294:21

card 243:7

care 239:14

career 22:19 25:3

carefully 155:11

Carlos 231:14

carrying 70:5

case 1:8 9:1910:4,2111:6,16,1912:1,4,2013:2,17 15:226:2130:12,13,14,1631:2,16 37:238:20 40:16 44:645:1,6,7,9 52:463:22 87:14120:18 124:4,16125:13 154:2,13161:18 170:20179:13 195:4,18228:9,12,16245:11 258:9261:20,22 262:2265:9,12 267:14268:11 279:15280:11,14,16,17,19,22 281:2,3282:1 285:22292:4 300:7

cases 10:5 44:4120:5,6 124:8162:17,20241:12 276:9281:7 284:18

cash 12:16

catalogs 208:22

categories 10:10

causation 42:6,943:8,11 44:8,1147:10,11,12 48:749:10 61:14,16137:2 138:2139:12173:6,10,16,17175:14 230:3,6239:16,20250:2,9,16252:16 254:12277:19 287:8295:8

cause 44:2 48:1793:11,12 142:19155:8 158:20159:6 169:21170:21 172:22175:3,12 185:22193:5 204:5,6240:7,12 276:21282:12 287:8

caused 36:5 47:2195:1,4124:11,12,19172:9 175:21,22229:4268:3,7,11,13277:14,22290:11

causes 42:16,1944:6,14 47:1649:3 86:14 87:20169:14 185:7193:18 198:9200:22

causing 174:22

cautiously 138:12

caveats 88:22

cc 299:22

CD 230:19

cellular 42:14

Celotex 26:11

census 65:17,1966:1,8,12,1867:6 76:19,20,22107:17 152:4,5,8162:17 213:14217:19

center 3:14 19:420:10 299:3

certain 61:6 75:16100:4,17 123:4211:3,4 257:17282:14,15

certainly 33:936:21 51:16127:11 145:4148:11 151:15185:22 189:12192:15 217:8219:11 227:21275:13 286:22

certificate 128:20297:1

certified 6:1416:12

certify 297:3

cervical 175:20277:3,5,14,22

cervix 174:16175:7

cetera 217:2

chance 29:5133:2,4 148:8248:10

change 62:1672:16 74:18219:9 235:15281:8 289:19292:5 300:10

Change/Reason300:10

changed 184:21185:1,2 254:22273:12 290:4

changes 236:2289:7 290:11291:14 298:8

changing 74:17185:18

characteristics138:11

Charleston 8:11

chart 4:15 54:490:21 200:19

charts 84:2,3,8143:12

cheap 276:14

check 105:4,8128:2 159:16213:22

chemical 34:11

chemicals 175:2

Chevron 1:6 6:227:1,2,22 33:241:4,6 135:11268:2 299:6300:7

Chevron's 40:2141:5

Chicago 12:6

chip 32:12



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chitchatted 226:4

choice 55:17 143:2

choose 55:18143:4

chose 54:19 269:4

Chris 120:11231:13 232:9243:3

cigarette 287:4

cigarettes 287:5

citation 37:5 112:3

citations 37:8

cite 112:2

cited 37:10,17107:17

City 18:3

civil 241:17

CL 183:5,10,20,21

claim 10:17 26:627:17,21 28:2129:14 121:7122:6 164:21290:19

claimants 12:14

claimed 215:11

claims 9:9 10:1813:18 14:525:4,15 26:1927:1,3,7,9,1328:12,22 32:1036:10 40:22112:19 121:18266:18

classification158:21 159:8160:4 185:3294:17

classifications150:13 159:6160:13 184:21

classified 294:20

cleaning36:1,2,3,5,1238:2 69:15

clear 72:12 155:21158:12 167:9177:13 178:10254:12 260:9277:18

clearly 59:10193:11 263:19273:7

client 25:19 27:1128:3 29:10 281:9287:16

clients 22:15 24:1825:20 35:11

clinical 17:17

close 112:22186:14 295:11

closed 295:21

Closer 126:16

co-authors 147:13156:15

code 185:4 280:1,7281:12 285:5

coded 159:6

codes 186:3

cognitive 17:9

College 34:2

Colonos 216:1

color 244:9

colored 244:6

Coltec 13:19 14:3

Columbia 1:21297:20

column 50:1968:1469:4,6,13,2070:4,7,8,15,16,17 74:3,4,13 75:283:19 136:14,15138:9 144:7174:14183:5,10,19,20209:5

combinations 40:3

comes 93:2

comfortable256:15

coming 122:18174:2 200:20

comment 177:18238:18

commentary50:11 167:2,5177:11

commented214:16 238:8

commenting 77:18131:6

comments234:6,7,9 290:10

Commercial 63:11

commercially 63:7279:15

commission297:21

committee 12:14

communications224:3,20

225:3,16 226:14

community202:2,6 203:8204:18

compact 265:2

companies 25:16

company 1:206:14,17 32:3,6,854:15 267:13269:21 300:1

comparable125:15

compare 82:16108:21 160:6181:9 182:4

compared 109:1144:12 150:12151:1 152:16153:9 154:4160:1 171:12194:19 197:10239:16 256:20257:1 274:5,11

comparing 77:14168:6

comparison152:10 181:21182:5

comparisons182:15

compelling 124:16157:10 174:20176:8

compensable 28:9121:11,14

compensate121:17

compensation



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228:8

competitor 22:12

competitors280:18

complete 246:20260:11290:5,6,15291:19 298:7

completed 247:18

completely 185:12197:20

completing 177:4235:1 246:11296:7

complex 218:18240:18

complicated 75:2278:9 87:17106:18 207:8284:16

component 22:934:18 36:1 92:21

components34:9,11 125:8,15

compounds239:17

comprise 34:11

computation 70:5219:12

computational291:16

computations58:19 80:11100:2 289:9

compute 62:4,685:6 86:1 87:7241:1,6

computed 81:5

98:7

computer 20:9,1021:15,22 22:8127:9 222:9232:14279:13,14,20,21,22 280:1,7,21284:16 291:2

computer-assisted297:8

computer-based18:7

computers 19:10

computing 19:420:10 89:16

conceivable175:13

concept 43:4,744:10,11 117:3

conceptual 128:7

conceptually 85:1

concern 81:1783:14 133:15174:17 284:13

concerned 81:1483:11 157:12194:8 210:8

concerning 22:1542:21

concerns 177:19

Concession 55:2256:6,7,1957:3,7,13,1458:5 64:14,1769:6,13,1771:12,1780:14,19 81:2,7179:18 191:13213:2,10 215:13

229:15 243:16261:11 264:7,20268:16

conclude 164:7210:12 273:19

concluded 143:17147:21

concludes 147:16

concluding 79:13134:3

conclusion 46:18162:4 163:9167:20 168:16169:8 195:13

conclusions 61:15147:12 163:7

condition 85:7,8

conditional 90:1,3

conduct 232:15

conducted 42:5,7166:15

confidence179:8,11 275:22

confined 261:14

confirm 154:7196:15

confounder170:20 171:3

confounding167:14 169:9170:9 173:18

confused 96:11

confuses 67:10

confusing 153:18203:22

Connecticut 1:183:6 6:10

connection 259:20293:11,14

consequence114:14,15,17117:6 277:11

consequent 117:13

consider 38:741:19 42:11168:7 247:19258:13 270:9,10284:2 295:20

considered 41:1359:13 62:13136:19 138:1185:5 287:17

considers 270:14

consist 282:11

constant 68:15214:12

consultant 15:14

Consultants 23:1324:15

consulting21:14,19 23:9129:9 261:18262:22

contact 185:10

contacting 299:12

contains 37:20168:10

contaminated253:16

contamination240:19 253:9,11254:1,10 255:6261:15 262:11263:3,22264:6,15 265:6



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continual 211:22212:3

continually 64:18

continued 2:22 3:34:22 25:5 32:17135:8

continues 64:8

continuing 18:465:2

continuous 212:9

contract 227:15228:3 287:5

control 171:13

conversant 283:14

conversation223:15

conversations226:2,5 288:19289:1

convert 160:9

converted 22:1

conveyed 124:4

conveying 130:17

copy 128:19221:14,17275:14 285:6299:9

copyright 282:6

copyrighted 282:3

core 291:16

corner 84:6

Corning 26:10

corporation 1:66:4,22 7:2215:8,12 20:12,1723:4 26:2,6,7,11

135:11 299:6300:7

correct 11:2 14:917:21 18:1122:2033:13,17,20,2136:13 37:12,1938:2,3 41:8 42:444:21 45:1946:6,19 47:14,1852:20 53:2,5,1656:3 60:17 61:762:3,7,19,2067:16,17,2168:6,8,9 70:971:13 74:18 76:677:2078:12,13,1680:17,21 81:4,584:9,21 86:11,2087:1 90:8 91:1893:19,22 94:9,1499:14,18,22100:21 106:9107:3108:11,17,19109:9,16110:1,10,20113:18115:13,16116:18119:11,14 120:7122:14,21123:1,4,7 124:20130:19 135:21137:4 138:19141:1,2,16142:12 143:4144:20147:14,18148:1,15,20149:3 151:13152:20 154:4

156:9,12 159:15160:14 167:4170:2 171:11181:11 183:4184:17 187:18188:9,14,17189:16,20190:2,4,9,14,19191:20 193:20194:2 198:16199:8200:8,12,13202:3,7,20,21203:2,14 204:19206:2 207:21210:21 211:1,4213:11,16214:9,10,18215:8 218:8223:2 237:8,9246:12 250:16251:10,16253:6,13,21254:6 256:9257:14 264:16268:4,8,19269:12 271:9,16275:1 276:5277:4,7283:7,9,20286:6,10 287:12291:1 292:19293:9 295:3298:6

corrected 85:7

corrections 298:8

correctly 57:19110:12 145:2202:17

correlated 72:16

correlates 84:22

correlation 47:22

167:11

correlational 48:8

correspondence176:17 285:22

corresponding70:16 77:7

cost 15:19 40:1746:3

costs 259:10

counsel 1:16 4:36:19 7:22 45:12135:11214:20,22 221:3225:5,21 228:3262:6 285:21294:1 295:18297:9 299:16

Counsel's 295:3

count 10:18 93:1396:9 126:7 158:7214:5 275:22276:5

counted 102:18103:3 117:17

counties 53:1357:8 74:2144:11,14,18151:16,21152:15 153:8,12

counting 9:21,2210:6,15 122:22184:16

country 94:1096:15

country-wide193:21

counts 152:9 153:2155:4 179:13,15181:1,5 182:7



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193:10,11

county 51:1 52:1874:1 118:8 206:7

couple 230:11283:2 285:11

course18:14,16,19,2077:19 147:9295:13

court 1:1 6:6,157:10 13:18 35:17237:15 267:14285:4

court-appointed128:5

covariates 138:14

covered 18:1563:5 272:22

create 30:13 65:4251:9 280:9,10281:2

created 30:14,1631:2 154:13191:7 218:12235:9 238:7,11269:20 280:21291:2

creates 199:1,7

credentials 233:20

credit 176:2

criteria 53:6206:10 210:13

criticism 165:16

criticisms 47:5133:13 165:12173:5

criticized 165:11

criticizing 133:10

critique 166:1

critiques 173:9

crucial 143:11

crude34:6,9,12,15,1837:2038:2,5,9,1639:3,14,18294:18,19

crudest 167:15

Crutcher 1:182:5,12 3:5

cum 17:4

current 66:16257:4

currently 8:12,1723:21 24:1106:10 256:16

curve 116:10211:22 217:10

CV 16:17,20227:9,10

DD.C 1:12,19 3:7

6:11,18 288:7300:4

daily 286:11

Daleo222:16,17,18223:5

Dalkey 20:19

damage 9:11 11:560:7,11 122:6177:20

damages 10:3 45:2

125:4 257:6

dance 198:19

Daniel 1:9,15 3:224:6 5:6 6:3,5,137:13 8:6 15:17135:7 296:9298:4,22299:6,10 300:7,8

data 21:17 64:1665:21 66:1,19,2267:1,6,776:9,19,20 86:13101:5 103:20104:16,17 105:1106:2,22107:10,11,12,17110:2 123:9,20138:12,22 139:7141:15143:5,9,15,19144:3150:14,16,20,22151:16,18,21154:1,5,13,19155:7 158:20159:1,11,13,16,18,20160:1,2,9,22162:4,9,13,16163:1,6 164:22165:1 180:20182:16,20184:11,16 186:7190:6 193:4194:15,18196:14199:11,19200:14,21213:13,14,20214:1,2 216:9236:13,14,16,21237:1,4,20 240:1241:16 255:16

256:12 259:3261:4 265:13274:13 275:5

database 66:4181:3,6

date 8:8 51:2 55:2152:9 220:11225:8 230:20298:22 300:9,22

dated 245:17247:2 259:9

dates 9:3 18:954:18,19 220:18240:16

David 12:6 129:8131:5

day 23:7 25:154:11 64:8,12246:14,19290:16

days 21:19 299:14

de 131:19

dead 115:7,15130:2 196:1

deadlines 233:22

deal 14:4 143:2239:22

dealing 111:9169:19

dealt 48:22 153:4286:4

Dear 299:8

death 45:2 81:1185:6,7 86:1487:8 89:2,6,2193:12 102:4,5,18113:13 117:17118:1 119:21



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120:18123:11,22125:18,20126:6,20158:7,20 159:6180:22 182:7185:7 191:3195:20 212:14241:16 268:17282:12 292:22

deaths 15:2046:3,13 81:582:3 86:18 94:1795:19 96:5 103:3115:6122:13,16,19,20123:1 179:16180:1,6,11,19191:2,11,19,22192:5 193:4201:5,10 203:2229:18 235:12241:12,14 247:4259:10268:10,18276:22 277:2282:22

deceased120:6,18,21196:3,4

decedent125:19,20

December 1:136:11 79:13,17135:4 177:7234:22 235:4296:6 300:9

decide 37:2 55:561:1 126:2179:22 258:8260:5,12

decided 21:2 22:6

148:16 278:8

decimated 215:12

decision 126:1,9179:20 240:21257:12

declaration 5:7278:14,22

decline 78:6225:22

declines 78:4

decrease 101:15260:20 261:2

decreased260:6,13

decreases 260:22

defect 170:14

defendant 11:18124:11 268:11

defendants 11:17

defended 13:6

defending 35:11

define 48:1,2

defined 49:1950:22 86:3 164:6

defines 203:14272:2

defining 78:7

definitely 107:21

definition 78:17250:16 269:16

definitive 137:2

degreasers 36:3

degree 17:3,6 21:733:12 34:10180:21 198:18233:21 234:7

degrees 22:18,21

delay 100:8 197:7212:1

delayed 100:6

delete 286:3,11

deleted 286:5

delivered 230:20

Delphi 20:20

demonstrate124:15 126:1

demonstrates 77:5

demonstration176:16

Denise 1:19 6:16297:2,18 299:21

denominator74:20 94:18145:14 146:9153:20161:14,16164:10

denominators144:14 145:21146:1 153:4,11154:10163:12,20

denser 49:4

Denver 129:9261:18

department20:18,19 21:424:12,14

departures 108:7

depend 28:2,5101:19 104:5117:21 118:5201:21

depended 25:1927:10

depending 27:1429:10 104:8287:15

depends 30:431:12 165:15192:16 241:15

DEPONENT298:1

deposed 8:2011:3,14,20 12:813:4

deposition 1:144:5 5:1 6:3,97:17,19 13:1016:1 50:13 88:11127:4 128:13131:7 147:3158:2 182:21191:4 208:11213:3 216:6219:17 221:11223:12 225:19226:3,9,12234:17 238:3242:7 245:19259:13 278:16295:11,20 296:8297:3,5 299:10300:9

derive 85:21

derived 84:2,4137:7

describe 16:2234:20 90:12217:7

description 155:18283:15

descriptions



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242:17 246:8

design 20:22 22:11136:19 177:12178:9

detail 160:11161:19

details 9:7

determination48:16 53:20180:21 269:19

determine 46:1364:16 82:21121:6 155:18156:4 160:16178:19,21180:18 182:16193:22 207:12220:6 223:1239:2,8 241:12261:1 264:15276:21

determined 128:6179:14,19213:15 260:17

determines 277:21

determining95:10,12 179:2197:13 198:1

Detroit 18:12

develop 61:21200:19

developed 26:343:1,22 208:3272:8 291:10

development21:15 291:15

deviated 182:7

diagnosed120:6,22 162:18

275:8

diagnoses 266:19

diagnosing 44:20

die 100:20102:11,17103:3,4,6 123:4212:7

died 120:7,22

differ 160:2

difference 109:21112:21 189:5274:6,9

differences 50:5169:16,17

different 11:1 22:754:6 55:21 56:468:874:4,9,10,13,2081:9 142:20147:12 160:19162:14 163:4170:7,8 179:4220:12 225:8244:6 281:9292:13

differential 144:9145:16 152:13176:13

differentially175:18

differentials155:14 156:5163:8 164:5

differently 241:14

differing 207:2244:9 255:19

difficulty 88:2

direct 158:17

226:11

direction 22:7140:18

directly 85:1 230:8

disagree 174:5257:10

disclosed 284:18285:1

disclosing 31:2

disclosure 284:13

discover 66:13

discuss 102:20233:10,16235:19 239:7261:14 262:4283:21

discussed 93:20215:2 218:21224:21 244:15270:14 272:4291:21

discussing 227:9259:20

discussion 49:10

discussions 224:7225:1

disease 27:14 43:744:11 57:1465:4,9 124:19160:4 185:3266:19

diseases 159:8286:15,16

dispute 249:1,19287:16

dissertation132:18

distance 59:11

distances 56:2059:5

distinction 215:22

distributed 207:1

distribution 28:830:6 80:18 97:2098:5,6,14,17105:9,17106:11,12,16,22107:2,6,16,20108:10,12,15,18143:16 187:8203:16 282:13

distributions105:5 138:13173:22 178:21

District 1:1,2,216:6,7 297:20

divided 73:3145:10

dividing 95:21130:21

division 11:12

doctor 42:3 77:3273:1

document 28:7107:18,21127:3,8,21129:20 130:4,10192:12 208:6,14213:1 217:1,19218:13 230:10238:6,11 242:10262:18263:13,14271:10 290:6

documentation44:5 119:7,9



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214:14,15

documented 51:852:18 53:4 264:7

documents14:8,10,14 29:566:5,6,21 107:13109:5 112:4131:21 221:20222:4,7,22 228:2230:8 261:13263:17 291:20

dollar 60:12,14

dollars 178:1256:17 257:6

done 24:22 31:1634:14,1745:11,17 58:2085:10 130:1142:1 143:14158:10 193:10218:6,9 221:8233:6 246:21247:21249:9,14,20255:2 266:17276:21 289:9,14290:7

Donziger 287:22288:5,17 289:21

door 25:1

dose 42:2243:4,16,2244:6,10 168:7,8265:18,21266:10

double-count 90:9

doubt 239:15285:3

Doug 243:17,18,20244:17

262:19,21

downloaded159:19

downward 93:20219:7

Dr 6:3,5 8:2 15:1716:5 45:21 50:1667:10 79:21 80:288:5,8 127:3,15130:15 131:10132:12,17133:9,17 135:13137:22 147:7156:8,11,15165:10,18166:4,14,22167:7,19 168:16169:7 171:5173:4,19 176:20177:11,18 178:8183:3 190:22191:7 208:15213:1,7 216:11219:20221:10,15231:14 232:3234:16 235:7238:6 242:11243:4,12 244:19245:22 259:17262:21 278:15285:18 294:15296:3

DR00000037.xlsb4:15

DR37.xlsb 182:20

draft 241:7 290:7

drafted 129:3,5,6234:5

draw 244:8

Drive 2:6

drop 83:8 100:13219:11

dropped 112:7185:21

dry 36:3

due 27:1,7 28:2299:16 147:17171:1 172:20180:12 187:3241:12,14

duly 7:14 135:9297:5

Dunkelberger227:8,10 228:17229:7 231:9,10236:18 243:3245:6 288:13

D-U-N-K-E-L-B-E-R-G-E-R227:8

Dunn 1:18 2:5,123:5 6:10,21 7:1,2

duration 203:22204:21 207:5210:7,9,18

durations 207:2

during 32:1653:19 89:21147:9 213:16223:22 226:3242:18 245:13

dying 83:7 87:9,1888:17 89:1,2092:20

dynamics 104:5106:19

EEaglePicher

26:1,2 291:9

earlier 93:20103:13107:18,22116:11 133:7152:8 153:13180:6 206:3220:1 227:2232:17 238:7243:6 270:1272:4

earliest 209:5,8

early 216:17226:20 234:2249:10

ecologic137:7,11,21138:11,15265:10,13

ecological135:20,22136:18 266:5

ecology 263:4264:1

economic 125:8,15

Ecuador 4:1315:22 16:1439:4,9,15,1940:4,12,22 41:1145:7 46:5,1050:6 55:6 56:866:7 79:281:6,9,16 82:394:5 96:21125:15,20 126:1128:3,4 132:5146:22 193:19194:19 195:1,2208:7 215:12222:19 226:18227:6 228:9



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229:19 232:14236:9,12,18237:2 245:11259:12 265:8,12268:11280:5,6,10,13281:13,16283:11,21 284:2293:6

Ecuadorian 7:454:15 65:17,19121:14237:7,15,19241:17,21 242:2293:10,13294:4,10

Ecuador's 82:16208:19

edited 234:6

Edmund 147:1

educational 16:22

effect 59:13101:11 130:2169:12,19,20170:1 173:18270:21

effective 92:1

effectively 111:20

effects 34:15,1838:5,9,1639:4,14,18143:13

Eh 176:4

either 24:10 26:1546:7 87:6 120:22124:6 129:6168:4 194:14243:18 275:1

electronic 285:6

eliminate 253:3

Elli 13:7

Ellis 13:8

else 33:11 36:658:2 86:7 101:20129:21 149:22160:17 171:12186:1 191:17231:11 245:11253:8 266:14269:9 288:11

elsewhere 103:19

e-mail 131:5262:19 285:21286:11 295:13

e-mails 286:3

emergency 176:22

employed 140:11297:10

employee 120:13

employment 24:7

empty 286:9

emulate 198:21

Enclosed 299:9

endeavored175:14

endpoints 239:2,8

engine 36:3

English 16:10128:19,21

enter 117:10,11,15

entered 115:18248:6

entering 269:10

entire 94:10 96:15110:7 136:3,16

148:19 156:18170:5

entirely 112:12156:1 256:10

entitled 50:5,18

entity 14:4 132:9

entrants 106:17107:1 248:5

entry 54:7,18 55:697:21 102:3117:22 118:5191:17 192:17242:20

environmental147:2 240:19

EPA 43:21 112:4

epi 36:14,15 39:8

epidemiologic166:15

epidemiological34:1 35:2,838:8,15 39:13137:8 164:16

epidemiologist33:4 166:3233:4,11,18

epidemiologists165:10

epidemiology33:7,13,16,1934:3 40:7,1250:9,11 136:20137:21 231:17233:20

equal 78:2,4 89:21195:9 196:17287:1,4

equals 89:6

equity 23:10 24:1

era 19:9

Eric 3:12 7:3222:16,17288:12 299:1

errata 298:9299:15

error 128:7,8137:6,9 178:17179:3,5188:11,18 193:5

errors 144:8145:15 146:5,6152:12

escapes 231:18

especially 42:22

Esquire 2:4,113:4,12299:1,16,22

essence 167:11

establish 263:16

established 56:9225:19 263:19291:17

establishes 150:22267:2,4,9

Estes 18:4

estimate 10:1714:2,16 15:1925:3 36:10 40:1746:2 60:7,1161:20 66:1771:3,4,6,12,1775:7 80:13122:15,16145:11 146:2177:20 178:5,14179:11 181:22182:5 187:13



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188:10,17 195:3197:11 213:9217:16,18228:19229:3,8,18243:15 257:6,17259:10 267:1293:6

estimated 9:871:21 97:3,2198:7 115:21126:4 153:19

estimates 11:712:16 26:18 63:680:22 98:17107:6 123:2135:21 144:9,10145:16,17,22146:6,7152:13,14 153:5168:11 178:17186:19 187:5202:20 203:1236:5,8 283:6286:16 289:16

estimating 10:8,1627:1,2,7 28:22101:9 103:6187:18 286:14

estimation 32:1056:22 178:4

et 217:2

ethnic 167:15169:9,16

etiology 42:12,13

evaluating 28:12

evenly 67:19

event 76:7176:17,18

everybody 19:10

115:7 204:22

everything 178:22

evidence 47:3 48:3117:2 124:14129:12 207:17250:19 253:10

evidence-based273:22 275:4

evident 56:12,15

evidently 76:16149:19 165:21222:9 247:21

[email protected]:18

exact 287:8

exactly 108:12154:9 185:5223:6,7 279:19284:10 290:2

examination 1:164:2 7:22135:8,11

examined 7:15135:9 298:5

example 21:2249:2 60:2 86:590:16 93:4 95:1397:11 116:13125:18 170:11171:4 176:19185:6 240:7244:5 265:4286:22

Excel 192:2

except 24:15 83:7222:9

excess 15:1940:5,19 45:2

46:3,13 48:1749:3 58:961:2,6,21 77:2278:4,5,8,11,1783:19 84:10,2285:15,18 86:1687:6,10,12,13,16,20 90:6 91:2192:13 93:1,7,995:10,13,18,2196:18 97:699:12,18 101:15102:18103:3,8,12110:13 113:4115:19,22117:7,17118:7,14 119:1121:8,10,13,18122:6,13123:6,7,12,22126:7,21 137:12140:11 147:22148:15 156:22157:1 178:6179:16180:1,6,11,19181:15,16186:18187:3,14,15,19188:10191:2,11,18193:3 195:3,8196:8,10,16197:11 198:12201:5,10 202:19205:14,16209:19 210:7217:14,18228:20,22229:4,8,18 240:6247:3 250:7,17251:5,15 252:7254:15,16

259:10 265:9268:17 269:19277:5 282:11283:1,5 290:19291:3 292:22293:7

excesses 58:22

exclude 78:11

excluded 12:20277:5

exclusion 149:1

exclusively 46:2047:1 77:13 99:16157:7

excuse 19:13 152:3170:15 174:10179:8 215:18

exercise 239:13

exhibit 15:1716:1,4,16 45:2150:4,13 77:384:4 88:4,1190:2,5 127:2,4,7128:10,13,17,19130:12 131:1,5,7132:22133:14,17135:17 138:8,21146:20 147:3,7151:12 158:16167:8 173:22182:19,21184:3,10 187:12191:4 208:11213:3 216:6,13218:9,10219:15,17,20221:11,14234:13,17 235:6238:1,3,6242:5,7,10



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245:16,19,22259:9,13 262:18263:2 278:16,20

Exhibits 4:5,225:1,22

exist 66:2,5 76:1581:13 194:10255:17 274:9

existed 62:19194:10204:15,18236:13

existing 107:2

exists 23:21 66:3118:15

exit 212:14

expanding64:13,18

expansion 64:765:2

expect 175:1 180:8

expected 48:1478:21 115:5174:17 183:13240:17

expedition 236:17

experience 44:20111:9

experimental17:8,11,14,2220:21 22:18

experimentation17:12

experiments 18:8

expert 11:19 12:1041:19 42:11126:22 128:3215:1 224:5

230:5 253:15254:1 257:14294:4

expertise 15:6 33:634:5,8 82:21227:9 232:6

experts 230:3243:9,13 244:20261:14,16,18291:20

expires 297:21

explain 129:19

explainable 88:21

explained 161:6292:7

explaining 170:21272:7

explanation169:12 170:20172:14,22173:18 195:17

explanations169:20

explicitly 84:1

exploitation 51:1,952:19 53:14118:8,11 206:7

exploration 45:353:1 56:8 63:6

exploratory 138:1

explore 248:10

explored 142:22

exponential 89:7

exposed 31:944:12,13 49:1950:22 54:7 85:797:4 103:15109:22

144:11,18146:10,12,15150:11,12 151:1152:15 153:8,17154:4,21 160:20164:6 168:9170:18 171:5173:21 195:22196:2,4,11 200:5203:5,8 205:5212:4,20 251:2,9253:11 264:16269:11

exposition 260:8

exposure 9:10 25:426:19 27:2,828:22 35:3,13,2036:12 38:543:1,16 44:3,550:19,21 94:13100:4 112:7,10123:7 124:16,19138:13 167:16196:20 197:1,5198:7,9,16199:1,7,12,20200:7,22 201:4204:1 207:2,3,5210:18,20212:1,2,3,10,17239:3,9 251:5,7277:14 278:1286:17 287:1,2

exposures 138:11196:21 292:18

expressing 172:22260:3

extend 153:2251:7

extending251:5,20

extends 263:4264:1

extensive 53:18249:10

extent 72:18 94:1896:7 122:2 215:3219:1 224:1,18225:15 248:8252:13 256:4257:11 264:10270:3

extra 247:1

extrapolate 67:15

extrapolated214:7

Ffacilities 56:1,9,21

57:22 58:14,1659:11 62:1864:7,13,1765:3,10 174:22191:14,15201:17,22202:3,7203:9,16,20204:14,22206:15 208:3218:19 220:7,12264:22 274:8

facility 61:13,19172:10 205:4220:2,11 272:5274:7

fact 13:16 32:7,1136:2 37:16 39:249:18 68:7 87:13117:7 143:11152:20 154:14161:17 162:8171:1,9 172:21



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197:9 205:8241:6 249:20253:8 258:18284:19

factor 89:17110:13 157:1170:9 196:10198:13

factors 97:10139:13 140:11141:18 167:15169:10 187:4195:8 196:8,16209:19 210:8241:16 272:8,18273:20 274:4,6

facts 124:14129:12

factual 13:21

faculty 18:11

faint 275:14

fair 22:17 27:1833:6 43:5 49:6106:10 271:8

fall 141:17

fallible 67:5131:20

familiar 38:8121:21 231:17247:7 261:19294:15

faster 160:21

fault 72:12

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favor 119:20

federal 13:19

feelings 32:20 33:1

fellow 232:13

fellowship 18:2

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field 31:2233:7,9,18,2137:18 91:894:8,13 255:21

fields 50:7 177:15

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figure 84:5,1695:18 119:12136:14 178:2184:14 189:3192:13 197:17207:8 220:1

figures 69:2,4,584:5 217:22219:6 220:3237:19 267:15

file 127:12,14244:8

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files 56:16 127:3158:7 160:5165:8 182:20190:22 222:9232:14 243:22244:1

filings 266:18

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findings 156:15164:17

fine 12:8 79:1088:10,15 133:22157:20 177:1192:19 225:20

finished 246:11

finishes 136:14

firm 6:13 11:2212:3,6 13:8 15:924:16 35:1045:14

firms 25:6

first 7:14 9:2 18:1420:15 25:1 63:1366:7 68:14 69:2073:3 89:2 175:6176:18 205:14209:18 210:5212:2 218:12221:16,18,19226:18 227:1228:14 231:15234:3 239:18253:16 263:10289:15 290:6,7291:8

firstly 140:4181:21 197:6

fit 75:14 169:3

five 9:1,2 39:7,1756:1,10 57:2258:14 59:775:15,17 80:15159:4 191:13202:7 220:2,10229:15 234:20264:21 265:5268:16

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five-year 151:22159:3

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Floor 300:3

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focus 48:21 169:22178:4 183:9217:12 218:4

focused 41:2136:7,11 169:18

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forecasts 25:16

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formal 33:15

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forming 38:1941:13 147:10156:2

formula 74:4,9,1887:9,21 88:5,889:2,3 90:591:22

formulas 74:990:14 114:5115:22

forward 62:3116:13 218:15299:14

foundation 263:17

founders 15:9

fourth 69:6 205:15

fractional 102:19

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free-standing 23:9

French 16:11

Frente 131:19132:1,4,11

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Friday 223:14,18288:3,14289:10,11

friend 21:18

Frog 20:1

front 45:21 50:259:4 79:20131:19 135:18193:14 235:6292:10

full 8:4 55:2,19174:14 198:12262:14

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future 9:9 10:1725:3 26:6,1927:1,7,13 28:22

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Garlock 14:6

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generally 9:727:19 30:6 41:2243:15 77:6112:18 133:16136:19 138:1150:20 168:10237:21 245:10255:11

genetic 170:14,19171:2,22 173:1

genetically 170:16

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geographic 56:17107:15 108:2146:4 150:19203:15 210:1219:1 243:21265:2 282:16

geographical 50:5167:11

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geographies229:11,12,14

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germane 47:2

gets 30:8 75:18201:17 203:15

getting 74:10103:18 112:15145:14 153:18175:14 179:9182:16 194:8212:20 235:20252:15

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GIS 219:22244:1,5

gist 123:16

given 15:4 44:354:19 58:1982:22 89:20 90:195:13 101:5108:5 116:7142:4,8 145:10146:3 150:22177:11 178:8179:21 196:11239:22 240:17259:2 284:21298:7

gives 37:5 89:2200:5 244:9

giving 36:4 47:1593:5 233:22

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God 243:17

gone 91:13 272:15

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government 13:19

Grace11:4,6,7,11,1512:4 13:5,1326:5

gradually 198:13260:20,22

graduate 18:14,1620:15 21:3,19

graduated 17:2,4

Grand 2:13

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grasping 200:1

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greater 48:6 59:1183:5,6 110:8,15144:13 153:10178:15 287:2

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group 15:5 23:1057:4 92:10 97:7110:11 120:13138:10 159:3170:17227:13,16,22228:4,15230:16,22231:11 236:15242:6245:1,3,4,18

288:6 289:18,19

grouping 183:5

groups 86:15,1687:10,19 88:1791:12 205:18

grow 210:1

growth 67:18,2268:3,4,14,15,20150:11 151:1153:16 154:3,20155:8,14 156:5160:19161:17,18 163:8164:5 213:18,19214:6,9,12215:10 216:10

guarantee 276:19

guess 26:11 33:1035:4 40:6 47:1052:12 54:1355:12 71:1976:21 82:15119:6 126:17164:13 172:19175:15 179:22191:12 192:22199:4 201:1215:4 218:16,19251:1 253:14271:1

guidance 43:22

Hhairs 269:15

half 19:12 25:8277:2

half-hour 288:10

half-time 21:5,14

halfway 59:2

136:15

hand 40:14,1591:8

handover 55:3

handwriting130:15

handwritten 4:8130:12

happen 83:1298:13 100:2,5126:3,4 127:12149:19 170:17173:2,17

happened 171:11227:4

happens 83:5106:11 178:2198:11 200:16

happy 188:21279:11

hard 140:19 220:4272:7

hate 75:21 170:15

haven't 26:9 33:22222:5 223:10294:19295:12,14

having 7:14 14:1129:5 45:11 55:1088:1 106:2,22135:8 161:16272:7 283:19293:5

hazy 9:3 243:5

head 201:15 207:6

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healthy 32:14

hear 199:15226:18

heard 26:13,15131:18 227:1248:22 261:20279:8

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heavy 239:16

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here's 70:14126:13289:2,13,14

he's 129:4 166:2174:15,19210:14,16 224:4258:10263:17,18 295:1

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high 17:2 83:7146:7,16 168:5

higher 27:16 29:2158:1 59:16 83:10161:17,18170:22 173:21187:2 194:18237:11

highest 28:20

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hint 177:14

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history 49:1764:21 208:6,19

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hold 66:20

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home 245:3,8

hope 67:4

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hour 79:8 225:13228:10

Huaorani 216:14

human 17:9,1318:7,17175:18,22 176:1

humans 294:21

hundred 92:1594:5 222:18

Hurtig 46:1648:4,15 49:1250:7 57:9 58:7110:3 117:8118:21 151:19153:3 154:10,18157:9 161:13,19162:15 165:22167:6 172:1248:3 274:10

hydrocarbons175:16

hypothesis 174:19274:5

hypothesizing

123:3,5

hypothetical122:19123:11,22126:6,20 191:19192:5 203:2229:18 251:15268:18 278:7293:6

Ii.e 211:15

IARC 294:15,17

IBM 21:20,2122:2,6 279:20

IBM-style 279:21

ICD-10 158:8,18159:9,12,14160:3,10,12162:9 164:21165:8 184:20,22185:5,8,10,18186:3

ICD-9 158:9,18160:9 162:9165:1,4,6 184:22185:18

I'd 41:22 68:20,2173:11,14 82:7106:6 155:10186:4 189:2234:20 256:12271:11 276:10

idea 112:13 189:21251:18

Ideally 105:3

identification 16:250:14 88:12127:5 128:14131:8 147:4

182:22 191:5208:12 213:4216:7 219:18221:12 234:18238:4 242:8245:20 259:14278:17

identified 265:5276:4

identify 7:7 84:3239:20 240:11

ignore 249:13252:1

ignored 101:10

ignoring 201:8202:22

Ilaan 288:14289:22

ill 32:20 33:1

I'll 18:10 21:12108:14 143:18146:20 169:3184:8 200:3202:16 257:3262:21

Illinois 8:11

illustrate 59:12

I'm 9:3,20 10:115:16 19:22 20:829:12,13 40:342:1 43:20 49:1751:13,22 52:466:9,13 67:469:11,14 70:1872:5,12 74:11,1981:4 82:1485:12,22 87:2,1788:1,7 89:1691:14 93:5 95:696:11 98:4



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imagine 270:21

immediately 248:9

impact 99:6

196:20 197:11207:5 247:19248:12 263:4,22295:6

impacts 205:9

implying 201:3

important 37:1574:12 238:20,21239:8,11

impression 249:18

inaccurate 267:16

in-between214:3,4

incidence 43:250:6 110:8,16153:15195:18,20 196:6266:4

incidence-based195:9 196:17

incident 173:20

incidentally 74:21

include 43:11125:3 228:3236:11 237:6,18240:5,21 241:4276:1 278:9285:21 287:10

included 94:20,2195:2,896:12,14,15,18156:20 157:5183:6 217:18237:11 240:13262:13 275:19

includes 90:1092:22 114:1125:8 195:20

including 136:14

143:1 144:9145:16 152:13

inclusion 53:7

incomplete 180:20

incorporate 280:6

incorporated105:2

incorrect 90:20145:21,22170:5,6186:18,19

increase 72:6171:8,16 172:7185:19 195:2197:12 216:20247:6 251:4

increased 52:1753:13,20 87:6147:17 156:16171:6 191:20198:13 207:21211:16 213:15216:18254:11,13,17271:15 275:20

increases 99:15205:15

increasing 59:1379:2,6 144:11,18152:15 153:9197:4 217:10219:1

increment 93:1

independently30:17

index 22:14 37:569:12 70:472:2,5,10 73:3,474:14,16,17,20

75:8 76:10107:22

indexed 73:799:10

indicate 65:1368:10 110:3112:5,21 113:4141:16 165:8186:4

indicated 109:21182:7 247:20281:19

indicating57:3,12,21194:20 217:9222:2

indigenous 4:18215:12,22216:4,10217:13,17,20

individual 116:3,4120:6 138:10,14

individuals 116:5269:10

Industries 13:20

industry 31:17174:2

INEC 65:13 66:367:6 68:19 76:9104:20 107:13150:14,16,22154:5,13 158:8159:7,19,20160:1,2 163:4164:22 165:6181:2,6 182:7186:7 190:6194:18

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inferring 47:11,12

inflation 28:14,15

information 53:1856:18 66:1476:12 80:10109:4 124:3189:6 197:1210:20224:3,13,19225:17 243:22264:5,11

informed 64:15

in-house 18:5

initial 288:18

initially 23:9 292:4

injuries 10:14,1528:10 112:19265:19

injury 10:9 11:526:5,9 33:9

input 281:8

inputs 282:11,18

insensitive272:9,19,20273:20 274:4

inside 57:8,13

installed 201:22

instead 138:13158:18

Institute 152:4

institutional166:12

instructing 224:9

instruction 19:8

intellectual 281:15

intended 60:1197:6 235:16

intending78:11,14 84:1986:4,6

intent 236:11

interact 231:12234:1 243:12244:20 261:21

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interest 55:13 69:173:14,18 228:11239:3 240:16282:17 297:11

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international 40:750:8 147:1 159:8160:3 294:16

interpolate 68:16

interpret 42:20173:12

interpretation128:8 130:3

interpreted 138:12173:9

interval 179:9,11

intervals 159:3

introduce 6:19

introduction197:9

invalid 104:8189:20

invalidated 164:20

investigation276:20

invoice 242:5

245:17 290:13

involved 9:18 15:240:12

involvement 227:5

involving 40:4

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Irvine 2:7

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issues 41:6 160:22173:10,12175:12209:17,19 210:2215:2 218:17270:13 277:19295:8

item 185:6

it's 43:20 48:755:2 60:19 64:1267:12 71:4 74:1288:10 89:1193:18 98:12100:7 103:11104:14 107:19113:20 114:5,14117:12 118:18119:5 120:16128:21 133:19140:19 141:3

148:7 150:5152:9,19 160:16163:1,4 172:17174:11 175:12176:22 178:20190:2,9,13191:1,3 192:3193:1 196:3,12205:1,2 208:9,18211:2 218:6220:4 225:2,18228:10 248:15250:8 252:12254:21 257:5260:17 261:3262:7,18 263:9266:17 269:3276:12 278:7279:8 284:11,12285:10 287:3294:20 295:8,21

I've 26:16 33:869:14 89:1 133:1158:6 161:13199:15 216:12221:16,18 223:8254:11 263:14266:4 279:8286:3 289:13

JJack 50:12

Jamieson 48:4

Jefferson 6:17300:2

Jersey 3:16 299:4

job 21:5 231:20

John 223:19225:11

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journal 40:7 41:750:8 138:8143:14 144:4,5147:2 156:7,9265:22

journals 41:3

JPEGs 56:17

judge 12:21 61:1126:2 179:22231:4 257:21258:3,8,11,12267:17 270:2277:21 278:6,8

judge's 125:22126:9 178:3179:20 257:12

judgment 132:4

judgments 236:12

June 209:1

jurisdiction 30:9

jurisdictions 30:3

jury 28:19 29:21119:13,18 120:5124:17

justice 241:17

KKappa 17:5

Kelsh 3:20 7:8146:22 147:20148:4,19,22149:4 150:5152:19 153:22154:12,20 155:8157:12 158:1,14162:14 163:7164:4,16,20165:7,10 184:9

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Kelsh's156:8,11,15162:1

Kenneth 3:21 7:9

Kermit 20:1

Kerry 231:13233:3,4 235:18243:3

K-E-R-R-Y231:13

Kill 12:2

kilometer 59:3

kilometers 56:1,1057:22 58:1459:3,7 75:15,1780:15 191:13,14220:2,10 229:15264:21 265:5268:16

kinds 175:1239:21

Kirkland 13:8

knowledge 35:18125:17 237:14

known 55:11175:2 180:9266:21 286:16

KPMG23:12,13,16,1924:10,11,14,1625:9

Llabel 84:10

labeled 61:6 83:19191:1,3

labels 84:11

laboratory 18:4,721:6,13

lack 177:12 178:9

Lago 118:11,15215:1

languages 16:989:13

laptop 150:2

laptops 149:15

large 44:3 69:1161:15 173:10179:13 187:5,6197:21 209:20274:9

largely 197:18

larger 37:7 59:1073:7 150:19180:16 193:5

largest 290:18

last 8:6 16:1654:6,1855:2,5,6,11,1977:3 102:3,4,5113:13 114:19118:1 130:11191:17 192:16223:14,18231:16 241:11243:17 246:19263:11 273:12292:21,22

late 243:4

latencies 111:11

latency 110:22111:5 112:14,16116:2,10 211:22240:17

later 15:13 191:12193:6 209:11

253:20

latter 126:16

Lau 147:1 158:15162:14

laude 17:4

laureates 121:18

law 1:17 6:9 11:2212:3,6 13:735:10 121:11,14294:10

laws 293:11

lawyers 13:155:7,10,12,15258:9 290:9

lead 138:14

learning18:8,17,19

least 18:10 28:839:3 41:9 49:1656:18 69:1 82:589:1 120:17197:7 202:20220:19 273:7

leave 100:19101:7,8

led 21:21 48:6,7144:19

Lee 1:9,15 2:116:3,5 7:1,13 8:6135:7 296:9298:4,22299:6,10 300:7,8

left-hand 50:19138:8 144:7191:1

legal 3:14 6:15284:7 299:3

less 9:15,17 29:19



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lesser 178:15

lets 62:4,6 244:8

let's 23:11 50:472:20 79:11 88:492:6,9 134:1157:15 170:13196:3 204:12,22206:15 208:5286:21

letter 186:4

leukemia 36:5121:1

leukemias 35:6,12120:21

level 42:14 76:13138:14 159:2198:2 276:1

Levenstein 13:7

liability 14:2 25:1760:17,18,21256:17

liable 60:8

Libby 146:22

life 110:7 236:6283:15

lifestyle 169:16

likelier 287:5

likelihood 83:6287:2

likely 44:12 48:9195:7

limit 40:11 112:6,7

limitation 201:9

limitations 167:9177:12 178:9

line 147:19 185:6261:3 300:10

linearly 205:15

link 176:17

linked 170:17

list 46:16 222:4,6

listed 40:21 62:14

literally 75:14157:10

literature35:3,8,15 37:838:9,15,19 39:1442:21 43:1557:2,21 175:19

litigation 32:6121:16 226:19227:6 281:22297:11

little 64:2,21 88:1122:3 140:19144:7 157:14191:12 210:17217:4 219:11231:18 243:5246:22 247:5270:20 275:16

live 48:2 58:13,1561:12,18 75:1794:8 106:13108:3,6171:10,12 173:2200:6 253:5

lived 71:12,16110:6 116:14171:4 172:21

204:7 207:20211:17 218:3

lives 110:9

living 47:15 48:1649:2,20 51:152:18 57:15,2258:1 75:195:2,7,19 96:697:12 98:14105:17110:14,19 118:7147:17 171:17172:9,14180:7,12 195:20196:3,5 203:8204:13,17 206:7210:22 211:2229:1,9,19 248:7250:4,18 253:1284:12

LLP 2:5,12 3:5,13299:2

lo 244:11

load 244:11

local 22:16

located 6:17262:12

location 149:12218:18

locations 262:11264:6,15

long 18:21 49:1382:14 101:22102:21 110:19113:5,16,17114:1,12 115:10117:3 157:13204:1 223:20225:12 260:18288:8,10 290:16

longer 23:7 29:5157:14 200:6,19201:17 250:4

Lorena 262:19

Los 2:14

lost 14:10,14 22:8125:11,18,20

lot 203:2 219:2248:16

lots 241:15

low 168:5,7 216:16

lower 99:18,20,21103:16 156:21194:18 195:1196:8 237:11

lowering 197:11

lowest 244:13

lucrative 21:11

lunch 133:20226:4

luncheon 134:4

lung 10:13120:20,22265:18 266:2,20267:1 286:21,22287:3,6

MMaazel 288:14

289:22

machine 279:4

Macintosh 279:22

magic 89:10

magnitude 139:12140:5

mails 286:7

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mainly 136:8

majority 192:4

males 174:18175:11

malignancy 186:4

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manifest 240:20

manner 142:20143:6,10

Manville 26:4,8

map 62:4,6,10,13213:1 218:20219:15,22 220:6244:12

maps 56:12,1759:4 62:1

March 263:2

margin 178:17179:2,5 188:18

mark 15:16 50:459:1 88:4 127:2128:9 131:4146:20 182:19190:21 208:5212:22 215:17216:3 219:14221:9 234:13237:22 242:4245:16 259:8278:13 295:11

marked 16:1 50:1388:11 127:4128:13 131:7147:3,6 182:21

191:4 208:11213:3 216:6219:17 221:11234:17 238:3242:7 245:19259:13 262:18278:16

market 21:21 22:3

markets 22:16

Marla 231:15,20243:16

Maryland 1:2 6:78:13

match 106:12107:2,5

material 179:21

materials 180:4222:19 262:3

math 20:19 75:4247:4

mathematical 17:918:17 60:189:8,12

mathematically85:22

matter 6:4 11:413:5 16:532:9,11 36:175:18 76:5105:20110:18,21115:17 128:4131:13 132:5147:10 210:13215:2 221:10222:20 227:13241:6 242:14246:3 250:1263:1 278:14279:1 283:22

284:19 291:7297:12

matters 15:1474:13 203:18,21205:3,7 210:7

Matthew 227:22

maximum 198:2205:16

may 37:4 44:661:21 66:5 67:369:8 86:17 87:11119:6 125:5127:10 129:22131:21 138:14140:13,20,22144:10 148:9151:14 162:15168:20 177:14178:14,15183:22 192:12210:15 243:4247:10 264:11265:21 275:11282:13 291:17

maybe 157:14192:13 225:13288:3

mean 40:1842:13,15 47:8,2148:7 56:14 60:1276:11 81:1882:13 91:14,20101:20 103:14112:17 139:12142:11 143:8,9145:20 149:18174:8 180:5189:21 198:19206:19 229:14239:17 261:16272:6 274:8279:19 281:22

290:14

meaning 71:18

means 47:22 87:12103:14 110:14120:2 195:19294:9 297:8

meant 129:19218:14

measure 60:21126:6 170:1

measures 167:15

mechanism 260:2

medical 42:3,16125:11

medication 8:17

meet 223:17288:5,8

meeting 25:2223:20,22 225:7242:21 243:2,10288:16

meetings 290:10

MELE 232:16

member 17:4

memory 9:14 18:729:9,19 45:1652:13 67:5 128:1131:20 243:5

men 275:15276:6,16

mental 188:6192:20

mentioned 10:1026:9 107:22129:2 133:6227:2 234:16243:6 247:2



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270:1

mesh 244:10

meso 102:22

mesothelioma10:5 27:16,2128:21 29:14,2042:21 43:144:1,2 112:13113:6 117:2185:6,22 265:18

Mesotheliomas10:13

messenger 230:20

messy 196:12

met 131:10 223:14287:22 293:20

metadata 127:8234:14 238:1

metal 239:16

method 17:1320:20 155:18

methodologies283:15

methodology255:12 270:19

methods 68:1173:6 136:8,13

Michael 2:11 3:207:1,8 146:22

Michelson 2:6

Michigan 18:1220:10 21:4,8,16

mid-1960s 216:17

MIDAS 21:16

mid-August230:20

middle 54:4 66:12

136:13

midpoint 56:11

migrating 101:14

migration 144:9145:16 152:13176:10 263:6264:2

Miguel 50:8

million 29:20119:12 120:2,3125:3,8 126:5178:2 283:6,9293:8

million-dollar124:9

millions 9:15,17

Mills 129:8 131:6

mind 92:20 115:3

mine 21:18 84:16229:13 257:12276:12

minimum 49:2151:2,9 52:8,1953:14 118:9,12201:11 203:9206:1,8 211:18

minus 89:7,1793:2

minute 90:2295:14 103:8132:21 152:12201:4 233:1

minutes 157:14234:20 285:11

mirror 198:18

Mischaracterization 144:1 185:15186:13

mischaracterizes252:14 270:4

misinterpreted129:21

misread 267:17

miss 221:2

missed 149:19

missing 282:14

mistaken 49:1752:1 72:6

misunderstanding204:20


model 10:20 11:126:18,2030:11,16 31:143:22 44:7 63:1964:3 65:2,968:15 75:15 76:777:22 100:18101:22 105:2106:10,19108:18 109:7110:6,10,19113:17,19,21114:1,4,6,12,16,18,20115:1,11,14,18116:2 117:15118:15123:11,21 126:8148:4 179:2,10181:17 182:9183:13 186:7,17187:7 189:9192:5 193:3198:11,15,17,20200:10 201:11202:22 206:18

209:9,14211:10,13212:8,9,16253:17 254:22255:5,12256:1,7,16257:2,5 258:19259:20 260:4265:18,21 271:7278:3,11 291:3,9

modeling 199:6

models 28:2129:15 42:22111:10 112:5,18266:11287:10,19

model's 186:9

moderately 33:10117:9

moment 21:12124:4

moments 88:14275:22

Monday 1:13

monetary 283:6

money 257:2

months 148:8

Morimoto 147:1158:15 162:14

morning 6:168:2,3

mortality 4:1277:12,19,21 79:181:1,8 82:1,983:11 86:1387:3,6,19 88:2089:16 90:1791:4,9,20,2292:1,14,16,21,22



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mortality-based195:8 196:16

move 88:21 100:18168:12 200:4273:1

moved 251:2

moving 99:17105:15,20278:12

multiple 272:16,22273:2,6

multiplicative67:22 213:18

multiplied 283:8

multiplier 266:18

multipliers 74:22

multiply 91:20,2192:13 97:5266:22 283:5

multiplying69:16,22 70:473:1 293:8

multivariate 18:16

myelitic 35:4

Myelogenous 35:5

myself 39:21 243:5

N

Napo 76:17

nation 56:8

national 22:13152:4,5 162:18194:5

nature 9:6 12:1513:21 14:1525:14 165:15228:8 261:15

nearly 195:8196:16

necessarily 43:1262:15 145:22170:8 187:1211:7

necessary 15:577:5

negative 89:15

neglected 218:14

neither 297:9

neoplasms 81:2

Neuman 2:4 6:217:20 8:1 10:1114:17 15:16 16:320:2,13 25:1227:6 28:4 29:730:1,2231:7,15,2136:11,17 37:138:13 39:5 41:1842:2 43:3,1344:9,18 49:1,1150:4,15 51:15,1852:2 53:3 54:255:4,14 57:11,2058:10,2159:15,2260:13,19 61:4,1062:17 63:3 64:565:8 73:16 75:10

76:4 79:8,11,1981:22 82:2083:13 85:1388:4,15,18 91:394:22 96:4,2298:9 99:4101:6,13104:7,12 105:11106:4,21 107:8109:14,20111:12,18113:10 114:11116:20 117:14118:3 119:8121:5 122:4,11123:17 124:7,18125:2 126:18127:2,6 128:9,15129:14131:4,9,17132:10,16133:19 134:1135:12 136:9137:3,20 138:6139:14,20140:6,21141:6,12,20142:10,18 144:6145:4,7 146:20147:5 148:10151:9 153:6155:6,20157:3,20,21158:11161:7,10,22163:3,18 164:3165:3,17166:7,13,20168:12,14,22169:6 172:3,16173:11 175:9177:1,9 178:7,16180:10,17182:12,19 183:1

185:17 186:16187:9 188:8,16189:15,21190:5,10,15,21191:6 192:14193:13 196:13197:19 198:8199:5,17 200:2201:19 203:6205:20 206:17207:18 208:5,13210:19 211:9212:15,22 213:5215:17,20216:3,8 217:11219:14,19220:16 221:9,13224:4,9,14 225:6226:1,17 228:6,7231:5 232:5234:13,19 235:5237:17,22238:5,16 240:14242:4,9245:16,21247:12,22249:2,8,22250:12 251:3,14252:6,18 254:8255:1,20256:6,22257:13,19258:4,10,14259:4,8,15260:19262:7,9,17,20263:20 264:13265:3 266:7267:10 269:17270:5,12271:3,21272:13,17273:3,9,13,18274:2,18



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Newark 3:16299:4

newborns 100:13

newly 54:7 98:5,6205:18

nice 217:8

Nicholson's 43:21

Nielson 22:13

night 290:8

Nobel 121:18

nobody 103:21222:6

non-disclosure281:20 284:19285:2

none 86:17 87:11201:2 265:20

non-exposed144:12 152:16153:10 195:22196:2

non-malignancies266:13

non-responsive168:13

nor 230:13 297:10

Norm 20:19

Northwest 1:183:6 6:11,18300:2

Notary 1:20297:1,19

note 49:5 87:11130:7

notes 130:6 234:5290:1

nothing 74:14,15149:13,22154:17 171:8259:7

notice 1:17

noticed 158:6

notion 113:15176:3 237:16

November 297:22

numerator 94:1796:9 155:4 163:2196:1

numerators146:18

numeric 182:9187:7

numerical 19:7

numerous 166:15210:15

Oobject 27:5 88:7

166:6 168:22169:3 258:4,6283:12

objection 10:714:12 24:2027:22 29:2,1630:19 31:4,10,18

36:7,16,1938:10,2141:15,20 42:1743:9,18 44:1548:19 49:7,2251:11 52:2153:21 54:21 55:857:5,16 58:3,1759:8,19 60:961:8 62:11,2263:20 65:5 73:975:5,19 79:381:19 82:11 83:285:2 94:15 95:2296:19 98:2,22101:2,12104:2,10105:6,22 106:14107:4 109:10,17111:7,15 113:7114:7 116:16117:4,19 119:2121:2,22 122:7123:13124:2,13,21126:14 129:11131:14 132:6,13136:5,21 137:17138:3 139:9,17140:2,16 141:5,8142:6,14 143:22144:21 148:5151:4 152:21155:1,15 156:17161:1 162:10163:15,22164:18 165:13166:9,17168:1,19 171:18172:11 173:7175:4 177:21178:12 180:2,13181:19 185:14186:11,20

188:4,12189:10,18192:10 193:7195:14 197:15198:3199:2,13,21201:12 203:3205:11 206:11207:14,22210:14 211:5212:11 215:14220:13 224:1,17225:15 226:13231:1,21 237:13238:12 240:8247:8,15 248:20249:5,16250:10,21251:12 252:3,13254:3,19 255:14256:2,18257:8,15258:1,20 260:15264:8,17 265:15267:3 269:13270:3,15 271:17272:12,21273:16 274:16275:2,9 277:8,16278:4 279:6281:4,17 282:5284:4 285:8286:18 287:13292:14294:5,11,22

objections 217:1258:5,7 273:21

obtained 17:22

obvious 82:18

obviously 174:1216:22 295:20

occasion 14:19



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occasions 8:22

Occupational147:2

occur 44:4 179:6192:5,9 260:18

occurred 169:20179:17 181:11182:1 187:3261:10

occurring 186:10

occurs 67:19292:22

offer 172:14

offered 61:15

offering 61:11112:10126:10,19,21169:12257:14,16268:2,6

officer 297:2

offices 1:17 6:10244:22 245:2,5288:6

official 68:18

oh 10:1 13:9 17:366:9 69:10 72:1076:14 102:22112:16127:10,18,22132:21 135:17150:4 174:11183:9,21 190:5192:7 193:1202:12 208:18244:4 269:6273:11 275:13285:20

oil 4:12 31:22

32:2,5,8,1534:6,9,12,15,1838:2,5,9,1639:3,14,18 45:249:20 50:751:1,8 52:1953:14,18 54:1556:1,20 57:2261:13,18 62:1863:7 64:6,13,1765:3,10 79:2,591:8 94:8,13110:7 118:8,11146:21 171:1174:2 177:15201:16,21202:9,18203:9,16204:1,18 205:3,9206:1,7 208:3209:8,15211:11,14,17218:18 220:2,10229:4252:9,12,16,20,21 254:17 255:6269:21 271:15274:8 294:18,19

oil-producing15:21 46:4,1447:16 48:12,1749:3,13 52:858:14 94:1995:2,7,19 96:6,897:12 100:19110:15 116:15147:18 171:9,17172:10 174:22191:14,15202:3,7 203:20204:8,22229:9,19 235:13248:8

250:4,7,8,18253:1,5 254:9259:11 264:22269:10 274:7

oil-production272:5

old 100:6,15102:11,12,15115:7 117:21

older 83:5109:8,15

Olick 12:2

one-and-a-half19:5

ones 37:7 82:3195:9 196:17215:6 237:11275:22

ongoing 51:2,952:19 118:8206:1,8 209:16

online 219:3220:7,12

operated 55:6209:1

operating 269:21

operations 53:1854:12 55:3 94:14171:9 175:2208:7,19209:5,6,8,15211:4,11,14,17215:11 229:5252:12,19,22253:3 254:17268:22 269:9,12

opine 204:6

opining 230:3

opinion 41:14

47:15,19 60:1561:11 112:10126:10,19,22253:12,15 254:1257:14,20268:2,6,10,12,14

opinions 36:438:19 121:20138:19 147:10156:2 189:17230:6 295:6

opportunity 21:11165:16 190:19271:10

opposed 32:5 60:368:3 81:9 99:16172:22 205:7245:7 274:12276:2 279:21

options 142:22

or/and 187:4,5

oral 1:16

oranges 152:10

order 18:10145:14 161:13179:11 220:9

Orellana 15:2146:4 71:776:15,18 77:181:13 150:18194:1,5 259:12

organization131:18

Oriente 208:7,20216:10

original 5:2273:15 127:12128:22 175:15236:11 247:3



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originally 184:19247:5 261:17291:21

ostensibly 173:20

others 7:18 40:8215:5 232:20239:12

otherwise 297:12

ourselves 23:16

outcome 138:13168:18 228:11297:12

outcomes167:13,22

outline 234:4,15235:9,12,17

out-migration101:9 104:6,9,16105:1

output 291:18293:1,8

outputs 4:14 183:2223:8 282:20

outside 39:15 45:957:14 265:8,12

overall 173:20237:16

overestimate144:19153:14,21

overestimated154:8,14 163:10

overestimates151:3

overpredicting

193:3

overstated 164:8

Owens 26:10

owned 14:5 26:8

Pp.m 134:2,4

135:2,3 177:3,6234:21 235:3285:12,15296:5,8

packaging 11:11

pads 263:5 264:1

page 2:22 4:2,5,225:1 15:18 46:150:16 54:3 55:2063:4 67:12 69:877:2,4,1880:1,2,5 83:1784:6,7 91:2 93:4111:19 116:21119:13,15,16127:7 130:11136:13,15138:7,8 144:8150:6 151:19155:22 158:19162:16 167:7174:12 176:20177:10,11 195:6196:19 208:18216:13221:19,20,22263:10 278:19290:17 292:9299:11,15300:10

pages 16:16 298:5

paper 46:17,2147:1,6 48:2288:5 110:3

136:16 153:2,3154:18 158:16162:15,16172:1,2 174:3222:8,10 230:11241:7 274:11

papillomavirus175:22 176:1

paragraph 50:1864:6 112:15

paragraphs 80:7

parameters 64:4120:8 257:17

parcel 239:11

parishes 74:1

parse 169:13

partially 168:3

participated 290:9

particular 15:622:21 24:1240:12 61:1787:14 89:11114:20 116:3210:22 211:3230:17 244:10

particularly284:11,14

parties 1:22297:10

partly 167:10

part-time 20:16

party 13:12

passed 180:19

past 100:4 103:12221:19 248:6,7251:8

patented 282:3

pathway 263:6264:3

patients 17:15,18

pattern 68:4,8173:22 174:8,18

patterns 144:9145:16 152:13

Patton 3:13 7:345:13 222:13,14223:14,17236:19 299:2

pause 178:10

pay 60:15 257:2,7262:16

Paycor 26:13

PC 22:2,8

PDF 149:10,11

peaks 192:1

peculiar 170:14

peer-reviewed41:3,7 144:4,5156:7,9 190:11

PEL 112:6

pending 6:5 10:1852:3

people 9:2157:1,3,13,2258:1 59:12,16,1771:15,2175:1,3,14 83:1092:2,20 94:5,798:6,11,14 99:17100:13101:7,8,14102:11,14103:18105:15,20106:12 108:3,6



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people's 276:22

per 90:18 92:1494:4 191:2

percent 67:18 68:1129:17130:3,4,5,7175:20 192:21193:2 196:3,4213:21 214:13222:18

percentage 70:1173:7 75:1776:1,2 95:12,13188:19 189:5192:8 193:12

percents 282:13

perception 17:1018:19

perform 80:11

142:19280:14,15

performed 45:1142:17

performing 97:18105:16

perhaps 28:13,1548:3 72:1482:5,6 107:18187:6 193:12291:13

period 53:19 77:2078:1 111:5112:14,16 119:1182:6184:1,11,13186:7 187:13198:14251:6,7,20266:21 271:11291:15

permanently286:5

permissible 192:18276:17

Perrin 14:3,16

person 19:6 20:1861:17 64:20 91:8120:18 130:3230:17 231:16232:12 243:6,21244:17 251:2252:22 255:18

personal 19:921:21 22:726:4,8 279:21,22293:16,22

persons 52:18 54:780:14,19 120:21217:17

person's 91:9,19

perspective 42:16

pertained 41:10

Pete 7:2

PETER 3:4

petitioner 1:7,162:3 3:3 4:3 6:4299:16

Petroecuador54:16 64:12249:9,15 268:22269:7,11,20

petroleum 31:17111:13 240:7,11277:14 278:1

Pfizer 12:17

Ph.D 15:1817:7,14,22

Phi 17:5

Philadelphia26:13,14

phone 225:8,10,12227:1,4,7

phonetic 232:16

phrase 40:5

physician 42:1

pick 122:12

picked 56:10201:16 260:8

picking 22:1

Picone 231:14232:3 243:4,12244:19

P-I-C-O-N-E231:14

picture 198:14

piece 88:5

pipelines 218:20

pits 252:9,12253:8 263:5264:1

places 58:6

plaintiff 119:20120:17 293:17

plaintiffs 7:59:18,20 11:1745:1,6,12 131:13214:20,22215:11 221:3230:2,9 243:13247:14261:14,17 263:1293:19 294:1295:18

plan 12:17,1824:18 25:17 28:830:6

plane 295:15

Planning 15:8,1223:4,12 24:15

platform 279:18

Plaza 3:15 299:3

please 6:19 7:6,118:5 34:21 46:170:21 80:1 83:1895:14 123:16141:13 168:22224:15299:9,14,17

plethora 167:14169:9

pleural 10:14

plug 74:8

plus 10:10 89:17



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point 18:9 29:1933:8 58:1562:5,7 65:1168:17 100:5,17112:8 116:13149:17 165:2168:9 172:19194:11 197:8205:18 206:3218:7 240:15241:11 248:7255:19 265:1276:10 292:2

points 82:4 133:15168:7 213:20217:5 238:19,21

pollution 174:2

poor 192:21

population 31:949:4,19 50:2255:22 59:13 61:762:2,8 66:16,1767:15,18 68:4,2069:18 70:1971:2,5 72:1673:6,8 75:1876:5,22 81:1084:15,16 94:1897:4,12 99:15103:15 104:5105:4,8,9107:2,7108:8,15,19109:7,8 136:7,12143:15144:8,12,13,19145:11,13,15146:3,5,6,9,13,15151:1,7,13,16,18,21 152:12,16

153:4,9,11,16,20154:3,10,20155:8,14 156:5160:19 161:20163:7 164:5176:9 187:5203:8,13 207:12213:10,15215:2,10,12,22216:14217:20,21 218:3219:6 268:15272:1 282:13,16

populations 4:1851:8 53:13 55:2156:5 109:22118:7 204:7205:22 206:7,9214:16 216:4,10217:13,14264:16

portion 115:18,21

position 18:1119:3 20:16 22:923:10

possession 29:6

possibility167:12,14,21168:17 169:9

possible 19:1328:9 131:20144:10152:14,19169:15,19175:6,16,17178:6,20 218:2244:13

possibly 109:19142:2,22 194:16243:4

post-1997 162:9

post-doctoral 18:2

postulate 137:12144:17

potential 25:17132:4 139:22142:4 178:5

potentially 109:3146:11

power 89:11

practical 75:11105:19

Practically 281:1

precise 108:14

precisely 78:19161:19 164:2,10173:17 283:18

predict 112:19183:14 186:9

predicting186:8,18 188:1

predicts 192:5

predisposition171:3 173:1

predominantly18:13 57:8 58:766:17 185:16245:2

prefer 104:14270:17

preferred 245:7

prejudges 140:4

preparation225:18

prepare 150:1223:11 226:8245:22 279:4

prepared 14:2

16:20 130:4215:7 223:9231:4 234:4,16242:10259:16,19

preparing 158:1232:3

presence 245:7252:8

present 1:22 3:207:6,19 23:7 39:454:11 55:1664:8,12 65:4124:16 144:3240:16,19249:15 252:2288:11,18289:13,18

presented 47:3143:12 154:18155:7 180:16241:7 289:16,17290:3

presents 180:5

president 25:11

presume 252:7

presumed 159:22

pretend 92:6

pretty 23:14113:14 233:16248:15

previous 206:22207:1

previously 14:132:2,5 135:9262:18 268:21295:22

primary 25:14



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principals 25:11

print 297:7

printed 127:8

printout 182:20183:2

prior 31:16 53:1963:7 66:19,2267:14 76:2 77:1878:20 108:6156:2 185:7186:13 226:21

privileged224:2,8,19,20225:2,16 226:14

probability 87:889:2,5,21

probably 26:1640:8 71:18,2277:13 112:4,8178:20 182:4188:20 192:2,3201:20 218:4230:19 234:10283:2 287:7289:22 291:8295:16

problem 81:12162:8 177:14184:18

proceed 19:17135:15 285:18289:14

proceeding 14:22

process 115:2,3117:10,11

processes 17:9

produce 39:11223:1

produced 56:17

127:3 128:20149:8208:6,10,15213:1 219:15221:21 222:7223:4 228:6291:21 296:1

producing 49:2058:16 271:16

production 4:1245:3 56:9,2059:11 61:13,1862:1863:11,14,1764:7,13,1765:3,10 79:2,5146:21 149:5150:1 202:9,18204:14,18205:4,9 206:1,15208:3,22 209:15218:19 220:10274:8 285:20

products111:13,14 240:7277:15 278:1

professor 18:4,1319:1

profitable 239:13

program279:13,14,20280:2,6,10,14,21281:13,16282:2,7,9,18,21283:4,11,19,22284:2 285:6291:2 293:6295:12

programming89:13 260:4284:16

programs281:2,6,13284:17

project 108:10,15245:6

projected 105:5,9109:8

projection 152:7182:3 266:4

projections 68:1976:12 152:3181:9

pronounce 65:15

proper 141:3161:13

property 11:5281:16

proportion 87:18195:22

proportional266:1

proposed 12:17

protection 281:16

prove 124:10138:2

proved 173:6

provenance 217:6230:18

provide 53:1765:21 69:2 76:11188:21 202:19214:22 215:6221:3 235:17

provided 11:739:20 76:12120:14 132:22221:17 230:5,15261:13

262:2,3,10264:5,12 278:22293:13

provides 177:13

providing 144:12153:10

province 73:2276:15

provinces 15:2246:5,871:9,10,11,16,2173:13,20,21151:22 194:13259:12

proximity 171:1


psychological 18:5

psychologist17:16,17,19

psychology17:7,8,9,11,1518:1,13,18 22:18

Public 1:20297:1,19

published 31:133:18 37:6 38:442:8 47:5 144:4147:1

punitive 125:3

purchased 23:12

purely 29:18

purporting 189:19190:8

purpose 25:18225:14 288:16

purposes 110:18162:20 171:15



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172:5 249:13

pursuant 1:17

putting 27:8212:18

puzzled 217:4

Qqualitative 164:14

quality 143:21166:22 248:22249:19 276:12

Quebec 166:3,8

question 10:214:13 28:129:3,11 30:2031:5,11,19 33:1136:8,20 38:11,2241:16,21 42:1843:10,19 44:1648:20 49:8 50:151:12,14,19,21,22 52:3,22 53:2254:22 55:957:6,17,1958:4,18 59:9,2060:10 62:1263:1,21 65:670:14 73:1075:6,11,20 78:2079:4,6 81:2082:12 83:3 85:394:16 95:15 96:198:3,19 99:1101:3 104:3,11105:7106:1,15,18108:14109:11,18111:8,16 113:8114:8 116:17117:5,20 119:3

121:3 122:1,8123:16 124:22125:21126:10,15127:10,19129:12 130:1131:15 132:7,14136:6,22 137:18138:4 139:10,18140:3,17141:9,11,12142:7,15 144:22148:6 151:5,6152:22 155:2,16157:22 161:1,7162:1,11 163:16164:1,2,19165:14166:10,18,21168:2,15,21169:4 171:19172:4,12 173:8175:5 177:22178:13 180:3,14181:20 185:9186:12,21188:5,13189:11,19190:3,15 192:11193:8 194:7195:15 197:16198:4 199:3,14200:1 201:13205:12 206:12207:10,15 208:1210:15 211:6215:15 220:14224:14 225:22226:15 231:2238:13 240:9247:9 248:21249:6,17250:11,22251:13 252:4

254:4,20255:15,21256:3,14,19257:9,21258:2,21 260:12264:18 265:16266:19 267:6269:14 270:16272:10273:2,4,6,7,9,13,17 275:3,10277:9,17 278:5279:7 281:5,18283:13 284:5286:19 287:14294:6,12 295:2

questions 22:14159:12 161:4,9289:4 299:17

quickly 200:18201:21 247:21

Quigley 12:9,1013:1 267:13

quite 10:1 39:142:20 144:2163:1 182:11187:8 230:1253:21 259:22

Quito 110:9162:18,22

quote 85:7 150:12154:21

Rrace 170:15

Raising 89:10

ramifications82:14

ramp-up 197:8

RAND 20:11,16

range 28:8 29:863:5 86:6 90:493:15 145:10183:15

ranges 83:7

rapid 150:11,22153:16 154:3,21

rapidly 144:11,18152:15 153:9

rate 67:19,2268:3,4,15 77:6,778:3,5,22 83:1185:6,8 86:1387:6,14 88:2089:16,17,1890:9,10,11,1791:4,9,20,2292:1,16 93:15,2197:3,9 101:9104:6,9,16 117:9153:21160:19,21161:15,17,18170:22196:1,5,7,12213:18214:6,9,13

rate-limiting 76:7

rates 77:4,1281:1,5,8,11,1282:1,9,15,2283:8,1687:3,19,2294:2,4,7,1296:9,16 137:7,9143:13 145:9146:2 153:15163:13,21164:11 187:4193:18,21,22



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194:4,5,17,22195:2 196:7266:4 282:13

rather 82:2 138:10147:20 148:3,22157:6 169:19170:22 176:17209:15 248:11276:15

rating 22:13

Ratings 22:10

ratio 69:2170:3,11 73:274:9 85:9,10196:10266:21,22

ratios 74:6,7267:11

Re 267:13 299:6

reach 163:8

reached 147:13198:2

reaches 147:12162:5 205:16

reaction 234:6238:20

reading 16:1169:14 80:16110:12 111:11130:9 136:10148:17151:17,18157:22 180:4208:21

reads 242:20

ready295:17,18,19

real 122:20 123:1167:12,14,20

168:17 169:8178:4 179:13,15180:11 181:1,5182:15 198:11

reality 104:4198:18

realize 101:8

really 23:5 32:1439:1 48:21 100:1154:10 161:21180:9 181:10209:17 212:10249:20

reason 8:14 49:565:22 82:8,18106:17 127:13149:8 156:12162:5 163:6,19164:12 166:21171:2 173:2,3174:5 175:13176:2 204:10221:6 233:14237:18 250:6277:13 286:20

reasonable 82:1983:1 101:4 259:2

reasonably 260:8

rebuttal 40:21

rebuttals 41:4

recall 9:20 14:329:17 40:3 45:1149:9,16 52:659:5 64:20 66:2169:17 98:16105:13 107:15108:1 112:8132:2 148:16194:20 214:21215:5 217:21228:1 230:1

231:19 233:2234:11236:16,18,20243:7,11 247:11264:12 288:14289:6,21 290:1

recalled 14:8

receipt 299:14

received 15:317:3,7 56:13131:21 164:22223:10 227:7230:12,19232:13

recent 13:9 30:931:13

recently 40:9

recess 20:5 79:15134:4 177:5235:2 285:14

recognize 208:14

recognized 43:7

recollection 29:1351:6

record 6:19 8:519:18,1920:2,4,7 51:2079:11,13,17134:1,3 135:4141:14 145:5157:15,18,19158:12 177:2,4,7190:17 224:16234:20,22 235:4262:7 273:6,15285:11,13,16295:17,18,20296:6

recreate 283:10

redo 189:9

reduced 87:15103:14 297:7

reduces 115:5

reducing 250:17

reduction 100:7

refer 119:15192:12 266:5

references 37:6,746:15,16

referred 11:866:15 69:12107:19 116:10162:21 220:1232:17

referring 77:1196:17 167:8179:15 210:9238:7 284:15

refinements291:18

reflect 138:10242:13,18 246:2

refresh 51:6

regard 34:5 42:6143:21

regarding 34:835:8 61:7 299:12

regardless 268:15

region 4:13 48:12146:22 168:10171:12,13194:19 195:1208:7,20 211:1,3216:5 250:4,7,8251:2 274:8

regional 194:14

regions 94:19



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95:2,7 96:8168:10,11179:17 195:21229:9 244:6,9

registered 162:18

registry 162:19,21

regression 18:1528:14

regulations 293:14

reject 155:9 162:5163:6 173:3

rejected 185:12

rejecting 156:15

related 35:12 38:239:2 65:9 111:13171:17173:5,10,12,16209:18 218:13222:19 233:18235:13 239:3,9255:6 266:2285:22286:15,22287:3,11 297:10

relates 133:6160:13

relating 35:339:14,18 43:1117:2

relation 50:6

relationship 69:370:11 293:16,22

relative84:11,14,17,20,22 85:5,1486:1,3,5,8 103:7164:15 233:11

relatively 44:3142:16 144:13

153:10 200:17265:1 272:8

relevance 59:6

relevant 40:13172:17 232:1

relied 37:17 46:1547:6 53:9,1276:20 135:21138:1 147:20148:21 150:20

reluctant 109:13

rely 46:20 47:1140:14 161:4,6230:6 231:6

relying 67:4127:22 136:4177:19

remain 255:17

remains 146:19

remediate 249:21250:3 253:2

remediation247:20248:4,9,11,13,17249:4,10,14250:1,15251:6,18 252:1260:3,6,9,14,18261:10

remember 36:267:4 155:11208:21 230:19231:15 243:17269:3

remind 24:4

removed 111:21186:1

rendered 267:15

repeat 51:13 95:14123:16

repeated 145:3

rephrase 47:9200:3

report 4:6 5:615:17 16:4,1330:11 35:14,1741:8 46:2,1654:3 56:1960:4,20 61:563:4 69:8 76:2277:2 78:18 79:2083:17 84:1885:16,21 88:1,1090:12,15102:2,6,21107:19 111:19128:7,10 130:18133:11 161:4164:4 171:6184:12 192:15193:14 195:6196:19 198:6202:11,18 208:9213:6 215:3221:1 223:13228:21229:13,20230:9,14,15,22231:3,6 232:3235:10,14237:7,12,19244:15246:10,17,20247:2,3 254:6258:15 259:21262:14,15264:11 267:17269:1,4 270:2275:11 283:16290:14 291:22

294:10

reported 159:2160:2,6 172:8184:11,15 186:6190:11 194:4

reporter 6:15 7:1051:20 141:14145:5 190:17224:16 273:15

Reporter/Notary299:21

reporting 1:206:14,17 83:15300:1

reports39:10,17,1940:22 41:1143:6,9 162:4190:11 215:1230:12 283:7

represent 6:2072:17 96:5183:10 197:6262:21

representative26:7

represented 12:5218:16

representing294:10

represents 96:3183:5

reproduce 283:16

request 214:19228:6 231:4236:14,16,21237:1,4

requested 51:20141:14 145:5



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190:17 224:16273:15

required 125:1260:3

requires224:2,12,19225:16

rerun 278:3,11

rescaled 72:15

research 15:8,1118:5 21:6,13,2123:4 34:14,17,2035:1 38:1 42:5,7132:12 294:16

reserve 216:22

reserves 63:8

reside 8:12 170:18

residents 15:2039:3 46:3,12,1448:5,11 50:7235:13 259:11

resides 91:8

residing 80:14,19

Resource 23:1224:14

resources 24:19

respect 22:7203:20 239:12

respectfully225:22 257:10

respective 1:22

respond 22:14

respondent 1:103:11 6:5 7:4

response 42:2243:4,16,2244:7,10 167:5

265:18,21266:10

responsibilities22:11

responsible 19:6232:12 299:12

responsive 223:1

rest 194:19

result 28:14,1868:18 69:16,2170:3,16 73:176:16 113:19,20114:3,5 115:22130:21 146:6147:19 180:7195:19

results 110:2129:21 136:8,14139:15140:13,14143:6,10 148:14154:18 170:7172:1 177:13178:10 219:10258:19 259:5274:10 283:16288:18,20

retained 228:14

retired 15:13 24:3

retirement 24:4

reveal 224:2,12,19225:16

reversal 83:9

review 14:15 29:535:7,1539:2,6,13 43:14147:9 148:8,19156:18 158:1161:3 165:16

167:5 222:6,22271:10 295:14

reviewed 14:116:12 38:1939:17 41:11137:6 149:8167:3

reviewers 165:21

reviewing 35:267:2 173:4

revise 190:19

revision 159:9219:5,7

revisit 189:13271:12

rework 189:17

right-hand 84:6136:13 174:14209:4

right-most 136:15

rights 217:1

risk 40:5 48:652:17 53:4,13,2057:4,1458:1,9,1359:14,16 61:765:4,9 83:1984:11,12,14,17,20,2285:1,6,15,1886:2,3,5,8 87:1289:17 90:6 91:2192:13 97:6,1099:13 103:15109:22110:8,12,13111:5 117:7118:6,7,15 119:1121:8,10,13,18122:6,13

123:4,7,12,22126:7,21 135:21137:7,12 139:13140:1,11 141:3144:10,20145:17 146:7,16147:17 151:3152:14 154:8,14155:9156:3,16,22157:1,2 164:8166:16 170:4171:6,16172:6,7,9 178:11187:3 191:20195:8196:8,10,16197:4,7,11,12198:1,12 199:1,7200:5,7202:1,5,20203:1,12204:6,16205:14,16,21206:6207:11,12,21209:19 210:7211:16 250:17252:8,11253:7,12254:11,13,14,16260:5,13,20265:9,14 266:2268:18 269:20271:15272:1,8,18273:20 274:3,6275:20 277:6282:12 293:7

risks 87:18 163:9

Riverfront 3:15299:3



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road 117:18

Roche 231:13233:4,10,17235:18 238:7239:2 240:2243:3

R-O-C-H-E231:13

Roche's 233:3

Rockefeller 18:3

Rockville 8:13

role 11:6 228:15232:2,11 233:3

room 288:13

roster 25:20

rough 192:8

Rourke 1:9,15 4:65:6 6:3,5 7:138:2,6 15:17,1816:5 45:21 50:1667:10 79:21 80:288:5,8 127:3130:15 131:10135:7,13 147:7183:3 190:22191:7 208:15213:1,7 216:11219:20221:10,15234:16 235:7238:6 242:11245:22 259:17262:21 278:15285:18 294:15296:3,9 298:4,22299:6,10 300:7,8

R-O-U-R-K-E 8:7

Rourke-Native4:7,9,10,16,17,1

9 5:2,3,4,550:10,12,17 84:7129:15 208:6219:16 234:14238:1

routinely 286:3

row 74:21

rows 183:10

RPC 23:4,14,2025:1,2

RPR 1:19 299:21

Rudnick 13:3

rule 176:18

rulemaking 44:1

ruling 47:9267:19,20,21

run 22:1

running 22:16

runs 66:7 279:20282:9

rushed 142:13

Ssales 22:2,8

sample 22:15

samples 25:6,7,8

San 46:17,20 47:648:15 49:1250:8,17 51:752:1753:5,9,11,1757:9 58:8 83:2284:11 85:14,17110:3 117:8118:6,21 131:5133:9 135:18137:22 138:22139:22 143:20

144:20 146:15147:13,21148:3,22 150:13151:2,10,11,19152:11 153:3154:1,7,9,14,17155:9,12 156:3,4157:9 161:14,20162:7,8,16163:9,12,20164:7,9,15165:11,22167:3,6,8,20168:16 169:8170:5 171:5172:2,8 173:4177:19 178:11201:9 202:1,5,19203:1,7 204:2,6205:21 206:19207:11 210:13211:10,13229:10,20233:12 248:3271:14 273:20274:11 275:7,19277:6

Satin 3:21 7:9

saw 154:12 160:7194:20 211:16263:13 265:22

scenario 123:10,21

schedule248:11,14

school 17:221:2,3,19

science 37:5 205:6254:18

sciences 20:17

scientific 17:1337:8 57:2 81:18

85:17,19 89:1297:22 98:20 99:2100:22 111:4118:17,19,22119:5,10 148:2156:14 195:12197:13,22199:11,18 206:5250:19 251:22255:11 261:6271:7,14,22272:11 273:19

scientifically140:12 255:22

scientist 30:18

scientists 37:10,1741:5 121:19

scientists/statisticians31:3

scratch 291:3

Sealed 11:8,12

search 40:2,11120:8,9,10232:16

searches 40:4232:13,15

Sebastian46:17,20 47:648:15 49:1250:8,17 51:752:1753:5,9,11,1757:9 58:8 84:11110:3 117:8118:6,21131:5,10133:9,17 135:18137:22 138:22139:22 143:20144:20 146:15



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147:13,21148:3,22151:2,10,11,19152:11 153:3154:1,8,9,14,17155:9,12 156:3,4157:9 161:14,20162:7,8,16163:12,20164:7,9,15165:22167:3,6,8,20168:16 169:8170:5 172:2,8173:4 177:19201:9 202:1,5,19203:7 204:2,6211:10,13229:10,20233:12 248:3274:11 275:19277:6

Sebastian's 83:2285:14,17132:12,17150:13 163:9165:11 171:5178:11 203:1205:21 206:19207:11 210:13271:14 273:20275:7

second 19:19 25:232:16 68:18 69:473:3,4 89:4,5127:7 176:19209:21 234:5

secondly 140:5239:20

secret 284:3,11

section 151:18155:18 236:5

237:7

sections 81:16

seek 233:17 282:2

seem 60:4 74:10146:11

seemed 40:13 47:2232:13

seen 14:8 51:8116:1 132:21133:1 147:6209:10 211:15216:9,12221:14,16,18222:4 223:8263:14,18295:12

selecting 148:3

selection 176:14

Seley 3:4 7:2

seminars 18:17

send 222:11,14227:9,10

senior 22:10 23:1324:11,12

sense 70:2 94:2096:10 125:22137:1,15 188:22193:11 247:18271:2 284:7295:8

sensitivity141:18,21 142:4143:1,3 156:21157:5

sent 222:8,18223:5 242:5286:7

sentence 77:4

159:5174:10,11,14263:11

separate 246:17

separating 76:16276:16

September 15:1821:22 245:17246:4 259:9288:3

series 69:11,13,2072:2,5,10 73:3,474:14,15,16,2075:9 76:10107:22 179:12216:4

serve 254:6

served 15:14 56:22221:10

serving 18:5

session 135:1296:7

setting 18:6

settlement 26:5,928:17,18 30:231:8

settlements 28:18

seven 198:13

seven-hour 295:10

severe 138:14139:8,22 140:4141:1,4 143:3

severely 140:14

sex 159:3

shareholder 11:1413:6 24:1

sheet 298:9 299:15

ship 32:10,12,13

shipyards 32:16

short 20:5 79:15127:19 142:16176:22 177:5235:2 285:14

shoved 75:3

shoves 75:4

showed 143:12253:20 271:9

showing 53:1283:10 191:9216:4

shown 94:12130:19 154:19189:16190:2,13,18198:14

shows 49:14 89:4150:11 152:19160:20 164:5191:10,18 199:9

sic 48:4 70:5 73:190:18 153:12154:21 167:7173:20 275:19

sides 180:5

Siemiatycki 50:12165:18 166:4,14167:2,7 173:19177:10,11,18178:8

Siemiatycki's166:22 167:19168:16 169:7173:4

sign 294:9

signatory 129:4



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signature 278:19299:11,13,15300:22

signed 227:20298:9 299:15

significant 275:20

significantly188:3,11

similar 125:19126:12 174:18216:9 261:9

similarity 281:7

simple 29:11 43:21256:14 278:10

simply 61:5 100:7168:15 207:10263:6 264:2

single 87:5265:10,13

sip 19:22

sir 16:7 52:1560:14 95:16 99:9118:4 125:6133:3 136:1209:12 244:7263:11 266:8276:3,18 284:9

sit 82:8 253:22

site 45:3 173:21

sites 40:8

situation 28:11

six 39:7,17

size 146:8,14

Skipping 159:5

slides 4:18 216:4

slight 182:2

small 26:12,14

76:8 115:9161:14,16 220:4225:11 288:12289:20

smaller 58:6 73:1992:4 145:13

smallest 68:22

small-ish 270:21

smoke 287:5

smoking 287:4

smooth 216:21

smoothly 217:9

so-called 160:20

social 20:17167:15 169:10

software 19:7,8,921:14 22:1279:16,18

sold 11:11

solely 29:9 61:2081:6 97:11,14,15

solid 200:18

solvent 36:12

solvents 36:1,3,538:2

somebody 269:9295:16

someone 15:560:7,15 90:16110:8 222:22223:2 283:14

someplace 7:18171:12

Sometime 226:20

somewhat 22:648:6,13 119:6

somewhere 7:1829:19 192:2265:22 292:11293:2

sorry 9:20 29:1243:20 51:13 52:467:13 72:13 81:487:17 91:1,1495:6 131:2 139:2141:10 158:14199:15,22202:15 213:17217:9 246:7263:8 267:7271:2 290:1

sort 17:16 24:2270:10 115:2125:21 150:4230:7 278:9281:20

sorts 88:22 125:11

sought 281:15

sound 19:22

sounds 70:1

source 159:11162:14 163:1285:5

South 2:13

span 101:22112:22 113:2,12145:11 213:16291:4

Spanish 16:7,13128:22

speak 16:9

speaking 103:13258:4,7

specialist 6:15

specializations

17:8

specific 137:10194:14 276:2,8

specifically 49:1560:17 280:11

specificity 34:10107:15 108:2

specified 28:10229:12,22

specify 49:15,18

speculated 107:1

speculates 106:11

speculating 101:18106:6,8

spell 30:8

spent 22:19 25:797:12 242:13246:3

spills 263:5 264:1

split 86:15 87:9,1788:17 218:2

Splitting 269:15

sponsored 132:11

spreadsheet 189:4

spreadsheets223:9

standard 87:889:3 143:15

standards 294:4

stands 89:9156:6,12 224:17270:18

start 157:22202:16209:5,9,14,22254:7 264:20



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282:14

started 40:9 63:14161:2209:10,12,15216:16 246:14254:2

starting 17:162:19 202:22271:7

startup 220:18

state 8:4 18:11,2119:4 20:9 50:2263:6 73:22 77:4138:9 139:7144:8 195:7202:17 241:11257:4,11

stated 153:13198:5

statement 61:1663:10 64:22116:19 122:9123:5 126:16,17215:18,19 295:3

states 1:1 6:645:10 66:8 167:8263:3

station 218:17

stations 63:14,17209:1 218:16

statistical 19:6,1121:6,13,14 279:3

statistically 275:19

statistician 18:622:10,19

statistics 18:14,1520:21 21:4,722:22 152:4255:22

status 50:19,21242:21 243:2

stay 157:19212:4,6

steadily 216:18

steady 215:10

steer 254:12277:18

stenographically297:7

Steven 287:22

stop 115:12 212:18282:15

stopped 117:22165:2

stops 102:2

straight 261:3

strata 151:22

stratified 159:2

Stratus 129:9261:17,19262:10,22

Stratus-Native4:11

streamed 7:17

strength 164:15

strengths 233:11

stress 49:4

strike 168:12

Stripped 167:10

strong 119:4177:13

struck 242:1

student 20:15

studied 42:15,19

48:6 57:8 58:7211:11,14

studies 36:1537:3,9 39:857:12 136:19137:8,11,16,22138:11 165:11166:15 233:12

studying 211:16

study's 167:9

stuff 149:18222:12

stupid 205:1

subcontract295:13,22

subject 234:9

submitted 16:4,13278:14

subpoena 4:20221:10,17

subsequent 287:3

subsequently 26:3227:7

subset 71:11

subsets 72:17

subsidiary 14:5

substance 225:1288:22 289:6

substantial 117:9

substantially 77:7182:8 211:19228:20 290:6291:18

substantive 226:5290:11

substantively234:1 235:19

substitute 92:5

subtle 119:6

subtract 90:6,1392:17 93:9

Sucumbios 15:2146:4 71:7 150:19193:22 194:5,9259:12

suffer 115:19,21

sufficient 263:4,22

suggested 48:5

suggesting 126:5145:12 194:12

suggests 44:5

suit 11:15 13:6,19119:21

suits 35:12

sum 35:2

Sun32:8,10,12,13,15

supplemental247:11

supplied 159:7

support 261:6271:11 274:1,13

supports 200:21256:12

supposed 149:17

sure 19:21 20:1452:7 66:13 69:10106:20 109:6115:10 123:18125:7 132:2143:8 145:1170:12 176:5,7194:10 208:18217:21 227:22



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233:6,19,20256:10 262:13289:9

surmised 175:15

surprised 113:16149:9

survey 25:6 66:16129:22 133:6,10

suspect 29:20102:19 118:13182:8 252:16254:7 262:15

suspicion 174:1

swear 7:11

Swedish 265:22

switch 73:5

switched 68:1191:12

switching 74:19

sworn 7:15 135:9297:5

synonyms 235:16

system 21:15,1722:13 56:18119:17 121:17122:17,18 126:2185:1 236:8,20237:15241:13,17243:22

Ttable 55:20

67:9,10,11 74:575:2 77:5,1479:1 83:18 84:1986:1,4 91:593:14 94:7,1295:3,8,11 96:2

97:5,6,10 102:3103:8 119:15150:5,10 154:1160:18 162:2163:7 164:4184:9 193:17208:22 213:9253:20 254:5271:9 283:15

tables 76:21

tail 102:21113:4,16,17114:1,12 117:3,6

taking 8:17 69:2170:3 95:20112:20 140:15148:18 206:5

talk 20:22 247:13

talked 228:15263:18

talking 90:22158:18 174:15188:18

tape 6:2

target 175:17

task 15:6 40:14,15281:7

taskmaster 233:8

taught 18:1833:21

T'D 3:1

TDP 28:7 29:1030:4,5

teaching 18:1366:9

team 242:21 243:2

Ted 227:7 228:17231:9 236:18

243:3 245:6288:12

telephone 15:3223:15

television 22:14

ten 46:17 59:2197:5 198:2200:7 202:3211:14 212:1260:10,21 272:9273:7

tend 105:19149:16 200:17

tended 37:6,9153:14

tends 143:13

ten-minute 79:9

tenth 197:9 205:16

ten-year 66:7197:17

term 78:18 115:4137:9 284:6

terms 40:20164:16 206:14227:5 265:1

test 104:15 214:11

testified 7:1513:15 14:18,21135:9 156:18190:6 268:21295:1

testify 8:18 14:11

testifying 11:17,1912:10,13 13:1214:7 224:4

testimony 8:15 9:612:15,20 13:22144:1 176:9,13

185:15 186:13226:6 252:14262:5 270:4297:4,6 298:6,7

testing 171:22

Texaco 32:2154:13 209:1

Texaco's 208:7,19

TexPet 55:6249:14 268:7269:20

TexPet's 215:11229:4

thank 19:16 51:1779:10 95:17123:19 148:12235:20 243:20296:3 299:18

that's 12:8 17:1626:12 27:1936:13 38:18 42:445:19 46:1650:18 52:3,6,1068:6 70:2 71:273:17 74:11 76:680:10 86:888:9,15,2189:3,7,1590:8,20 91:1492:1,16 93:2,2094:4 97:1,8103:20 106:9,18108:3 110:2,4112:22 115:4,13116:18 119:4,11122:9,21 123:5125:1 127:17130:5,9,15,21136:12 137:9141:2 142:12144:2 147:19



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148:1 149:21150:16 154:6155:19 157:20159:15,22160:14,16 165:8170:16 171:11174:19 177:1178:3 179:20183:4 188:22192:17,19193:20 194:2199:9 200:11201:7 202:21203:21 211:20214:10,18 215:8218:8,21 225:20230:10 244:14246:13 250:8251:10 253:19258:10 265:20267:8,20268:1,20269:5,6,8 276:19277:4,10283:9,20286:6,10 290:3291:1 292:12

themselves 6:2056:21

theoretical 240:6283:5 291:3

theoretically239:3,9

thereafter 297:7

therefore 153:20268:13

therein 247:5

there's 43:16 49:458:13 70:10 76:780:5 88:22 99:12109:21 115:3

118:22 119:5120:18 130:12149:7,13,22155:17 158:5171:9 175:12179:4 180:1,18181:5 187:10198:16199:1,10,18204:20 205:8207:3,4 217:3218:16 244:5247:20 250:6252:11 253:8265:17,20

thesis 132:17,19

they're 25:7 66:1173:19 74:6 81:694:10 96:12,14113:6 114:22115:15 123:2146:12 149:17168:6 184:16189:20 190:3200:4 202:15212:20 219:3224:7 286:5

they've 116:14

third 159:5 162:17198:7 240:15

thoroughly 38:8

thousand 92:1594:5 205:1

three-week 291:15

three-year 197:7

Throughout58:11,12

throw 174:1

thrust 295:7

Thursday 288:14

till 23:15 165:9229:22 261:20289:10 290:8

Tillinghast-Towers 14:2,16

tissues 200:18

title 15:18 24:946:2 172:13191:1 228:20

titles 40:10

today 8:15 190:9223:12 226:3,12253:22 259:20269:3 270:14272:22

today's 296:7

Tom 15:3 227:3

top 80:5

tort 119:12,17121:17122:17,18 124:8126:2 232:18236:8,12,20237:7 241:13

total 10:8,1277:15,21 78:281:1 86:1,1287:3,6,1889:17,20 90:9,1092:1,21,2293:5,6,10,11,1294:20 95:11,2096:8,12,13 97:9102:1181:10,16,22182:6 183:18,22184:12187:13,19,22189:2,4

193:1,10,11276:2,15 282:22283:1,4

totally 54:14148:14 232:1,4

totals 95:3,8 96:17203:2 282:16283:8

towards 32:2133:2 263:2 293:1

townships 74:2

toxic 232:18

toxicology 231:17

TPH 239:4,9,16,21

track 116:2,12

tracking 22:3

trade 284:3,10

train 295:15

training 33:15

trait 170:19

transaction 11:9

transcript 5:22273:10

transcription297:8 298:7

translate 206:13

translation 16:12128:20,22

translators 246:21

treated 17:18

treating 17:1544:20

trend 77:5,22 78:183:9 93:21

trended 77:19



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trending 79:1 94:2

trial 13:15 14:18

tribe 170:16

tried 254:11

trip 236:17

true 16:19 27:1952:10 62:1597:16 118:20122:9 123:5151:2 152:20153:13 154:6155:19 170:3185:7 186:19192:17199:10,18201:7,8 211:7233:2 242:1246:13 250:13252:10 265:20268:20 298:6

truncate 100:3

truncated 100:16

trust26:3,4,5,9,11,1228:6,8 30:2,4,631:12

trusts 30:7,931:8,13

try 88:3 104:17155:11 163:14229:3 295:7

trying 14:3 40:395:18 109:2113:15 117:12123:15 188:6198:11,18,20201:14 206:13239:11

turn 46:1 50:16

54:3 63:4 77:280:1 83:17111:19 117:12129:15 130:11137:11,16 195:6221:19

turned 16:17,1854:12 56:16172:20 246:22266:1 268:22269:9

turns 25:5 32:1575:22 195:18,21

TV 22:15

twice 11:4 93:13288:3

two-year 18:2

type 13:17 14:2127:14 32:9 35:20137:15 239:15276:8

types 40:20 146:5211:3 276:7

typo 91:14

UU.S 13:18 119:17

120:17 121:7,11122:5,13,16,18123:10,21125:13,19 126:2237:12241:13,18,22

UCLA 17:3,821:19

Uh-huh 11:1372:22 183:12203:11

ultimate 86:16

ultimately 56:2199:18 126:9227:12 231:6237:6 240:3

unable 226:9

unaware215:16,19

unclear 140:10153:1 172:17

uncorrected 141:7

undercount 81:15

underestimate144:13 153:19

underestimated146:8,14 153:11

undergraduate18:14,18,19

underlying 159:6

understand 7:1738:18 43:4 47:2057:18 60:688:9,19 98:10119:14 123:15124:9 138:21139:6 145:20170:10 172:4190:12 206:14236:22

understanding10:1 56:660:20,22 64:11120:16 121:1217:6 238:10246:20 292:1,3

understood269:5,8

unexposed 85:8109:22 146:10150:12 151:2

154:4,22 160:21164:6 196:5,11251:2

unfair 190:2

uniform 58:9

unique 149:14

United 1:1 6:645:10 66:8

university18:3,12,2219:5,10 21:3,8166:3,5

university's 19:7

unless 100:19212:6 270:18

UNR 26:4

unreasonable126:3 267:15

unremediated252:8

unvalidated257:4,11

upon 138:1 149:15

upshot 218:22219:5

useful 245:10

usually 83:5,12195:19 198:6266:17,20,21

Vvalid

62:10,13,14,21104:1,14 189:20

validate 105:14255:2,8 256:5258:19 259:6



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validated 30:17255:13,17 256:8258:18

validating 179:2256:1

validation 179:6256:13

validity 104:15214:11

valuable 36:15

valuation 27:12,13126:11

valuations 27:9

value 9:9 12:1627:16 28:2029:10,14 40:1886:2 122:17,19126:2,20 141:19236:6 241:12,13

valued 61:3

values 27:2028:9,17,18 30:231:8 142:9241:16,18,21,22281:9 291:4

vanish 113:1

variable 176:10,14260:7

varied 255:18

varies 29:9

various 40:356:11,14,20283:6

vary 27:13 30:231:9 82:17140:11 141:18281:3

Vasquez 15:3

227:3,5

vast 192:4

verdict 119:18120:5

verdicts 28:1929:21 119:13120:2 121:7232:18

version 16:13,19114:20 253:17

versus 6:5 164:15168:7 196:21223:4 233:12239:21

viable 63:7

vice 25:11

video 6:1579:14,17 134:3135:4 177:4,7235:1,4

Videographer3:22 6:2 7:6,1020:3,6 79:12,16134:2 135:3177:3,6 234:21235:3 285:12,15296:5

videotape 6:3296:7

Videotaped 1:14

view 172:19216:20 255:19

voice 179:8

vs 1:8 299:6 300:7

WW.R

11:4,6,7,11,15

12:4 13:5,1326:5

wage 125:18

wages 125:11,20

wait 19:18 90:2295:14 103:7233:1

walked 25:1

War 32:16

Washington1:12,19 3:76:11,18 300:4

wasn't 70:15149:10,12 182:8218:4 226:10232:1 258:18271:4,5 290:7

waste 273:14

water 19:14

Wayne 18:11,2119:4 20:9

weak 136:19 137:1

weaknesses 233:12

weathered 37:20

web 40:8232:12,15

website 66:6,13,2168:19 104:20,21107:14 159:19

websites 30:7

week 234:3

weekends 245:3

weeks 290:22291:4,10

weighed 121:19

weighted 84:15

Weinberg 15:5120:13227:12,16,21,22228:4,14230:16,22231:11 236:14242:5245:1,2,4,18288:6

welcome 296:4

we'll 72:4 86:17102:22 150:4157:18 177:1296:2

well-developed81:15

well-known 33:10166:2

wells 171:1 263:5264:2

we're 20:4,7 52:1692:6 127:22157:17 161:12184:18 188:18218:9 252:15272:3 285:16

Westenberger3:12 7:3,16,2110:7 14:12 24:2027:5,22 29:2,1630:19 31:4,10,1836:7,16,1938:10,2141:15,20 42:1743:9,18 44:1548:19 49:7,2251:11 52:2153:21 54:21 55:857:5,16 58:3,1759:8,19 60:9,1661:8 62:11,22



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198:3199:2,13,21201:12 203:3205:11 206:11207:14,22210:14 211:5212:11 215:14216:22 220:13223:19224:1,6,11,17,22225:15 226:3,13231:1,21 237:13238:12 240:8247:8,15 248:20249:5,16250:10,21251:12 252:3,13254:3,19 255:14256:2,18257:8,15258:1,6,20260:15 262:5263:12,16264:8,17 265:15267:3,5,8 269:13270:3,7,9,15271:17272:12,15,21273:5,11,16,21274:16 275:2,9277:8,16 278:4279:6 281:4,17282:5 283:12284:4 285:8286:18 287:13288:12 289:20292:14294:5,11,22295:15,19299:1,8

we've 79:8 87:16168:3 225:19259:20 270:14

whatever 44:1354:15 69:10 98:7112:7 168:8169:17 171:2,13173:2 175:13,16179:18 204:10205:16 271:10279:12

what-if 261:8

whenever 179:7203:19 254:9

whereas 186:7200:18

Whereupon 7:1220:5 79:15 134:4135:6 177:5235:2 285:14296:8

whether 36:437:20 60:17,1962:9 82:22121:10,13,17,20125:14,17 156:4169:22 171:16172:6,9,17 173:6180:1,18,22196:6,15 212:10217:13 230:2240:21 249:20258:2 269:19276:20

whoever 128:6129:20

whole 76:10102:22148:13,17155:10 157:11185:1 208:17282:10 286:9295:7

whom 6:20 35:7

41:5 235:17297:2

Who's 165:5

whose 86:2231:15,17 243:6297:4

Wilcox 26:6

William 18:443:21

withdraw 30:1547:19 60:5 78:10108:8 116:21143:18 172:6189:17 198:21211:12 253:10257:3 293:3

witness 7:11,1410:8 13:16 14:1420:8 24:21 28:229:4,18 30:2131:6,12,2036:9,21 38:1239:1 41:17,2242:19 43:11,2044:17 48:21 49:950:2 51:13,19,2153:1 54:155:1,10 57:7,1858:5,19 59:10,2160:11,22 61:962:13 63:2,2265:7 73:1175:7,21 79:5,1081:21 82:13 83:485:4 88:13,1694:17 96:2,2098:4 99:2 101:4104:4 105:8106:2,16 107:5109:12,19111:9,17 113:9114:9 116:18



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251:1 252:5,15254:5,21 255:16256:4,20257:10,16258:12,22260:17 262:8,17263:14264:10,19265:17 267:4,7,9269:15270:8,10,17271:19273:14,22275:4,11277:10,18 278:8279:8 281:6,19282:6 283:14284:6 285:9286:20 287:15292:16294:4,7,13 295:3296:4 297:4,6299:12 300:8

woman 231:15

women 275:15276:6,16 277:2

work 11:22 12:313:1 15:1125:5,14,2131:1,17 32:8,1841:6 44:22 45:553:8,11 67:273:15 132:3133:21142:11,13,17,19149:15 166:22171:20,22175:15 190:20201:15 207:6217:17 218:5227:13 228:9229:22 233:18234:1 238:22

244:22 247:1,18248:16 249:1,20261:21265:8,12,17266:3,9,12,13267:12 281:3283:10 286:14290:5,11,21293:11,14

worked 11:1920:17,18 23:5,1325:19 26:4 32:245:6,9,13 131:12245:3 262:2280:16

working 20:1925:8,13 33:845:12 233:15245:4,8,13262:22 280:18

workplace 9:1025:4 26:1927:2,8 29:1111:21

works 31:3 110:10192:2 205:13212:8 237:15

worksheet 183:3

world 32:16198:12

write 35:14 87:2188:5 183:22184:2 240:18241:15 280:1,7

writing 88:8227:18 246:10290:13

written 227:15280:17

wrong 37:4 54:14

129:17 130:5,8137:12,16182:9,13,14,17187:10 232:1,4270:6

wrongful 119:20120:17125:18,20180:22 241:16

wrote 64:21 128:6129:17,20165:22 176:6177:11 234:7239:2 241:5262:3

Yyear-by-year

282:16,22

yesterday 223:15225:9

yet 179:14 223:10240:20 290:5

York 18:3

you'll 150:10221:20

young 114:21115:1 117:10,11

younger 83:10105:21 109:15

yours 280:18299:19

yourself 38:739:19 41:1942:11 60:6 180:6223:2

you've 10:2133:18,21 40:2161:6 76:1 91:12



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116:1 133:2161:8 217:4218:6,18 263:18267:20 272:15273:7 280:16290:18 292:13

ZZefutie 223:19

225:11

zero 122:14 147:22148:15 156:22180:12 253:7,12

Zevnik 35:10

zoning 52:4

12/20/10 Deposition Transcript- Rourke

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Transcript of 12/20/10 Deposition Transcript- Rourke