UNITED ST A TES ENVIRONMENT AL PROTECTION AGENCY

WASHINGTON

, D .C. 20460

OFFICE OF CHEMICAL SAFETY AND

POLLUTION PREVENTION



### MEMORANDUM

DAT E: March 1 5, 2016



### SUBJECT: Ame nd ed: ( Amende d] Paraquat & 

### H e rbicid e Alternatives: Incident Rat e Ana l ys i s 

### (Changes: This document i s the seco nd amended memo

. This amendme n t make s corrections to

the proprietary designations in Appendix 

### C. Specifica ll y, four tabl es h ave been moved out of
Append ix C i n to the non -p ropr i etary Appendix B because they did not contain propri e tar y information.)

P C Code: 016061. 016063

D ec i s i o n No. 5 I 4946

Petiti o n No .: N A OP Barcod e: 432238

R egist r a tion No.: NA

R eg ulat o r y Act i on: NA

Case No.: NA Ri s k Assess m e nt T y p e: NA

T X R N o.: NA CAS No

. : 72%3- 72-5

40 CF R: NA MRID No .: NA



### FROM:

# /~ames Ng uy en

, Mathematical Statisticiani)i,/J

· C hemical Expos ure Branch (

Health Effects Di vis ion (7509 P )

and

E li zabeth Evans

, E n v iro nmental Prot ect i on Specialist

To x icolo gy and Epidem i ology Branch

Health Effects Di v ision (7509P)



### THRO U GH: David J. 

### Miller, Acti n g Branch Ch i ef
Toxicology and Epidemiology Branch

Health Effec ts Division (7509P)



### TO : Marianne Manni x, Chem i cal Review Manager

Ri sk Management and I mp l eme ntati o n Branch II I

Pesticide R e

- eval u at i on Division (7508P)

a nd
Tracy Pen y, Team Leader

Risk Mana gemen t and I mplementation Branch III

Pe s ticide Re-evaluation Di v ision (7508P)

and Kelly Sherman, Branch Chief

Risk Mana ge m e nt & 

### Im plementation Bran c h III

Pesticide Re-evaluat i on Di vision (7508 P ) V e r. Apr.08

Page 1 of 1 3Page 2 of 13



### This memorandum is an amendment to the previous amended memorandum #D 431236

. This document is the second amended memo. This amendment makes corrections to the proprietary designations in Appendix C.

I. Summary of Incident Rate Comparison of Paraquat to Potential Alternatives: Diquat, Glufosinate and Glyphosate

 

### The incident rate (on a usage

- normalized basis) for high and moderate severity poisonings associated with paraquat use was significantly and substantially higher than that of two potentially comparable herbicides, glufosinate and glyphosate. More specifically, the incident rate associated with paraquat when considering high and

moderate seve rity poisoning outcomes (after normalizing by amount a.i. used) was estimat ed to be about 10 times that of glufosinate and 23 times that of glyphosate

. Diquat data was not part of this analysis because there are no high or moderate severity incidents. ( Ta ble 3)

 T he incident rate of all paraquat poisoning cases including high, moderate, and low severity outcomes ( normalizing by amount a.i. used ) w as estimated to be about 7

times higher than glufosinate and 13 times higher than g ly phosate. There is no

evidence t o indicate the incident rates for diquat and paraquat are different

. (Table B1, Appendix B)

 

### Among the cases reported to SENSOR

- Pesticides, poisoning incidents associated with paraquat tended to be more severe than those ass ociated glyphosate incidents

. The observed tendency of more severe incidents associated with paraquat than glufosinate was not statistically significant. Diquat was considered as an alternative to paraquat but was excluded from this portion of the analysis because diquat had no high or moderate severity incidents. (Table 4)

II. Background

At the 2/19/15 monthly manager’s meeting, the OPP Paraquat Team was tasked with developing an estimate of the incident rate of poisonings normalized to a “ per amou nt of

a

. i

. used ” basis , for paraquat (PC Code s 016061 and 016063) compared to other “comparable” chemicals. The team selected three other herbicides considered as potential paraquat alternatives : diquat (PC Code s 032201 and 032202 ) , glyphosate (PC

Code s 4 1730, 103607, 103605, 103604, 103608, 103601, 103603, and 128501 ) , and glufosinate (PC Code 128850 )

.

Data Sources To obtain the number of incidents, single active ingredient case reports 1 

### of the four

herbicides (paraquat + the 3 potential alternatives ) during the period 1998

- 2011 were

obtained from the SENSOR database. All cases of intentional exposures or multiple

1 Conducting single active ingredient queries is the standard protocol when conducting Tier I scoping, Tier I updates, and Tier II incident analyses. This is primarily in place to help ensure that symptoms and circumstances surrounding the cases for a chem ical are related to the chemical exposure and not related to another pesticide.Page 3 of 13



### active ingredient exposures were excluded from the analysis . Further, only SENSOR cases where the “application site” was recorded as being on a farm for a n agricultural crop were analyzed to ensure valid rate comparisons could be made

. ( See Appendix A

for additional details regarding the SENSOR data query parameters .) The final number

of SENSOR cases included in the analysis are presented on the left hand side of Table 1.

Table 1

. SENSOR

- Pesticides Case Counts / Agricultural Use Data from 1998

- 2011

a.i. Number of Incidents

Of Given Severity

(numerator) Usage Metric 

(denominator)

High Moderate Low Total Pounds used Acres applied Respondents

Diquat 0 0 3 3 700,000 2,200,000 9,500

Glufosinate 0 1 4 5 13,600,000 30,300,000 140,000

Glyphosate 0 24 124 148 732,000,000 870,000,000 2,400,000

Paraquat 1 19 52 72 27,000,000 58,000,000 200,000

 Usage metric from OPP Biological And Economic Analysis Division (BEAD) has been rounded to protect proprietary data

* Exact calculations using proprietary data are included in Appendix C.



### The right hand side of Table 1 p rovides the pesticide usage metrics. In order to

normalize the incident counts obtained from SENSOR

- Pesticide database to the “amount” of product use

, use/usage data was obtained from an agricultural marketing

firm which produces survey estimates regarding use/usage data . Three normalization (or standardization) parameters were chosen for each of the four herbicides during 1998

- 2011 period :

 the amount of AI used in pounds

 the area treated with the AI in acres)

 the (estimated) number of people handl ing the AI

These data reflect the same time period over which the incidents were accumulated and was

restricted to the specific states participating in the SENSOR program

. 2 

### The three normalization
(or standardization) factors can be considered as very rough measures of the geographical extent
of the use (acres treated), the intensity of use (pounds applied) and the number of users (projected sample )

.

III. Statistical Method

A s i s common practice when examining incident rates and incident rate ratios, a Poisson regression model w as used to estimate and compare the incident rates between the

herbicides. The number of incidents were normalized by setting t he logs of the total amount of a.i. used ( expressed per unit of 100,000 pounds), the total areas treated ( in

2 SENSOR states included: Arizona, California, Florida, Iowa, Louisiana, Nebraska, New Mexico, New York, North Carolina, Michigan, Oregon, Texas, and Washington.Page 4 of 13



### units of 10 0,000 acres) , and the number of respondents (in thousands ) as off set values in

the models (i.e. using as denominators to calculate incident rates)

.

Two different analyses were done to estimate two different incident rates: (i) i ncident rates of cases including only high and moderate severity outcomes ; and (ii) incident rates

of all cases including h igh, moderate a nd low severity outcomes

. The differences in

incident rate s among the herbicides were estimated and presented in term s of incident

rates ratios (IRRs)

.

In addition, a second analysis of the data was performed which explores t he t endency of one pesticide to have a systematically more severe outcome given a case than another (second “comparison”) pesticide. This potential for a systematic “shift” towards more

severe cases was evaluated by using a proportional odds ( aka ordinal logistic regression)

model . The tendency toward a case resulting in a more severe outcome with one herbicide co mpared to another is expre ssed in terms of an estimated odds ratios. For example, if an odds ratio > 1 results when herbicide “X” is compar ed to herbicide “Y”

, this indicates that incidents associated with exposure t o herbicide “X” are more likely to

be more severe than incidents associated with exposure to herbicide “Y”.

IV. Results of Analysis

The estimated incident rate of case s for high 3 

### and moderate sever ity 4 

### outcomes of the

herbicides a re presented in Table 2.

Table 2. Inciden t Rate for High & Moderate Sever ity

SENSOR

- Pesticides Cases

AI Incident rate per

100,000 pounds applied Incident rate per

100,000 acres treated Incident rate per

1,000 respondents

Diquat 0.0 (NA, NA) 0.0 (NA, NA) 0.0 (NA, NA)

Glufosinate 0. 01 ( < 0.01, 0.05) 0.003 ( < 0. 001 , 0.023) 0. 01 ( < 0.01, 0.05)

Glyphosate 0.003 (0.002, 0.005) 0.003 (0.002, 0.004) 0.01 ( < 0. 01 , 0. 02 )

Paraquat 0.07 (0. 05 , 0. 12 ) 0.03 (0.02, 0.05) 0. 1 ( < 0. 1 , 0. 2 )

* Exact calculations using proprietary data are included in Appendix C.



### As can be seen, t he results in Table 2 indicate that the incident rate for incidents classified as having high and moderate severity outcomes for paraquat are 0.07 incidents

per 100,000 thousand lbs. used. Alternatively, t hese rates can also be expressed in terms

of acres tr e ated and rate per 1

, 000 respondents and

– when expressed in this way

– are 0.03 incidents per 100,000 acres treated or 0. 1 incidents per 1,000 respondents. Thes e rates are substantially higher than those for glyphosate, glufosinate, and diquat.

3 SENSOR defines “high” severity cases as “The illness or injury is severe enough to be considered life threatening and typical ly requires treatment. This level of effect commonly involves hospitalization to prevent death.”

4 SENSOR defines “moderate” seve rity cases as cases of less severe illness or injury often involving systemic manifestations. Generally treatment was provided.Page 5 of 13



### The se comparisons can also be usefully expressed as incident rate ratios (IRRs) along

with the 95% c onfidence intervals which is standar d practice in the descriptive epidemiology literature. For high and moderate sever ity outcomes

, thes e IRRs are presented in Table 3.

Table 3. Comparisons of Incident Rate Ratio for High & Moderate Sever ity O utcomes

SENSOR

- Pesticides Case s

by pounds applied by acres applied by respondents

Comparison Incidence Rate Ratio (95% CI) P

value Incidence Rate Ratio

(95% CI) P

value Incidence Rate Ratio

(95% CI) P

value

Glufosinate vs. Glyphosate 2 .2 ( <1

, 17 ) 0.43 1.2 (0. 2 , 8. 9 ) 0.86 0.7 (0. 1 , 5.2) 0.7 4

Paraquat vs. Glufosinate 10 (1, 76) 0.02 10 (1, 77) 0.02 13 ( 2

, 97 ) 0.01

Paraquat vs. Glyphosate 2 3 (1 3 , 41) < 0 .01 12 ( 7 , 2 3 ) < 0 .01 9 (5, 1 7 ) < 0 .01

* Exact calculations using proprietary data are included in Appendix C..



### The results in Table 3 show that t he incident rate of high and moderate severity outcome

cases for p araquat was significantly higher than that of g lufosinate or g lyphosate using any of the three methods for normalizing exposure

. For example, the incident rate for high and moderate severity outcome s normalizing by amount a.i. used for p araquat is

about 10 times greater than the corresponding rate for g lufosinate a nd 23 times that of

g lyphosate.

The estimated incident rate for all cases (i.e., high, moderate, and low severity ) outcomes are presented in Table B 1 in Appendix B

, while the comparisons of incident rates of all cases including high, moderate, and low severity outcomes among the herbicides are presented in Table B 2

.

Finally, t he analysis of the tendency for exposure of one pesticide to result in a systematically more severe outcom e compared to another pesticide using the numbers of

SENSOR

- Pesticides i ncidents are presented in Table 4

.

Table 4

. Odds Ratios Associated With More Severe Outcomes

Comparison Odds Ratio (95% CI) P

- value

Glufosinate vs. Glyphosate 1.29 (0.27, 6.22 ) 0.75

Paraquat vs. Glufosinate 1.56 (0.32, 7.63 ) 0.58

Paraquat vs. Glyphosate 2.02 (1.25, 3.25 ) < 0. 01

* Exact calculations using proprietary data are included in Appendix C..



### The results in Table 4 show that reported c ases associated with p araquat exposures tended to be more severe than cases associated with g lyphosate ( OR= 2.02 ( 95% CI: 1.25, 3.25))

. More specifically, t he odds of a random case associated with paraquat to be more severe than one associated with glyphosate was a statistically significant 2.02 , orPage 6 of 13



### about double the odds . Comparing paraquat with glufosinate, on the other hand, produced

a non

- s ignificant Odds Ratio of 1.56 (95% CI: 0.32, 7.63 ) , reflecting about 1 1⁄2 times the

odds

.

V. Conclusion

The OPP Paraquat Team estimated the incident rate of paraquat poisonings compared to other herbicides with a normalized pesticide use metric. A Poisson regression model was used to estimate and compare two different incident rates between the herbicides a nd presented in terms of incident rates ratios (IRRs). The incident rate of moderate and high severity paraquat poisonings was estimated to be about 10 times that of glufosinate and 23 times that of glyphosate. 5 

### The incident rate of all paraquat poisoning cases was estimated to be about 7 times higher than glufosinate and 13 times higher than glyphosate. There is no evidence to indicate the incident rates for diquat and paraquat are

different.

Another analysis of the data was performed to explore the tendency of one pesticide to have a systematically more severe outcome given a case than another pesticide. The incident rate associated with paraquat when considering high and moderate severity cases, those associated with paraquat tended to be more sever e than those associated glyphosate incidents. The observed tendency of more severe incidents associated with paraquat than glufosinate was not statistically significant. 6

5 Diquat had no high or moderate severity cases for this analysis.

6 Diquat had no high or moderate severity cases for this analysis.Page 7 of 13 

# Appendix A.

Paraquat/Diquat/Glyphosate/Glufosinate Incident Query Methodology:

1. Q uery of SENSOR

- Pesticides case reports from 1998

- 2011

2. PC Codes query

a. Pc_code1 = 417300 or 103607 or 103605 or 103604 or 103608 or 103601 or

103603 or 128501 or 128850 or 032201 or 032202 or 016061 or 016063

b. Pc_code2 is null

c. This yields the total cases wi th a single active ingredient from above only

3. Intentional Exposures Removed: “Intnexpo does not = 1”

4. Includes only Application site (Appsite) = 01

a. Appsite 01 = Farm, all farms, orchard crop production excluding nursery,

livestock and forest

5. Results:

a. N= 228 cases total

b. (see “Single AI HerbicideCaseCompareAPPSITE1_becker.xlsx)

6. In the Excel output from Access, “Chemical” column was added and populated with
chemical name (diquat, glufosinate, glyphosate, paraquat) that corresponded to pc_code1

SENSOR

- Pesticides Query Results

Chemical Severity Total

High Mod Low

diquat 3 3

glufosinate 1 4 5

glyphosate 24 124 148

paraquat 1 19 52 72

Total 1 44 183 228Page 8 of 13 

# A ppendix B

.

Table B1 . Inciden t Rate for All

SENSOR

- Pesticide Cases

AI Incident rate per

100,000 thousand pounds used Incident rate per

100,000 thousand acres applied Incident rate per

1,000 respondents

Diquat 0.4 (0. 1 , 1.3) 0.1 ( < 0. 1 , 0.4) 0.3 (0.1, 1

. 0 )

Glufosinate 0.0 4 (0. 0 2 , 0. 0 9 ) 0.01 2 (0. 0 1 , 0.04) 0. 0 4 (0. 0 2 , 0. 0 9 )

Glyphosate 0.02 ( < 0. 0 1 , 0. 0 3 ) 0. 0 2 (0.01, 0.02) 0.06 (0.05, 0.07)

Paraquat 0. 3 (0.24, 0.34) 0.1 (0.09, 0.16) 0.3 (0. 2 5 , 0.4)



### Table B2

. Comparisons of Incident Rate Ratio for All

SENSOR

- Pesticide Cases

by pounds applied by acres applied by respondents

Comparison Incidence Rate Ratio (95% CI) P

value Incidence Rate Ratio (95% CI) P

value Incidence Rate Ratio (95% CI) P

value

Paraquat vs. Diquat 0.6 (0.2, 2) 0.44 0. 9 (0. 3 , 2.8) 0.84 1. (0.3, 3.3) 0.94

Paraquat vs. Glufosinate 7 ( 3 , 18 3 ) < 0 .01 7 (3, 18) < 0 .01 9 ( 4 , 23) < 0 .01

Paraquat vs. Glyphosate 13 (10, 1 8 ) < 0. 01 7 (5, 10 ) < 0 .01 5 (4, 7) < 0 .01

Diquat vs. Glufosinate 1 2 ( 3 , 4 9 ) < 0.0 1 8 (2, 35) < 0. 01 9 (2, 3 8 ) < 0. 01

Diquat vs. Glyphosate 21 ( 7 , 6 6 ) < 0 .01 8 ( 3 , 2 6 ) < 0.0 1 5 ( 2 , 16) < 0. 01

Glufosinate vs. Glyphosate 2 ( <1 , 4) 0.19 1 (0. 1 , 2 .4 ) 0.95 0. 6 (0. 2 , 1.4) 0.22



### The results show that the incident rates of all cases including high, moderate, and low severity

outcomes of p araquat and d iquat were significantly higher than that of g lufosinate or g lyphosate

in all three ways of normalizing by amount a.i. used (pounds), by number of treated acres, and by the number of respondents. For example, the incident rate of all cases including high, moderate, and low outcomes normalizing by am ount a.i. used of p araquat was estimating about 7 times of

g lufosinate and 13 times of g lyphosate.Page 9 of 13 Table B3

. Incident Rate for High & Moderate Severity

SENSOR

- Pesticides Case s

a.i. Incident rate per

100,000 pounds applied Incident rate per

100,000 acres treated Incident rate per 1,000 respondents

Diquat 0.000 (NA, NA) 0.000 (NA, NA) 0.000 (NA, NA)

Glufosinate 0.007 (0.001, 0.052) 0.003 ( <0.001 , 0.023) 0.007 (0.001, 0.050)

Glyphosate 0.003 (0.002, 0.005) 0.003 (0.002, 0.004) 0.010 (0.007, 0.015)

Paraquat 0.075 (0.048, 0.116) 0.034 (0.022, 0.053) 0.092 (0.059, 0.143)

Table B4 . Comparisons of Incident Rate Ratio for High & Moderate Severity Outcomes

SENSOR

- Pesticides Cases

by pounds applied by acres applied by respondents

Comparison Incident Rate Ratio (95% CI) P

value Incident Rate Ratio

(95% CI) P

value Incidence Rate Ratio

(95% CI) P

value

Glufosinate vs. Glyphosate 2.24 (0.30, 16.54) 0.4301 1.20 (0.16, 8.88) 0.8576 0.71 (0.10, 5.24) 0.7359

Paraquat vs. Glufosinate 10.21 (1.37, 76.11) 0.0233 10.37 (1.39, 77.30) 0.0224 12.98 (1.74, 96.70) 0.0124

Paraquat vs. Glyphosate 22.85 (12.62, 41.36) <.0001 12.46 (6.88, 22.55) <.0001 9.20 (5.08, 16.65) <.0001

Table B5 . Inciden t Rate for All

SENSOR

- Pesticide Cases

a.i. Incident rate per 100,000 pounds used Incident rate per

100,000 acres applied Incident rate per

1,000 respondents

Diquat 0.425 (0.137, 1.318) 0.139 (0.045, 0.430) 0.318 (0.103, 0.987)

Glufosinate 0.037 (0.015, 0.088) 0.017 (0.007, 0.040) 0.035 (0.015, 0.085)

Glyphosate 0.020 (0.017, 0.024) 0.017 (0.014, 0.020) 0.062 (0.053, 0.073)

Paraquat 0.270 (0.214, 0.340) 0.123 (0.098, 0.156) 0.332 (0.263, 0.418)

Table B6

. Comparisons of Incident Rate Ratio for All

SENSOR

- Pesticide Cases

by pounds applied by acres applied by respondents

Comparison Incidence Rate Ratio (95% CI) P

- value Incidence Rate Ratio (95% CI) P

- value Incidence Rate Ratio (95% CI) P

- value

Paraquat vs. Diquat 0.634 (0.200, 2.013) 0.4400 0.891 (0.281, 2.828) 0.8450 1.042 (0.328, 3.306) 0.9447

Paraquat vs. Glufosinate 7.354 (2.971, 18.205) <.0001 7.469 (3.017, 18.490) <.0001 9.344 (3.775, 23.132) <.0001

Paraquat vs. Glyphosate 13.340 (10.066, 17.679) <.0001 7.273 (5.488, 9.639) <.0001 5.370 (4.052, 7.117) < .0001

Diquat vs. Glufosinate 11.592 (2.770, 48.506) 0.0008 8.381 (2.003, 35.071) 0.0036 8.970 (2.144, 37.535) 0.0027

Diquat vs. Glyphosate 21.027 (6.705, 65.945) <.0001 8.161 (2.602, 25.595) 0.0003 5.155 (1.644, 16.167) 0.0049

Glufosinate vs. Glyphosate 1.814 (0.744, 4.423) 0.1903 0.974 (0.399, 2.374) 0.9533 0.575 (0.236, 1.401) 0.2231Page 10 of 1 3



# Appendix C ( pages 11

- 13 ) contains
proprietary pesticide usage data and is to be
removed prior to post ing in the docket.