Review of 2,4-dichlorophenoxyacetic acid (2,4-D) biomonitoring and epidemiology

Figures & data

Table 1.  General population, no a priori exposure, in µg/L (ppb).

Source	Group	Number of subjects	LOD	% < LOD	Selected percentiles				Max
					25th	GM (AM)	50th	75th
Centers for Disease Control and Prevention (CDC) (2009)	US 2001-2 population, Ages 6–59	2413	0.2	NR	<LOD	NC	<LOD	<1	95th = 1.3
Health Canada (2010)	Canadian population, Ages 6–79	5480	NR	95	<LOD	NC	<LOD	<LOD	95th < LOD
Hill et al. (1995)	US adults	983	1	88	<LOD	(<1)	<LOD	<LOD	37
Morgan et al. (2008)	NC adults	66	0.2	14	<1	NR	<1	1	5
Morgan et al. (2008)	OH adults	69	0.2	13	<1	NR	1	1	8
Morgan et al. (2008)	NC children	66	0.2	12	<1	NR	<1	1	3
Morgan et al. (2008)	OH children	69	0.2	3	<1	NR	1	2	13
Panuwet et al. (2009)	Thailand children	207	0.5	83	NR	<1 (<1)	<1	NR	3

AM, arithmetic mean; GM, geometric mean; LOD, limit of detection; NC, not calculated; NR, not reported.

Table 2.  Bystanders and rural populations, source of exposure expected to be indirect, in µg/L (ppb).

Source	Group	Number of subjects	LOD	% <LOD	Selected percentiles				Max	Comment
					25th	GM (AM)	50th	75th
Alexander et al. (2007)	Crop applicator, pre-application	34	1.0	30	NR	4	2	NR	231
Alexander et al. (2007)	Children of applicator day 2	52	1.0	11	NR	4	3	NR	263	20 children were present during the application process, 4 had opportunity for direct contact.
Alexander et al. (2007)	Spouses of applicator day 2	34	1.0	32	NR	1	1	NR	25	8 spouses were present during the application process, 1 had opportunity for direct contact.
Arbuckle et al. (2002)	Crop applicator did not use 2,4-D	83	1.0	NR	<LOD	1	<LOD	1	66
Arbuckle et al. (2004)	Children of applicator day 2	92	1.0	86	NR	(1.9)	NR	<LOD	100	Child with highest concentration was directly involved in the handling of the herbicide.
Arbuckle and Ritter (2005)	Spouses of applicator day 2	125	1.0	86	NR	1 (2)	<LOD	<LOD	100	Spouse with the highest concentration helped with the handling of the herbicide
Arcury et al. (2007)	Children of farm worker	60	0.2	58	NR	NR	<1	NR	NR	Only % detected reported
Arcury et al. (2010)	Farm worker	196 (784 samples)	0.2	2	NR	NR	NR	NR	NR	Only % detected reported, 4 samples per person, 30% had all 4 detections
Bakke et al. (2009)	Crop applicator of atrazine, Pre season	30 (61 samples)	0.2	16	<LOD	1 (3)	NR	NR	NR	90th percentile = 4
Bakke et al. (2009)	Crop applicator of atrazine, planting season	30 (157 samples)	0.2	4	NR	3 (23)	NR	NR	NR	90th percentile = 67
Bakke et al. (2009)	Extension agent, all seasons	10 (49 samples)	0.2	53	<LOD	<1 (<1)	NR	NR	NR	90th percentile = 2
Cooper et al. (2001)	Farm worker at pregnancy delivery	9	20	89	NR	NR	NR	NR	120
Curwin et al. (2005)	Crop applicator, did not use 2,4-D	27	0.2	30	<LOD	<1	NR	NR	NR
Curwin et al. (2005)	Farmer, 2,4-D sprayed by others	4	0.2	0	NR	2	NR	NR	NR
Curwin et al. (2005)	Nonfarmer	45	0.2	31	<LOD	<1	NR	NR	NR
Garry et al. (2001)	Licensed applicator, did not apply	15	0.6	80	<LOD	(0.5)	<LOD	<LOD	2
Harris et al. (1992)	Household members, turf application, did not apply 2,4-D	19	NR	100	<LOD	<LOD	<LOD	<LOD	<LOD	A controlled study
Panuwet et al. (2008)	Crop applicator (Pon Yaeng) did not use 2,4-D	67	0.2	91	<LOD	NC	<LOD	<LOD	30
Panuwet et al. (2008)	Crop applicator, (Inthakhin), not timed with application	69	0.2	35	<LOD	<1	<1	3	598

AM, arithmetic mean; GM, geometric mean; LOD, limit of detection; NC, not calculated; NR, not reported.

Table 3.  Applicators, source of exposure expected to be direct, in µg/L (ppb).

Source	Group	Number of subjects	LOD	% <LOD	Selected percentiles				Max	Comments
					25th	G mean (AM)	50th	75th
Alexander et al. (2007)	Crop applicator, Day 2	34	1	3	13	45	80	145	2236	Levels associated with glove use, repairing equipment and acres treated.
Arbuckle et al. (2002)	Crop applicator, Day 1	43	1	52	1	5	6	13	410	Levels associated with protective gear, application equipment, handling practice, and personal hygiene practice.
Bhatti et al. (2010)Figgs et al. (2000)	Seasonal applicator	31 (136 samples)	1	NR		63 (259)	94		2857	16% of between worker variance explained by method, month, concentration, gloves
Curwin et al. (2005)	Crop applicator	16	0.2	6		13				Levels associated with time since application, amount applied, and acres treated
Garry et al. (2001)	Forestry applicator	24	0.6	1	20	49	61	145	1700	Levels associated with application method
Harris et al. (1992)	Turf home applicators; granular, protected	11	4	1/11	<LOD	(9.8)	<LOD	<LOD	108
Harris et al. (1992)	Turf home applicators; granular, unprotected	9	4	1/9	<LOD	(19)	<LOD	<LOD	169
Harris et al. (1992)	Turf home applicators; liquid, protected	11	4	2/11	<LOD	(9)	<LOD	<LOD	63
Harris et al. (1992)	Turf home applicators; liquid, unprotected	9	4	6/9	85	(204)	111	289	744
Harris et al. (2010)	Turf applicators 24-h urine samples	135 (513 samples)		42			15		3658
Knopp (1994)	Manufacturers, 1985–1986,	41	1	0	332	(1345)	731	1391	12,963	Within first two hours of working shift (Friday)
Knopp and Glass (1991)	Crop applicator, Day 3	2	0.5	0		(1317)			2480
Knopp and Glass (1991)	Forestry applicator, Pilot, Day 1	1	0.5	0	NA	NA	NA	NA	52	.
Knopp and Glass (1991)	Forestry, mixer-loader, Day 1	1	0.5	0	NA	NA	NA	NA	365
Lerda and Rizzi (1991)	Crop applicator	32	NR	NR	NR	(9000)	NR	NR	NR	Poor information on methods
Panuwet et al. (2008)	Crop applicator, not timed with application	45		35	<LOD	<1	<1	3	598
Thomas et al. (2010)	Crop applicator, Day 5	26	0.2	0	NR	17	NR	NR	2500	Lower GM levels associated with glove use and application method.

AM, arithmetic mean; GM, geometric mean; LOD, limit of detection.

Table 4.  Carcinogenicity: summary of findings of 2,4-D exposure and cancer.

Author, year	Design	Exposure	Exposed cases	Results	Comments/conclusions
Alavanja et al. (2003)	Nested C–C	Applied 2,4-D (self report)	NR	Data not shown	No significant increase, AHS
	Prostate cancer				Very large sample size (566 cases, 54 766 controls)
Band et al. (2011)	Nested C–C	Ever exposed to 2,4-D (self report and job exposure matrix)	11	OR = 2.72 (1.12–6.57)	Results inconsistent.
	Prostate cancer			Dose response not significant (not shown)	Large sample size (1153 cases and 3999 controls
Boers et al. (2010)	Cohort	Worked in chlorophenoxy manufacturing		Hazard ratios for Factory A:	No significant increase
	All cancers		81	All cancers: 1.31 (0.86–2.01)	Moderate cohort (Factory A = 539 exposed, 18 811 person years and Factory B = 411 exposed, 12 946 person years)
	NHL		4	NHL: 0.92 (0.19–4.47) (1 case in Factory B)	Not specific to 2,4-D, exposures include 2,4,5-T, MCPA, other pesticides and possibly TCDD.
	Stomach		5	Stomach: 2.23 (0.38–13.20)
	Melanoma		3	Melanoma: 0.72 (0.07–7.03)
	Prostate		6	Prostate: 2.93 (0.61–14.15)
Burns et al. (2011)	Cohort	Worked in 2,4-D manufacturing		SIR for Cohort 2	No significant increase
	All cancers		244	All cancers: 0.88 (0.78–1.00)	Significant deficit of prostate cancer
	NHL		14	NHL 1.36 (0.74–2.29) 14 cases	Other exposure include benzene, asbestos and possibly TCDD
	Stomach		4	Stomach: 0.85 (0.23–2.18)	Moderate cohort of 1256 workers, 23 354 person years)
	Melanoma		9	Melanoma: 1.06 (0.48–2.01)
	Brain cancer		3	Brain: 0.87 (0.17–2.54)
	Prostate		62	Prostate: 0.74 (0.57–0.94)
Carreón et al. (2005)	C–C	Exposed to 2,4-D on a farm (self-report)	24	OR = 0.9 (0.5–1.6) including proxy	No significant increase
	Glioma		11	OR = 0.8 (0.4–1.6) excluding proxy	Moderate sample size (341 cases, 527 controls)
Chiu et al. (2006)^a; See Lee et al. (2004a) and De Roos et al. (2003)	C–C	Used phenoxy herbicides (self-report)		T(14:18) positive	Results inconsistent.
	NHL by t(14:18) status			No use: OR = 5.0 (1.2–20.8)	Not specific to 2,4-D
			14	Used: OR = 2.9 (1.0–8.4)	Risk from no use higher than risk of use
				T(14:18) negative	Large sample size (172 cases, 1432 controls)
				No use: OR = 1.5 (0.5–4.8)	All pesticide analyses higher for t(14:18) positive status than negative
			14	Used OR = 0.8 (0.4–1.6)
De Roos et al. (2003)^a; See Chiu et al. (2006) and Lee et al. (2004a)	C–C	Used 2,4-D (self-report)	123	OR = 0.9 (0.6–1.2)	No significant increase
	NHL			No increase both malathion and 2,4-D	Large sample size (870 cases, 2569 controls)
Dennis et al. (2010)	Nested C–C	Applied 2,4-D (self report)	NR	data not shown	No significant increase, AHS
	Melanoma				Very large sample size (150 cases and 24 554 noncases)
Engel et al. (2005)	Nested C–C	Applied 2,4-D (self report)	41	Adj RR 0.8 (0.6–1.1) (also by state and menopausal status)	No significant increase, AHS
	Breast cancer				Very large sample size (309 cases, 30 145 noncases)
Eriksson et al. (2008)	C–C	Used 2,4-D and/or 2,4,5-T (self-report)	33	Multivariate OR = 1.24 (0.68–2.26)	No significant increase
	NHL and specific entities		21	OR = 2.08 (0.99–4.38) for ≤ 29 days	Large sample size (910 cases, 1016 controls)
			12	OR = 1.33 (0.57–3.13) for >29 days.	Significant results for phenoxy herbicides driven by MCPA results.
				Non significant Ors for 8 entities
Flower et al. (2004)	Nested C–C	Parent used 2,4-D (self-report by parent)	7	Mother used OR = 0.72 (0.32–1.60)	No significant increase, AHS
	Cancer in children		26	Father used OR = 1.29 (0.71–2.35)	Large sample size (17 280 children) 50 cancer cases, 2 NHL cases
Hartge et al. (2005)	C–C	2,4-D in carpet dust	257	OR = 1.10 (0.78–1.55) <500 ng/g in dust	No significant increase
	NHL		86	OR = 0.91 (0.58–1.45) 500–999 ng/g	Large sample size (1321 cases, 1057 controls; with dust 679 cases, 510 controls)
			165	OR = 0.66 (0.45–0.98) 1000–9999 ng/g
			24	OR = 0.82 (0.41–1.66) >10,000 ng/g
Hohenadel et al. (2011); See McDuffie 2001, 2005^a	C–C	Used 2,4-D (self-report)	49	2,4-D alone OR = 0.94 (0.67–1.33)	Risk only in combination
	NHL		61	Malathion and 2,4-D OR = 2.06 (1.45–2.93)	Large sample size (513 cases, 1506 controls)
Lee et al. (2004a)^a; See Chiu 2006 and De Roos et al. (2003)	C–C	Used 2,4-D (self-report)	172	Non asthmatics OR = 1.0 (0.8–1.3)	No significant increase
	NHL		17	Asthmatics OR = 1.3 (0.7–2.5)	Large sample size (872 cases, 2381 controls)
					Asthmatics tended to have higher Ors for many other pesticides
Lee et al. (2005)	C–C	Used 2,4-D (self report)	25	Proxy OR = 3.3 (1.5–7.2)	Potential recall bias in proxies
	Glioma		7	Self OR = 0.6 (0.2–1.6)	Moderate sample size (251 cases, 498 controls)
Lee et al. (2004b)	C–C	Used 2,4-D (self-report)	27	Stomach OR = 0.8 (0.4–1.3)	No significant increase
	Stomach, Oesophagus		20	Oesophagus OR = 0.7 (0.4–1.2)	Moderate sample size (170 stomach, 137 oesophagus, 502 controls)
				Proxy respondents for 80% of stomach and 76% of oesophageal cases	Potential recall bias in proxies
Lee et al. (2007)	Nested	Used 2,4-D (self-report)	135	Colon OR = 0.6 (0.4–0.8), also significant by lifetime exposure days (p = 0.011)	Significant inverse association for colon, AHS
	C–C Colorectal		69	Rectum OR = 1.1 (0.6–2.0)	Very large sample size (305 cases, 56 508 noncases)
McDuffie et al. (2001)^a	C–C	Used 2,4-D (self report)	111	Adj OR = 1.32 (1.01–1.73)	Results inconsistent
	NHL	More than 10 hours per year		No dose response by days per year	Large sample size (517 cases, 1506 controls)
				Conditional, most parsimonious model excluded phenoxy herbicides.	Results confounded by other factors
McDuffie et al. (2005)^a	C–C	Used 2,4-D (self-report)	37	Exp to 2,4-D, no DEET OR = 1.05 (0.69–1.6)	No significant increase
	NHL	More than 10 hours per year	73		Large sample size (513 cases, 1506 controls)
			20	Both 2,4-D and DEET OR = 1.17 (0.84–1.64)	OR highest when used rubber gloves.
				Glove,2,4-D and DEET OR = 1.77 (0.90–3.45)
Miligi et al. (2003)^a	C–C	Exposed to 2,4-D (Expert assessment from self-report)	6	Men OR = 0.7 (0.3–1.9)	No significant increase
	NHL		7	Women OR = 1.5 (0.4–5.7)	Large sample size (1575 cases, 1232 controls)
Miligi et al. (2006); See Miligi 2003^a	C–C	Exposed to 2,4-D (Expert assessment from self-report)	17	Overall OR = 0.9 (0.5–1.8)	Results imprecise
	NHL		9	Lack of protective equipment OR = 4.4 (1.1–29.1)	Large sample size (1925 cases, 1232 controls)
Mills and Yang (2005)	C–C	Work records matched with use registry		Cases diagnosed <1995	Results inconsistent by diagnosis date. OR’s similar for low and high use.
	Breast cancer		12	OR = 0.61 (0.20–1.86) for low use	Exposure subject to misclassification: assumed from working in fields but metrics not timed with application and entry.
			8	OR = 0.62 (0.23–1.69) for high use	Moderate sample size (128 cases, 640 controls)
				Cases diagnosed ≥1995
			19	OR = 2.16 (0.95–4.93) for low use
			21	OR = 2.14 (1.06–4.32) for high use
Mills et al. (2005)	C–C	Work records matched with use registry	NR	NHL High (v Low) OR = 3.8 (1.85–7.81)	Results imprecise (only 15% exposed to 2,4-D)
	NHL, MM, Leukemia			NHL extranodal OR = 9.73 (2.68–35.3)	Exposure subject to misclassification: assumed from working in fields but metrics not timed with application and entry.
				NHL males OR = 3.79 (1.58–9.11)	Moderate sample size (60 NHL, 51 leukemia, 20 MM cases and 5 controls per case)
				NHL females OR = 5.23 (1.3–20.9) N = 15
				Leukemia OR = 1.03 (0.41–2.61)
				MM, “no elevated risks”
Mills and Yang (2007)	C–C	Work records matched with use registry	17	Significant at lowest pounds of use	Results inconsistent for reference and no dose response.
	Gastric (stomach) cancer		14	OR = 2.16 (1.02–4.56) for 1–14 lbs	Moderate sample size (100 cases, 210 controls)
			11	OR = 1.57 (0.71–3.51) for 15–86 lbs	Exposure subject to misclassification: assumed from working in fields but metrics not timed with application and entry.
				OR = 2.09 (0.87–5.05) for 86–1950 lbs
				No increase using low as the reference.
Pahwa et al. (2006) and Pahwa et al. (2011)	C–C	Used 2,4-D (self report)	57	HL OR = 0.96 (0.67–1.37)	No significant increase
	HL, MM, STS	More than 10 hours per year	80	MM OR = 1.21 (0.89–1.65)	Large sample size (316 HL, 342 MM, 357 STS, 1506 controls)
			69	STS OR = 0.97 (0.71–1.32)
				No increases if both DEET and 2,4-D
Schroeder et al. (2001)	C–C	Used phenoxy herbicides (self report)	17	T (14:18) positive OR = 0.9 (0.5–1.5)	No significant increase
	NHL specific entities		30	T (14:18) negative OR = 1.1 (0.7–1.5)	Not specific to 2,4-D
					Large sample size (622 cases, 182 cases with translocation status, 1245 controls)

^aSame population.

C–C, case–control study; HL, Hodgkin lymphoma; MM, Multiple myeloma; NR, not reported; STS, soft tissue sarcoma.

Case–control size: Very large (>10 000); Large (>1000); Moderate (>100). Cohorts small (<10 000 person years at risk).

Table 5.  Reproductive toxicity: summary of findings of 2,4-D exposure and reproductive endpoints.

Author	Design	Exposure	Exposed cases	Results	Comment/conclusions
Arbuckle et al. (2001)^a; Arbuckle et al. (1999)^a	Nested C–C	Used 2,4-D in farming during pregnancy period		<12 weeks gestation	Results inconsistent
	Spontaneous abortion (SAB)		26	Preconception exposure OR = 1.3 (0.9–2.0)	Large sample size (395 SAB among 3936 pregnancies)
			9	Post conception exposure OR = 0.6 (0.3–1.2)	Related analysis of Arbuckle 1999, “results were sensitive to the cutpoint used” (12 vs. 13 weeks gestation)
			22v5	Pre vs. Post OR = 2.9 (1.1–8.0)
			11v14	12–19 weeks, Pre vs. Post OR = 0.5 (0.2–1.1)
Swan et al. (2003)	Nested C–C	Urinary 2,4-D (≥0.1 µg/g cr)	5	Low semen quality OR = 0.8 (0.2–3.0) (Missouri, no exposed cases in Minnesota)	No significant increase
	Semen quality			Concentration, morphology, % motile (all p values >0.05)	Small sample size (65 cases and 101 controls with semen samples)
Waller et al. (2010)	C–C	2,4-D in surface water (>70 µg/L)	NR	OR = 1.05 (0.81–1.37) <10 km to water	No significant increase
	Gastroschisis (birth defect)				Large sample size (805 cases, 3616 controls)
Weselak et al. (2007)^a	Nested C–C	Used 2,4-D in farming during pregnancy period		In offspring:	Significant only for hayfever/allergies
	Allergies, hayfever in offspring		21	Bronchitis OR = 1.46 (0.80–2.66)	Large sample size (3405 children)
			24	Asthma OR = 0.74 (0.38–1.43)	Since a farming family, exposure not restricted to in utero.
			52	Hayfever/allergies OR = 1.66 (1.11–2.49)
Weselak et al. (2008)^a	Nested C–C	Used 2,4-D in farming during pregnancy period	7	Any defect OR = 0.97 (0.42–2.25)	No significant increase
	Birth defects		6	Pre-conception OR = 0.60 (0.25–1.46)	Large sample size (118 malformation in 3412 pregnancies

^aSame cohort.

C–C, case–control study; NR, not reported.

Table 6.  Genotoxicity: summary of findings of 2,4-D exposure and genetic, hormone, and immune related endpoints.

Author	Design	Exposure	Exposed cases	Results	Comments/conclusions
Figgs et al. (2000)	Cross sectional	Urinary 2,4-D in farmer applicators	12	Micronuclei scores, lymphocyte phenotypes, blood counts not significant.	Results inconsistent
	Micronuclei, replicative index (RI), lymphocyte phenotypes, complete blood counts			Replicative index (RI)	Only RI significant before/after and appl/control, no dose response
				Before/after (p = 0.016)	Small sample size(12 applicators, 9 controls)
				Applicator/control (p = 0.046)
				Trend by urinary level (p = 0.15)
Garry et al. (2001)	Cross sectional	Urinary 2,4-D and work records	21	LH correlated with urinary levels (p = 0.006)	Small sample size (24 applicators, 15 controls)
	Hormone levels, chromosomes aberrations			FSH, testosterone, chromosome aberrations not correlated with urinary levels	Exposure according to application method
				V(D)J rearrangement frequency correlated with urinary levels by application method
Semchuk et al. (2007)	Cross sectional	Used 2,4-D (occupational)	50	OR = 0.70 (0.27–1.77) ANA +	No significant increase
	Antinuclear antibodies (ANA)		48	OR = 0.30 (0.11–0.86), men only	Moderate sample size (N = 208)

^aSame cohort.

Table 7.  Neurotoxicity.

Author, year	Endpoint	Exposure	Exposed Cases	Results	Comments/conclusions
Dhillon et al. (2008)	C–C	Used 2,4-D	17	OR = 1.2 (0.6–2.8)	No significant increase
	Parkinson’s disease				Moderate sample size (100 cases, 84 controls)
Kamel et al. (2007)^a	Nested C–C	Used 2,4-D	47	OR = 0.9 (0.5–1.8) prevalent	No significant increase, AHS
	Parkinson’s disease		49	OR = 1.0 (0.5–2.1) incident	Very large sample size (83 prevalent cases, 78 incident cases, 79 557 controls)
Hancock et al. (2008)	C–C	Used chorphenoxy acid/ester	15	OR = 2.07 (0.69–6.23)	No significant increase
	Parkinson’s disease				Large sample size (319 cases, 296 controls)
Tanner et al. (2009)	Parkinsonism	Used 2,4-D	16	OR = 2.59 (1.03–6. 48)	Results inconsistent
				No association by duration of use	Moderate sample size (519 cases, 511 controls)
Tanner et al. (2011)^a; See Kamel et al. (2007)	Nested C–C	Used 2,4-D	76	OR = 1.2 (0.57–2.4)	No significant increase, AHS
	Parkinson’s disease				Moderate sample size (110 cases, 358 controls)

^aSame cohort.

C–C, case–control study.

Table 8.  General toxicity.

Author	Endpoint	Exposure	Exposed cases	Results	Comments/Conclusions
Hoppin et al. (2006a)^a	Nested C–C	Used 2,4-D in private farming and commercial application	NR	Farmers OR = 0.97 (0.86–1.10)	No significant increase, AHS
	Wheeze			Commercial OR = 0.99 (0.73–1.34)	Very large sample size (17 920 farmers and 2255 commercial applicators, 19 and 22% with wheeze)
Hoppin et al. (2002)^a	Nested C–C	Used 2,4-D in farming	2395	OR = 0.99 (0.88–1.11)	No significant increase, AHS
	Wheeze		77	OR = 1.25 (0.83–1.90) at highest use (>40 days)	Very large sample size (3838 cases, 16 630 controls)
					Highest OR at highest dose
Hoppin et al. (2006b)^a	Nested C–C	Used 2,4-D in commercial application	225	Current use OR = 1.27 (0.96–1.68)	No significant increase, AHS
	Wheeze				Large sample size (486 cases, 1769 noncases)
Hoppin et al. (2007a)^a	Nested C–C	Used phenoxy herbicide	363	OR = 1.12 (0.77–1.64)	No significant increase, AHS
	Farmer’s lung				Results not specific to 2,4-D
					Large sample size (395 cases, 16 563 controls)
Hoppin et al. (2007b)^a	Nested C–C	Used 2,4-D in farming	78	OR = 1.10 (0.90–1.35)	No significant increase, AHS
	Chronic bronchitis				Very large sample size (654 cases, 20 254 noncases)
Hoppin et al. (2008)^a	Nested C–C	Used 2,4-D in farming, women only	52	Atopic asthma OR = 1.53 (1.12–2.10)	Significant increase, AHS
	Asthma		66	Nonatopic OR 1.07 (0.82–1.41)	Very large sample size (282 atopic asthma, 420 nonatopic, 3086 atopy, 25,112 controls)
				Atopy alone OR = 1.20 (1.08–1.34)
			477	Growing up on farm OR = 0.55 (0.43–0.70)	OR > 1 for most pesticides reported
Schreinemachers (2010)	Cross sectional HDL, triglycerides, non-HDL, insulin, C-peptide, plasma glucose, thyroid stimulating hormone	Urinary levels in US population (> LOD)	102	Decreasing HDL (p < 0.05), increasing TSH (p < 0.05) among T4 < 8.5.	Only 14% above LOD
				Increasing triglycerides (p < 0.01), insulin (p < 0.05), c-peptide (p < 0.01) among HbA1c >5.1%	Moderate sample size (N = 727)
					Endpoints within normal range
					Cross sectional study with very low exposure.
Slager et al. (2009)^a	Nested C–C Rhinitis (stuffy, itchy, or runny nose in past 12 months)	Used 2,4-D as a commercial applicator	750	OR = 1.34 (1.09–1.64)	Results inconsistent, no dose response, AHS
			11	OR = 0.69 (0.25–1.89) at highest dose (≥ 60 days)	Large sample size (1664 cases, 581 controls)
Valcin et al. (2007)^a	Chronic bronchitis	Used 2,4-D in farming, women only	93	OR = 1.29 (1.02–1.63) adj for age, state	Results attenuate with modeling, AHS
				OR = 1.20 (0.89–1.63) multivariate	Very large sample size (583 cases, 20 958 noncases)

^aSame cohort.

C–C, case–control study; NR, not reported.

Figure 1.  Relative risk estimates, lower and upper limits for epidemiology studies of NHL from 1989 to 2011.

Table 9.  Comparison of results in an epidemiology study.

Group	OR	95% CI	Statistically significant	CI ratio
Men	0.7	0.3–1.9	No	6.3
Women	1.5	0.4–5.7	No	14.3
Overall	0.9	0.5–1.8	No	3.6–most precise
Lack of protective equipment	4.4	1.1–29.1	Yes	26.0–least stable

From Miligi et al. (2003, 2006).

Health Canada. (2010). Report on human biomonitoring of environmental chemicals in Canada. Available at: http://www.hc-sc.gc.ca/ewh-semt/pubs/contaminants/chms-ecms/index-eng.php. Accessed on: 28 December 2011.

 Google Scholar

Hill RH Jr, Head SL, Baker S, Gregg M, Shealy DB, Bailey SL et al. (1995). Pesticide residues in urine of adults living in the United States: reference range concentrations. Environ Res 71:99–108.

 PubMed Web of Science ®Google Scholar

Morgan MK, Sheldon LS, Thomas KW, Egeghy PP, Croghan CW, Jones PA et al. (2008). Adult and children’s exposure to 2,4-D from multiple sources and pathways. J Expo Sci Environ Epidemiol 18:486–494.

 PubMed Web of Science ®Google Scholar

Panuwet P, Prapamontol T, Chantara S, Barr DB. (2009). Urinary pesticide metabolites in school students from northern Thailand. Int J Hyg Environ Health 212:288–297.

 PubMed Web of Science ®Google Scholar

Alexander BH, Mandel JS, Baker BA, Burns CJ, Bartels MJ, Acquavella JF et al. (2007). Biomonitoring of 2,4-dichlorophenoxyacetic acid exposure and dose in farm families. Environ Health Perspect 115:370–376.

 PubMed Web of Science ®Google Scholar

Arbuckle TE, Burnett R, Cole D, Teschke K, Dosemeci M, Bancej C et al. (2002). Predictors of herbicide exposure in farm applicators. Int Arch Occup Environ Health 75:406–414.

 PubMed Web of Science ®Google Scholar

Arbuckle TE, Cole DC, Ritter L, Ripley BD. (2004). Farm children’s exposure to herbicides: comparison of biomonitoring and questionnaire data. Epidemiology 15:187–194.

 PubMed Web of Science ®Google Scholar

Arbuckle TE, Ritter L. (2005). Phenoxyacetic acid herbicide exposure for women on Ontario farms. J Toxicol Environ Health Part A 68:1359–1370.

 PubMed Web of Science ®Google Scholar

Arcury TA, Grzywacz JG, Barr DB, Tapia J, Chen H, Quandt SA. (2007). Pesticide urinary metabolite levels of children in eastern North Carolina farmworker households. Environ Health Perspect 115:1254–1260.

 PubMed Web of Science ®Google Scholar

Arcury TA, Grzywacz JG, Talton JW, Chen H, Vallejos QM, Galván L et al. (2010). Repeated pesticide exposure among North Carolina migrant and seasonal farmworkers. Am J Ind Med 53:802–813.

 PubMed Web of Science ®Google Scholar

Bakke B, De Roos AJ, Barr DB, Stewart PA, Blair A, Freeman LB et al. (2009). Exposure to atrazine and selected non-persistent pesticides among corn farmers during a growing season. J Expo Sci Environ Epidemiol 19:544–554.

 PubMed Web of Science ®Google Scholar

Cooper SP, Burau K, Sweeney A, Robison T, Smith MA, Symanski E et al. (2001). Prenatal exposure to pesticides: a feasibility study among migrant and seasonal farmworkers. Am J Ind Med 40:578–585.

 PubMed Web of Science ®Google Scholar

Curwin BD, Hein MJ, Sanderson WT, Barr DB, Heederik D, Reynolds SJ et al. (2005). Urinary and hand wipe pesticide levels among farmers and nonfarmers in Iowa. J Expo Anal Environ Epidemiol 15:500–508.

 PubMedGoogle Scholar

Garry VF, Tarone RE, Kirsch IR, Abdallah JM, Lombardi DP, Long LK et al. (2001). Biomarker correlations of urinary 2,4-D levels in foresters: genomic instability and endocrine disruption. Environ Health Perspect 109:495–500.

 PubMed Web of Science ®Google Scholar

Harris SA, Solomon KR, Stephenson GR. (1992). Exposure of homeowners and bystanders to 2,4-dichlorophenoxyacetic acid (2,4-D). J Environ Sci Health B 27:23–38.

 PubMed Web of Science ®Google Scholar

Panuwet P, Prapamontol T, Chantara S, Thavornyuthikarn P, Montesano MA, Whitehead RD Jr et al. (2008). Concentrations of urinary pesticide metabolites in small-scale farmers in Chiang Mai Province, Thailand. Sci Total Environ 407:655–668.

 PubMed Web of Science ®Google Scholar

Bhatti P, Blair A, Bell EM, Rothman N, Lan Q, Barr DB et al. (2010). Predictors of 2,4-dichlorophenoxyacetic acid exposure among herbicide applicators. J Expo Sci Environ Epidemiol 20:160–168.

 PubMed Web of Science ®Google Scholar

Figgs LW, Holland NT, Rothmann N, Zahm SH, Tarone RE, Hill R et al. (2000). Increased lymphocyte replicative index following 2,4-dichlorophenoxyacetic acid herbicide exposure. Cancer Causes Control 11:373–380.

 PubMed Web of Science ®Google Scholar

Harris SA, Villeneuve PJ, Crawley CD, Mays JE, Yeary RA, Hurto KA et al. (2010). National study of exposure to pesticides among professional applicators: an investigation based on urinary biomarkers. J Agric Food Chem 58:10253–10261.

 PubMed Web of Science ®Google Scholar

Knopp D. (1994). Assessment of exposure to 2,4-dichlorophenoxyacetic acid in the chemical industry: results of a five year biological monitoring study. Occup Environ Med 51:152–159.

 PubMed Web of Science ®Google Scholar

Knopp D, Glass S. (1991). Biological monitoring of 2,4-dichlorophenoxyacetic acid-exposed workers in agriculture and forestry. Int Arch Occup Environ Health 63:329–333.

 PubMed Web of Science ®Google Scholar

Lerda D, Rizzi R. (1991). Study of reproductive function in persons occupationally exposed to 2,4-dichlorophenoxyacetic acid (2,4-D). Mutat Res 262:47–50.

 PubMed Web of Science ®Google Scholar

Thomas KW, Dosemeci M, Hoppin JA, Sheldon LS, Croghan CW, Gordon SM et al. (2010). Urinary biomarker, dermal, and air measurement results for 2,4-D and chlorpyrifos farm applicators in the Agricultural Health Study. J Expo Sci Environ Epidemiol 20:119–134.

 PubMed Web of Science ®Google Scholar

Alavanja MC, Samanic C, Dosemeci M, Lubin J, Tarone R, Lynch CF et al. (2003). Use of agricultural pesticides and prostate cancer risk in the Agricultural Health Study cohort. Am J Epidemiol 157:800–814.

 PubMed Web of Science ®Google Scholar

Band PR, Abanto Z, Bert J, Lang B, Fang R, Gallagher RP et al. (2011). Prostate cancer risk and exposure to pesticides in British Columbia farmers. Prostate 71:168–183.

 PubMed Web of Science ®Google Scholar

Boers D, Portengen L, Bueno-de-Mesquita HB, Heederik D, Vermeulen R. (2010). Cause-specific mortality of Dutch chlorophenoxy herbicide manufacturing workers. Occup Environ Med 67:24–31

 PubMed Web of Science ®Google Scholar

Burns C, Bodner K, Swaen G, Collins J, Beard K, Lee M. (2011). Cancer incidence of 2,4-D production workers. Int J Environ Res Public Health 8:3579–3590.

 PubMed Web of Science ®Google Scholar

Carreón T, Butler MA, Ruder AM, Waters MA, Davis-King KE, Calvert GM et al.; Brain Cancer Collaborative Study Group. (2005). Gliomas and farm pesticide exposure in women: the Upper Midwest Health Study. Environ Health Perspect 113:546–551.

 PubMed Web of Science ®Google Scholar

Chiu BC, Dave BJ, Blair A, Gapstur SM, Zahm SH, Weisenburger DD. (2006). Agricultural pesticide use and risk of t(14;18)-defined subtypes of non-Hodgkin lymphoma. Blood 108:1363–1369.

 PubMed Web of Science ®Google Scholar

Lee WJ, Cantor KP, Berzofsky JA, Zahm SH, Blair A. (2004). Non-Hodgkin’s lymphoma among asthmatics exposed to pesticides. Int J Cancer 111:298–302.

 PubMed Web of Science ®Google Scholar

De Roos AJ, Zahm SH, Cantor KP, Weisenburger DD, Holmes FF, Burmeister LF et al. (2003). Integrative assessment of multiple pesticides as risk factors for non-Hodgkin’s lymphoma among men. Occup Environ Med 60:E11.

 PubMed Web of Science ®Google Scholar

Dennis LK, Lynch CF, Sandler DP, Alavanja MC. (2010). Pesticide use and cutaneous melanoma in pesticide applicators in the agricultural heath study. Environ Health Perspect 118:812–817.

 PubMed Web of Science ®Google Scholar

Engel LS, Hill DA, Hoppin JA, Lubin JH, Lynch CF, Pierce J et al. (2005). Pesticide use and breast cancer risk among farmers’ wives in the agricultural health study. Am J Epidemiol 161:121–135.

 PubMed Web of Science ®Google Scholar

Eriksson M, Hardell L, Carlberg M, Akerman M. (2008). Pesticide exposure as risk factor for non-Hodgkin lymphoma including histopathological subgroup analysis. Int J Cancer 123:1657–1663.

 PubMed Web of Science ®Google Scholar

Flower KB, Hoppin JA, Lynch CF, Blair A, Knott C, Shore DL et al. (2004). Cancer risk and parental pesticide application in children of Agricultural Health Study participants. Environ Health Perspect 112:631–635.

 PubMed Web of Science ®Google Scholar

Hartge P, Colt JS, Severson RK, Cerhan JR, Cozen W, Camann D et al. (2005). Residential herbicide use and risk of non-Hodgkin lymphoma. Cancer Epidemiol Biomarkers Prev 14:934–937.

 PubMed Web of Science ®Google Scholar

Hohenadel K, Harris SA, McLaughlin JR, Spinelli JJ, Pahwa P, Dosman JA et al. (2011). Exposure to multiple pesticides and risk of non-Hodgkin lymphoma in men from six Canadian provinces. Int J Environ Res Public Health 8:2320–2330.

 PubMed Web of Science ®Google Scholar

McDuffie HH, Pahwa P, McLaughlin JR, Spinelli JJ, Fincham S, Dosman JA et al. (2001). Non-Hodgkin’s lymphoma and specific pesticide exposures in men: cross-Canada study of pesticides and health. Cancer Epidemiol Biomarkers Prev 10:1155–1163.

 PubMed Web of Science ®Google Scholar

McDuffie HH, Pahwa P, Robson D, Dosman JA, Fincham S, Spinelli JJ et al. (2005). Insect repellents, phenoxyherbicide exposure, and non-Hodgkin’s lymphoma. J Occup Environ Med 47:806–816.

 PubMed Web of Science ®Google Scholar

Lee WJ, Colt JS, Heineman EF, McComb R, Weisenburger DD, Lijinsky W et al. (2005). Agricultural pesticide use and risk of glioma in Nebraska, United States. Occup Environ Med 62:786–792.

 PubMed Web of Science ®Google Scholar

Lee WJ, Lijinsky W, Heineman EF, Markin RS, Weisenburger DD, Ward MH. (2004). Agricultural pesticide use and adenocarcinomas of the stomach and oesophagus. Occup Environ Med 61:743–749.

 PubMed Web of Science ®Google Scholar

Lee WJ, Sandler DP, Blair A, Samanic C, Cross AJ, Alavanja MC. (2007). Pesticide use and colorectal cancer risk in the Agricultural Health Study. Int J Cancer 121:339–346.

 PubMed Web of Science ®Google Scholar

Miligi L, Costantini AS, Bolejack V, Veraldi A, Benvenuti A, Nanni O et al. (2003). Non-Hodgkin’s lymphoma, leukemia, and exposures in agriculture: results from the Italian multicenter case-control study. Am J Ind Med 44:627–636.

 PubMed Web of Science ®Google Scholar

Miligi L, Costantini AS, Veraldi A, Benvenuti A, Vineis P; WILL. (2006). Cancer and pesticides: an overview and some results of the Italian multicenter case-control study on hematolymphopoietic malignancies. Ann N Y Acad Sci 1076:366–377.

 PubMed Web of Science ®Google Scholar

Mills PK, Yang R, Riordan D. (2005). Lymphohematopoietic cancers in the United Farm Workers of America (UFW), 1988-2001. Cancer Causes Control 16:823–830.

 PubMed Web of Science ®Google Scholar

Mills PK, Yang RC. (2007). Agricultural exposures and gastric cancer risk in Hispanic farm workers in California. Environ Res 104:282–289.

 PubMed Web of Science ®Google Scholar

Pahwa P, McDuffie HH, Dosman JA, McLaughlin JR, Spinelli JJ, Robson D et al. (2006). Hodgkin lymphoma, multiple myeloma, soft tissue sarcomas, insect repellents, and phenoxyherbicides. J Occup Environ Med 48:264–274.

 PubMed Web of Science ®Google Scholar

Pahwa P, Karunanayake CP, Dosman JA, Spinelli JJ, McLaughlin JR; Cross-Canada Group. (2011). Soft-tissue sarcoma and pesticides exposure in men: results of a Canadian case-control study. J Occup Environ Med 53:1279–1286.

 PubMed Web of Science ®Google Scholar

Schroeder JC, Olshan AF, Baric R, Dent GA, Weinberg CR, Yount B et al. (2001). Agricultural risk factors for t(14;18) subtypes of non-Hodgkin’s lymphoma. Epidemiology 12:701–709.

 PubMed Web of Science ®Google Scholar

Arbuckle TE, Lin Z, Mery LS. (2001). An exploratory analysis of the effect of pesticide exposure on the risk of spontaneous abortion in an Ontario farm population. Environ Health Perspect 109:851–857.

 PubMed Web of Science ®Google Scholar

Arbuckle TE, Schrader SM, Cole D, Hall JC, Bancej CM, Turner LA et al. (1999). 2,4-Dichlorophenoxyacetic acid residues in semen of Ontario farmers. Reprod Toxicol 13:421–429.

 PubMed Web of Science ®Google Scholar

Swan SH, Kruse RL, Liu F, Barr DB, Drobnis EZ, Redmon JB et al. Study for Future Families Research Group. (2003). Semen quality in relation to biomarkers of pesticide exposure. Environ Health Perspect 111:1478–1484.

 PubMed Web of Science ®Google Scholar

Waller SA, Paul K, Peterson SE, Hitti JE. (2010). Agricultural-related chemical exposures, season of conception, and risk of gastroschisis in Washington State. Am J Obstet Gynecol 202:241.e1–241.e6.

 Web of Science ®Google Scholar

Weselak M, Arbuckle TE, Wigle DT, Krewski D. (2007). In utero pesticide exposure and childhood morbidity. Environ Res 103:79–86.

 PubMed Web of Science ®Google Scholar

Weselak M, Arbuckle TE, Wigle DT, Walker MC, Krewski D. (2008). Pre- and post-conception pesticide exposure and the risk of birth defects in an Ontario farm population. Reprod Toxicol 25:472–480.

 PubMed Web of Science ®Google Scholar

Semchuk KM, Rosenberg AM, McDuffie HH, Cessna AJ, Pahwa P, Irvine DG. (2007). Antinuclear antibodies and bromoxynil exposure in a rural sample. J Toxicol Environ Health Part A 70:638–657.

 PubMed Web of Science ®Google Scholar

Dhillon AS, Tarbutton GL, Levin JL, Plotkin GM, Lowry LK, Nalbone JT et al. (2008). Pesticide/environmental exposures and Parkinson’s disease in East Texas. J Agromedicine 13:37–48.

 PubMedGoogle Scholar

Kamel F, Tanner C, Umbach D, Hoppin J, Alavanja M, Blair A et al. (2007). Pesticide exposure and self-reported Parkinson’s disease in the agricultural health study. Am J Epidemiol 165:364–374.

 PubMed Web of Science ®Google Scholar

Hancock DB, Martin ER, Mayhew GM, Stajich JM, Jewett R, Stacy MA et al. (2008). Pesticide exposure and risk of Parkinson’s disease: a family-based case-control study. BMC Neurol 8:6.

 PubMed Web of Science ®Google Scholar

Tanner CM, Ross GW, Jewell SA, Hauser RA, Jankovic J, Factor SA et al. (2009). Occupation and risk of parkinsonism: a multicenter case-control study. Arch Neurol 66:1106–1113.

 PubMed Web of Science ®Google Scholar

Tanner CM, Kamel F, Ross GW, Hoppin JA, Goldman SM, Korell M et al. (2011). Rotenone, paraquat, and Parkinson’s disease. Environ Health Perspect 119:866–872.

 PubMed Web of Science ®Google Scholar

Hoppin JA, Umbach DM, London SJ, Lynch CF, Alavanja MC, Sandler DP. (2006a). Pesticides and adult respiratory outcomes in the agricultural health study. Ann N Y Acad Sci 1076:343–354.

 PubMed Web of Science ®Google Scholar

Hoppin JA, Umbach DM, London SJ, Alavanja MC, Sandler DP. (2002). Chemical predictors of wheeze among farmer pesticide applicators in the Agricultural Health Study. Am J Respir Crit Care Med 165:683–689.

 PubMed Web of Science ®Google Scholar

Hoppin JA, Umbach DM, London SJ, Lynch CF, Alavanja MC, Sandler DP. (2006b). Pesticides associated with wheeze among commercial pesticide applicators in the Agricultural Health Study. Am J Epidemiol 163:1129–1137.

 PubMed Web of Science ®Google Scholar

Hoppin JA, Umbach DM, Kullman GJ, Henneberger PK, London SJ, Alavanja MC et al. (2007a). Pesticides and other agricultural factors associated with self-reported farmer’s lung among farm residents in the Agricultural Health Study. Occup Environ Med 64:334–341.

 PubMed Web of Science ®Google Scholar

Hoppin JA, Valcin M, Henneberger PK, Kullman GJ, Umbach DM, London SJ et al. (2007b). Pesticide use and chronic bronchitis among farmers in the Agricultural Health Study. Am J Ind Med 50:969–979.

 PubMed Web of Science ®Google Scholar

Hoppin JA, Umbach DM, London SJ, Henneberger PK, Kullman GJ, Alavanja MC et al. (2008). Pesticides and atopic and nonatopic asthma among farm women in the Agricultural Health Study. Am J Respir Crit Care Med 177:11–18.

 PubMed Web of Science ®Google Scholar

Schreinemachers DM. (2010). Perturbation of lipids and glucose metabolism associated with previous 2,4-D exposure: a cross-sectional study of NHANES III data, 1988-1994. Environ Health 9:11.

 PubMed Web of Science ®Google Scholar

Slager RE, Poole JA, LeVan TD, Sandler DP, Alavanja MC, Hoppin JA. (2009). Rhinitis associated with pesticide exposure among commercial pesticide applicators in the Agricultural Health Study. Occup Environ Med 66:718–724.

 PubMed Web of Science ®Google Scholar

Valcin M, Henneberger PK, Kullman GJ, Umbach DM, London SJ, Alavanja MC et al. (2007). Chronic bronchitis among nonsmoking farm women in the agricultural health study. J Occup Environ Med 49:574–583.

 PubMed Web of Science ®Google Scholar