Table 4.

Nonlinear regression of post-application urine concentration on algorithm.

Y = [{α₀} + {α₁} × mix + {α₂} × method + {α₃} × repair] × [1 − {β₁} × gloves − {β₂} × ppe_other].

2,4-D (n = 88)	R-Squared = Regression	0.36
Variable 1	Coefficient	P-value
Intercept α0	27	0.76
Mix α1,	58	0.53
Method α2	123	0.02
Repair α3	32	0.59
Gloves β1	0.75	<0.001
PPE other β2	0.26	0.26
Chlorpyrifos (n = 17)	R-Squared = Regression	0.77
Variable1	Coefficient	P-value
Intercept α0	8	0.22
Mix α1,	Na 2	Na 2
Method α2	33	0.006
Repair α3	15	0.89
Gloves β1	0.51	0.014
PPE other β2	0.21	0.59

¹ α₀ represented the urinary concentration at the referent level of all factors, where α₁, α₂ and α₃ parameters represented the increase in Y for mixing (1 = yes, 0 = no), use of hand spray (method = 1) or boom spray (method = 0) for 2,4-D, or boom spray (method = 1) or in-furrow (method = 0) for chlorpyrifos, and repairing equipment (1 = yes, 0 = no), respectively, and where β₁ and β₂ parameters represented the reduction factors for use of CR gloves (1 = yes, 0 = no) and/or other PPE (1 = yes, 0 = no), respectively.

² na: all participants mixed chlorpyrifos and the regression omitted the variable.