Table A1.

Hierarchical Linear models: Associations of baseline neighborhood socioeconomic status and relative income with allostatic load at wave 2, controlling for Years in the US

	Model 1			Model 2			Model 3			Model 4			Model 5			Model 6
	Estimate	S.E.	p	Estimate	S.E.	p	Estimate	S.E.	p	Estimate	S.E.	p	Estimate	S.E.	p	Estimate	S.E.	p
Relative Income ^a	-0.054	0.027	0.05	-0.078	0.030	0.01	-0.056	0.029	0.05	-0.063	0.033	0.06	-0.067	0.332	0.04	-0.039	0.268	0.15
NSES Factor^b	-0.039	0.056	0.49	-0.120	0.063	0.06	-0.076	0.057	0.18	-0.082	0.610	0.18	-0.087	0.061	0.16	-0.052	0.048	0.28
Age (centered at 45 y) Gender (ref = female)							0.063	0.074	<0.0001	0.059	0.008	<0.0001	0.057	0.008	<0.0001	0.027	0.007	<0.0001
Male							0.262	0.123	0.03	0.250	0.124	0.05	0.270	0.125	0.03	0.196	0.104	0.06
IPR (ref = above poverty level Below poverty level										0.045	0.130	0.73	0.039	0.130	0.77	-0.042	0.109	0.70
LOS(years)										0.005	0.005	0.27	0.005	0.005	0.32	0.001	0.041	0.74
City (ref = suburban area)
Urban													0.028	0.108	0.80	-0.055	0.077	0.48
Baseline AL																0.568	0.026	<0.0001
Sample Size	^a1089			1030			1030			1030			929			929
Sample Size	^b1030			1030			1030			1030			929			929

Income modeled as a 1-unit increase in the variable corresponds to a $10,000 increase

NSES (neighborhood socioeconomic status) factor is standardized, so a 1 unit increase corresponds to a 1-SD increase in NSES

Model 1: Relative Income or NSES Factor

Model 2: Relative Income + NSES Factor

Model 3: Model 2 + Age + Gender

Model 4: Model 3 + individual poverty ratio (IPR) + length of stay in the U.S. mainland (LOS)

Model 5: Model 4 + length of residence

Model 6: Model 5 + Baseline AL