Table 2.

Hierarchical Linear models: Associations of baseline neighborhood socioeconomic status and relative income with allostatic load at wave 2.

	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	-0.052	0.027	0.05	-0.076	0.030	0.01	-0.056	0.029	0.05	-0.063	0.033	0.06	-0.068	0.033	0.04	-0.044	0.028	0.10’
NSES Fact or ^b	-0.039	0.052	0.48	-0.120	0.061	0.05	-0.076	0.057	0.18	-0.086	0.061	0.16	-0.093	0.061	0.13	-0.060	0.050	0.23
Age (centered at 45 y) Gender (ref = female)							0.063	0.007	<0.0001	0.063	0. 007	<0.0001	0.060	0.008	<0.0001	0.029	0.007	<0.0001
Male							0.262	0.123	0.03	0.258	0.123	0.04	0.280	0.123	0.03	0.172	0.106	0.11
IPR										0.057	0.128	0.66	0.049	0.128	0.70	-0.010	0.109	0.93
Length of residence													0.014	0.008	0.08	0.009	0.007	0.20
City of residence													0.043	0.107	0.69	-0.038	0.087	0.67
Baseline AL																0.557	0.026	<0.0001
Sample Size	^a 1030			1030			1030			1030			1030			929
Sample Size	^b 1089			1030			1030			1030			1030			929

Model 1: Relative Income or NSES Factor

Model 2: Relative Income + NSES Factor

Model 3: Model 2 + Age + Gender

Model 4: Model 3 + individual SES

Model 5: Model 4 + length of residence + city of residence

Model 6: Model 5 + Baseline AL

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