A “Swiss paradox” in the United States? Level of spatial aggregation changes the association between income inequality and morbidity for older Americans

Table 2 Parameter estimates of rate of five health conditions and behaviors based on decile of state and county-level Gini index

Gini level	Obesity	Diabetes	Current smoker	Poor/fair SRH	Sedentary
Unadjusted
County	− 0.33 (− 0.54, − 0.13)	− 0.08 (− 0.12, 0.27)	0.05 (− 0.10, 0.20)	0.82 (0.59, 1.04)	0.19 (− 0.04, 0.42)
State	− 0.01 (− 0.25, 0.23)	0.55 (0.32, 0.78)	0.24 (0.07, 0.41)	1.11 (0.84, 1.38)	0.66 (0.39, 0.94)
Income-adjusted
County	− 0.39 (− 0.59, − 0.19)	0.03 (− 0.16, 0.23)	0.01 (− 0.14, 0.15)	0.66 (0.45, 0.88)	0.05 (− 0.17, 0.28)
State	− 0.09 (− 0.33, 0.15)	0.50 (0.27, 0.73)	0.18 (0.01, 0.36)	0.89 (0.64, 1.15)	0.48 (0.21, 0.75)
Fully adjusted^a
County	− 0.42 (− 0.63, − 0.20)	− 0.10 (− 0.31, 0.10)	0.01 (− 0.14, 0.17)	0.63 (0.41, 0.86)	0.23 (− 0.01, 0.47)
State	− 0.25 (− 0.50, 0.01)	0.30 (0.06, 0.55)	0.12 (− 0.06, 0.30)	0.71 (0.44, 0.98)	0.58 (0.30, 0.85)

Italics indicate significant association (p < 0.05)
^aFully adjusted models included the following covariates: age (65–69, 70–74, 75–79, 80+), sex, race/ethnicity (White, Black, Hispanic, Other), individual income level (in $25,000 increments), and education level (less than college degree, college degree or higher), county-level population density, and county-level per capita income

ISSN: 1476-072X