Table 3.
Multivariate modeling by sequential addition of potential confounders
Race + chronic | Race + chronic + age + urban | Race + chronic + age + urban + insurance + gender | |
---|---|---|---|
Health insurance | |||
Medicaid | Reference | Reference | Reference |
No insurance | 14.4 (12.7, 16.2)**** | 12.9 (11.3, 14.6)**** | 11.8 (10.2, 13.4)**** |
Private insurance | 3.4 (2.8, 4.0)**** | 3.4 (2.9, 4.0)**** | 2.8 (2.3, 3.4)**** |
Other | 6.7 (4.8, 8.5)**** | 6.4 (4.5, 8.3)**** | 5.7 (3.8, 7.6)**** |
Gender | |||
Female | – | Reference | Reference |
Male | – | 7.3 (6.7, 7.9)**** | 6.8 (6.2, 7.4)**** |
Race/ethnicity | |||
Caucasian | – | – | Reference |
African-American | – | – | 2.5 (1.7, 3.3)**** |
Hispanic | – | – | 1.7 (0.9, 2.5)**** |
Asian/Pacific Islander | – | – | 4.5 (2.9, 6.0)**** |
Native American | – | – | 0.7 (− 2.1, 3.5) |
Other | – | – | 1.4 (0.3, 2.5)* |
Risk differences per 1000 hospitalizations presented with 95% confidence intervals in parentheses
Race=race/ethnicity; Chronic=chronic illnesses; Age=age in years; Urban=household urbanization
Italics indicates ideal set of confounders to evaluate according to DAG depicted in Fig. 1. Additional confounders are shown for comparison but may potentially introduce additional bias
p-value < 0.05; **< 0.01; ***< 0.001; ****< 0.0001