Health Care Reform: Understanding Individuals’ Attitudes and Information Sources

Table 3

Predictors of health care usage.

	Doctor’s visit^+		Overnight hospital stay		Outpatient hospital care		ER visit
Predictors	AOR*	P	AOR	P	AOR	P	AOR	P
Female	1.84	0.004	1.09	0.689	1.12	0.440	1.08	0.640
Nonwhite	1.19	0.626	0.66	0.251	0.87	0.590	1.27	0.372
Age	1.01	0.013	1.01	0.034	1.00	0.621	0.99	0.029
Income	1.00	0.380	0.99	>0.001	1.00	0.441	0.99	>0.001
Some college	0.97	0.913	0.99	0.953	1.04	0.813	0.98	0.925
Democrat	1.67	0.049	1.55	0.057	1.19	0.349	1.00	0.998
Republican	1.01	0.974	1.00	0.993	0.84	0.360	0.71	0.088
Health insurance	5.32	>0.001	3.46	0.001	2.62	>0.001	1.48	0.088
Year	1.31	0.193	0.96	0.835	0.97	0.854	0.90	0.502

+The specific questions asked to elicit health services usage data are as follows: In the past 12 months, how many times have you, yourself, made a doctor visit?, In the past 12 months, how many times have you, yourself, had an overnight stay in a hospital?, In the past 12 months, how many times have you, yourself, gone to the hospital for outpatient care, not including ER visits?, In the past 12 months, how many times have you, yourself, gone to an emergency room for medical treatment? Our usage variables take two values with 0 indicating zero usage and 1 indicating at least one usage.
*AOR: adjusted odds ratio implies controlling for age, gender, race, education, insurance status, and political affiliation. These are for association of favorable attitudes with independent predictors. The binary dependent variable was “usage of health services” with 0 indicating no health care use.