How Do Consumer Health Coverage Decisions Affect the Schedule, Management, and Cost of Patient Care?

The implementation of the Patient Protection and Affordable Care Act (PPACA) will ensure that nearly every American will gain access to comprehensive health insurance through design features such as the individual mandate, health insurance exchanges, Medicaid coverage expansions, and the elimination of the pre-existing condition clause. In addition, the PPACA will give consumers more control over their health insurance coverage decisions, which has the potential for altering the way consumers interact with the healthcare system. For patients suffering from a chronic condition, these coverage decisions could have significant effects on disease management and health outcomes. Although policymakers are focused on expanding coverage to ensure universal access, it is important that they also consider how coverage decisions may impact inequalities in the use and cost of medical care. In this study, we will investigate the impact of an individual’s choice of insurance coverage on the management, schedule and cost of medical care among adults with chronic disease. In particular, we focus our analysis on patients diagnosed with coronary heart disease and stroke (CHDS).

We use data from the 2001-2010 Medical Expenditure Panel Survey to create a dataset of adults aged 18 and older. Multinomial and count data regression models will be used to model the impact of insurance decisions on healthcare utilization and cost function analysis will model how coverage decisions impact medical expenditures. Additionally, these models will be examined across the age distribution to control for the evolution of available coverage options and nonlinear controls will be utilized to examine the schedule of patient care. The primary outcome variables are routine visits, emergency room visits, and medical expenditures. Four different potential coverage options are examined: uninsured, private only, public only, and dual coverage. Respondents will also be assigned to one of three health status groups: diagnosed with CHDS, diagnosed with a major chronic disease (MCD) other than CHDS, or diagnosed with no MCD. We focus our subgroup analysis on the three largest racial groups: Hispanic, White non-Hispanic, and Black non-Hispanic. Individual factors such as age, sex, race/ethnicity, educational attainment, personal income, geographic region, and survey year will also be controlled for within our regressions. Sampling weights will be used to adjust for oversampling and the standard errors will be clustered by age to account for interclass correlation arising from the degenerative effects of aging.