Abstract: Disparities in Healthcare Access for People with Heart Conditions: A National Assessment (Society for Social Work and Research 24th Annual Conference - Reducing Racial and Economic Inequality)

82P Disparities in Healthcare Access for People with Heart Conditions: A National Assessment

Schedule:
Thursday, January 16, 2020
Marquis BR Salon 6 (ML 2) (Marriott Marquis Washington DC)
* noted as presenting author
Liana Petruzzi, MSW, PhD Student, University of Texas at Austin, TX
Weiwen Zeng, MSSc, PhD student, University of Texas at Austin, Austin, TX
Bethany Wood, MSW, PhD Student, University of Texas at Austin
Background & Purpose: Cardiovascular disease is the leading cause of death in the United States and accounts for over 800,000 deaths per year. While, the Affordable Care Act expanded access to health insurance to millions of Americans, racial and economic disparities in access to care still exist. However, little research has been done to assess the barriers to care among patients with heart conditions such as coronary heart disease (CHD) or myocardial infarction (MI). The purpose of this study was to understand disparities in healthcare access for people with heart conditions.

Methods: Data was analyzed from a population-based study, the 2017 National Health Interview Survey (NHIS). Adults with CHD or MI between the ages of 25-74 were identified (N = 2474). Variables examined as predictors of barriers to healthcare access included age, gender, marital status, race/ethnicity, education, income, and insurance status (no insurance, public insurance or private insurance). Two outcome variables were created based on 7 questions from NHIS about access to healthcare including trouble finding a doctor, doctor not accepting insurance, lack of available appointments, inability to afford follow-up care or specialty care and lack of transportation. We created a dichotomous variable called any barriers, and a continuous variable called number of barriers. Next, multivariate logistic regression and multivariate linear regression models were used to analyze predictors of barriers to healthcare.

Results: 7% of participants had no insurance, 63% had public insurance and 30% had private insurance. 27% of the sample had experienced at least one barrier to care. The logistic regression model found that lower age and low-income status were associated with higher odds of having a barrier to care compared to the odds of those who were older (OR= 0.985, 95% CI=0.973-0.998) or the odds of those with high-income (OR=2.7, 95% CI=1.542-4.737). Although not statistically significant, people with no insurance or public insurance had higher odds of having a barrier to care compared to those with private insurance (OR=1.60, 95% CI= 0.85-3.00; OR=1.33, 95% CI= 0.92-1.95). Our linear regression model found that being single or having low-income was associated with increased numbers of barriers to care compared to those who were married (B= 0.115, p= .028) or had high-income (B= 0.242, p=.003).

Conclusions & Implications: Understanding the barriers to healthcare for at-risk populations is an important step to reducing disparities in healthcare access. This is particularly important for people with heart conditions, as it is the leading cause of death in the United States. This study found that those who are younger or low-income have higher odds of experiencing any barrier to care, and those who are single or low-income have more barriers to care. Insurance, although a significant factor in healthcare access in other studies, was not found to be a predictor of barriers to healthcare. Social workers can benefit from considering these barriers to healthcare when assessing patients with heart conditions in hospital or outpatient settings.