Racial/Ethnic and Economic Disparities in Barriers to Healthcare for Women: A National Assessment Since the ACA

Purpose: Healthcare access has improved over the past decade due to the passage of the Affordable Care Act and Medicaid expansion. Healthcare social workers have contributed to implementation and improved access through further integration into healthcare settings.

While much has been written about the improvement to healthcare access, less has been written about the unique impact on women and little empirical research has examined racial/ethnic and economic health access disparities among women. Therefore, this study conducted a national assessment to understand the sociodemographic factors that influence barriers to healthcare for women since the passage of the ACA.

Methods: Data was analyzed from the Medical Expenditure Panel Survey utilizing a 5-year sample (2010-2015) and women ages 18-74 (N = 72,653). Predictors of barriers to healthcare included age, marital status, race/ethnicity, education, income, and insurance status. A dichotomous variable called “any barriers to care” was created based on four questions about access to healthcare. Multivariate logistic regression models were used to analyze predictors of barriers to healthcare.

Findings: 29% of respondents reported at least one barrier to care. In the multivariate logistic regression model, we found that being single (OR= 1.31, 95% CI=1.25-1.39) was associated with higher odds of having a barrier to care compared to those who were partnered. Women with no insurance (OR= 2.97, 95% CI=2.75-3.22) and public insurance (OR= 1.17, 95% CI=1.08-1.26) had higher odds of having a barrier to care compared to women with private insurance. Similarly, income was a significant predictor, where lower income meant having higher odds of having a barrier to care, which existed on a gradient. Finally, our analysis indicated that there were racial disparities in having a barrier to care, where Asian women (OR= 1.15, 95% CI=1.02-1.31) had higher odds of having a barrier to care compared to White women. However, Black women (OR= 0.93, 95% CI=0.87-1.00) and Hispanic women (OR= 1.05, 95% CI=0.97-1.13) did not have significantly different odds of having a barrier to care compared to White women when other socioeconomic factors were included in the model.

Income, education and insurance status each moderated the relationship between race/ethnicity and any barrier to care. Specifically, women who did not graduate from college had lower odds of having a barrier to care compared to college educated women across all racial groups, but with varying magnitude. While income was a predictor for all racial groups, there was a steeper gradient for white women. Not having insurance was especially harmful for Asian women, while public insurance was associated with higher odds of having a barrier to care compared to private insurance for white women only.

Conclusions: Sociodemographic variables significantly predicted having a barrier to healthcare. While all racial minority groups initially had higher barriers to care compared to White women, when additional sociodemographic variables such as income, education and insurance status were included in the model, only Asian women had significantly higher barriers to care compared to White women. This highlights the importance of taking an intersectional approach to research and policy practice.