Abstract
Objective
This study applied the vulnerability framework and examined the combined effect of race and income on health insurance coverage in the US.
Data source
The household component of the US Medical Expenditure Panel Survey (MEPS-HC) of 2017 was used for the study.
Study design
Logistic regression models were used to estimate the associations between insurance coverage status and vulnerability measure, comparing insured with uninsured or insured for part of the year, insured for part of the year only, and uninsured only, respectively.
Data collection/extraction methods
We constructed a vulnerability measure that reflects the convergence of predisposing (race/ethnicity), enabling (income), and need (self-perceived health status) attributes of risk.
Principal findings
While income was a significant predictor of health insurance coverage (a difference of 6.1–7.2% between high- and low-income Americans), race/ethnicity was independently associated with lack of insurance. The combined effect of income and race on insurance coverage was devastating as low-income minorities with bad health had 68% less odds of being insured than high-income Whites with good health.
Conclusion
Results of the study could assist policymakers in targeting limited resources on subpopulations likely most in need of assistance for insurance coverage. Policymakers should target insurance coverage for the most vulnerable subpopulation, i.e., those who have low income and poor health as well as are racial/ethnic minorities.
A National Survey of Trends in Health Insurance Coverage of Low-Income Adults Following Medicaid Expansion
