About

The Health Uninsurance Gap measures the ratio between Black and White individuals with no health insurance coverage in a county. Individuals from both Hispanic and non-Hispanic ethnicities are included. According to the U.S. Census Bureau, in 2019, the uninsured rate for non-Hispanic Black individuals under age 65 was 11.3%, compared to 8.8% for their non-Hispanic White counterparts ¹. Since the implementation of the Affordable Care Act’s (ACA) coverage provisions, the uninsured rate among non-elderly Black Americans decreased by 10 percentage points, from 20.9 percent in 2010 to 10.8 percent in 2022 ². Despite this progress, disparities persist, with Black individuals still more likely to be uninsured than their White counterparts. 

Why is the Health Uninsurance Gap important to the Structural Racism and Discrimination (SRD) Index?

The Health Uninsurance Gap between Black and White Americans serves as a critical measure of the impact of structural and institutional racism and discrimination on the healthcare system. Structural racism manifests through policies and practices that systematically disadvantage marginalized communities, resulting in unequal access to health insurance and, consequently, healthcare services ³. For example, discriminatory practices in employment, income distribution, and access to employer-sponsored insurance create significant barriers to coverage for minority populations, particularly Black and Hispanic individuals ⁴. The health uninsurance gap is not merely an indicator of inequities; it is a driver of broader health disparities. Addressing this gap requires dismantling institutional practices that perpetuate exclusion and investing in policies that prioritize health equity, ensuring that all individuals, regardless of race or ethnicity, have access to the care they need. For instance, the decision of states to expand Medicaid eligibility under the ACA significantly impacted coverage rates for both Black and White Americans, with states that expanded seeing greater gains.

What is the expected relation to Structural Racism and Discrimination?

A higher value of the Health Uninsurance Gap between the Black and White individuals with no health insurance coverage contributes to the higher value or score of the SRD Index.

How is the Health Uninsurance Gap calculated?

Data Source

We obtained data from the IPUMS National Historical Geographic Information System (NHGIS) ⁵. The data is publicly available.

Data

We used the following four variables at the county level.

Variables*	Year	Unit
Total Black or African American alone civilian noninstitutionalized population	2010 | 2020	Number
Total White alone civilian noninstitutionalized population	2010 | 2020	Number
Black or African American alone uninsured civilian noninstitutionalized population	2010 | 2020	Number
White alone uninsured civilian noninstitutionalized population	2010 | 2020	Number

* Individuals from both Hispanic and non-Hispanic ethnicities are included.

Methodology

We calculated the Health Uninsurance Gap using a ratio formula:
$$
RPrBlWhUnins = \frac{PrBlUnins}{PrWhUnins}
$$

Where:
RPrBlWhUnins: Ratio of proportions of (Black or African American alone to White alone) uninsured civilian noninstitutionalized population
PrBlUnins = BlUnins/BlCN: Proportion of Black or African American alone uninsured civilian noninstitutionalized population
PWhUnins = WhUnins/WhCN: Proportion of White alone uninsured civilian noninstitutionalized population

Missing Data

We replaced missing values (where applicable) by using functions that were based on Tobler’s First Law of Geography, i.e., “everything is related to everything else, but near things are more related than distant things” ⁶. This law also informs the concept of Spatial Autocorrelation (SA). The variables were highly spatially autocorrelated, and the missing value in a county was imputed using the median value of the eight nearest neighbors or counties. The eight nearest neighbors were identified using the KDTree algorithm in Python’s scipy.spatial module ⁷. After imputation of missing data, we have 13 counties with no data in 2020, and none for 2010.

Limitations

Data is not available for the years 1990 and 2000.

References

1. Centers for Disease Control and Prevention. (n.d.). Health insurance coverage. U.S. Department of Health and Human Services. Retrieved December 21, 2024.

2. Health Insurance Coverage and Access to Care Among Black Americans: Recent Trends and Key Challenges (Issue Brief No. HP2024-14). Washington, DC: Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services. June 2024. https://aspe.hhs.gov/reports/healthinsurance-coverage-access-care-black-americans

3. Trostel, S., & Reeves, R. V. (2024, April 17). Long shadows: The Black-White gap in multigenerational poverty. Brookings Institution. https://www.brookings.edu/articles/long-shadows-the-black-white-gap-in-multigenerational-poverty/

4. Chetty, R., Hendren, N., Jones, M. R., & Porter, S. R. (2020). Race and economic opportunity in the United States: An intergenerational perspective. The Quarterly Journal of Economics, 135(2), 711-783.

5. Manson, S., Schroeder, J., Van Riper, D., Knowles, K., Kugler, T., Roberts, F., & Ruggles, S. (2024). IPUMS National Historical Geographic Information System: Version 19.0 [Dataset]. IPUMS.

6. Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit region. Economic geography, 46(sup1), 234-240.

7. Virtanen, P., Gommers, R., Burovski, E., Oliphant, T. E., Weckesser, W., Cournapeau, D., … & Feng, Y. (2021). scipy/scipy: SciPy 1.6. 0. Zenodo.