From Politics to Parity: Using a Health Disparities Index to Guide Legislative Efforts for Health Equity

Abstract

Objectives. We created an index quantifying the longitudinal burden of racial health disparities by state and compared this index to variables to guide the construction of, and validate support for, legislative efforts aimed at eliminating health disparities.

Methods. We evaluated 5 focus areas of greatest racial disparities in health from 1999 to 2005 and compiled state health disparities index (HDI) scores. We compared these scores with variables representing the purported social determinants of health.

Results. Massachusetts (0.35), Oklahoma (0.35), and Washington (0.39) averaged the fewest disparities. Michigan (1.22), Wisconsin (1.32), and Illinois (1.50) averaged the greatest disparities. The statistical reference point for nationwide average racial disparities was 1.00. The longitudinal mixed model procedure yielded statistically significant correlations between HDI scores and Black state population percentage as well as with the racial gap in uninsured percentages. We noted a trend for HDI correlations with median household income ratios.

Conclusions. On the basis of the HDI-established trends in the extent and distribution of racial health disparities, and their correlated social determinants of health, policymakers should consider incorporating this tool to advise future efforts in minority health legislation.

The US Congress took its first stand against minority health disparities with the passage of the Minority Health and Health Disparities Research and Education Act in October 2000. This measure commissioned the creation of the National Center for Minority Health and Health Disparities and mandated health disparities research and reporting.¹ The goal was to conscientiously move the discourse from identifying health disparities to eliminating them.

The years that followed brought progress in researching causation, positing solutions, and supporting promising models for the elimination of these disparities. The congressionally mandated Institutes of Medicine report Unequal Treatment grouped factors of causation into 3 basic areas: health system–level factors, care process variables, and patient-level variables.² In addition, its authors proffered strategies for achieving health equity with recommendations that included legal, regulatory, and policy interventions.²

With increasing research and dialogue, legislators began considering the next appropriate intervention. Congress has continued to debate solutions to the health care issues facing minority communities, introducing 6 comprehensive minority health equity bills since 2000. In 2007, the 110th Congress saw the introduction of an unprecedented 16 health disparities–focused bills.³ Despite the increasing legislative interest in improving minority health, no comprehensive minority health measure has become public law since the Minority Health and Health Disparities Research and Education Act of 2000.

The proposition of legislative remedies for health disparities has not been unique to Congress. Since 1999, a majority of state legislatures have passed minority health legislation.⁴ These laws have addressed cultural competency, health professional recruitment and retention, and disease burden or risk factor management. Furthermore, 40 states created offices of minority health, and 4 states designated official minority health contacts.⁵

When considered in sum, these state and federal legislative efforts have ushered in a new age of health disparities discourse. Champions of comprehensive health equity legislation argue that it is a necessary adjunct to existing efforts to eliminate racial and ethnic health disparities. Skeptics assert that the disparities can be largely addressed through generally improving access to quality care for all Americans. Most, however, appreciate the utility in bringing the discussion into the legislative arena as this issue continues to gain momentum.

In this context, we recognized a need to create tools to guide legislative efforts for achieving minority health equity. The goals of our analysis were 3-fold: (1) to establish an index depicting variations in US racial health disparities; (2) to evaluate the association between this health disparities index (HDI) and known social determinants of health; and (3) to use statistical correlations to help guide minority health legislative interventions at the state and federal levels.

METHODS

Six of the most glaring indicators of racial and ethnic health disparities, as referenced by the Department of Health and Human Services, were the foundation for this HDI. These focus areas are: cancer screening and management, cardiovascular disease (CVD), diabetes, HIV/AIDS, immunizations, and infant mortality.⁶ Mortality was selected as the primary outcome for each condition as a proxy for disease severity, because of its value as a more precise endpoint than morbidity and its compelling significance to legislators. We omitted immunization statistics, as immunization is unique among the 6 focus areas as it is a preventive measure rather than a distinct cause of mortality.

We obtained mortality statistics from the National Center for Health Statistics Compressed Mortality File from 1999 to 2005⁷ for each of the 5 disease processes. We specifically calculated crude mortality rates per 100 000 for Black and White populations aged 20 to 64 years in all 50 states. For each disease process, we calculated a state disparity value (SDV). For example, we calculated the SDV for CVD in Maryland during 2003 by using the formula:

This value is representative of the ratio of excess Black mortality to the White mortality per 100 000 individuals for a single disease process in a specific state. Additionally, we calculated a cumulative US disparity value (USDV) for each disease process during a particular year. We calculated the HDI value per year, per disease process, per state with the formula:

We calculated a comprehensive HDI_COMP score for each year by using the following formula, for example:

We calculated these HDI scores for the 7 years in the analysis for all 50 states as sufficient data allowed.

A state HDI score of 1.00 represents racial health disparities equivalent to those existing among the entire US population. An HDI score of 0.00 would represent racial health parity, and states with HDI scores of 0.01 to 0.99 experience less racial health disparity in mortality than the inequalities among the US population at large. Lastly, states with HDI scores exceeding 1.00 exhibit more racial health disparity than US averages, indicating larger differences in Black and White mortality for the selected conditions.

Selection of Variables

To test the validity of the HDI as a tool to measure health disparities, we first evaluated its correlation to several of the known social determinants of health.⁸ These included income and social status, education and literacy, health services, culture, and social environments.

To evaluate income and social status, we calculated income disparities as a ratio of White-to-Black median household incomes by using the 2000–2005 Annual Social and Economic Supplements of the US Census Bureau's Current Population Survey.⁹ Next, as our variable for education and literacy, we calculated the ratio of Black-to-White high school dropout rates as reported by the National Center for Educational Statistics between 2000 and 2005.¹⁰ As a proxy for culture and social environment, we used 2 indicators: geographical region, as divided by the US Census Bureau,¹¹ and the percentage of each state's population that is Black. These variables were meant to loosely model the cultural and social environment in each state.

We depicted health services through 3 separate variables. First, differences in access to health services were represented by the difference between Black and White uninsured rates as reported in Behavior Risk Factor Surveillance System questionnaire responses. In addition, state Medicaid program eligibility was selected as a proxy for the size of the state's safety net for its most vulnerable populations. This value was extracted from Public Citizen Health Research Group's analysis of state Medicaid programs, which evaluated each state Medicaid program for its coverage of optional eligibility groups.¹² Finally, to capture the increased spending that would be associated with health disparities, we evaluated state health spending, calculated as a percentage of the gross state product.¹³

Statistical Analysis

We first employed Pearson's correlation coefficients to assess the association between state HDI scores and variables representing the social determinants of health. We completed a second, more sophisticated longitudinal mixed model analysis to reassess correlations in the data from 2000 to 2004. In the mixed model we used yearly data for disparities in uninsured percentages, state health spending, and disparities in median household income, but averaged data for high school dropout rate because of missing values. Additionally, 10 observations were removed from this latter analysis because of missing values for the gap in uninsured percentages. We omitted from the analysis states with unreliable mortality data, as defined in the Compressed Mortality File as values fewer than 20 deaths per annum in any HDI subcategory.¹⁴

RESULTS

Thirty-three states had sufficient data for the calculation of HDI scores from 1999 to 2005, with 17 states omitted.¹ Among the states for which scores were compiled, Massachusetts (0.35), Oklahoma (0.35), Washington (0.39), Nevada (0.53), and Kentucky (0.57) had the lowest 7-year average HDI scores, indicative of the least racial health disparity. California (1.17), North Carolina (1.20), Michigan (1.22), Wisconsin (1.32), and Illinois (1.50) had the highest 7-year average HDI scores, making them the states averaging the most racial health disparity between 1999 and 2005 (Table 1).

TABLE 1.

Health Disparities Index Scores by Rank, 1999–2005

Statea	1999	2000	2001	2002	2003	2004	2005	Average	Rankb
Massachusetts	0.27	0.44	0.32	0.33	0.52	0.22	0.31	0.35	1
Oklahoma	0.46	0.35	0.28	0.36	0.26	0.29	0.44	0.35	2
Washington	0.63	0.18	0.15	0.36	0.48	0.34	0.61	0.39	3
Nevada	0.44	0.38	0.57	0.51	0.51	0.67	0.64	0.53	4
Kentucky	0.65	0.52	0.42	0.69	0.57	0.53	0.59	0.57	5
New York	0.57	0.60	0.60	0.53	0.58	0.60	0.69	0.60	6
Florida	0.68	0.66	0.59	0.66	0.56	0.59	0.55	0.61	7
Minnesota	0.85	0.65	0.59	0.29	0.36	0.64	0.91	0.61	8
Colorado	0.62	0.82	0.66	0.93	0.78	0.36	0.60	0.68	9
Connecticut	0.63	0.84	1.02	0.73	0.69	0.58	0.81	0.75	10
Arkansas	0.90	0.89	0.73	0.73	0.73	0.65	0.76	0.77	11
Mississippi	0.76	0.72	0.81	0.88	0.75	0.69	0.85	0.78	12
Alabama	0.94	0.76	0.92	0.74	0.83	0.63	0.75	0.79	13
Georgia	0.86	0.82	0.78	0.78	0.78	0.82	0.78	0.80	14
Indiana	0.96	0.72	0.76	0.91	0.86	0.83	0.81	0.84	15
Kansas	0.80	0.87	0.78	1.08	0.69	0.72	1.15	0.87	16
Delaware	1.21	0.86	0.97	0.73	0.92	0.65	0.93	0.90	17
Ohio	0.82	0.86	0.85	0.95	0.95	1.02	0.94	0.91	18
Tennessee	0.96	1.03	0.93	0.95	0.85	0.85	0.84	0.91	19
Missouri	1.15	0.83	0.95	1.14	0.89	0.88	0.92	0.97	20
Texas	0.92	0.94	0.98	1.04	1.06	1.00	1.07	1.00	21
Nebraska	1.12	0.91	0.92	0.88	1.21	1.01	0.98	1.01	22
Louisiana	1.07	1.02	1.03	1.11	1.12	1.06	0.96	1.05	23
New Jersey	1.10	1.09	1.12	1.08	1.11	1.04	1.05	1.08	24
South Carolina	1.11	1.13	1.13	1.05	1.18	1.11	0.90	1.09	25
Maryland	1.25	1.24	1.23	1.02	1.11	0.96	0.93	1.11	26
Pennsylvania	1.33	1.07	1.09	1.03	1.21	1.08	1.02	1.12	27
Virginia	1.03	1.12	1.20	1.32	0.96	1.20	1.02	1.12	28
California	1.09	1.16	1.16	1.18	1.18	1.23	1.22	1.17	29
North Carolina	1.12	1.28	1.25	1.24	1.13	1.25	1.10	1.20	30
Michigan	1.24	1.05	1.18	1.29	1.21	1.24	1.35	1.22	31
Wisconsin	1.11	1.41	1.03	1.19	1.55	1.65	1.34	1.32	32
Illinois	1.47	1.44	1.42	1.52	1.57	1.58	1.51	1.50	33

Note. Health disparities index is an index depicting state variations in US racial health disparities. Values < 0.00 represent a Black mortality rate that is better than the White mortality rate. Values of 0.00 represent parity in Black–White mortality. Values > 0.00 but < 1.00 represent state mortality disparities that are below the national average for mortality disparities. Values of 1.00 represent state mortality disparities that equal the national average for mortality disparities. Values > 1.00 represent state mortality disparities that exceed the national average for mortality disparities.

^aAlaska, Arizona, Hawaii, Idaho, Iowa, Maine, Montana, New Hampshire, New Mexico, North Dakota, Oregon, Rhode Island, South Dakota, Utah, Vermont, West Virginia, and Wyoming had insufficient data for the calculation of a health disparities index score.

^bState rank with respect to 33 states with sufficient data to be included in the analysis.

When we used Pearson's correlation coefficients, the HDI was positively correlated to racial disparities in median household income (P < .001), state Black population (P < .001), and Medicaid eligibility scores (P < .01). We found a negative correlation between HDI scores and state health spending (P < .001).

The longitudinal mixed model analysis included 5 years of data (2000–2004) for each state rather than the entire 7-year data set. The 1999 and 2005 data were eliminated from the analysis because of the missing values for median household income and state health spending for those years. In the final analysis, 32 states were included (Tennessee was ultimately omitted because of a lack of a state Medicaid eligibility score), and a total of 150 observations. The longitudinal mixed model analysis yielded a correlation between HDI scores and both racial disparities in uninsured percentages (P < .01) and state Black population (P < .01). We found a trend toward significance between HDI scores and racial disparities in median household income (P = .074). Table 2 details the outcomes of the statistical analysis both for Pearson's correlation coefficients and the longitudinal mixed model analysis.

TABLE 2.

Statistical Correlations Between Health Disparities Index Scores and Selected Variables

	Health Disparities Index Scores
	Pearson's Correlation Coefficients
	Coefficient	P	Longitudinal Mixed Analysis, P
Geographical region	NA		.243
State Black population (n = 238)	0.65	<.001	.001
Disparities in uninsured percentages (n = 218)	0.1	.124	.008
Disparities in high-school drop-out rates (n = 224)	−0.09	.203	.466
Disparities in median household income (n = 204)	0.78	<.001	.074
State health spending (n = 204)	−0.38	<.001	.958
State Medicaid eligibility score (n = 238)	0.18	.006	.779

DISCUSSION

The HDI has several strengths, including its evaluation of health disparities. Rather than focusing the analysis on well described gaps between Black and White health statuses according to various indicators, the HDI was created to depict variations in the extent and distribution of the racial disparities that are known to persist. The HDI builds upon the corpus of health disparities literature to depict the racial health inequalities for each state within the larger, national context of health disparities discourse.

Tracking Progress in Reducing Disparities

According to HDI scores, most states failed to significantly reduce the extent of health disparities between 1999 and 2005, an observation consistent with the findings of the National Healthcare Disparities Reports issued during the study period.^15–17 The states with the fewest disparities in 1999 generally maintained their status among the most equitable states by 2005, whereas states with the greatest disparities largely remained among the states with the highest HDI scores throughout the duration of the study. The profiles of the states at either end of the rankings were noticeably divergent with regard to the Black population percentage and racial disparities in uninsured percentages, whereas disparities in median household income were not significantly different among states at either end of the HDI rankings (Table 3).

TABLE 3.

Changes Over Study Duration (1999–2005) in Health Disparities Index (HDI) Scores and Selected Variables Among States With the Lowest and Highest HDI Scores in 1999

State	HDI Score	HDI Rank	Black Population, %	Health Insurance Gap, %	Income Gap
Least disparities
Massachusetts
1999	0.27	1	5.34	6.80	1.76
2005	0.31	1	6.51	8.60	1.81
Nevada
1999	0.44	2	6.50	NA	1.75
2005	0.64	7	8.02	NA	1.53
Oklahoma
1999	0.46	3	6.38	−0.40	1.82
2005	0.44	2	8.11	9.00	1.55
Most disparities
Maryland
1999	1.25	31	27.72	5.50	1.47
2005	0.93	20	29.52	10.60	1.5
Pennsylvania
1999	1.33	32	9.60	4.30	1.66
2005	1.02	25	10.68	10.80	1.79
Illinois
1999	1.47	33	14.50	13.40	1.67
2005	1.51	33	15.13	15.40	1.82

Notes. NA = not available. Of the 17 states without HDI scores, 16 have Black populations of less than 4%, and represent the 16 states with the smallest Black populations.

Although few states exhibited any meaningful diminution of health disparities, some displayed steady improvement in their HDI scores over the study duration. Maryland, for example, began the analysis as the state exhibiting the third-most racial health care disparity in 1999, with an HDI score of 1.25. By 2005, however, this score had steadily decreased to a value of 0.93, a value that was below the average for racial health care disparity across the nation. The greatest improvement was made in the interval between 2001 and 2002, as the HDI score decreased from 1.23 to 1.02. Of note, Maryland passed a total of 4 state laws addressing health disparities in 2002 and 2003.¹⁸ We are not, however, attributing Maryland's decreasing HDI value solely to the passage of that state legislation. Rather, the legislative commitment to the reduction of disparities in Maryland reflects a statewide emphasis on improving the health of minority communities. Furthermore, this commitment was statistically reflected in the reduction of health care disparities as depicted by the HDI values.

Tracking Health Disparities in Minority Health Focus Areas

The HDI offers states the opportunity to track disparities in mortality among cancer, CVD, diabetes, HIV/AIDS, and infant mortality. Figure 1 illustrates the HDI disease-specific scores for these 5 focus areas in the 2 states at polar ends of the HDI rankings. This index constitutes another mechanism by which states can track disparities in certain disease processes to identify state priorities in addressing disparities. Importantly, each of these conditions can be addressed through better prevention, surveillance, and disease management. By allowing states to identify disease processes that contribute significantly to the health disparities burden in their state, specific interventions can be crafted to assist in addressing these inequalities in minority communities.

FIGURE 1.

Component disease processes of HDI for a) Massachusetts and b) Illinois. Y-axis values are HDI scores for each disease process or for state disparities as a whole.

Note: CVD = cardiovascular disease; HDI = health disparities index. Value of 0.00 represents racial/ethnic parity in mortality rates, while a value of 1.00 represents the average Black–White mortality disparities across the United States.

Application of the HDI to the Development of Minority Health Legislation

Through statistical correlations, the HDI becomes a valuable tool to evaluate health inequalities in relation to variations in the social determinants of health. As such, correlations found between the HDI and the selected variables have implications for both understanding racial and ethnic health disparities and crafting future minority health legislation.

Blacks are 3 times more likely to live in poverty than are Whites, a remarkable fact when one considers how income significantly influences health status, access to health care, and health insurance coverage.¹⁹ The positive correlation between the HDI and racial disparities in median family income corroborates studies touting the impact of disparities in income on racial health inequalities. Additionally, we found a correlation between states with the largest Black populations and those having the most racial health disparity. Notably, the income disparities and Black state population variables, themselves, were positively correlated, supporting claims of their interrelatedness in contributing to poor health outcomes.

We employed the longitudinal mixed model analysis because of the potential for confounding variables. Not only did it explicitly model state change in HDI scores over time, but it also simultaneously and explicitly modeled between- and within-state variation. In discerning the relationship of variables to the HDI over the duration of the study, this analysis afforded flexible modeling of covariance structure of the repeated measures, allowing us to determine whether variables such as Black percentage of the population and disparities in income were confounders or independently associated with the HDI.

Using the longitudinal mixed model analysis, we identified correlations between the HDI and both racial disparities in uninsured percentages as well as the state Black population. Additionally, we noted a statistically insignificant trend correlating the HDI and racial disparities in median household income. The presence of both the correlation for state Black population and the trend for disparities in income support claims that these variables are independently related to health disparities and not merely a function of each other. As such, we believe that legislation to eliminate racial health disparities must be accompanied by efforts to address the income disparities that impact health.

Higher education levels have been linked to the utilization of preventive services and longer life. Conversely, the lower rates of educational attainment documented among Blacks compared with Whites and Asians are considered a factor in poor health status.²⁰ With the impact of increased dropout rates unlikely to manifest for many years, these values function as an educational snapshot of the community in comparison with other states, allowing us to reasonably extrapolate those values to the community as a whole. In our analysis, however, we found no statistically significant correlation between dropout rates and the HDI, indicating that this factor alone is not related to health disparities across states.

As a primary reason for escalating health care costs, chronic diseases, such as those composing the HDI, account for more than 75 cents of every dollar spent on health care in the United States.²¹ Additionally, minority health disparities cost $229.4 billion in direct medical care expenses between 2003 and 2006.¹¹ In our analysis, state health spending was inversely related to racial health disparities in mortality. Although increased spending may be associated with less mortality, this is not a sustainable mechanism to combat disparities in health care in light of our nation's economic climate, and current health care trends in spending.

We found no correlation between the HDI and state inclusion of optional eligibility groups in their Medicaid programs. Greater access should not be confused with greater accessibility. For a state's most vulnerable populations, increasing access without the infrastructural support to increase actual accessibility to quality care will not sufficiently reduce racial health disparities.

In a review of comprehensive minority health equity legislation that has been introduced into Congress, several trends in policy-based interventions recur. Each bill has called for health disparities research and improving health care quality, and most included provisions to increase access to care, health workforce diversity, and training for cultural competency. Several bills proposed grants for community programming, public health education, and promoting healthy lifestyles. Each intervention is supported by extensive health disparities research and evidence-based recommendations for health disparities solutions. The challenge, however, comes in the prioritization of these interventions and the specification of efforts that constitute best practices in achieving health equity.

Advisory tools are needed for identifying specific community program grants that legislation should support, as well as the appropriate improvements to create quality, accessible care. Using the HDI as a guide, minority health legislation should focus primarily on decreasing the number of uninsured, as well as closing the significant income gaps that persist between Blacks and Whites. Furthermore, the preponderance of health disparities in states with larger Black populations indicates that the states with the greatest disparities are also the states with the largest segment of the population that would benefit from such targeted legislative initiatives. The percentage of the population affected in the states with the most racial health disparity should encourage legislators to seek to improve minority health on behalf of these individuals, their constituents.

For state legislators, the HDI would be an effective tool to identify the need for disease-specific minority health legislation, as well as a mechanism to track the progress in decreasing mortality. Federally, the reality that the Black–White mortality gap has not improved significantly over the past 4 decades warrants the utilization of the HDI to better define this egregious disparity and reliably track current and future efforts to close that gap.²² Although this analysis focused on evaluating trends in the social determinants of health, future variables could be increasingly specific to predict the impact that diversifying the physician workforce, improving patient lifestyle factors, and increasing funding for community grants would have on the elimination of health disparities. With both state and federal legislation, an understanding of the correlation between social determinants and the HDI can effectively serve as a sound basis for legislative initiatives using pertinent positive and negative correlations to identify targets for policy intervention upstream of the differences in health status among racial and ethnic minorities.

Limitations

We used mortality rather than incidence data to construct the HDI. Although mortality was selected as a surrogate for disease severity, many of the interventions to eliminate health disparities would be meant to prevent disease and might be better modeled by using incidence if sufficient data for these measures existed in all states. Additionally, a likely time lag exists between variations in the social determinants of health and resultant changes in mortality. Through this analysis, however, we hope to discern how long it would take for changes in policy to manifest in improvements in HDI scores.

Another limitation was our use of crude mortality rates as opposed to age-adjusted rates. As we were comparing 2 populations with different age structures, it would follow that age-adjusted rates could be beneficial. Because Whites have an older age distribution than do Blacks, age confounds the relationship between race and mortality. Future studies should consider adjusting the mortality rates accordingly.

Next, our index compares the mortality for Blacks in each state against the mortality of Whites within the same state. As such, it does not compare within-state Black mortality to a national standard for mortality rates, but rather state racial health disparities against national racial health disparities. We chose our point of comparison with focus on the unique disparities within each state. Even still, we recognize that this could understate particularly concerning Black mortality rates in states with higher mortality among Whites as well.

We were unable to calculate HDI scores for 17 states because of unreliable mortality data. Notably, of the states without HDI scores, 16 have Black populations of less than 4% and are the states with the smallest Black populations in the nation. To better elucidate health disparities causation and the most promising policy solutions, reporting for some primary outcome—be it mortality, incidence, or prevalence—must be sufficient to reliably evaluate the health status of minority populations in each state.

Finally, we realize that the index proves useful in assessing correlation between the extent of health disparities and the selected variables, although it does not assert causation. Instead, this analysis can serve 2 other major purposes: (1) to track progress in eliminating health disparities in each state and (2) to identify areas of interest among the social determinants of health and subsequently guide future legislation for reducing racial health disparities.

In conclusion, the HDI serves as a novel and useful mechanism to guide legislative efforts for improving minority health. By tracking the extent and distribution of racial health disparities, identifying correlated social factors, and positing potential initiatives to reduce disparities, the HDI displays its great potential utility in such analyses. Policymakers should strongly consider incorporating this tool to evaluate health disparities and strategically plan future legislative initiatives.