from politics to parity: using a health disparitiies index to guide legislative efforts for health...

From Politics to Parity: Using a Health Disparities Index toGuide Legislative Efforts for Health EquityBryant Cameron Webb, BA, Sean L. Simpson, PhD, and Kristen G. Hairston, MD, MPH

The US Congress took its first stand againstminority health disparities with the passage ofthe Minority Health and Health DisparitiesResearch and Education Act in October 2000.This measure commissioned the creation ofthe National Center for Minority Health andHealth Disparities and mandated health dis-parities research and reporting.1The goal was toconscientiously move the discourse from identi-fying health disparities to eliminating them.

The years that followed brought progress inresearching causation, positing solutions, andsupporting promising models for the eliminationof these disparities. The congressionally man-dated Institutes of Medicine report UnequalTreatment grouped factors of causation into 3basic areas: health system–level factors, careprocess variables, and patient-level variables.2

In addition, its authors proffered strategiesfor achieving health equity with recommen-dations that included legal, regulatory, andpolicy interventions.2

With increasing research and dialogue, legis-lators began considering the next appropriateintervention. Congress has continued to debatesolutions to the health care issues facing minoritycommunities, introducing 6 comprehensive mi-nority health equity bills since 2000. In 2007,the 110th Congress saw the introduction of anunprecedented 16 health disparities–focusedbills.3 Despite the increasing legislative interest inimproving minority health, no comprehensiveminority health measure has become public lawsince the Minority Health and Health DisparitiesResearch and Education Act of 2000.

The proposition of legislative remedies forhealth disparities has not been unique toCongress. Since 1999, a majority of statelegislatures have passed minority health legis-lation.4 These laws have addressed culturalcompetency, health professional recruitment andretention, and disease burden or risk factormanagement. Furthermore, 40 states createdoffices of minority health, and 4 states designatedofficial minority health contacts.5

When considered in sum, these state andfederal legislative efforts have ushered in a newage of health disparities discourse. Championsof comprehensive health equity legislationargue that it is a necessary adjunct to existingefforts to eliminate racial and ethnic healthdisparities. Skeptics assert that the disparitiescan be largely addressed through generallyimproving access to quality care for all Amer-icans. Most, however, appreciate the utility inbringing the discussion into the legislativearena as this issue continues to gain momen-tum.

In this context, we recognized a need tocreate tools to guide legislative efforts forachieving minority health equity. The goalsof our analysis were 3-fold: (1) to establish anindex depicting variations in US racial healthdisparities; (2) to evaluate the association be-tween this health disparities index (HDI) andknown social determinants of health; and (3) touse statistical correlations to help guide mi-nority health legislative interventions at thestate and federal levels.

METHODS

Six of the most glaring indicators of racialand ethnic health disparities, as referenced bythe Department of Health and Human Services,were the foundation for this HDI. These focusareas are: cancer screening and management,cardiovascular disease (CVD), diabetes, HIV/AIDS, immunizations, and infant mortality.6

Mortality was selected as the primary outcomefor each condition as a proxy for disease severity,because of its value as a more precise endpointthan morbidity and its compelling significanceto legislators. We omitted immunization statistics,as immunization is unique among the 6 focusareas as it is a preventive measure rather thana distinct cause of mortality.

We obtained mortality statistics from theNational Center for Health Statistics Com-pressed Mortality File from 1999 to 20057 foreach of the 5 disease processes. We specificallycalculated crude mortality rates per 100000 forBlack and White populations aged 20 to 64years in all 50 states. For each disease process,

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 de-

terminants of health.

Results. Massachusetts (0.35), Oklahoma (0.35), and Washington (0.39) aver-

aged 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. (Am J Public Health. 2011;101:554–560. doi:

10.2105/AJPH.2009.171157)

RESEARCH AND PRACTICE

554 | Research and Practice | Peer Reviewed | Webb et al. American Journal of Public Health | March 2011, Vol 101, No. 3

we calculated a state disparity value (SDV).For example, we calculated the SDV for CVDin Maryland during 2003 by using the for-mula:

ð1Þ SDVCVD in 2003 ¼ðBlack mortalityfrom CVD in MD during 2003

�White mortalityfrom CVD in MD during 2003Þ=White mortalityfrom CVD in MD during 2003

This value is representative of the ratio ofexcess Black mortality to the White mortalityper 100000 individuals for a single diseaseprocess in a specific state. Additionally, wecalculated a cumulative US disparity value(USDV) for each disease process during a par-ticular year. We calculated the HDI value peryear, per disease process, per state with theformula:

ð2Þ HDICVD in ðyearÞ ¼SDVCVD in ðyearÞ=USDVCVD in ðyearÞ

We calculated a comprehensive HDICOMP

score for each year by using the followingformula, for example:

ð3Þ HDI2003 ¼½HDICancer in 2003 1 HDICVD

1 HDIDiabetes 1 HDIHIV

1 HDIinfant mortility�=5

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

A state HDI score of 1.00 represents racialhealth disparities equivalent to those existingamong the entire US population. An HDI scoreof 0.00 would represent racial health parity,and states with HDI scores of 0.01 to 0.99experience less racial health disparity in mor-tality than the inequalities among the USpopulation at large. Lastly, states with HDIscores exceeding 1.00 exhibit more racialhealth disparity than US averages, indicatinglarger differences in Black and White mortalityfor the selected conditions.

Selection of Variables

To test the validity of the HDI as a toolto measure health disparities, we first evalu-ated its correlation to several of the known

social determinants of health.8 These inclu-ded income and social status, education andliteracy, health services, culture, and socialenvironments.

To evaluate income and social status, wecalculated income disparities as a ratio ofWhite-to-Black median household incomesby using the 2000–2005 Annual Social andEconomic Supplements of the US CensusBureau’s Current Population Survey.9 Next,as our variable for education and literacy, wecalculated the ratio of Black-to-White highschool dropout rates as reported by the NationalCenter for Educational Statistics between 2000and 2005.10 As a proxy for culture and socialenvironment, we used 2 indicators: geographicalregion, as divided by the US Census Bureau,11

and the percentage of each state’s population thatis Black. These variables were meant to looselymodel the cultural and social environment ineach state.

We depicted health services through 3separate variables. First, differences in accessto health services were represented by thedifference between Black and White unin-sured rates as reported in Behavior RiskFactor Surveillance System questionnaireresponses. In addition, state Medicaid pro-gram eligibility was selected as a proxy forthe size of the state’s safety net for its mostvulnerable populations. This value wasextracted from Public Citizen Health Re-search Group’s analysis of state Medicaidprograms, which evaluated each state Med-icaid program for its coverage of optionaleligibility groups.12 Finally, to capture the in-creased spending that would be associated withhealth disparities, we evaluated state healthspending, calculated as a percentage of the grossstate product.13

Statistical Analysis

We first employed Pearson’s correlation co-efficients to assess the association betweenstate HDI scores and variables representing thesocial determinants of health. We completeda second, more sophisticated longitudinalmixed model analysis to reassess correlationsin the data from 2000 to 2004. In the mixedmodel we used yearly data for disparities inuninsured percentages, state health spending,and disparities in median household income,but averaged data for high school dropout rate

because of missing values. Additionally, 10observations were removed from this latteranalysis because of missing values for the gapin uninsured percentages. We omitted from theanalysis states with unreliable mortality data, asdefined in the Compressed Mortality File asvalues fewer than 20 deaths per annum in anyHDI subcategory.14

RESULTS

Thirty-three states had sufficient data forthe calculation of HDI scores from 1999 to2005, with 17 states omitted.1 Among thestates for which scores were compiled, Massa-chusetts (0.35), Oklahoma (0.35), Washington(0.39), Nevada (0.53), and Kentucky (0.57)had the lowest 7-year average HDI scores, in-dicative of the least racial health disparity.California (1.17), North Carolina (1.20), Michigan(1.22), Wisconsin (1.32), and Illinois (1.50) hadthe highest 7-year average HDI scores, makingthem the states averaging the most racial healthdisparity between 1999 and 2005 (Table 1).

When we used Pearson’s correlation coeffi-cients, the HDI was positively correlated toracial disparities in median household income(P < .001), state Black population (P < .001),and Medicaid eligibility scores (P < .01). Wefound a negative correlation between HDIscores and state health spending (P < .001).

The longitudinal mixed model analysis in-cluded 5 years of data (2000–2004) for eachstate rather than the entire 7-year data set.The 1999 and 2005 data were eliminatedfrom the analysis because of the missingvalues for median household income andstate health spending for those years. In thefinal analysis, 32 states were included (Ten-nessee was ultimately omitted because ofa lack of a state Medicaid eligibility score),and a total of 150 observations. The longi-tudinal mixed model analysis yielded a cor-relation between HDI scores and bothracial disparities in uninsured percentages(P< .01) and state Black population (P< .01).We found a trend toward significance be-tween HDI scores and racial disparities inmedian household income (P= .074). Table2 details the outcomes of the statisticalanalysis both for Pearson’s correlation co-efficients and the longitudinal mixed modelanalysis.


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DISCUSSION

The HDI has several strengths, including itsevaluation of health disparities. Rather thanfocusing the analysis on well described gapsbetween Black and White health statusesaccording to various indicators, the HDI was

created to depict variations in the extent anddistribution of the racial disparities that areknown to persist. The HDI builds upon thecorpus of health disparities literature to depictthe racial health inequalities for each statewithin the larger, national context of healthdisparities discourse.

Tracking Progress in Reducing

Disparities

According to HDI scores, most states failedto significantly reduce the extent of healthdisparities between 1999 and 2005, an ob-servation consistent with the findings of theNational Healthcare Disparities Reports issuedduring the study period.15–17 The states with thefewest disparities in 1999 generally maintainedtheir status among the most equitable states by2005, whereas states with the greatest disparitieslargely remained among the states with thehighest HDI scores throughout the duration ofthe study. The profiles of the states at either endof the rankings were noticeably divergent withregard to the Black population percentage andracial disparities in uninsured percentages,whereas disparities in median household incomewere not significantly different among states ateither end of the HDI rankings (Table 3).

Although few states exhibited any meaningfuldiminution of health disparities, some displayedsteady improvement in their HDI scores overthe study duration. Maryland, for example,began the analysis as the state exhibiting thethird-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 of0.93, a value that was below the average forracial health care disparity across the nation.The greatest improvement was made in theinterval between 2001 and 2002, as the HDIscore decreased from 1.23 to 1.02. Of note,Maryland passed a total of 4 state laws ad-dressing health disparities in 2002 and 2003.18

We are not, however, attributing Maryland’sdecreasing HDI value solely to the passage ofthat state legislation. Rather, the legislativecommitment to the reduction of disparities inMaryland reflects a statewide emphasis on im-proving the health of minority communities.Furthermore, this commitment was statisticallyreflected in the reduction of health care dispar-ities as depicted by the HDI values.

Tracking Health Disparities in Minority

Health Focus Areas

The HDI offers states the opportunity totrack disparities in mortality among cancer,CVD, diabetes, HIV/AIDS, and infant mortal-ity. Figure 1 illustrates the HDI disease-specificscores for these 5 focus areas in the 2 states atpolar ends of the HDI rankings. This index

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 representa 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 > 1.00represent 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 disparitiesindex score.bState rank with respect to 33 states with sufficient data to be included in the analysis.



constitutes another mechanism by which statescan track disparities in certain disease pro-cesses to identify state priorities in addressingdisparities. Importantly, each of these condi-tions can be addressed through better pre-vention, surveillance, and disease

management. By allowing states to identifydisease processes that contribute significantlyto the health disparities burden in their state,specific interventions can be crafted to assistin addressing these inequalities in minoritycommunities.

Application of the HDI to the

Development of Minority Health

Legislation

Through statistical correlations, the HDIbecomes a valuable tool to evaluate health-inequalities in relation to variations in thesocial determinants of health. As such, corre-lations found between the HDI and the se-lected variables have implications for bothunderstanding racial and ethnic health dis-parities and crafting future minority healthlegislation.

Blacks are 3 times more likely to live inpoverty than are Whites, a remarkable factwhen one considers how income signifi-cantly influences health status, access tohealth care, and health insurance coverage.19

The positive correlation between the HDIand racial disparities in median familyincome corroborates studies touting the im-pact of disparities in income on racial healthinequalities. Additionally, we found a correla-tion between states with the largest Blackpopulations and those having the most racialhealth disparity. Notably, the income dispar-ities and Black state population variables,themselves, were positively correlated, sup-porting claims of their interrelatedness incontributing to poor health outcomes.

We employed the longitudinal mixedmodel analysis because of the potential forconfounding variables. Not only did it ex-plicitly model state change in HDI scoresover time, but it also simultaneously andexplicitly modeled between- and within-statevariation. In discerning the relationship ofvariables to the HDI over the duration of thestudy, this analysis afforded flexible model-ing of covariance structure of the repeatedmeasures, allowing us to determine whethervariables such as Black percentage of thepopulation and disparities in income wereconfounders or independently associatedwith the HDI.

Using the longitudinal mixed model analysis,we identified correlations between the HDIand both racial disparities in uninsured per-centages as well as the state Black population.Additionally, we noted a statistically insignifi-cant trend correlating the HDI and racialdisparities in median household income. Thepresence of both the correlation for state

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

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 the16 states with the smallest Black populations.



Black population and the trend for disparitiesin income support claims that these variablesare independently related to health disparitiesand not merely a function of each other. Assuch, we believe that legislation to eliminateracial health disparities must be accompanied byefforts to address the income disparities thatimpact health.

Higher education levels have been linkedto the utilization of preventive services andlonger life. Conversely, the lower rates ofeducational attainment documented amongBlacks compared with Whites and Asians areconsidered a factor in poor health status.20

With the impact of increased dropout ratesunlikely to manifest for many years, thesevalues function as an educational snapshot ofthe community in comparison with otherstates, allowing us to reasonably extrapolatethose values to the community as a whole. In

our analysis, however, we found no statisti-cally significant correlation between dropoutrates and the HDI, indicating that this factoralone is not related to health disparities acrossstates.

As a primary reason for escalating healthcare costs, chronic diseases, such as thosecomposing the HDI, account for more than75 cents of every dollar spent on health carein the United States.21 Additionally, minorityhealth disparities cost $229.4 billion in directmedical care expenses between 2003 and2006.11 In our analysis, state health spendingwas inversely related to racial health disparitiesin mortality. Although increased spending maybe associated with less mortality, this is nota sustainable mechanism to combat disparities inhealth care in light of our nation’s economicclimate, and current health care trends inspending.

We found no correlation between theHDI and state inclusion of optional eligibilitygroups in their Medicaid programs. Greateraccess should not be confused with greateraccessibility. For a state’s most vulnerablepopulations, increasing access without theinfrastructural support to increase actual ac-cessibility to quality care will not sufficientlyreduce racial health disparities.

In a review of comprehensive minorityhealth equity legislation that has been in-troduced into Congress, several trends inpolicy-based interventions recur. Each billhas called for health disparities researchand improving health care quality, and mostincluded provisions to increase access to care,health workforce diversity, and training forcultural competency. Several bills proposedgrants for community programming, publichealth education, and promoting healthylifestyles. Each intervention is supported byextensive health disparities research andevidence-based recommendations for healthdisparities solutions. The challenge, how-ever, comes in the prioritization of theseinterventions and the specification of effortsthat constitute best practices in achievinghealth equity.

Advisory tools are needed for identifyingspecific community program grants thatlegislation should support, as well as the ap-propriate improvements to create quality, ac-cessible care. Using the HDI as a guide,minority health legislation should focusprimarily on decreasing the number ofuninsured, as well as closing the significantincome gaps that persist between Blacksand Whites. Furthermore, the preponder-ance of health disparities in states withlarger Black populations indicates that thestates with the greatest disparities are alsothe states with the largest segment of thepopulation that would benefit from suchtargeted legislative initiatives. The percent-age of the population affected in the stateswith the most racial health disparity shouldencourage legislators to seek to improveminority health on behalf of these individ-uals, their constituents.

For state legislators, the HDI would bean effective tool to identify the need fordisease-specific minority health legislation, aswell as a mechanism to track the progress in

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.

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.



decreasing mortality. Federally, the realitythat the Black–White mortality gap has notimproved significantly over the past 4 decadeswarrants the utilization of the HDI to betterdefine this egregious disparity and reliablytrack current and future efforts to close thatgap.22 Although this analysis focused on evalu-ating trends in the social determinants of health,future variables could be increasingly specific topredict the impact that diversifying the physicianworkforce, improving patient lifestyle factors,and increasing funding for community grantswould have on the elimination of health dispar-ities. With both state and federal legislation,an understanding of the correlation betweensocial determinants and the HDI can effectivelyserve as a sound basis for legislative initiativesusing pertinent positive and negative correlationsto identify targets for policy intervention up-stream of the differences in health status amongracial and ethnic minorities.

Limitations

We used mortality rather than incidencedata to construct the HDI. Althoughmortality was selected as a surrogate for dis-ease severity, many of the interventions toeliminate health disparities would be meant toprevent disease and might be better modeledby using incidence if sufficient data for thesemeasures existed in all states. Additionally,a likely time lag exists between variations in thesocial determinants of health and resultantchanges in mortality. Through this analysis,however, we hope to discern how long it wouldtake for changes in policy to manifest inimprovements in HDI scores.

Another limitation was our use of crudemortality rates as opposed to age-adjustedrates. As we were comparing 2 populationswith different age structures, it would followthat age-adjusted rates could be beneficial.Because Whites have an older age distributionthan do Blacks, age confounds the relationshipbetween race and mortality. Future studiesshould consider adjusting the mortality ratesaccordingly.

Next, our index compares the mortality forBlacks in each state against the mortality ofWhites within the same state. As such, it doesnot compare within-state Black mortality toa national standard for mortality rates, butrather state racial health disparities against

national racial health disparities. We chose ourpoint of comparison with focus on the uniquedisparities within each state. Even still, werecognize that this could understate particu-larly concerning Black mortality rates in stateswith higher mortality among Whites as well.

We were unable to calculate HDI scoresfor 17 states because of unreliable mortalitydata. Notably, of the states without HDI scores,16 have Black populations of less than 4%and are the states with the smallest Blackpopulations in the nation. To better elucidatehealth disparities causation and the mostpromising policy solutions, reporting for someprimary outcome—be it mortality, incidence, orprevalence—must be sufficient to reliably evaluatethe health status of minority populations ineach state.

Finally, we realize that the index provesuseful in assessing correlation between theextent of health disparities and the selectedvariables, although it does not assert causation.Instead, this analysis can serve 2 other majorpurposes: (1) to track progress in eliminatinghealth disparities in each state and (2) toidentify areas of interest among the socialdeterminants of health and subsequently guidefuture legislation for reducing racial healthdisparities.

In conclusion, the HDI serves as a novel anduseful mechanism to guide legislative efforts forimproving minority health. By tracking theextent and distribution of racial health dispar-ities, identifying correlated social factors, andpositing potential initiatives to reduce dispar-ities, the HDI displays its great potential utilityin such analyses. Policymakers should stronglyconsider incorporating this tool to evaluatehealth disparities and strategically plan futurelegislative initiatives. j

About the AuthorsBryant Cameron Webb is an MD candidate at WakeForest University School of Medicine, Winston-Salem, NC,and a JD candidate at Loyola University Chicago School ofLaw, Chicago, IL. Kristen G. Hairston, MD, MPH, is withthe Section on Endocrinology and Metabolism at WakeForest University School of Medicine. Sean L. Simpson,PhD, is with the Department of Biostatistical Sciences atWake Forest University Health Science’s Division of PublicHealth Sciences.

Correspondence should be sent to Bryant CameronWebb, 973 E 61st St, Apt 1, Chicago, IL 60637 (e-mail:[email protected]). Reprints can be ordered at http://www.ajph.org by clicking the ‘‘Reprints/Eprints’’ link.

This article was accepted June 21, 2010.

ContributorsB.C. Webb originated the study, completed the analysis,led the writing, and supervised all aspects of its imple-mentation. K. G. Hairston assisted with study designand oversaw study implementation. S. L. Simpson pro-vided statistical support. All authors helped to concep-tualize ideas, interpret findings, and review drafts of thearticle.

AcknowledgmentsThis research was supported financially by the StudentNational Medical Association/Pfizer David M. SatcherResearch Fellowship.

Ronny A. Bell, PhD, Capri G. Foy, PhD, Jaimie Hunter,MPH, David L. Mount, PsyD, John H. Stewart IV, MD,Ashley C. Augustus, MPH, and Orita McCorkle allcontributed to creative, analytical, and structural guid-ance and support on this project.

Human Participant ProtectionNo human participants were involved.

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