As it did in Healthy People 2000 and Healthy People 2010, disparity once again plays a prominent role in the recently released Healthy People 2020 (U.S. Department of Health and Human Services 1990, 2000, 2010), a decennial national road map for health in the United States. One of the road map's four overarching goals is to “achieve health equity, eliminate disparities, and improve the health of all groups.” Healthy People 2020 regards health disparity as an ethical concept of unfair health differences associated with certain characteristics or attributes (U.S. Department of Health and Human Services 2008, 2010).
Reporting the progress (or lack of progress) on eliminating health disparities and comparing them across communities and over time is fundamental for this renewed call to succeed (Truman et al. 2011). Among the various challenges of reporting on health disparity, one that is often overlooked is how best to report health disparities associated with multiple attributes. In Healthy People 2020, the key attributes of health disparities are race/ethnicity, gender, sexual identity and orientation, disability status or special health care needs, and geography (U.S. Department of Health and Human Services 2010). While recognizing the multidimensionality of health disparity is important, measuring all aspects of health disparity, even when all the relevant data are available, is a formidable task for researchers, policymakers, and community partners. This is the focus of this article, and we propose a new approach that measures health disparities associated with multiple attributes at the same time and summarizes them as the overall health disparity in the population.
Researchers and policymakers most commonly measure health disparities in a bivariate (Wolfson and Rowe 2001) or pair-wise (Truman et al. 2011) fashion, as a joint distribution of health and another attribute such as income, sex, or race/ethnicity. The degree of health disparity across groups within the chosen attribute (e.g., low-, middle-, and high-income groups) can be quantified by an index. An index can be simple (e.g., a range measure) or complex (e.g., the Concentration index) (Harper and Lynch 2005). A more sophisticated approach uses individual-level data rather than aggregate-level data to examine the level of health and the level of the attribute (e.g., income) of each individual rather than the average level of health of each group. Regardless of the unit of analysis (group or individual) or the disparity index used, the bivariate approach always measures health disparity in relation to one other attribute. The previous literature on this subject used this approach extensively (e.g., Cameron et al. 2009; Frohlich, Ross, and Richmond 2006; Harper et al. 2007; James et al. 2007; Mackenbach et al. 2003; Mustard and Etches 2003), and it has also influenced policymaking. For example, responding to the second goal of Healthy People 2010, “to eliminate health disparities among segments of the population, including differences that occur by gender, race or ethnicity, education or income, disability, geographic location, or sexual orientation” (U.S. Department of Health and Human Services 2000), analysts measured health disparities in a bivariate fashion (Hines et al. 2011). Measuring and reporting health disparities for Healthy People 2020, therefore, would most likely use a bivariate approach.
While the bivariate measurement approach has created a wealth of information useful for many purposes and contexts, this vast array of information obscures the overall picture of health disparities. Suppose that state A has large health disparity due to race/ethnicity but small disparities associated with sex, income, and education and that state B has modest health disparities due to each of these attributes. Given the different degrees of health disparities associated with various attributes from state to state, it is difficult to determine which state has the largest health disparities. Such a determination presumes a systematic mechanism to compare multiple bivariate health disparity relationships (e.g., health/race/ethnicity, health/sex, health/income, and health/education) across states, which is an ambitious task given the current state of the literature. Easily accessible information about the overall picture of health disparities could enhance policymaking, particularly if this information included a set of relevant bivariate disparities, since policy often develops around target groups.
To our knowledge, the only attempt to summarize multiple bivariate health disparities is the measure developed for the Health of Wisconsin Report Card 2007 (Booske et al. 2007, 2010). After measuring the health disparities associated with different attributes (gender, education, rurality, and race/ethnicity) in a bivariate fashion, the Wisconsin researchers calculated the Index of Disparity, a modified version of the coefficient of variation developed by Pearcy and Keppel for Healthy People 2010 (Pearcy and Keppel 2002; U.S. Department of Health and Human Services 2000), using all fourteen groups (two gender, three education, four rurality, and five race/ethnicity groups).
Although the information about the overall picture of health disparities in the population offered by this measure shows significant progress, this measure needs improvement regarding the following two properties. First, this measure is insensitive to the group's population size, as it treats all groups as having equal importance. Second, it uses the healthiest group as the reference group, regardless of attributes, and compares its health with the average health of the other groups (e.g., as the reference, the highest-income group's health is compared with the health of the low-education group, that of women, and that of rural residents). Measuring overall health disparity in this way does not readily offer between-group disparities specific to attributes (e.g., health disparities across education groups).
We expanded the Wisconsin researchers’ approach by proposing a new analytic approach to summarizing social disparities in health. Our approach derives from the bivariate health disparity measure developed by Gastwirth (Gastwirth 2007), which has a number of attractive policy-relevant features, including intuitive between-group comparisons within each attribute (e.g., income groups) and the minimum (i.e., group rather than individual level) data requirement. While maintaining these features, we extended the Gastwirth index, originally developed to measure health disparity associated with a single attribute, to a summary measure of health disparities, which simultaneously accounts for multiple attributes. Our approach, an extended Gastwirth index, improves these two properties of the Wisconsin measure by incorporating sensitivity to the group size and preserving bivariate health disparity information. As an empirical demonstration, we applied our approach to the 2009 American Community Survey (U.S. Census Bureau n.d.a) and ranked overall health disparity as well as health disparities by income, education, sex, and race/ethnicity across all fifty states plus the District of Columbia (Washington, DC).
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Generally, our results show a lack of consistency in the rankings of overall and attribute-specific disparities in age-adjusted rates of functional limitation across states. None of the best-performing states in overall disparity, as reported in table 1, fare consistently well in all of the four attribute-specific disparity rankings. The strength of correlations between rankings by attribute-specific disparities is generally weak to modest. For example, across all states Spearman's correlation coefficient for the rankings by sex- and race/ethnicity–specific disparities is 0.11, and that by income- and race/ethnicity–specific disparities is 0.48 (appendix Appendix), with two exceptions. First, rankings by income- and education-specific disparities tend to be similar (Spearman's correlation coefficient of 0.83). Second, the worst-performing states, as reported in table 1, have similar rankings across overall and income-, education-, and race/ethnicity–specific disparities.
Table 1. States with the Smallest and Largest Disparities in Functional Limitation, 2009
|Six States with the Smallest Overall Disparity||Five States with the Largest Overall Disparity|
| ||Disparity||Contribution (%)|| ||Disparity||Contribution (%)|
|Source: U.S. Census Bureau n.d.a.|
|Overall (1, 8)||0.018|| ||Overall (47, 41)||0.061|| |
|Income (12)||0.034||45.77||Income (49)||0.069||28.37|
|Education (14)||0.030||40.26||Education (47)||0.060||24.54|
|Sex (27)||0.004||4.76||Sex (20)||0.003||1.16|
|Race/Ethnicity* (4)||0.007||9.21||Race/Ethnicity (43)||0.112||45.93|
|Overall (2, 1)||0.021|| ||Overall (48, 45)||0.062|| |
|Income (2)||0.024||28.20||Income (47)||0.062||25.01|
|Education (5)||0.025||29.30||Education (49)||0.064||25.95|
|Sex (41)||0.008||9.28||Sex (10)||0.002||0.77|
|Race/Ethnicity (6)||0.028||33.21||Race/Ethnicity (46)||0.119||48.26|
|Overall (2, 21)||0.021|| ||Overall (49, 48)||0.063|| |
|Income (16)||0.036||44.30||Income (46)||0.061||24.20|
|Education (11)||0.029||34.97||Education (46)||0.059||23.73|
|Sex (41)||0.008||9.55||Sex (27)||0.004||1.72|
|Race/Ethnicity (5)||0.009||11.18||Race/Ethnicity (49)||0.126||50.35|
|Overall (4, 5)||0.022|| ||Overall (50, 50)||0.078|| |
|Income (1)||0.022||24.92||Income (50)||0.070||22.63|
|Education (1)||0.017||19.08||Education (51)||0.073||23.41|
|Sex (1)||0.000||0.00||Sex (20)||0.003||1.08|
|Race/Ethnicity (21)||0.050||55.99||Race/Ethnicity (50)||0.165||52.89|
|Overall (5, 3)||0.023|| ||Overall (51, 51)||0.083|| |
|Income (18)||0.037||39.99||Income (51)||0.077||23.21|
|Education (18)||0.032||33.88||Education (50)||0.072||21.66|
|Sex (50)||0.020||21.54||Sex (45)||0.012||3.77|
|Race/Ethnicity* (3)||0.004||4.59||Race/Ethnicity (51)||0.170||51.37|
|New Jersey|| || || |
|Overall (5, 4)||0.023|| || || || |
|Income (4)||0.027||29.36|| || || |
|Education (16)||0.031||34.20|| || || |
|Sex (20)||0.003||3.41|| || || |
|Race/Ethnicity (8)||0.030||33.03|| || || |
The states thus appear to have different profiles of attribute-specific health disparities, and three profiles emerge from our examination of attribute contribution to overall disparity (figure 2). In a large majority of the states (thirty-four), race/ethnicity–specific disparity has the biggest contribution to overall disparity (profile 1). An example of a state in profile 1 is New Hampshire, in which race/ethnicity has a 64 percent contribution to overall disparity—the largest contribution from race/ethnicity among all states—followed by 19 percent from education and 15 percent from income. In ten states, race/ethnicity and socioeconomic factor(s) (income and/or education) have roughly equal contributions to overall disparity (profile 2). For example, Kansas, which of all the states has the largest (roughly equal) contributions from race/ethnicity and a socioeconomic factor, the contribution of race/ethnicity is 37 percent, that of income is 36 percent, and that of education is 24 percent. Finally, seven states have the largest contribution from socioeconomic factor(s) (income and/or education) (profile 3). In Vermont, which of all the states has the largest contribution from a socioeconomic factor, income and education contribute 51 percent and 42 percent, respectively, while race/ethnicity contributes 2 percent. Regardless of the profile of health disparities, in all states except Montana, sex-specific health disparity is the smallest of the four attribute-specific disparities. In four states (California, Nevada, New York, and Pennsylvania) we observed no sex-specific disparity. The largest degree of sex-specific disparity is 0.020 in Montana and North Dakota, suggesting that an additional 2 percent of men must become free of functional limitation in order to eliminate sex-specific disparity in these states (see appendix Appendix for each state's attribute-specific disparities and their contributions to overall disparity).
Figure 2. Three profiles of attribute-specific disparities in functional limitation across the United States, 2009.
Notes: 1. “No functional limitation” is defined as not having a limitation in any of the following six areas: hearing, vision, cognition, ambulation, self-care, and independent living.
2. The profiles of disparities are based on the attribute(s) that makes the largest contribution to overall disparity in each state. We consider attribute contributions within ±5% as the same.
3. Due to the small numbers (cell counts less than 50), combined groups, rather than a single group, are used as the reference in the calculation of race/ethnicity–specific disparity in the six states (Maine, Montana, North Dakota, South Dakota, Vermont, and Wyoming).
4. New Hampshire has the largest contribution from race/ethnicity (64%) among all the states, followed by 19% from education and 15% from income.
5. Kansas has the largest roughly equal contributions from race/ethnicity and a socioeconomic factor across all the states. The contribution of race/ethnicity is 37%; that of income is 36%; and that of education is 24%.
6. Vermont has the largest contribution from a socioeconomic factor among all the states: income and education have a contribution of 51% and 42%, respectively, while race/ethnicity has a 2% contribution.
7. Regardless of the profile of health disparities, in all states, except Montana, sex-specific health disparity is the smallest of the four attribute-specific disparities.
8. All analyses are weighted, and functional limitation is age standardized using the U.S. 2000 standard population.
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The measure of overall disparity gives a summary view of attribute-specific disparities in each state (table 1 and appendixes Appendix and Appendix). Wyoming exhibits the smallest overall disparity, in which in order to eliminate disparities in functional limitation on average across the four attributes examined, an additional 1.8 percent of the population from the less healthy (and often disadvantaged) groups must become free of functional limitation. West Virginia, in contrast, shows the largest overall disparity, in which an improvement similar to that in Wyoming must be made by an additional 8.3 percent of the population. The map of the overall disparity ranking (figure 3) does not show a clear geographic pattern, although it does indicate a divide between north (small) and south (large). The ranking of overall disparity is most closely correlated with that of race/ethnicity–specific disparity, with Spearman's correlation coefficient of 0.91. Most of the rankings of overall disparity according to our proposed measure and the Wisconsin measure are similar (table 1 and appendixes Appendix, Appendix, and Appendix), with Spearman's correlation coefficient of 0.91, although in five states (Georgia, Louisiana, Maine, Minnesota, and Vermont), the rankings differ by more than ten placements.
Figure 3. State ranking of overall disparity in functional limitation, 2009.
Notes: 1. “No functional limitation” is defined as not having a limitation in any of the following six areas: hearing, vision, cognition, ambulation, self-care, and independent living. 2. Overall disparity is an average of the income-, education-, sex-, and race/ethnicity–specific disparities in each state. 3. Rankings are based on the degree of overall health disparity, from the smallest to the largest. 4. All analyses are weighted, and functional limitation is age standardized using the U.S. 2000 standard population. Source: U.S. Census Bureau n.d.a.
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- Appendix 1
- Appendix 2
- Appendix 3
- Appendix 4
In this article we proposed an analytic approach to summarizing social disparities in health and demonstrated its application using the 2009 ACS. Our approach expands the effort to integrate information about multiple bivariate health disparities for policy relevance. One attractive feature of our proposed approach is its ability to measure and compare multiple bivariate health disparities. Our approach reveals that states generally did not perform consistently better or worse across all the income-, education-, sex-, and race/ethnicity–specific disparities that we examined. This result indicates that examining only one attribute-specific health disparity provides partial information about health disparities in the population and underscores the importance of a systematic examination of multiple health disparities in the population health assessment. Despite the historical emphasis on race/ethnicity-specific health disparity in the United States, we have shown that it is unlikely that it tells the whole story of health disparities.
A comparative assessment of multiple attribute-specific disparities also allows us to identify patterns of disparities common across the states. Our analysis shows that of the four attributes we examined, sex-specific disparity was the smallest in all but one state. Moreover, three typical profiles of disparities emerge across the states, with the most prevalent profile, observed in two-thirds of all states, having the largest contribution from race/ethnicity–specific disparity among the four attributes. Whether our observation of the general patterns of disparities indicates the seriousness of (or lack thereof) sex- and race/ethnicity–specific health disparities, however, requires careful interpretation. The consistently small sex-specific disparity probably was due to our choice of the particular measurement of health, functional limitation. If we were to use other measures of health, such as life expectancy, we might not observe such a small disparity. A large race/ethnicity–specific disparity probably can be traced to the non-Hispanic Asian/Pacific Islander group that we used in our analysis as the reference in most states. That is, this group sets a high reference level (the fraction of persons free from functional limitation ranges from 0.870 to 0.985) but has a small population size relative to that of other racial/ethnic groups (the population share ranges from 0.5% to 8%). The high reference level set by a small group creates a large shortfall shared by the majority of the population. For example, in West Virginia, which had the largest race/ethnicity–specific disparity in our calculation, the fraction of persons free from functional limitation and the population share of each racial/ethnic group are, respectively, 0.939 and 0.5 percent for non-Hispanic Asian/Pacific Islanders, 0.777 and 0.8 percent for Hispanics, 0.771 and 94.5 percent for non-Hispanic whites, 0.753 and 2.9 percent for non-Hispanic blacks, and 0.586 and 1.3 percent for non-Hispanic others. With the reference set by the non-Hispanic Asian/Pacific Islander group, the disparity is calculated as 0.170, but if the reference is set by the non-Hispanic white group, the disparity is 0.003.
As we did, previous population health reports have also tried examining multiple attribute-specific health disparities. Healthy People 2010 Final Review is one of the most recent, and notable, examples (Hines et al. 2011). One of our main contributions is to expand this effort by providing a measure of overall health disparity. The use of the interpretable 0–1 index makes it easier to compare across states and across time if desired. For example, we showed that to eliminate state-level disparities in functional limitation on average across the four attributes considered, an additional 2 to 8 percent of the population from the less healthy (and often disadvantaged) groups must become free from functional limitation. Easily comparable and interpretable information about overall health disparity in turn facilitates a dialogue about policy. Monitoring health disparities is the fundamental exercise that supports the health disparity goals of Healthy People 2020, for example, and our approach offers ready answers to questions such as where we are (as this study shows), how far we have come (by applying our approach to historical data), and what a challenging, yet feasible, future goal should be.
The policy applicability of our approach crucially depends on data availability. Our approach requires only aggregate-level data, a measure of health by group defined by the attributes that analysts wish to use. Such data are often publicly available for states or large counties. Although in our empirical demonstration we used individual-level data to obtain the group-level estimates, it is possible to apply our approach using only aggregate-level information, such as that from CDC Wonder (Centers for Disease Control and Prevention n.d.). In addition, our approach can be used with different measures of health and for county-level analysis, for example, by using the Selected Metropolitan/Micropolitan Area Risk Trends (SMART) Behavioral Risk Factor Surveillance System (BRFSS) (Office of Surveillance, Epidemiology, and Laboratory Services n.d.). Certain data constraints still apply though. Even in our state-level analysis, our racial/ethnic groups are not as refined as the seven racial/ethnic groups recommended by Healthy People 2010 (Hines et al. 2011; U.S. Department of Health and Human Services 2000), owing to our desire for reliability. Nonetheless, the proposed approach is practical within the current data infrastructure in the United States. Future application of the proposed approach, especially to small areas, such as counties, will benefit from variance estimation or uncertainty bounds to ensure the data's reliability. This is not a pressing issue for this study, however, as it uses the ACS, with more than 2 million observations for the state-level analysis with relatively large group categories. Different methods of variance estimation depend on the particular data used in the analysis, but given the complex sampling methods used in most population-based data, the appropriate variance estimation methods likely involve replication methods (Rust and Rao 1996).
The high correlation between rankings of overall disparities based on the Wisconsin measure and our approach gives face validity to our approach. Discrepancies, however, do exist and are large for some states (appendix Appendix). They likely come from differences in two measurement properties and, fundamentally, relate to value questions of what we wish to measure as health disparities. The first difference is sensitivity to the group's population size. The Wisconsin measure is insensitive, meaning that it treats all groups as having the same population size, whereas our approach is sensitive, reflecting the population size of each group in the measurement. Thus, when the population sizes of less healthy groups are proportionally larger, all else being equal, our approach gives a worse ranking than the Wisconsin measure does. The choice of the two measures is value laden: whether we should consider the same health gap as worse if it is experienced by a proportionally greater number of people (Asada 2007; Temkin 1993). According to the current literature on the measurement of health disparities, the answer is yes (Harper and Lynch 2005; Wagstaff, Paci, and van Doorslaer 1991), which corresponds to our proposed approach.
Another difference between the Wisconsin measure and our approach is the selection of the reference group. According to the Wisconsin measure, the reference group is the healthiest group in the population, while in our approach the reference group is set separately for each attribute. Accordingly, when the healthiest group in the population is much healthier than the healthiest groups among other attributes, all else being equal, the Wisconsin measure gives a worse ranking than our approach does. The literature currently does not agree on the reference group (Asada 2007; Harper and Lynch 2005). We argue, however, that a common understanding of health disparities relates to attribute-specific between-group health disparities and that it is intuitive to set the reference separately for each attribute. Whether or not it sets the reference for each attribute, a summary measure is most informative when it preserves information regarding attribute-specific between-group health disparities, as our approach does.
Our proposed approach thus has a number of attractive features: a uniform assessment of multiple attribute-specific disparities, interpretable and comparable information on the overall picture of disparities in the population, a minimum data requirement, and sensitivity to the group's population size. As does any measure of disparity, however, our approach has important constraints that analysts should recognize, namely, nonmutual exclusivity and a crude assessment of fairness. First, our proposed approach (and the Wisconsin measure) is a composite measure of health disparities associated with mutually nonexclusive attributes. This means that each attribute-specific health disparity is likely confounded by other attribute-specific health disparities. This is clearly the case in our analysis for income and education, as is evident from the high correlation between rankings of these attribute-specific disparities. When attributes are correlated in a predictable direction (e.g., the poor tend to be uneducated), all else being equal, the overall disparity calculated by our approach is larger than it would be if there were no such correlation.
While we believe this property is compatible with a commonly shared sense of when health disparity is worse, some analysts may wish to assess the health disparities associated with mutually exclusive attributes. For example, in a recent article, Williams and his colleagues argue for the importance of an “intersectional perspective” that integrates multiple social statuses simultaneously (Williams et al. 2012). This can be accomplished by refining groups. In our data, the thirteen groups from four attributes can be segmented mutually exclusively into ninety groups. Our sensitivity analysis suggests that mutually exclusive overall disparity is much larger than nonmutually exclusive overall disparity (e.g., respectively, 0.158 and 0.018 for Wyoming, 0.122 and 0.021 for Hawaii, 0.127 and 0.021 for Minnesota, 0.224 and 0.063 for Arkansas, 0.222 and 0.078 for Kentucky, and 0.232 and 0.083 for West Virginia). This is not because of the increased number of groups but because of the increased gap between the healthiest group and the least healthy group, with the former having a greater impact than the latter, given its role as the reference against which all other groups are compared.
The choice between mutually exclusive and nonexclusive measures depends on the purpose of the measurement. Nonmutually exclusive measures appear to capture basic ethical and policy concerns. It is unlikely that we would, for example, discredit the importance of sex-specific health disparity if it were mainly explained by the generally lower education and income levels of women than men. Because the value we attach to the attribute sex is socially meaningful, the information about sex-specific health disparity would still be important even if the causal mechanisms were compounded with other attributes. In contrast, a mutually exclusive measure can help us understand health disparities well by identifying the specific attributes of the healthiest and least healthy persons. Notice that at the end of the refinement of mutually exclusive groups, we measure health disparity across individuals. As we increase the number of mutually exclusive groups, each group becomes smaller, and at the end, there is one person in each mutually exclusive group, which is measuring health disparity across individuals. Thus, if the purpose of the measurement is to understand health disparities well, our approach with refined mutually exclusive groups is cumbersome. Individual-level analysis would be better suited for this purpose.
The second constraint in our approach is the crude assessment of fairness. Our proposed approach builds on the common practice of measuring health disparity in association with an attribute, one attribute at a time. Any measurement of health disparities involves ethical judgment (Asada 2007; Harper et al. 2010), which is implicit in both this common bivariate measurement approach and our proposed approach. Our analysis reflects, for example, the following ethical judgments. By identifying attributes to examine, we implicitly make an ethical judgment that the health disparities associated with these attributes are unfair. By using an age-standardized measure of health, we are implicitly declaring that health disparities due to age are not of ethical concern; while by not using a sex-standardized measure of health, we are implicitly suggesting that health disparities due to sex are unfair. By treating each attribute-specific health disparity equally, we are implicitly judging that all attribute-specific health disparities are equally important.
There is a growing literature that opposes such implicit and crude assessments of fairness and calls for more sophisticated and explicit approaches (Fleurbaey and Schokkaert 2009, 2011). This critique is largely aimed at the wide use of the Concentration index. Using the Concentration index, analysts start by measuring the health disparity associated with one particular attribute and, using its decomposition, explain what factors might cause that attribute-specific health disparity (O'Donnell, van Doorslaer, and Lindelow 2007). Fleurbaey and Schokkaert argue that rather than starting by focusing on one particular attribute, analysts should examine as many attributes as possible, classify them into groups—such as biologically determined health endowments, individual preferences, available information, social background, and health care supply—and make an explicit judgment as to which of these are “legitimate” (i.e., pose no ethical concern) and “illegitimate” (i.e., lead to unfairness) causes of health inequality (Fleurbaey and Schokkaert 2009, 2011).
Though attractive, the feasibility of empirical application and the policy usefulness of this alternative sophisticated approach await future assessment. Few data sets offer information about determinants of health to the extent of allowing refined ethical judgments, as Fleurbaey and Schokkaert suggest. Such rich, individual-level data are hard to find for small jurisdictions, in which the health of a population is increasingly reported (County Health Rankings and Roadmaps n.d.; United Health Foundation n.d.). Moreover, despite the intensive research and policy efforts over the past decades, there has been no agreed-on, precise definition of health disparity or inequity (Norheim and Asada 2009). Given this, parallel to the effort to encourage explicit, sophisticated assessments of fairness, we believe there is room for the development of a simple approach that builds on the common practice with incremental improvements, as we have proposed.
In summary, the analytic approach we propose in this article offers policy-relevant health disparity information, both summary information about the health disparities observed in the population and information about attribute-specific health disparities, in a comparable and interpretable manner. Its measurement properties capture commonly held conceptions of health disparities, and currently publicly available data support its application. With these strengths, it is our hope that this approach will spark a discussion regarding how best to systematically track health disparities across communities or within a community over time in relation to the health disparity goal of Healthy People 2020.