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Chapter 4: Adopting a More Quantitative and Transparent Measure Select

Future Directions for the National Healthcare Quality and Disparities

Tools for Assessing Equity

A high-value health care system, by definition, requires the provision of equitable, high-value care to all individuals; therefore, metrics that assess equity in health care delivery should be considered in the prioritization process for measure selection. Measures in which the nation as a whole is performing well (i.e., for which there is little or no gap between the national average and achieving recommended care for the entire applicable population) may show performance gaps when the data are stratified by population subgroups. Therefore, the goal of achieving value in health care must be balanced by considering the needs of population groups that differ in age, race, ethnicity, gender, disability, and socioeconomic status. Chosen quality measures should promote the core quality dimension of equity in health care.

An inequity is a measurable, observable difference that can and should be closed (Carr-Hill, 2001; Whitehead and Dahlgren, 1991). For example, because the incidence of AIDS is more than 20 times higher among Black than White women, and two-thirds of new AIDS cases among women are in Black women (Kaiser Family Foundation, 2009), the CPB of interventions related to AIDS, such as use of highly active antiretroviral therapy, is much greater among Black women than among the population of all women.

As is further discussed in Chapter 5, the identification of disparities is often hampered by sample sizes and a lack of systematic, standardized collection of sociodemographic data. Yet large disparities that are statistically insignificant due to small sample sizes may still be indicative of problems with equity (Siegel et al., 2009). This section explores some of the established techniques and tools that allow for the identification of disparities. It is important to consider (1) whether the disparity is measured on a relative or absolute scale, (2) the reference point from which differences are measured, and (3) whether the disparity is weighted by population size or degree of inequity.

Relative and Absolute Difference

Absolute and relative measures of disparity can provide contradictory evidence regarding changes in a disparity over time. In the context of health care quality improvement, increasing relative but decreasing absolute inequality occurs when the rate of improvement is smaller for the group with the worst performance rate (Harper et al., 2010). In concert with one another, absolute and relative differences can provide a more comprehensive picture of a disparity than either method alone. The committee does not recommend a single approach to measuring disparities and instead emphasizes that the method of measurement can determine the size and direction of a potential disparity.

AHRQ presents information on disparities in terms of both relative and absolute differences in either adverse or favorable outcomes. In the Highlights section of the 2008 NHDR, AHRQ presents the three largest disparities in quality for different groups using relative differences (AHRQ, 2009c). The committee was not able to assess the validity of these rankings. A relative measure expresses the disparity as a ratio relative to the reference point or group, so that reference point becomes the unit of comparison. Absolute measures of disparity are simply the difference between a group rate and the reference group; most of the AHRQ graphs reflect absolute differences. Go to Table 4-2 for a list of ways to measure absolute and relative health disparity.

The following example highlights how examining relative and absolute differences can lead to different conclusions, especially when comparing over time. In 2000, the rate of a specific disease was 8 percent in the African American population and 4 percent in the White population. In absolute terms, this was a 4-point difference, whereas in relative terms, the African American rate was twice the White rate. In 2010, the African American rate is 6 percent, and the White rate is 3 percent. Both groups have better rates, and the African American rate has improved more than the White rate. In absolute terms, the gap has shrunk from 4 points to 3 points. In relative terms, the African American rate is still double the White rate. In this case, the relative rate does not reflect that the situation is better in 2010 than it was in 2000. A 2005 report released by the CDC advised that to promote a more complete understanding of the "magnitude of disparities," disparities should be measured in both absolute and relative terms, especially when making comparisons over time, geographic regions, or populations (Keppel et al., 2005). Additionally, Harper and colleagues have urged researchers against always using a single measure (e.g., a rate ratio), and instead advised researchers to "pay more attention to the normative choices inherent in measurement" (Harper et al., 2010, p. 22).

When both absolute and relative difference cannot be presented (due to space constraints, for instance), major medical journals are trending toward presenting absolute differences (Braveman, 2006; Dombrowski et al., 2004; Regidor et al., 2009; Rosvall et al., 2008). The advantage of this approach is that it is more consistent with using population health burden as a metric for prioritizing within populations. When both measures cannot be presented, the committee suggests AHRQ might include absolute rates in graphs and tables and add a comment in the text about whether the relative disparity is changing.

Calculating Disparities Using Odds Ratios

By expressing disparities in terms of odds ratios, researchers can calculate and present the risk of one group over another (similar to relative rate).12 AHRQ employs this method to calculate the "odds," for example, for uninsurance for Black and Asian adults to White adults. This method allows AHRQ to easily convey that the risk of uninsurance is 0.9 times higher for Blacks and 1.1 times higher for Asians (AHRQ, 2009c). Odds ratios should be used with caution as they can exaggerate differences and may be misleading in terms of clinical significance. For any notion of causality, notations of the absolute difference should be readily available (that is, on the probability scale).

The Reference Population

As Nerenz and Fiscella have noted, the quality measures that matter to the overall population also matter to minority populations (Fiscella, 2007; Nerenz et al., 2006). Disparities may be assessed by stratifying quality data by various population groups. Indeed, AHRQ presents data on measures for priority populations in this way in the NHDR. This method also has the benefit of being able to use the same measures to assess performance levels for both disparities and quality among populations. However, additional measures of disparity may be relevant and necessary to fully document the extent or presence of inequities.

Measuring disparities requires a comparison group. The reference group or point can be the unweighted mean of all groups, the weighted mean of the total population, the most favorable rate among population groups, or an external deliberate standard such as a Healthy People 2010 target or benchmark. Although each of these reference points can be useful, the group with the most favorable rate is often chosen as the reference point in disparities studies because it assumes that every group in the population has the potential to achieve the health of the best-off group. (In Chapter 6, the committee suggests that in the NHDR, AHRQ use benchmarks based on the best-in-class performance rate not just the highest population rate, which often is worse than the best-in-class performance rate.)

An Index of Health Care Disparities

Indices of disparities summarize average differences between groups and express the summation as a ratio of the reference rate (Harper et al., 2008). Most disparity indices measure statistically significant disparities across all populations for a given condition or disease (e.g., among all races in a given state), but do not always measure variance for a single discrete population group (Gakidou et al., 2000). Pearcy and Keppel's Index of Disparity gives equal weight to each group, even when each group represents different proportions of the population (Pearcy and Keppel, 2002). This kind of unweighted measure of disparity means that an individual in a larger population group may receive more weight than an individual in a smaller population group. To be clinically relevant to providers, a disparity index needs to measure disparities in care among discrete subpopulations and needs to give greater weight to disparities that affect greater numbers of patients (Siegel et al., 2009). Doing so captures population impact. Siegel and colleagues developed a disparities index that takes in account the quality of health care being provided to all patients, the size of the affected population, and changes over time (Siegel et al., 2009). Another benefit of using population-weighted measures is that they are able to account for changes in the distribution of the population that inevitably occur over time (Harper and Lynch, 2005).

For the purposes of the national healthcare reports, measures of equity may need to consider more than just the number of individuals affected in the entire population. For instance, a very large gap in quality of care between one relatively small subpopulation and the overall population may have significant implications for quality. A report prepared for the National Cancer Institute on measuring cancer disparities adopted a population health perspective on disparities. This perspective means that the researchers were primarily concerned with the total population burden of disparities and thus considered both absolute differences between groups and the size of the population subgroups involved (Harper and Lynch, 2005).


The methods discussed above should be considered when analyzing data relevant to assessing disparities in performance among different populations and prioritizing measure selection. Measures that reveal an equity gap, even when those same measures are equivalent in assessments of value, should be considered for prioritization as they exhibit an important attribute of the health care system where greater improvements in health care quality can be made.


The Future Directions committee has recommended improving the process for selecting performance measures for the NHQR and NHDR to make the process more transparent and quantitative. It has also recommended establishing a Technical Advisory Subcommittee for Measure Selection to advise AHRQ through the NAC. Although there are limits to applying more quantitative techniques in valuing measurement areas, they should be used whenever feasible. Their use is common in prioritization practices for resource allocation. National prioritization of measures can influence where resources are devoted to quality improvement. The potential impact of focusing quality improvement on closing the performance gaps of specific measure choices should be analyzed with care, particularly as the committee believes the national reports should be driving action rather than passively reporting on past trends.


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12 "Odds ratios are a common measure of the size of an effect and may be reported in case-control studies, cohort studies, or clinical trials. Increasingly, they are also used to report the findings from systematic reviews and meta-analyses. Odds ratios are hard to comprehend directly and are usually interpreted as being equivalent to the relative risk. Unfortunately, there is a recognized problem that odds ratios do not approximate well to the relative risk when the initial risk (that is, the prevalence of the outcome of interest) is high. Thus there is a danger that if odds ratios are interpreted as though they were relative risks then they may mislead" (Davies et al., 1998, p. 989).

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Internet Citation: Chapter 4: Adopting a More Quantitative and Transparent Measure Select: Future Directions for the National Healthcare Quality and Disparities . December 2010. Agency for Healthcare Research and Quality, Rockville, MD.