Number 25 September 2004

Measuring Progress in Healthy People 2010 

Kenneth G. Keppel, Ph.D.; Jeffrey N. Pearcy, M.S.; and Richard J. Klein, M.P.H., Office of Analysis and Epidemiology 

Abstract 

Background—Healthy People 2010 (HP2010) objectives are 

based on two overarching goals: 1) to increase years and 

quality of healthy life, and 2) to eliminate disparities among 

subgroups of the population. Four hundred and sixty-seven 

specific objectives consistent with these goals were outlined, 

baseline data were identified when available, and specific targets were set for the year 2010. This report discusses the techniques that are being used to measure progress toward these 

two goals. 

Process—In order to promote consistency in monitoring 

across different objectives, a minimum template of subgroups 

was adopted for the population-based objectives in HP2010. 

A workgroup of individuals representing the U.S. Department 

of Health and Human Services agencies involved in HP2010 

was convened to consider the issues related to monitoring 

progress toward the two goals of HP2010. The workgroup 

concurred with the recommendations in this report. 

Recommendations—Progress toward target attainment can be 

monitored for all objectives with at least one data point 

beyond the baseline. For those objectives that are based on 

data for a population, progress toward target attainment can 

also be measured for subgroups of the population. 

Progress toward the elimination of disparity for individual 

population subgroups can be measured in terms of the percent 

difference between each subgroup rate and the most favorable 

or ‘‘best’’ subgroup rate in each domain. For purposes of 

measuring disparity relative to the ‘‘best’’ subgroup rate, all 

measures are expressed in terms of adverse events. 

Keywords: goals c objectives c health disparity 

Introduction 

The first goal of HP2010 is to increase years and quality 

of healthy life for all individuals (1). This goal is embodied 

in 467 objectives that are intended to improve the length and 

quality of life. These objectives correspond to specific 

measures of health and targets were set for the year 2010 

that would require substantial improvement for each 

measure. The second goal of HP2010 is to eliminate health 

disparities among subgroups of the population. Subgroups 

include those defined in terms of gender, race and ethnicity, 

Acknowledgments 

The contributions of the following individuals to these discussions is 

gratefully acknowledged: Ernest Moy (Agency for Health Care Research 

and Quality); Wilma Tilson and Andrea Pernack (Office of the Assistant 

Secretary for Planning and Evaluation); John Aberle-Grasse, Kenneth 

Keppel, Richard Klein, Elsie Pamuk, Jeffrey Pearcy, and Robert Robinson 

(Centers for Disease Control and Prevention); Colleen Ryan (Indian Health 

Service); Susan Queen (Health Resources and Services Administration); 

Nancy Breen, Shanita Williams-Brown, Lisa Colpe, Rob Fulwood, Jeanette 

Guyton-Krishnan, Howard Hoffman, Wendy Johnson-Taylor, Barry Portnoy, 

Frederick Stinson, and Martina Vogel-Taylor (National Institutes of Health); 

Carter Blakey, and Omar Passons (Office of Disease Prevention and Health 

Promotion); Audrey Burwell, Olivia Carter-Pokras, Tuei Doong, and Valerie 

Welsh (Office of Minority Health); Wanda Jones (Office on Women’s 

Health); and Nancy Brady, Deloris Hunter, Aquila Mitchell, and Joyce 

Weddington (Substance Abuse and Mental Health Services Administration). 

Jennifer Madans, Associate Director for Science, Diane Makuc, Acting 

Associate Director, Office of Analysis and Epidemiology, and Joe Fred 

Gonzalez from the Office of Research Methodology also provided 

comments on the manuscript. This report was edited by Thelma W. Sanders 

and typeset by Zung Le, Information Design and Publishing Staff, Office of 

Information Services. 

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Disease Control and Prevention

National Center for Health Statistics
education or income level, disability status, geographic 

location, or sexual orientation. This goal applies to 383 

population-based objectives for which disparities between 

these subgroups are to be eliminated. The techniques that are 

being used to measure progress toward these two goals are 

described here (2). 

A workgroup of individuals representing the U.S. 

Department of Health and Human Services agencies 

involved in the HP2010 initiative was convened to discuss 

the measurement of progress toward the goals of Healthy 

People. Representatives from the following agencies 

participated in the workgroup: Agency for Health Care 

Research and Quality, Centers for Disease Control and 

Prevention, Indian Health Service, Health Resources and 

Services Administration, National Institutes of Health, Office 

of the Assistant Secretary for Planning and Evaluation, 

Office of Disease Prevention and Health Promotion, Office 

of Minority Health, Office on Women’s Health, and the 

Substance Abuse and Mental Health Services Administration. 

This workgroup met three times between July 12 and 

October 24, 2002. Agreement was reached on the techniques 

described here. These techniques have been successfully 

applied to a wide variety of HP2010 objectives. Some 

adjustment to these procedures may be necessary in specific 

instances. The workgroup also recommended that the 

National Institutes of Health and other Federal agencies 

should encourage researchers to investigate other measures 

of disparity, to identify the determinants of disparity, and to 

consider other measures of progress toward the elimination 

of disparity. 

Two Distinct Goals to Be Monitored 

The first goal of HP2010 is to increase the years and 

quality of healthy life for individuals of all ages. Consistent 

with this goal, 467 objectives that would contribute to an 

increase in the length and/or quality of life were outlined. 

One or more specific measures of health were identified for 

each objective. These measures focus on health-related 

behaviors, risk factors, outcomes, preventive services, and 

characteristics of the health care and public health systems. 

Baseline values have been established for each measure for 

which data are available (3). In order to increase years and 

quality of healthy life, specific targets were set for each of 

these objectives for the year 2010. One of the following 

target setting methods was used for each measure: 

+ Set a target to surpass the value achieved by any racial or 

ethnic group at the baseline (‘‘better than the best’’). 

+ Set a target that represents improvement for a substantial 

proportion of the population. 

+ Set a target of ‘‘total coverage’’ or ‘‘total elimination.’’ 

+ Set a target consistent with another national program (for 

example, national education goals). 

+ Retain the Healthy People 2000 target. 

As more recent data points become available, progress 

toward the target can be measured. For each objective, 

progress toward target attainment represents an increase in 

years and quality of healthy life. 

The second goal of HP2010 is to eliminate health 

disparities among segments of the population (1). This goal 

applies to 383 objectives for which measures are based on 

individuals in a population. For the population-based 

objectives, a minimum template of subgroups was defined 

for each of the following domains: race and ethnicity, 

gender, family income level, and education level. The 

template does not apply to population-based objectives that 

are tracked using counts of events rather than rates or 

percents. Additional domains were defined for selected 

measures based on geographic location, health insurance 

status, disability status, and sexual orientation. A single 

target for the year 2010 was set for each measure and this 

target applies to the total population and to each population 

subgroup. Data are not available for all subgroups for all 

measures. However, when data are available, measurable 

reductions in disparities between subgroups are expected. 

For each population-based objective, progress toward the 

elimination of disparities can be monitored in terms of the 

differences between subgroups. 

Progress toward target attainment and progress toward 

elimination of disparity are measured separately. Progress 

toward target attainment can be measured for all objectives 

with at least one data point beyond the baseline. For the 

population-based objectives, progress toward target 

attainment will be measured for the total population and for 

each population subgroup. Progress toward elimination of 

disparity can be measured for population-based objectives 

with at least one data point beyond the baseline. Techniques 

for measuring progress toward target attainment and 

techniques for measuring progress toward the elimination of 

disparity are described below. 

Because the two overarching goals are fundamentally 

distinct, it is important to remember that progress toward 

target attainment does not necessarily entail progress toward 

the elimination of disparity and vice versa. Progress toward 

the target could occur for all subgroups in a domain without 

any reduction in the disparity between subgroups and 

progress toward reduction in the disparity between groups 

could occur without any progress toward the target for 

specific subgroups. HP2010 calls for both progress toward 

attainment of the targets and progress toward reduction in 

disparities among subgroups. 

Defining Population Subgroups for 

Healthy People 2010 

HP2010 calls for monitoring progress for social and 

demographic subgroups of the population. In order to 

promote consistency in tracking progress for specific 

subgroups, a minimum template was adopted for monitoring 

the population-based objectives in HP2010 (1). The initial 

template was altered based on new standards for Federal 

data on race and ethnicity published by the Office of 

Management and Budget (4). The new standards allow each 

2 
individual to identify with one race only or with more than 

one race. The revised minimum template for populationbased objectives includes the following characteristics and 

categories: 

Race: 

American Indian and Alaska Native only 

Asian and Pacific Islander only 

Asian only 

Native Hawaiian and Other Pacific Islander only 

Black or African American only 

White only 

2 or more races 

American Indian and Alaska Native; White 

Black or African American; White 

Ethnicity: 

Hispanic or Latino 

Not Hispanic or Latino 

Black or African American only 

White only 

Gender: 

Female 

Male 

Family income level: or Education level: 

Poor Less than high school 

Near poor High school graduate 

Middle/high income At least some college 

Data systems are revising their collection and tabulation 

procedures to comply with the new standards on racial and 

ethnic identification. All Federal data systems were required 

to implement the new standards by January 1, 2003. Some 

data systems began reporting data using the new standards 

beginning with data for calendar year 1999. Some data 

systems implemented the new standards between 1999 and 

2003 and others are still in the process of revising their 

collection and tabulation procedures; therefore, templates for 

race and Hispanic origin will vary across objectives. The 

on-line data system for Healthy People, DATA2010, is 

adding data for the new categories as soon as they become 

available (3). The data systems used to track the populationbased objectives in HP2010 may not provide data for all of 

these domains and subgroups. However, some data systems 

provide data for additional subgroups, for example, Hispanic 

origin subgroups: Cuban, Mexican American, and Puerto 

Rican. 

This template does not apply to nonpopulation-based 

objectives such as those that measure schools, worksites, or 

States. Because of problems in interpreting risk, the template 

also does not apply to population-based objectives that are 

tracked using counts of events rather than rates or 

percents (5). Additional subgroups are included for specific 

objectives, including: geographic location (urban/rural), 

health insurance status, disability status, chronic disease 

status, sexual orientation, and specific age groups. These 

subgroups are defined in Tracking Healthy People 2010 (5). 

In the discussion that follows, the terms ‘‘group’’ and 

‘‘subgroup’’ are used interchangeably. 

Measuring Progress Toward Target 

Attainment 

In the Healthy People 2000 Midcourse Review and in 

the Final Review, progress toward the Healthy People 2000 

targets was measured using the percentage of targeted 

change that was achieved or the ‘‘progress quotient’’ (6,7). 

The progress quotient expresses any change from the 

baseline relative to (as a percent of) the initial difference 

between the baseline and the target. The progress quotient is 

being used again to monitor progress in HP2010. In 

addition, an absolute measure of the difference remaining 

between the target and the most recent data value is being 

used to compare the remaining progress needed among the 

subgroups. 

Baseline data values were published at the beginning of 

the decade for those measures for which data were 

available (1). Baseline data for additional measures have 

become available since the publication of Healthy People 

2010 (3). As subsequent data points become available, 

changes between the baseline and the most recent data value 

can be measured. These changes can be measured in 

absolute terms (in terms of the units in which the objective 

is measured) or they can be measured in relative terms 

(relative to some standard). Absolute and relative measures 

provide complementary information about changes over 

time. The workgroup concluded that both absolute and 

relative measures were important in monitoring progress 

toward target attainment. 

Absolute differences 

The absolute difference between the target and the most 

recent data value provides information about the progress 

that is still required to attain the target. In table 1 the 

absolute difference between the target and the most recent 

data value for objective 16–1c that calls for a reduction in 

infant mortality rates is shown in the last column. In order to 

achieve the year 2010 target, the annual total infant mortality 

rate would have to decline by an additional 2.4 infant deaths 

per 1,000 live births. The difference between the target and 

the most recent value can be used to compare the absolute 

progress required among subgroups for the population-based 

objectives. In this example, the infant mortality rate for 

infants of American Indian or Alaska Native mothers would 

have to decline by an additional 3.8 infant deaths per 1,000 

live births to achieve the target, and the rate for infants of 

non-Hispanic black or African American mothers would 

have to decline by an additional 9.1 infant deaths per 1,000 

live births to achieve the target. 

Relative changes 

The progress quotient is a relative measure of change 

over time. It can be used to monitor progress for both 

3 
--- --- --- --- ---

--- --- --- --- ---

Table 1. Measuring progress toward target attainment for Objective 16–1c: Reduction in infant mortality rates 

Selected characteristic 

Target attainment 

Infant mortality rate 

(per 1,000 live births) Difference 

between 

most 

recent 

and 

baseline 

b–a 

Percent 

change 

between 

most 

recent and 

baseline 

(b–a)/a × 100 

Difference 

between 

target 

and baseline 

c–a 

Progress 

quotient 

(percent) 

(b–a)/(c–a) × 100 

Difference 

remaining 

between the 

target and 

the most 

recent 

value 

c–b 

Baseline 

(1998) 

a 

Most 

recent 

(2000) 

b 

Target 

(2010) 

c 

Total........................ 7.2 6.9 4.5 *−0.3 −4.2 −2.7 11.1 −2.4 

Mother’s race and ethnicity 

American Indian and Alaska Native .... 9.3 8.3 4.5 −1.0 −10.8 −4.8 20.8 −3.8 

Asian and Pacific Islander .......... 5.5 4.9 4.5 *−0.6 −10.9 −1.0 60.0 −0.4 

Asian...................... 5.0 4.5 4.5 −0.5 −10.0 −0.5 100.0 0.0 

Native Hawaiian and other 

Pacific Islander ............... 10.0 8.2 4.5 −1.8 −18.0 −5.5 32.7 −3.7 

Black or African American .......... 13.8 13.5 4.5 −0.3 −2.2 –9.3 3.2 −9.0 

White ....................... 6.0 5.7 4.5 *−0.3 −5.0 −1.5 20.0 −1.2 

Hispanic or Latino ............... 5.8 5.6 4.5 −0.2 −3.4 −1.3 15.4 −1.1 

Not Hispanic or Latino............. 7.5 7.2 4.5 *−0.3 −4.0 −3.0 10.0 −2.7 

Black or African American ......... 13.9 13.6 4.5 −0.3 −2.2 −9.4 3.2 −9.1 

White ..................... 6.0 5.7 4.5 *−0.3 −5.0 −1.5 20.0 −1.2 

Gender 

Female ...................... 6.5 6.2 4.5 *−0.3 −4.6 −2.0 15.0 −1.7 

Male ....................... 7.8 7.5 4.5 *−0.3 −3.8 −3.3 9.1 −3.0 

Mother’s education level 

(aged 20 years and over) 

Less than high school ............. 8.2 7.9 4.5 *−0.3 −3.7 −3.7 8.1 −3.4 

High school graduate ............. 7.6 7.3 4.5 *−0.3 −3.9 −3.1 9.7 −2.8 

At least some college ............. 5.3 5.0 4.5 *−0.3 −5.7 −0.8 37.5 −0.5 

Mother’s disability status 

Mothers with disabilities............ DNC DNC 4.5 

Mothers without disabilities.......... DNC DNC 4.5 

*The difference between the most recent value and the baseline is statistically significant.


- - - Data are not available.


DNC = Data were not collected.


0.0 Quantity more than zero but less than 0.05.


SOURCE: National Vital Statistics System, National Center for Health Statistics, Centers for Disease Control and Prevention.


population-based and nonpopulation-based objectives. The 

progress quotient measures the percent of the targeted 

change that has been achieved. The formula for the progress 

quotient is: 

most recent value – baseline value * 100 

year 2010 target – baseline value 

For example, school-based objective 7–2c calls for an 

increase in the proportion of middle, junior high, and senior 

high schools that provide school health education to prevent 

violence, from a baseline of 58 percent in 1994 to a target of 

80 percent in 2010. In 2000, 73 percent of schools provided 

education to prevent violence. Using the formula above, 

68.2 percent of the difference between the baseline and the 

year 2010 target had been achieved in 2000. 

73–58 * 100 = 68.2 percent 80–58 

For the population-based objectives, the progress 

quotient can also be used to measure progress toward the 

target for each population subgroup for which at least two 

reliable data points are available. In table 1, for example, the 

progress quotient is computed for objective 16–1c to reduce 

infant mortality rates. For the total population, 11.1 percent 

of the difference between the baseline and the year 2010 

target had been achieved in 2000. The progress quotient 

ranged from 100 percent (target attained) among infants of 

Asian mothers, to 3.2 percent among black or African 

American mothers. 

The progress quotient will be positive when the rate has 

moved toward the target. A negative value would indicate 

that the rate moved away from the target (see the example in 

table 2 below). The progress quotient can also be used to 

compare progress for one objective, relative to its baseline, 

with progress for other objectives, relative to their baselines. 

4 
Table 2. Measuring progress toward target attainment for Objective 5–3: Reduce the prevalence of diabetes 

Selected characteristic 

Target attainment 

Prevalence of diagnosed diabetes 

(age adjusted per 1,000 standard population) 

Difference 

between 

most 

recent and 

baseline 

b–a or b–a’ 

Percent 

change 

between 

baseline and 

most recent 

(b–a)/a × 100 

or 

(b–a’)/a’ × 100 

Difference 

between 

target 

and 

baseline 

c–a or 

c–a’ 

Progress 

quotient1 

(percent) 

(b–a)/(c–a) × 100 

or 

(b–a’)/(c–a’) × 100 

Difference 

remaining 

between the 

target and 

the most 

recent value 

c–b 

Baseline 

(1997) 

a 

Baseline 

for raceorigin 

groups2 

(1999) 

a’ 

Most 

recent 

(2001) 

b 

Target 

(2010) 

c 

Total .................... 39.7 41.3 48.3 25 *8.6 21.7 −14.7 −58.5 −23.3 

Race and ethnicity3 

American Indian and Alaska Native 

only .................... 

NA 

DSU 106.3 25 DSU DSU DSU DSU –81.3 

Asian only ................. 34.0 37.4 25 3.4 10.0 −9.0 −37.8 −12.4 

Native Hawaiian and other Pacific 

Islander only ............... DSU DSU 25 DSU DSU DSU DSU DSU 

Black or African American only .... 69.1 76.9 25 7.8 11.3 −44.1 −17.7 −51.9 

White only ................. 37.2 44.0 25 *6.8 18.3 −12.2 −55.7 −19.0 

2 or more races ............. 70.7 85.6 25 14.9 21.1 −45.7 −32.6 −60.6 

Hispanic or Latino ............ 64.5 69.3 25 4.8 7.4 −39.5 −12.2 −44.3 

Not Hispanic or Latino.......... 39.5 46.7 25 *7.2 18.2 −14.5 −49.7 −21.7 

Black or African American only . . . 69.2 77.5 25 8.3 12.0 −44.2 −18.8 −52.5 

White only ............... 35.7 42.4 25 *6.7 18.8 −10.7 −62.6 −17.4 

Gender 

Female ................... 40.4 NA 45.7 25 *5.3 13.1 −15.4 −34.4 −20.7 

Male .................... 39.2 51.5 25 *12.3 31.4 −14.2 −86.6 −26.5 

Education level 

(aged 25 years and over) 

Less than high school .......... 92.1 

NA 

107.8 25 *15.7 17.0 −67.1 −23.4 −82.8 

High school graduate .......... 57.8 79.6 25 *21.8 37.7 −32.8 −66.5 −54.6 

At least some college .......... 44.5 55.3 25 *10.8 24.3 −19.5 −55.4 −30.3 

Geographic location 

Urban .................... 40.4 NA 46.2 25 *5.8 14.4 −15.4 −37.7 −21.2 

Rural .................... 38.2 54.0 25 *15.8 41.4 −13.2 −119.7 −29.0 

Disability status 

Persons with disabilities ........ 86.6 

NA 

99.4 25 *12.8 14.8 −61.6 −20.8 −74.4 

Persons without disabilities....... 28.1 36.7 25 *8.6 30.6 −3.1 −277.4 −11.7 

*The difference between the most recent value and the baseline is statistically significant. 

NA = Not applicable. 

DSU = Data do not meet the criteria for statistical reliability, data quality, or confidentiality (data are suppressed). 

DNA = Data have not been analyzed. 1The progress quotient is the percent of the original difference between the baseline value and the target that has been eliminated based on the most recent data value. When the progress 

quotient is negative (in bold) the difference from the target has increased, that is, the most recent value is further from the target than the baseline. See footnote 2 concerning the baseline year for 

data by race and Hispanic origin. 2For purposes of calculation, the baseline year for data by race and ethnicity has been changed to the first year for which data are available according to the new OMB standards. 3Starting with data year 1999, estimates by race and ethnicity are tabulated using the 1997 Standards for Federal data on race and ethnicity. 

SOURCE: National Health Interview Survey, National Center for Health Statistics, Centers for Disease Control and Prevention. 

There are, however, some limitations to the inter- In order to help interpret these comparisons, the baseline 

pretation of this statistic. First, the progress quotient year and the most recent data year will be shown when 

measures the difference between the baseline year and the progress quotients are compared across objectives. In table 1, 

most recent year only. Fluctuations in the measure during the the baseline data year is 1998 and the most recent data year 

intervening years are not reflected. In addition, the number is 2000. Second, the progress quotient is based on the actual 

of years between the baseline and the most recent data value change between the baseline and the most recent data value 

and the number of years between the baseline and the year without any consideration of variability in the data that may 

2010 will vary from one objective to another. When progress substantially affect the size of the progress quotient from 

quotients are compared across objectives, it is important to year to year. Third, the absolute change required to attain the 

remember that they may be based on different time periods. target may differ among subgroups or across measures 

5 
having the same progress quotients. Therefore, equal 

progress quotients do not reflect equal absolute progress 

from the baseline. This is why the absolute difference 

remaining between the target and the most recent data value 

is also employed to monitor progress and to compare 

progress among subgroups. Finally, the progress quotient is 

not applicable to subgroups of the population where the year 

2010 target was already equaled or exceeded at the baseline. 

The percent change between the baseline and the most 

recent data value is included so that the size and direction of 

changes can be examined for each subgroup even if the 

target had been achieved at the baseline. 

Interpreting progress among subgroups 

These absolute and relative statistics can be interpreted 

together to better understand differences among subgroups. 

Subgroups begin with different baselines but have the same 

target. Groups that have experienced similar proportions of 

progress may, therefore, have very different absolute levels 

of progress remaining to achieve the target. While about 

20 percent of the difference from the target was eliminated 

for both infants of American Indian and Alaska Native 

mothers and infants of non-Hispanic white mothers, the 

absolute change in the infant mortality rate required to 

achieve the target for American Indian and Alaska Native 

mothers (3.8 infant deaths per 1,000 live births) is more than 

three times the change required by non-Hispanic white 

mothers (1.2 infant deaths per 1,000 live births) (table 1). 

Statistical significance 

When estimates of variability for the data values are 

available, the statistical significance of the difference 

between the baseline and the most recent rate or percent for 

a subgroup can be tested directly using a Z statistic. 

2 2 Z = (Rmr – Rb)/ √ SEmr + SEb 

Where: 

Rmr = rate or percent based on the most recent data, 

Rb = rate or percent at the baseline, 

SEmr = standard error of the most recent data value, and 

SEb = standard error of the rate or percent at the 

baseline. 

This formula assumes that the group rates are 

independent. The comparison of the Z statistic with some 

Z-critical value determines the significance of the difference 

between the two rates. If |Z| ≥ 1.96, the difference is 

significant at an alpha (α) level of 0.05. The difference 

between the most recent data value and the baseline is 

flagged (*) when it is statistically significant at the 0.05 

level. In table 1, for example, the standard error for the total 

infant mortality rate was 0.043 in 1998 and 0.041 in 2000. 

When they are available, estimates of standard errors are 

included in the HP2010 database, DATA2010 (3). Using the 

formula above, the change in the total infant mortality rate 

from 1998 to 2000 was statistically significant and the 

difference between the most recent value and the baseline is 

flagged accordingly. 

2 2 Z = (Rmr – Rb)/ √ SEmr + SEb 

Z = (6.9 – 7.2) / √ 0.0412 + 0.0432 

|Z| = 5.05 

When measures of variance cannot be produced, it is 

not possible to assess the statistical significance of the 

difference between the most recent data value and the 

baseline. The difference might be the result of sampling 

error or random variability. This is particularly true for 

smaller subgroups such as Asians, Native Hawaiians and 

other Pacific Islanders, and American Indians and Alaska 

Natives. 

Defining racial and ethnic subgroups 

The data in table 1 come from the National Vital 

Statistics System prior to the implementation of the 1997 

OMB standards for the classification of race and ethnicity. 

Until data based on the new standards become available, 

progress quotients will be computed for the old race-origin 

groups. Because the subgroups defined under the old 

standards are not strictly comparable to the subgroups 

defined under the new standards, it is not appropriate to 

compute progress quotients when the baseline and the most 

recent data are based on different standards. For those data 

systems that change to the new standards, the first year for 

which data are available according to the new standards will 

be used as the baseline for computing the progress quotient 

for racial and ethnic subgroups. This truncates the period 

between the baseline and the most recent data value. 

In table 2, progress quotients are computed for objective 

5–3, which calls for a reduction in the prevalence of 

diabetes. Between 1997 and 2001 the progress quotient for 

the total population was −58.5 percent, indicating movement 

away from the target. Stated differently, the difference 

between the year 2010 target and the diabetes prevalence 

rate was 58.5 percent greater in 2001 than in 1997. 

Beginning in 1999, data for this objective from the 

National Health Interview Survey are available for the new 

race and Hispanic origin groups. Progress quotients for the 

race and Hispanic origin groups are computed with 1999 

data as the baseline rather than the original 1997 data. 

Between 1999 and 2001 the progress quotient for Asians 

was −37.8 percent, indicating movement away from the 

target. This change is not due to any change in the way in 

which Asians are defined. The change for racial and ethnic 

groups is, however, based only on the period from 1999 to 

2001. Progress quotients for groups in the gender, education 

level, geographic location, and disability status domains are 

computed with the original 1997 data as the baseline. These 

groups are not affected by the change in race-origin 

categories, therefore, progress quotients for groups in these 

domains are based on 2 additional years of data and are not 

strictly comparable with progress quotients for the 

race-origin groups in this example. 

6 
Measuring Progress Toward the 

Elimination of Disparity 

The words disparity and difference are synonyms 

according to most dictionaries. The population-based 

objectives in HP2010 are measured in terms of rates, 

percents, means, or some other quantitative measure. In the 

context of public health, a disparity is the quantity that 

separates a group from a reference point on a particular 

measure of health that is expressed in terms of a rate, 

proportion, mean, or some other quantitative measure. 

Disparity cannot be measured for those HP2010 objectives 

based on counts of events rather than rates or percents. 

The second goal calls for the elimination of disparities 

among subgroups of the population. It does not presume that 

membership in a particular group is the primary or even a 

necessary cause of differences between groups. The purpose 

of the second goal of Healthy People is to call attention to 

differences in health between groups regardless of the cause. 

In order to eliminate disparities, specific causes or 

determinants need to be identified. The National Institutes of 

Health Strategic Research Plan and Budget to Reduce and 

Ultimately Eliminate Health Disparities indicates that a 

multifactorial approach is needed to identify the 

determinants of disparities in health (8). 

In a similar vein, when there is no disparity or 

difference between groups in one of the Healthy People 

measures it does not prove that none exists. For example, 

the age-adjustment procedures employed in measuring 

Healthy People objectives may obscure differences between 

groups at specific ages or failure to compare groups by both 

race and sex may obscure differences associated with these 

two factors combined. 

Three questions were considered by the workgroup in 

order to determine how to measure progress toward the 

elimination of disparities in HP2010. First, should disparity 

be measured in absolute or in relative terms? Second, should 

disparity be measured in terms of favorable events or in 

terms of adverse events? And finally, what reference point 

should disparity be measured from? The answers to these 

questions provide a framework for measuring disparity in 

HP2010. 

Measuring disparity in absolute and relative 

terms 

As discussed in the section on target attainment above, 

differences between rates can be measured in absolute or in 

relative terms. Changes in disparity over time can also be 

measured in absolute or in relative terms. However, absolute 

and relative measures of disparity may lead to different 

conclusions about changes over time. In figure 1, for 

example, tuberculosis case rates are shown for non-Hispanic 

white persons and Hispanic persons in 1990 and in 2000 (9). 

The absolute difference between rates for non-Hispanic 

white persons and Hispanic persons decreased from 17.3 

cases per 100,000 population in 1990 (21.5 − 4.2), to 8.9 

cases per 100,000 population in 2000 (10.8 − 1.9). However, 

Figure 1. Tuberculosis case rates for not Hispanic white persons 

and Hispanics: United States, 1990 and 2000 

when these rates are compared based on the percent 

difference, a relative measure of disparity, we are led to a 

different conclusion. In 1990 the percent difference between 

the tuberculosis rates for non-Hispanic white persons and 

Hispanic persons is equal to 412 percent of the rate for 

non-Hispanic white persons ((21.5 − 4.2) / 4.2 * 100 = 412). 

In 2000 the percent difference is equal to 468 percent of the 

rate for non-Hispanic white persons ((10.8 − 1.9) / 1.9 

*100 = 468). In this example the absolute difference declined 

but the relative difference increased. 

When rates are declining, a reduction in the absolute 

difference between group rates can occur without a reduction 

in the relative difference (as in the example above). When 

rates are declining, a reduction in the relative difference 

between group rates always corresponds to a reduction in the 

absolute difference. On the other hand, when rates are 

increasing, a reduction in the absolute difference between 

group rates always corresponds to a reduction in the relative 

difference but reduction in the relative difference can occur 

without a reduction in the absolute difference. A decline in 

both absolute and relative differences is the best evidence of 

progress toward the elimination of disparity. In Healthy 

People 2000 a relative measure of change in disparity was 

employed to determine whether progress was being made 

toward the goal of reducing disparity. A ‘‘ratio of percent 

change’’ was used to compare the change in the rate for each 

special population group with the change in the rate for a 

reference population (7). Relative measures of disparity will 

again be the primary basis for assessing progress toward the 

elimination of disparity in HP2010 because relative measures 

adjust for changes in the level of the reference point over time. 

Relative measures of disparity have the additional advantage of 

adjusting for differences in the level of the reference point 

when comparisons are made across objectives. 

It is important to remember that the absolute and 

relative perspectives are fundamentally different yet 

complementary. Large relative differences do not necessarily 

imply large absolute differences. For example, the relative 

difference between rates of 1 and 2 deaths per 100,000 

population is the same as the relative difference between 50 

7 
and 100 deaths per 100,000 population. However, more lives 

would be saved by eliminating the second disparity than 

would be saved by eliminating the first disparity. The 

absolute difference will be used as a secondary means of 

assessing disparity among subgroups in a domain. 

Measuring disparity in terms of favorable or 

adverse events 

Some HP2010 objectives are expressed in terms of 

favorable events or conditions that are to be increased and 

others are expressed in terms of adverse events or conditions 

that are to be reduced. The choice to measure objectives in 

terms of favorable or adverse events can have a substantial 

impact on relative measures of disparity. On the left side of 

figure 2, for example, the percent of females with health 

insurance is 2.4 percent greater than the percent of males 

with health insurance ((84.2−82.2) / 82.2 * 100 = 2.4 

percent) (10). On the right side of the figure, the percent of 

females without health insurance is 11.2 percent less than the 

percent of males without health insurance ((15.8−17.8) / 17.8 

* 100 = −11.2 percent). While the absolute difference is the 

same for both favorable and adverse conditions (2 percentage 

points), the size and direction of the percent difference 

depends on whether it is computed for the percent with 

health insurance or for the percent without health insurance. 

Similarly, conclusions about changes in disparity over time 

also depend on whether the objective is expressed in terms 

of favorable events or in terms of adverse events. 

It would not be appropriate to compare the relative 

disparity for one objective expressed in terms of favorable 

events (which tend to occur more frequently than adverse 

events among HP2010 objectives) with the relative disparity 

for another objective expressed in terms of adverse events 

(which tend to be more rare). In order to facilitate 

comparisons of relative disparity across different objectives, 

disparity will be measured in terms of adverse events in 

HP2010. Objectives that call for an increase will be 

transformed for purposes of measuring disparity. These 

objectives will not be restated but when relative measures of 

disparity are computed, the objective will be expressed in 

terms of adverse events or conditions. 

Reference point for measuring disparity 

Disparity can be measured in relation to various 

reference points, including the year 2010 target rate, the total 

population rate, an average of group rates, the rate for a 

specific group such as the largest group, or the group with 

the ‘‘best’’ or most favorable rate. There are advantages and 

disadvantages associated with each of these choices. The 

advantages of the year 2010 target include the fact that it is 

fixed for a decade and it has no sampling or other random 

error associated with it. However, there is no advantage to 

using a relative measure of disparity to measure change over 

time when the target does not change. The total population 

rate is a weighted average of the subgroup rates. It reflects 

the average experience of all individuals in the population. 

However, the total population rate is not a good reference 

Figure 2. Percent of persons under 65 years of age with health 

insurance coverage and without health insurance coverage, 

female and male (age adjusted), United States, 2000 

point for measuring changes in disparity among several 

groups over time because the total rate depends on the 

distribution of the population among the groups and this 

distribution can change over time. The mean of group rates 

in a domain is an unweighted average of the group rates 

and, therefore, not affected by changes in the distribution of 

persons among groups, but it is more affected by outlying 

group rates than some of the other choices. The largest 

group rate is generally the most reliable of the group rates 

but it singles out one group as the standard of comparison 

for all objectives. The ‘‘best’’ or most favorable group rate is 

theoretically achievable by other groups. Implicit in the use 

of the ‘‘best’’ rate as the reference point is the idea that all 

differences are inequitable. However, the ‘‘best’’ group rate 

will differ from one domain to the next, the group with the 

‘‘best’’ rate may change over time, and the ‘‘best’’ group rate 

might be the least reliable. 

The ‘‘best’’ group rate was chosen as the reference point 

for measuring disparity in HP2010. Choosing the ‘‘best’’ rate 

eliminates the difficulty of describing the disparity for any 

group with a rate better than the reference point. Use of the 

‘‘best’’ rate is also consistent with the idea behind using a 

rate ‘‘better than the best’’ as a target rate. The ‘‘best’’ rate is 

employed whether it is the ‘‘best’’ group rate in a domain of 

two groups or the ‘‘best’’ group rate in a domain of three or 

more groups. It is important to remember that, for a 

particular objective, the ‘‘best’’ rate may differ from one 

domain to the next. The ‘‘best’’ group rate among race-origin 

groups may differ from the ‘‘best’’ group rate among groups 

defined in terms of gender, education, location, disability or 

sexual orientation. The ‘‘best’’ group may also change over 

time. At baseline, females might have the ‘‘best’’ rate and 

several years later males might have the ‘‘best’’ rate. 

8 
Concerns about the stability of the ‘‘best’’ group rate 

can be alleviated by imposing a standard of reliability on the 

selection of the group with the ‘‘best’’ rate. When estimates 

of reliability are available, the rate for the ‘‘best’’ group 

must have a relative standard error less than 10 percent; 

otherwise the next most favorable rate will be used as the 

reference point. This is more stringent than the 30 percent 

relative standard error used by many HP2010 data 

sources (11). When estimates of variance are available, 

differences between each of the other group rates and the 

‘‘best’’ group rate will be evaluated for statistical 

significance (see the discussion below). Finally, it should be 

kept in mind that change in the ‘‘best’’ group rate itself may 

be responsible for changes in disparity. For example, if the 

rates for other groups remained unchanged, any change in 

the ‘‘best’’ group rate would result in a change in disparity. 

In summary, in the context of HP2010, disparity is 

being measured in both absolute and relative terms. 

Disparities are measured with the ‘‘best’’ group rate in each 

domain as the reference point and the objectives/measures 

are expressed in terms of rates or percents of adverse events. 

Different choices might lead to different conclusions about 

changes in disparity over time or across objectives. 

However, in the interests of consistency, disparity and 

changes in disparity in HP2010 will be governed by these 

choices. 

Measuring Disparity 

Nearly all of the population-based objectives in HP2010 

are measured in terms of rates, percents, and means. Most of 

these objectives are essentially dichotomous and are 

expressed in terms of a rate or percent for which the 

techniques described below are appropriate. In the interest of 

brevity, the term ‘‘rates’’ is used to refer to both rates and 

percents. Similar principles can be applied to the 

measurement of disparities in means. 

Absolute disparity will be measured as the simple 

difference between other group rates and the rate for the 

‘‘best’’ group in the domain: 

Ri – Rr 

where Ri is the rate for a group of interest and Rr is the rate 

for the reference point, the ‘‘best’’ group rate. 

Relative disparity will be measured in terms of the 

percent different between the rates for other groups and the 

rate for the ‘‘best’’ group in the doman. The percent 

difference indicates how much larger the rate for each group 

is compared to the rate for the ‘‘best’’ group in the same 

domain. The formula for the percent difference is: 

(Ri − Rr)/ Rr * 100 

where Ri is the rate for a group of interest and Rr is the rate 

for the reference point, the ‘‘best’’ group rate. The ‘‘best’’ 

group rate is the lowest rate of adverse events among the set 

of mutually exclusive and exhaustive groups in each domain. 

For example, the simple difference and the percent 

difference between subgroup rates and the ‘‘best’’ group rate 

in each domain are calculated for Objective 5–3 in table 3. 

This objective calls for a reduction in the prevalence rate of 

diabetes. Baseline data for this objective come from the 

1997 National Health Interview Survey. As discussed 

previously, for purposes of measuring disparity by race and 

Hispanic origin, baseline data for this objective come from 

the 1999 National Health Interview Survey. In the race and 

ethnicity domain, the ‘‘best’’ group rate was chosen from the 

following groups: American Indian and Alaska Native only; 

Asian only; Native Hawaiian and other Pacific Islander only; 

2 or more races; Hispanic or Latino; Not Hispanic or Latino, 

black or African American only; and Not Hispanic or Latino, 

white only. For purposes of measuring disparity, these 

groups are treated as if they were mutually exclusive 

although small numbers of persons in the first four groups 

also identified themselves as Hispanic. These persons are 

included in both the race-specific group and the Hispanic 

group. Among the race-origin groups in 1999, Asians had 

the lowest rate of diabetes (34.0 per 1,000 population). The 

rate of diabetes among American Indians and Alaska Natives 

was statistically unreliable. The differences are not 

applicable for Asian only because this group had the most 

favorable rate in 1999. The rate for Native Hawaiians and 

other Pacific Islanders was statistically unreliable. The rate 

for black persons was 35.1 cases per 1,000 population higher 

than the‘‘best’’ group rate or 103.2 percent higher than the 

‘‘best’’ group rate, etc. 

For domains other than race and ethnicity, baseline data 

are shown for 1997. In 1997, the diabetes rate for females 

was 1.2 cases per 1,000 population or 3.1 percent higher 

than the rate for males. Among persons 25 years and over, 

the diabetes rate for those with less than a high school 

education was 47.6 cases per 1,000 population or 

107.0 percent higher than the diabetes rate for persons with 

at least some college and the diabetes rate for high school 

graduates was 13.3 cases per 1,000 population or 

29.9 percent higher than the diabetes rate for persons with at 

least some college. The diabetes rate for persons in urban 

areas was 2.2 cases per 1,000 population or 5.8 percent 

higher than the diabetes rate in rural areas. The diabetes rate 

for persons with disabilities was 58.5 cases per 1,000 

population or 208.2 percent higher than the diabetes rate for 

persons without disabilities. Trends in disparity will be 

assessed primarily in terms of changes in the percent 

difference from the ‘‘best’’ group rate. 

Statistical significance of the disparity between 

each group rate and the ‘‘best’’ group rate 

When methods for assessing the variability of rates and 

percents are available, the statistical significance of the 

difference between the two rates on which the percent 

difference is based will be tested. The methods used to 

assess the variability of rates and percents depend on the 

source of the data (11). Methods are not available for 

assessing the variability of rates and percents for all 

HP2010 objectives. Assuming that estimates of the standard 

errors for rates are available, the statistical significance of 

9 
Table 3. Measuring progress toward disparity elimination for Objective 5–3: Reduce the prevalence of diabetes 

Domains 

Disparity elimination 

Prevalence of diagnosed diabetes (age adjusted per 1,000 standard population) 

Change in 

disparity 

from 

baseline to 

most recent 

(percentage 

points) 

Baseline (1997) Baseline for race-origin groups2 (1999) Most recent (2001) 

Rate 

Simple 

difference 

from 

‘‘best’’

1 

Percent 

difference 

from 

‘‘best’’

1 Rate 

Simple 

difference 

from 

‘‘best’’

1 

Percent 

difference 

from 

‘‘best’’

1 Rate 

Simple 

difference 

from 

‘‘best’’

1 

Percent 

difference 

from 

‘‘best’’

1 

Total.......................... 39.7 NA NA 41.3 NA NA 48.3 NA NA NA 

Race and ethnicity3 

American Indian and Alaska Native only . . . DSU DSU DSU 106.3 68.9 *184.2 DSU 

Asian only ...................... 34.0 NA NA 37.4 NA NA NA 

Native Hawaiian and other Pacific Islander 

only .......................... DSU DSU DSU DSU DSU DSU DSU 

Black or African American only ......... 69.1 35.1 *103.2 76.9 39.5 *105.6 2.4 

White only ...................... NA NA NA 37.2 3.2 9.4 44.0 6.6 17.6 8.2 

2 or more races .................. 70.7 36.7 *107.9 85.6 48.2 *128.9 21.0 

Hispanic or Latino ................. 64.5 30.5 *89.7 69.3 31.9 *85.3 −4.4 

Not Hispanic or Latino............... 39.5 5.5 16.2 46.7 9.3 24.9 8.7 

Black or African American only ........ 69.2 35.2 *103.5 77.5 40.1 *107.2 3.7 

White only .................... 35.7 1.7 5.0 42.4 5.0 13.4 8.4 

Gender 

Female ........................ 40.4 1.2 3.1 NA NA NA 45.7 NA NA NA 

Male ......................... 39.2 NA NA 51.5 5.8 *12.7 9.6 

Education level (aged 25 years and over) 

Less than high school ............... 92.1 47.6 *107.0 

NA NA NA 

107.8 52.5 *94.9 −12.1 

High school graduate ............... 57.8 13.3 *29.9 79.6 24.3 *43.9 **14.0 

At least some college ............... 44.5 NA NA 55.3 NA NA NA 

Geographic location 

Urban ......................... 40.4 2.2 5.8 NA NA NA 46.2 NA NA NA 

Rural ......................... 38.2 NA NA 54.0 7.8 *16.9 11.1 

Disability status 

Persons with disabilities ............. 86.6 58.5 *208.2 

NA NA NA 99.4 62.7 *170.8 −37.4 

Persons without disabilities............ 28.1 NA NA 36.7 NA NA NA 

*The difference between the rate for this group and the ‘‘best’’ group rate is statistically significant. 

**The difference between the percent difference at baseline and the percent difference for the most recent year is statistically significant. 

NA = Not applicable. 

DSU = Data do not meet the criteria for statistical reliability, data quality, or confidentiality (data are suppressed). 1For each domain (Race and ethnicity, Gender, Education level, Geographic location, and Disability status) the percent difference is computed between the rate for each of the other groups and 

the rate for the group with the ‘‘best’’ or lowest rate. The lowest rate in each domain is shown in italics. In the race and ethnicity domain, the ‘‘best’’ group rate was chosen from the following 

groups that are treated is if they were mutually exclusive: American Indian and Alaska Native only; Asian only; Native Hawaiian and other Pacific Islander only; 2 or more races; Hispanic or Latino; 

Not Hispanic or Latino, Black or African American only; and Not Hispanic or Latino, White only. 2The baseline year for data by race and ethnicity is the first year for which data are available according to the latest OMB standards. 3Starting with data year 1999, estimates by race and ethnicity are tabulated using the 1997 standards for Federal data on race and ethnicity. 

SOURCE: National Health Interview Survey, National Center for Health Statistics, Centers for Disease Control and Prevention. 

the difference between groups can be assessed using the 

following Z statistic: 

Z = (Ri – Rr)/ √ SEi 

2 2 + SEr 

where: 

Ri = rate for a group of interest, 

Rr = rate for the reference group, 

SEi = standard error of the rate for a group of interest, 

and 

SEr = standard error of the rate for the reference group. 

This formula assumes that the groups are independent. 

The comparison of the Z statistic with some Z-critical value 

determines the significance of the difference between the 

two rates, and by inference the significance of the percent 

difference between the two rates. If |Z|≥ 1.645, the difference 

is significant at an alpha (α) level of 0.05. Because the 

comparison is made to the ‘‘best’’ rate the other group rate 

can only be larger, therefore, a one-tailed test is employed. 

The difference between the rate for a group of interest and 

10 
the ‘‘best’’ group rate is flagged (*) when it is statistically 

significant at the 0.05 level. 

In table 3, for example, the diabetes prevalence rate for 

blacks or African Americans only was 69.1 per 1,000 and 

the rate for Asians only was 34.0 per 1,000. Standard errors 

for these two point estimates were calculated using 

SUDAAN (12), a statistical package that adjusts for the 

effects of the complex design of the National Health 

Interview Survey (3.5 and 6.4 per 1,000, respectively). Using 

the formula above: 

Z = (Ri – Rr)/ √ SEi 

2 2 + SEr 

Z = (69.1 – 34.0) / √ (3.52 + 6.42) 

|Z| = 4.81 

Because Z is greater than the critical value (1.645), the 

difference between the diabetes prevalence rate for black or 

African Americans only and Asians only is statistically 

significant at the 0.05 level, and the percent difference is 

flagged accordingly. 

If the purpose of the analysis were to determine whether 

the rate for more than one group in a domain was different 

from the ‘‘best’’ group rate, the Z-critical value should be 

adjusted for the number of comparisons by dividing the 

alpha (α) level by the number of comparisons. In the 

example above, if both of the other education groups were 

compared with the ‘‘best’’ group in 1997, the alpha (α) level 

would be (0.05 / 2 = 0.025). The Z-critical value for an 

alpha (α) of 0.025 is 1.96 for a one-tailed test. Z values for 

the other education groups were 10.5 and 4.0, respectively. 

The prevalence of diabetes among persons 25 years and over 

with less than a high school education and for those with a 

high school education was significantly different from the 

prevalence for the ‘‘best’’ group. The Z-critical value for 

both groups exceeded the critical value for multiple 

comparisons (13). The more stringent requirements of the 

multiple comparison test compensate for the increased 

probability that at least one significant difference might 

occur by chance. 

Measuring change over time in disparity for 

individual groups 

The change in disparity for individual groups can be 

measured in terms of the change in the percent difference 

from the ‘‘best’’ group rate. The absolute change in the 

percent difference from the ‘‘best’’ rate is computed by 

subtracting the percent difference at the earlier period from 

the percent difference at the later period as illustrated in the 

example for Objective 5–3 in table 3. An increase in the 

percent difference is indicative of an increase in disparity 

and a decrease in the percent difference is indicative of a 

decrease in disparity. 

Between 1999 and 2001, the percent difference from the 

‘‘best’’ rate increased by 8.4 percentage points for the nonHispanic white group. The term ‘‘percentage points’’ is used 

to signify that this is an absolute difference between two 

percents. The absolute change over time in the percent 

difference from the ‘‘best’’ group can be used to identify the 

group with the greatest change relative to the ‘‘best’’ 

group—Asians only in this example. Among the race-ethnic 

groups in table 3, persons who identified with 2 or more 

races had the greatest increase in the rate of diabetes (that is, 

the greatest increase in disparity) relative to the ‘‘best’’ 

group. Relative to the ‘‘best’’ group, the percent difference 

for Hispanics declined by 4.4 percentage points. This was 

the only race-origin group for which the percent difference 

from the ‘‘best’’ group decreased. 

Because they are not affected by changes in racial and 

ethnic classification, baseline data for the other domains 

(gender, family income level, and geographic location) are 

shown for 1997. Between 1997 and 2001, the disparity in 

diabetes rates by gender increased by 9.6 percentage points 

relative to the ‘‘best’’ rate. Note that the group with the most 

favorable rate changed from males in 1997 to females in 

2001. In 1997 the rate for females was 3.1 percent higher 

than the rate for males. In 2001 the rate for males was 

12.7 percent higher than the rate for females. The net change 

in the percent difference from the ‘‘best’’ rate was therefore 

9.6 percentage points (12.7 − 3.1 = 9.6 percentage points). 

In the education domain the disparity between persons with 

less than high school and persons with at least some college 

decreased by 12.1 percentage points relative to the ‘‘best’’ 

rate and the disparity between high school graduates and 

persons with at least some college increased by 

14.0 percentage points relative to the ‘‘best’’ rate. The 

disparity in diabetes rates by geographic location increased 

by 11.1 percentage points relative to the ‘‘best’’ rate. In the 

disability status domain, the disparity between persons with 

disabilities and persons without disabilities decreased by 

37.4 percentage points relative to persons without 

disabilities, indicating a decrease in disparity. 

Statistical significance of changes in disparity 

over time 

In order to evaluate the statistical significance of a 

change in the percent difference over time, standard errors 

for the percent difference at the baseline and at the most 

recent data point are needed. The percent difference is based 

on the ratio of the simple difference to the reference point as 

shown above. The relative standard error of a ratio is 

computed based on the relative standard error of the 

numerator and the denominator. The relative standard error 

(RSE) for the simple difference (SD) is calculated as: 

RSESD = √ SEi 2 / (Ri−Rr) 2 + SEr 

where SEi is the standard error of the rate for a group of 

interest (i), SEr is the standard error for the reference group 

rate (r), Ri is the rate for the group of interest, and Rr is the 

reference group rate. 

The relative standard error of the rate for the reference 

point is computed as: 

RSEr = SEr / Rr. 

11 
The relative standard error for the percent difference 

RSEPD is computed based on the relative standard errors of 

the numerator (RSESD) and the denominator (RSEr) as: 

2 2 RSEPD = √RSESD + RSEr . 

The standard error of the percent difference is obtained 

from the relative standard error as: 

SEPD = RSEPD * PD. 

The statistical significance of a change in the percent 

difference from the ‘‘best’’ group rate over time can be 

assessed using the following Z statistic: 

2 2 Z = (PD1−PD0)/ Œ SEPD1 + SEPD0 

where 

PD1 = percent difference at time 1 (the most recent data 

point), 

PD0 = percent difference at time 0 (the baseline), 

SEPD1 = standard error of the percent difference at time 1, 

and 

SEPD0 = standard error of the percent difference at time 0. 

In table 3, the diabetes prevalence rate for females was 

3.1 percent higher than the rate for males in 1997 and the 

rate for males was 12.7 percent higher than the rate for 

females in 2001. In this example, the net change in the 

percent difference from the ‘‘best’’ group rate is 

9.6 percentage points. Standard errors for the four group 

rates were calculated using SUDAAN (12). The relative 

standard error for the simple difference between the rate for 

the group of interest and rate for the reference point is 

calculated as: 

RSESD = √SEi 2 / (Ri−Rr). 2 + SEr 

Baseline: 

RSESD,0 = √1.42 + 1.52 / (40.4−39.2) 

= √4.21 / 1.2 

= 1.708 or 170.8% 

Most recent: 

RSESD,1 = √1.92 + 1.42 / (51.5−45.7) 

= √5.57 / 5.8 

= 0.407 or 40.7% 

The relative standard error for the reference point rates are 

calculated from the standard error as: 

RSEr = SEr / Rr. 

Baseline: 

RSEr, 0	 = 1.5 / 39.2 

= 0.038 or 3.8% 

Most recent: 

RSEr,1	 = 1.4 / 45.7 

= 0.031 or 3.1% 

The relative standard error for the respective percent 

differences are calculated as: 

2 2 RSEPD = √RSESD + RSEr . 

Baseline: 

RSEPD,0 = √1.7082 + 0.0382 

= √2.919 

= 1.709 or 170.9% 

Most recent: 

RSEPD,1 = √0.4072 + 0.0312 

= √0.617 

= 0.409 or 40.9% 

The standard errors for the percent differences are calculated 

as: 

SEPD = RSEPD * PD. 

Baseline: 

SEPD,0 = 1.709 * 3.1% 

= 5.30% 

Most recent: 

SEPD,1 = 0.409 * 12.7% 

= 5.19% 

Finally, the Z test for the significance of the change in the 

percent difference: 

2 2 Z = (PD1−PD0)/ √SEPD,1 + SEPD,0 

= (12.7−3.1) / √5.192 + 5.302 

= 9.6 / 7.42 

= 1.29. 

That is, |Z| = 1.29. Because the percent difference from the 

‘‘best’’ group rate could increase or decrease, a two-tailed 

test is employed (|Z| ≥ 1.96). Because the absolute value of Z 

is not equal to or greater than the critical value, there was no 

significant change in the disparity between males and 

females in the prevalence of diabetes between 1997 and 

2001. 

12 
Table 4. Measuring progress toward elimination of gender disparity for selected Healthy People 2010 objectives 

Objectives and gender 

Disparity elimination 

Prevalence 

Change in 

disparity 

from baseline 

to most recent 

(percentage 

points) 

Baseline 1997 Most recent 2001 

Rate or 

percent 

Percent 

difference 

from ‘‘best’’

1 

Rate or 

percent 

Percent 

difference 

from ‘‘best’’

1 

Objective 5–3 

Prevalence of diagnosed diabetes 

(age adjusted per 1,000 standard population) 

Female ............................. 40.4 3.1 45.7 0.0 NA 

Male ............................... 39.2 0.0 51.5 *12.7 9.6 

Objective 5–15 

Percent without an annual dental visit among persons 

with diabetes (persons aged 2 years and over) 

Female ............................. 41.3 0.0 39.1 0.0 NA 

Male ............................... 47.9 16.0 45.7 *16.9 0.9 

Objective 22–1 

Percent of persons with no physical activity 

(adults aged 18 years and over) 

Female ............................. 43.0 *18.8 40.1 *15.6 −3.2 

Male ............................... 36.2 0.0 34.7 0.0 NA 

Objective 22–2 

Percent of persons without moderate physical activity 

(adults aged 18 years and over) 

Female ............................. 71.3 *10.2 71.2 *10.2 0.0 

Male ............................... 64.7 0.0 64.6 0.0 NA 

*The difference between the rate for this group and the ‘‘best’’ group rate is statistically significant. 

**The difference between the percent difference at baseline and the percent difference for the most recent year is statistically significant (none in this table). 

NA = Not applicable. 

0.0 Quantity more than zero but less than 0.05. 1The percent difference is computed between the rate for each of the other groups and the rate for the group with the ‘‘best’’ or most favorable rate. The most favorable rate in each domain is 

shown in italics. 

SOURCE: National Health Interview Survey, National Center for Health Statistics, Centers for Disease Control and Prevention. 

Comparisons Across Healthy People 

Objectives 

As a relative statistic, the percent difference can be 

compared among groups in a domain or over time as 

illustrated in table 3. When the percent difference at one 

point in time is compared with the percent difference at a 

later point in time, it is essential that the groups of interest 

and the reference point group be defined in the same way at 

both points in time. As noted previously, the specific group 

with the ‘‘best’’ rate may change over time, but whichever 

group has the ‘‘best’’ rate should be chosen from the same 

set of group rates at each point in time. The percent 

difference can also be compared across objectives. In table 4, 

the change in the percent difference by gender between 1997 

and 2001 is shown for four HP2010 objectives based on data 

from the National Health Interview Survey. Note that for 

purposes of measuring disparity, all of these objectives are 

expressed in terms of adverse conditions or events. 

As indicated above, the gender disparity in the 

prevalence of diabetes increased by 9.6 percentage points 

relative to the ‘‘best’’ rate, which changed from males in 

1997 to females in 2001. Relative to the change in the 

‘‘best’’ rate, this change was not statistically significant. The 

gender disparity in the percent without an annual dental visit 

among persons with diabetes increased by 0.9 percentage 

points. The gender disparity in the percent of persons with 

no leisure-time physical activity decreased by 3.2 percentage 

points and the gender disparity in the percent of persons 

without moderate physical activity was unchanged. None of 

these changes in disparity between the baseline in 1997 and 

2001 is statistically significant. 

Measuring Disparity for Domains of 

3 or More Groups 

In HP2010 several population domains are defined in 

terms of two groups (gender, geographic location, and 

disability status). When there are just two groups in a 

domain the disparity from the ‘‘best’’ group can be measured 

directly in terms of the percent difference and the change in 

13 
Table 5. Measuring disparity and changes in disparity among several groups: Objective 16–1c: Reduction in infant mortality rates 

Domain 

Infant mortality rate per 1,000 live births Percent difference from ‘‘best’’

1 

Baseline 

1998 

Most recent 

2000 

Baseline 

1998 

Most recent 

2000 

Mother’s race and ethnicity 

American Indian and Alaska Native .................. 9.3 8.3 86.0 84.4 

Asian ..................................... 5.0 4.5 NA NA 

Native Hawaiian and other Pacific Islander .............. 10.0 8.2 100.0 82.2 

Hispanic or Latino ............................. 5.8 5.6 16.0 24.4 

Not Hispanic or Latino, black or African American ......... 13.9 13.6 178.0 202.2 

Not Hispanic or Latino, white ...................... 6.0 5.7 20.0 26.7 

Index of disparity (percent) ........................ NA NA 80.0 84.0 

NA = Not applicable.
 1The ‘‘best’’ group rate for each year is shown in italics.


SOURCE: National Vital Statistics System, National Center for Health Statistics, Centers for Disease Control and Prevention.


disparity can be measured in terms of the absolute change in 

the percent difference, as described previously. When there 

are more than two groups in a domain (race and ethnicity, 

education, and income) the disparity among several group 

rates or percents can be measured using an index of 

disparity that summarizes the differences among the 

groups (14,15). While the percent difference measures 

disparity from the ‘‘best’’ group rate for individual groups, 

an index of disparity can be used to measure the disparity 

between several groups and the ‘‘best’’ group. An index of 

disparity provides a way to determine whether the disparity 

between several groups in a domain is getting larger or 

smaller relative to the ‘‘best’’ group rate. The formula for an 

index of disparity is: 

n–1 PDi ( Σ )/(n−1) i=1 

where PDi is the percent difference for each group rate of 

interest and n is the total number of groups. Because this 

statistic is based on the average of differences from the 

‘‘best’’ group rate, the average is computed using the number 

of groups minus 1 (n−1). 

In table 5 the index of disparity is computed based on 

six race and ethnic groups for infant mortality rates in 1998 

and 2000. Ideally the groups in a domain should be mutually 

exclusive and exhaustive. In this example, however, small 

numbers of mothers in the first three groups also identified 

themselves as Hispanic. These mothers are included in both 

the race-specific group and the Hispanic group. When the 

index of disparity is calculated for categories of race and 

ethnicity based on the new standards, seven groups would be 

used and treated as if they were mutually exclusive and 

exhaustive: American Indian and Alaska Native only, Asian 

only, Native Hawaiian and other Pacific Islander only, 2 or 

more races, Hispanic or Latino, Not Hispanic or Latino 

Black only, and Not Hispanic or Latino White only. 

In both 1998 and 2000, infants born to Asian mothers 

had the most favorable infant mortality rate. The percent 

difference for each of the other groups is calculated as 

described previously. The index of disparity is equal to the 

mean of the five percent differences from the ‘‘best’’ group 

rate. In 1998, the average percent difference from the ‘‘best’’ 

group rate was equal to 80.0 percent of the ‘‘best’’ group 

rate. Like the percent difference, the index of disparity is a 

relative statistic. The average disparity from the ‘‘best’’ 

group rate is expressed as a percent of the ‘‘best’’ group rate. 

The index of disparity is based on an average of relative 

differences. Similar index values can be obtained when the 

rate for only one group is very different from the reference 

group, or when the rates for several groups are only 

moderately different from the reference point. The index of 

disparity should always be interpreted in terms of the 

group-specific rates on which it is based. The procedure for 

estimating the standard error for the index of disparity is 

described below. 

Measuring change over time in disparity for a 

domain of several groups 

The index of disparity can be used to assess changes in 

disparity among groups in a domain over time. In table 5, 

the index of disparity by race and ethnicity was computed 

for both 1998 and 2000 for Objective 16–1c. The index of 

disparity increased from 80.0 percent in 1998 to 84.0 percent 

in 2000, an increase of 4 percentage points. Once again the 

term percentage points is used to emphasize the fact that this 

is an absolute change in a relative measure of disparity. The 

average percent difference from the ‘‘best’’ group rate, 

therefore, increased relative to the ‘‘best’’ group rate. This 

finding is consistent with increases in the percent difference 

from the ‘‘best’’ rate for Hispanic, white non-Hispanic, and 

black non-Hispanic mothers in table 5. An increase in the 

index of disparity can be interpreted as an increase in 

disparity by race and ethnicity. 

The index of disparity does not indicate whether any of 

the group rates are increasing or decreasing. A decrease in 

the statistic does not necessarily indicate that any of the 

subgroup rates are decreasing; it simply means that there is 

proportionally less variation in subgroup rates relative to the 

‘‘best’’ subgroup rate. Because it is based on an average, the 

index of disparity is sensitive to the number of groups. 

14 
When the index of disparity is used to make comparisons 

over time, across objectives, or across geographic areas the 

index must be computed using the same number of 

groups—defined in the same way. If the ‘‘best’’ group rate 

has a relative standard error greater than or equal to 

10 percent, it is excluded from the calculation and the next 

‘‘best’’ group rate is used as the reference point. The index 

of disparity can also be used to make comparisons of 

disparity for a particular domain across geographic areas or 

across populations. Comparisons of the index of disparity 

across domains may be of interest; however, such 

comparisons should be made with caution. Differences in the 

index of disparity should also be interpreted in terms of the 

group-specific rates on which they are based. 

Statistical significance of changes in the index of 

disparity 

In order to obtain a standard error for the index of 

disparity a type of resampling or ‘‘bootstrap’’ procedure is 

employed (16). This procedure uses the rate and standard 

error for each group to re-estimate each group rate 25,000 

times assuming a random normal distribution. Based on 

these group rates, 25,000 estimates of the index of disparity 

are generated and the distribution of these estimates is used 

to estimate the standard error of the index. 

The bootstrap procedure is used to estimate standard 

errors for the index of disparity at time (1) and time (0) to 

determine whether the index of disparity changed over time. 

A Z test for the difference between the index of disparity at 

time(1) and time(0) can be computed as follows: 

Z = (ID1−ID0)/ √(SE1 2), 2 + SE0 

where, 

ID1 = the index of disparity at time (1) 

ID0 = the index of disparity at time (0) 

SE1 = the standard error of the index of disparity at time 1 

SE0 = the standard error of the index of disparity at time 0. 

Standard errors for the index of disparity in 1998 and in 

2000 were obtained using the bootstrap methodology. The 

difference between the index value in 1998 and in 2000 for 

the infant mortality rate (Objective 16–1c) was tested using 

the formula above. 

Z = (ID1−ID0)/ √(SE1 2) 2 + SE0 

Z = (84.0−80.0) / √(9.22 + 9.32

)


Z = 4.0 / 13.08


|Z| = 0.31


Because the value of the index could increase or decrease, a 

two-tailed test is employed (|Z| ≥ 1.96). Because the absolute 

value of Z is not greater than or equal to the critical value, 

the change in the index of disparity by race and ethnicity for 

infant mortality rates between 1998 and 2000 was not 

statistically significant at the 0.05 level. 

Summary 

There are two overarching goals of the HP2010 

initiative: 1) to increase years and quality of healthy life, and 

2) to eliminate disparities among subgroups of the 

population. Progress toward attainment of these two goals 

can be measured in terms of the specific HP2010 objectives. 

Progress toward target attainment is measured in terms of a 

progress quotient— the percent of the difference between the 

year 2010 target and the baseline that was achieved based on 

the most recent data value. The absolute difference between 

the most recent data value and the target is also measured. 

These measures will be produced for the total population 

and for each population subgroup for the population-based 

objectives. The relative progress made and the absolute 

progress required to reach the target can be compared among 

subgroups of the population. 

Progress toward the elimination of disparity can be 

measured in terms of the percent difference between each 

subgroup rate and the most favorable or ‘‘best’’ subgroup 

rate in each domain. For purposes of measuring disparity, all 

measures are expressed in terms of adverse events. A 

reduction in the percent difference from the ‘‘best’’ group 

rate is interpreted as a reduction in disparity for the other 

group. For domains of three or more groups (race and 

ethnicity, education, or income) disparity for the domain is 

measured using an index of disparity based on an average of 

the percent differences from the ‘‘best’’ group rate. A 

reduction in the index is interpreted as a reduction in 

disparity in group rates relative to the ‘‘best’’ group rate. 

References 

1.	 U.S. Department of Health and Human Services. Healthy 

People 2010. 2nd ed. With understanding and improving health 

and objectives for improving health. 2 vols. Washington: U.S. 

Government Printing Office. 2000. 

2.	 National Center for Health Statistics. Methodological issues in 

measuring health disparities. Series 2. (forthcoming) 

3. DATA2010 is an interactive, on-line database containing the 

baseline and tracking data for the Healthy People 2010: 

Objectives for Improving Health. http://wonder.cdc.gov/data2010. 

4.	 Office of Management and Budget. Revisions to the standards 

for the classification of Federal data on race and ethnicity. 

1997. 

5.	 U.S. Department of Health and Human Services. Tracking 

Healthy People 2010. Washington: U.S. Government Printing 

Office. 2000. 

6.	 U.S. Department of Health and Human Services. Healthy 

People 2000 Midcourse Review and 1995 Revisions. 

Washington: Public Health Service. 1995. 

7.	 National Center for Health Statistics. Healthy People 2000 

Final Review. Hyattsville, Maryland: Public Health Service. 

2001. 

8.	 U. S. Department of Health and Human Services. Strategic 

Research Plan and Budget to Reduce and Ultimately Eliminate 

Health Disparities, Volume I. Draft: October 2003. 

http://www.ncmhd.nih.gov/strategicmock/our_programs/ 

strategic/pubs/VolumeI_031003EDrev.pdf 

15 
9.	 Centers for Disease Control and Prevention. Reported 

Tuberculosis in the United States, 2000. 2001. 

10.	 National Center for Health Statistics. Health, United States, 

2003, With Chartbook on Trends in the Health of Americans. 

Hyattsville, Maryland: 2003. 

11. Klein RJ, Proctor SE, Boudreault MA, Turczyn KM. Healthy 

People 2010 criteria for data suppression. Statistical Notes, no 

24. Hyattsville, Maryland: National Center for Health 

Statistics. 2002. 

12.	 Shah BV, Barnwell BG, Bieler GS. SUDAAN User’s Manual: 

Software for analysis of correlated data, release 7.5. Research 

Triangle Institute, Research Triangle Park, North Carolina, 

1997. 

13.	 Miller RG. Simultaneous Statistical Inference, 2nd edition. 

Springer Series in Statistics, New York, New York: 1981. 

14.	 Keppel KG, Pearcy JN, Wagener DK. Trends in racial and 

ethnic-specific rates for the health status indicators: United 

States, 1990–98. Healthy people statistical notes, no 23, 

Hyattsville, Maryland: National Center for Health Statistics. 

2002. 

15.	 Pearcy JN, Keppel KG. A summary measure of health 

disparity. Public Health Rep (117) 273–80. May–June 2002. 

16.	 Efron B. The jackknife, the bootstrap, and other resampling 

plans. SIAM Pub. Co., Philadelphia, Pennsylvania: 1982. 

U.S. DEPARTMENT OF

HEALTH & HUMAN SERVICES

Centers for Disease Control and Prevention


National Center for Health Statistics


3311 Toledo Road


Hyattsville, Maryland 20782-2003


FIRST CLASS MAIL 

POSTAGE & FEES PAID 

CDC/NCHS 

PERMIT NO. G-284 

OFFICIAL BUSINESS


PENALTY FOR PRIVATE USE, $300


To receive this publication regularly, contact


the National Center for Health Statistics by


calling 1-866-441-NCHS


E-mail: nchsquery@cdc.gov


Internet: www.cdc.gov/nchs


Suggested citation


Keppel KG, Pearcy JN, Klein RJ. Measuring 

progress in healthy people 2010. Statistical Notes, 

no 25. Hyattsville, Maryland: National Center for 

Health Statistics. September 2004. 

DHHS Publication No. (PHS) 2004-1237


04-0271(9/04) 04-0271