Selected Comparisons of

Measures of Health Disparities

A Review Using Databases

Relevant to Healthy People 2010

Cancer-Related Objectives

NCI Cancer Surveillance Monograph Series

Number 7

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U.S. DEPARTMENT

OF HEALTH AND

HUMAN SERVICES

National Institutes

of Health

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Sam Harper John Lynch

Department of Epidemiology, Biostatistics, and Occupational Health

McGill University

1020 Pine Avenue West

Montreal, Quebec H3A 1A2 CANADA

sam.harper@mcgill.ca / john.lynch@mcgill.ca 

Phone: (514) 398-6261

Fax: (514) 398-4503

This report was written under contract from the Surveillance Research Program (SRP) and the Applied Research 

Program (ARP) of the Division of Cancer Control and Population Sciences of the National Cancer Institute, NIH. 

It represents the interest of these organizations in health disparities related to cancer, quantitative assessment 

and monitoring of these disparities and interventions to remove them. NCI Project Officers for this contract are 

Marsha E. Reichman, Ph.D. (SRP), Bryce Reeve, Ph.D. (ARP) and Nancy Breen, Ph.D. (ARP).

This monograph was written as a follow-up to: 

Harper S, Lynch J. Methods for Measuring Cancer Disparities: Using Data Relevant to Healthy People 2010 Cancer-Related Objectives. 

NCI Cancer Surveillance Monograph Series, Number 6. Bethesda, MD: National Cancer Institute, 2005.

NIH Publication No. 05-5777.

Selected Comparisons of Measures of

Health Disparities

A Review Using Databases Relevant to

Healthy People 2010 Cancer-Related Objectives
III

Executive Summary 1

Introduction 7

Methods 9

Measures of Absolute Disparity 9

Measures of Relative Disparity 10

Presentation of Results 12

Results 13

Case Study 1: Racial Disparities In Lung Cancer Incidence, 1990-2001 13

Case Study 2: Area Socioeconomic Disparities in Lung Cancer Incidence, 1988-1999 18

Case Study 3: Area Socioeconomic Disparities In Colorectal Cancer Mortality, 1950-2000 26

Case Study 4: Area Socioeconomic Disparities in Prostate Cancer Mortality, 1950-2000 34

Case Study 5: Socioeconomic Disparities in Smoking, 1965-2003 41

Case Study 6: Race and Ethnic Disparities in Breast Cancer Incidence, 1990-2001 48

Case Study 7: Socioeconomic Disparities in Obesity, 1960-2000 54

Case Study 8: Race and Ethnic Disparities in Cervical Cancer Incidence, 1990-2001 60

Case Study 9: Social Disparities in Mammography Screening, 1987-2003 67

Case Study 10: Geographic Disparities in Stomach Cancer Mortality, 1950-2001 70

Results Summary 77

Socioeconomic Disparity Trends 77

Race and Ethnic Disparity Trends 78

Geographic Disparity Trends 79

Comparing Socioeconomic and Race and Ethnic Disparity Trends 79

Conclusions 83

Appendix: Random Variation 87

References 91

Table of Contents
IV

Figures

Figure S1. Graphical Summary of Selected Disparity Trends 3

Figure S2. Trends in Lung Cancer Incidence among Males by Race and Trends in Overall Absolute and 

 Relative Racial Disparity, 1990-2001 4

Figure 1. Lung Cancer Incidence by Race among Males 45-74, 1990-2001 13

Figure 2. Trends in Racial Disparity in Lung Cancer Incidence among Males 45-74, 1990-2001 16

Figure 3. Lung Cancer Incidence among Females 45-74, 1990-2001 16

Figure 4. Trends in Relative Racial Disparity in Lung Cancer Incidence among Females 45-74, 1990-2001 19

Figure 5. Lung Cancer Incidence among Males 45-74, by Area Socioeconomic Position, 1988-1999 20

Figure 6. Trends in Relative and Absolute Socioeconomic Disparity in Lung Cancer Incidence 

 among Males 45-74, 1988-1999 22

Figure 7. Lung Cancer Incidence among Females 45-74, by Area Socioeconomic Position, 1988-1999 24

Figure 8. Trends in Relative and Absolute Socioeconomic Disparity in Lung Cancer Incidence 

 among Females 45-74, 1988-1999 26

Figure 9. Trends in Colorectal Cancer Mortality among Males 45-74, by Area Socioeconomic Position, 

 1950-2000 27

Figure 10. Trends in Relative Socioeconomic Disparity in Colorectal Cancer among Males 45-74, 

 1950-2000 (3-Year Moving Average) 29

Figure 11. Trends in Colorectal Cancer Mortality among Females 45-74, by Area Socioeconomic Position, 

 1950-2000 30

Figure 12. Trends in Relative Socioeconomic Disparity in Colorectal Cancer among Females 45-74, 

 1950-2000 (3-Year Moving Average) 32

Figure 13. Observed Change in Colorectal Cancer Mortality among Females 45-74, by Area Socioeconomic 

 Position, 1990 and 2000 34

Figure 14. Trends in Prostate Cancer Mortality among Males 45-64, by Quintile of Area Socioeconomic 

 Position, 1950-2000 35

Figure 15. Trends in Area Socioeconomic Disparity in Prostate Cancer Mortality among Males 45-74, 

 1950-2000 38

Figure 16. Trends in Prostate Cancer Mortality by Area Socioeconomic Position among Males 75 

 and Over, 1950-2000 39

Figure 17. Trends in Area Socioeconomic Disparity in Prostate Cancer Mortality among Males 75 

 and Over, 1950-2000 41

Figure 18. Trends in the Prevalence of Smoking by Years of Education among Males, 1965-2003 42

Figure 19. Trends in Educational Disparity in Smoking among Males 25 and Over, 1965-2003 44

Figure 20. Trends in the Prevalence of Smoking by Years of Education among Females, 1965-2003 45
V

Figure 21. Trends in Educational Disparity in Smoking among Females 25 and Over, 1965-2003 47

Figure 22. Trends in Breast Cancer Incidence by Race and Ethnicity among Women 45-74, 

 1990-2001 49

Figure 23. Trends in Race and Ethnic Disparity in Breast Cancer Incidence among Women 45-74, 

 1990-2001 51

Figure 24. Trends in Breast Cancer Incidence by Race and Ethnicity among Women 75 and Over, 

 1990-2001 52

Figure 25. Race and Ethnic Trends in Relative Disparity in Female Breast Cancer Incidence among 

 Those 75 and Over, 1990-2001 54

Figure 26. Trends in Obesity by Years of Education among Males, 1960-2000 55

Figure 27. Trends in Educational Disparity in Obesity among Males, 1960-2000 57

Figure 28. Trends in Obesity by Years of Education among Females, 1960-2000 58

Figure 29. Trends in Educational Disparity in Obesity among Females, 1960-2000 60

Figure 30. Trends in Cervical Cancer Incidence by Race and Ethnicity among Women <45 Years of Age, 

 1990-2001 61

Figure 31. Trends in Relative Race and Ethnic Disparity in Cervical Cancer Incidence among 

 Women <45 Years of Age, 1990-2001 63

Figure 32. Trends in Cervical Cancer Incidence by Race and Ethnicity among Women 45-74, 1990-2001 64

Figure 33. Trends in Relative Race and Ethnic Disparity in Cervical Cancer Incidence among Those 45-74, 

 1990-2001 67

Figure 34. Trends in the Proportion of Women 40 and Over Not Receiving a Mammogram in 

 the Past 2 Years, by Education and Income, 1987-2003 National Health Interview Surveys 68

Figure 35. Trends in the Proportion of Women 40 and Over Not Receiving a Mammogram in 

 the Past 2 Years, by Race and Ethnicity, 1987-2003 National Health Interview Surveys 69

Figure 36. Mortality from Stomach Cancer among Those 60 and Over, US Geographic Areas, 1950-2001 71

Figure 37. Geographic Disparities in Stomach Cancer Mortality among Those 60 and Over, 1950-2001 

 (3-Year Moving Average) 74

Figure 38. Graphical Summary of Disparity Trends 81

Tables

Table 1. Trends in Lung Cancer Incidence and Population Distribution, by Race, among Males 45-74, 

 1990-2001 14

Table 2. Changes in Racial Disparity in Lung Cancer Incidence between 1990 and 2001 among 

 Males 45-74 15
VI

Table 3. Trends in Lung Cancer Incidence and Population Distribution, by Race, among 

 Females 45-74, 1990-2001 17

Table 4. Changes in Racial Disparity in Lung Cancer Incidence between 1990 and 2001 

 among Females 45-74 18

Table 5. Lung Cancer Incidence and Population Distribution among Males 45-74, by Quintile of 

 Socioeconomic Position, 1988-1999 20

Table 6. Changes in Socioeconomic Disparity in Lung Cancer Incidence between 1988 

 and 1999 among Males 45-74 21

Table 7. Changes in Socioeconomic Disparity in Lung Cancer Incidence between 1988 

 and 1999 among Males 45-74 Using Alternative Measures of Disparity 23

Table 8. Lung Cancer Incidence and Population Distribution among Females 45-74, by Quintile of 

 Socioeconomic Position, 1988-1999 24

Table 9. Changes in Socioeconomic Disparity in Lung Cancer Incidence between 1988 

 and 1999 among Females 45-74 25

Table 10. Rates of Colorectal Cancer Mortality and Population Share by Area Socioeconomic Quintile 

 among Males 45-74, 1950-2000 28

Table 11. Measures of Disparity in Each Decade and Percent Change in Disparity by Decade among 

 Males 45-74, 1950-2000 28

Table 12. Rates of Colorectal Cancer Mortality and Population Share by Area Socioeconomic Quintile 

 among Females 45-74, 1950-2000 31

Table 13. Measures of Disparity in Each Decade and Percent Change in Disparity by Decade among 

 Females 45-74, 1950-2000 31

Table 14. Changes in Socioeconomic Disparity in Colorectal Cancer Incidence between 1990 

 and 2000 among Females 45-74 Using Alternative Measures of Disparity 33

Table 15. Changes in Socioeconomic Disparity in Colorectal Cancer Incidence between 1990 

 and 2000 among Females 45-74 for Different Orderings of Socioeconomic Groups 34

Table 16. Rates of Prostate Cancer Mortality and Population Share by Area Socioeconomic Quintile 

 among Males 45-74, 1950-2000 36

Table 17. Measures of Socioeconomic Disparity in Prostate Cancer Mortality in Each Decade and Percent 

 Change in Disparity by Decade among Males 45-74, 1950-2000 37

Table 18. Rates of Prostate Cancer Mortality and Population Share by Area Socioeconomic Quintile 

 among Males 75 and Over, 1950-2000 39

Table 19. Measures of Disparity in Each Decade and Percent Change in Disparity by Decade among 

 Males 75 and Over, 1950-2000 40

Table 20. Prevalence of Current Smoking and Population Distribution among Males 25 and Over, 

 by Years of Education, NHIS Selected Years 1965-2003 42
VII

Table 21. Measures of Educational Disparity in Current Smoking in Selected Years and Percent 

 Change in Disparity by Year among Males 25 and Over, 1965-2003 43

Table 22. Prevalence of Current Smoking and Population Distribution among Females Age 25 and Over, 

 by Years of Education, NHIS Selected Years 1965-2003 45

Table 23. Measures of Educational Disparity in Current Smoking in Selected Years and Percent 

 Change in Disparity by Year among Females 25 and Over, 1965-2003 46

Table 24. Changes in Educational Disparity between 1965 to 2003 in Current Smoking Holding Constant 

 the Population Distribution Equal to That Observed in 1965 48

Table 25. Incidence of Female Breast Cancer and Population Distribution among Females 45-74, 

 by Race and Ethnicity, 1990-2001 49

Table 26. Changes in Race and Ethnic Disparity in Female Breast Cancer Incidence among 

 Females 45-74, 1990-2001 50

Table 27. Incidence of Female Breast Cancer and Population Distribution among Females 75 and Over, 

 by Race and Ethnicity, 1990-2001 52

Table 28. Changes in Race and Ethnic Disparity in Female Breast Cancer Incidence among 

 Females 75 and Over, 1990-2001 53

Table 29. Prevalence of Obesity and Population Distribution by Education among Males, 1960-2000 56

Table 30. Changes in Educational Disparity in Obesity among Males, 1960-2000 56

Table 31. Prevalence of Obesity and Population Distribution by Education among Females, 1960-2000 58

Table 32. Changes in Educational Disparity in Obesity among Females, 1960-2000 59

Table 33. Incidence of Cervical Cancer and Population Distribution among Females <45 Years of Age, 

 by Race and Ethnicity, 1990-2001 61

Table 34. Changes in Race and Ethnic Disparity in Cervical Cancer Incidence among 

 Females <45 Years of Age, 1990-2001 62

Table 35. Incidence of Cervical Cancer and Population Distribution among Females 45-74, 

 by Race and Ethnicity, 1990-2001 65

Table 36. Changes in Race and Ethnic Disparity in Cervical Cancer Incidence among Females 45-74, 

 1990-2001 66

Table 37. Changes in Education, Income, and Race and Ethnic Disparity in the Proportion of Women 

 40 and Over Not Receiving a Mammogram in the Past 2 Years, 1987 and 2003 NHIS 70

Table 38. Changes in Geographic Disparity in Stomach Cancer Mortality by Different Levels of 

 Geographic Aggregation, 1950-2000 72

Table 39. Ratio of Level of Disparity in Stomach Cancer Mortality Calculated Across US States Relative 

 to Disparity Calculated Across US Regions, 1950-2000 73
1

 The purpose of this report is to empirically evaluate 

the performance and suitability of various measures 

of health disparity for the purpose of monitoring 

disparities in cancer-related health outcomes. As such, 

it extends the work of a prior monograph in which 

we evaluated several measures of health disparity on 

theoretical grounds (1), and it is worthwhile to briefly 

revisit the overall conclusions of that report. 

Overall Conclusions from the Theoretical 

Review (1)

 First, we concluded that all measures of health 

disparity implicitly or explicitly contain value 

judgments concerning the relative importance of 

capturing different aspects of health disparity. Two of 

the most important considerations concern -

1) How much weight to give to individuals? For 

example, if we measure the disparity in prostate 

cancer mortality among U.S. states in 2000 without 

weighting states by their population size, California 

and Wyoming receive equal weight despite the fact 

that California has nearly 70 times as many males 

as Wyoming. Thus, in an unweighted analysis of 

U.S. states individual males in California receive 

approximately 1/70th the weight of males in Wyoming. 

Both are correct but they reflect contrasting values 

about how to treat groups and individuals in 

measuring health disparity.

2) How much to weight the health of individuals of 

different social groups? Should our measures of health 

disparity be more sensitive to health improvement 

among the socially disadvantaged than the 

advantaged?

 It would be advantageous if such value judgments 

were made more explicit by researchers when 

measuring health disparities. 

 Second, for the purpose of measuring and 

monitoring trends in health disparities we argued 

for a population health-oriented approach, which 

is characterized by measuring health disparities as 

differences from the population average, taking 

account of the population size of the social groups 

under consideration, and measuring disparities on 

both the absolute and relative scale. Some measures 

of health disparity use the “best” rate or prevalence 

as their reference point. This may be problematic in 

some circumstances in cancer-related disparities when 

the best rate is among a very small, or heterogeneous 

population sub-group, such as American Indians and 

Alaska Natives.

The Empirical Assessment

 Despite these conclusions from the theoretical 

review, it remains an empirical question whether, 

given a particular set of data, the particular method 

for measuring health disparity makes any substantive 

difference or would lead to different conclusions about 

the disparity. 

 This report presents the results of 22 separate 

analyses in 10 case studies of trends in selected 

cancer-related health disparities, for which we 

empirically compared various summary measures 

of health disparities. We included assessments of 

socioeconomic, race / ethnic and geographic disparities 

in a selected range of cancer-related outcomes, 

including mortality, incidence, risk factors and 

Executive Summary
2

2. How often does the choice of disparity measure 

matter? 

Of the 22 separate analyses conducted, 9 (41%) 

revealed situations where the overall conclusion about 

the trend in disparity was difficult to make without 

some apriori judgment about what dimensions of 

disparity are important (e.g., relative or absolute 

disparity, whether or not to weight social groups by 

population size, etc.)

3. Why does the choice of disparity measure matter? 

Most of the cases of disagreement between measures 

of disparity depended on two issues. One is the scale 

on which disparity should be evaluated. In many cases 

relative measures of disparity moved in one direction, 

while absolute measures moved in the opposite 

direction. For example, the left side of the Figure 

below shows trends in lung cancer incidence among 

males for 4 race groups (Whites, Blacks, American 

Indian/Alaska Natives, and Asian Pacific Islanders) and 

the right side shows the percentage change since 1990 

in two summary measures of absolute and relative 

disparity. Over this period, absolute disparity declined 

by roughly 40% while relative disparity increased by 

roughly 40%. Whether, given this data, one concludes 

that the situation with respect to racial disparity in 

lung cancer incidence among males is getting better or 

worse depends on whether one thinks of disparity as 

absolute or relative. Thus, specifying whether absolute 

or relative disparities are more important prior to 

undertaking any analyses will assist in minimizing 

disagreement about disparity trends.

 The second source of disagreement among disparity 

measures was whether they weight social groups 

by population size. In several cases we found that 

population-weighted disparity measures differed 

in either magnitude or direction from unweighted 

disparity measures. In particular, and as might be 

expected, unweighted measures of disparity appear to 

screening. The goal of these analyses was to examine 

the consistency of different measures of health 

disparity across a range of cancer-related outcomes. 

 Summaries of selected results are shown in Figure 

S2. The numbers in the table represent % changes in 

the value of the disparity measure over the specified 

period. Dark red means disparity has increased by 

more than 30%, light red indicates disparity increased 

between 10-29%, yellow means a change (increase 

or decrease) of less than 10%, light green indicates 

declines in disparity of 10-29%, and dark green means 

that disparity has declined by more than 30%. Overall, 

these graphical examples reinforce the conclusion that 

how you measure disparity matters. For instance, for 

race / ethnic disparity in mammography screening, 

no firm conclusion can be reached about whether 

disparity got better or worse between 1987 and 

2003 - there are both red and green cells indicating 

increases and decreases depending on which measure 

is used. The only sensible way to make a conclusion 

on race / ethnic disparity trends in mammography 

screening is to decide whether disparity should be 

measured on a relative or the absolute scale. This is 

not the case for obesity where all cells are dark green 

indicating declining socioeconomic disparities or for 

smoking, where one can reasonably conclude that 

socioeconomic disparity increased regardless of which 

measure is used.

To summarize the results of these analyses:

1. Does the choice of a measure of disparity matter 

for assessing cancer-related disparity trends? 

Yes. The 10 case studies revealed a number of 

situations where substantively different interpretations 

concerning the level and trend in disparity resulted 

from using different measures of health disparity on 

the exact same data. Such differences in interpretation 

could not be reconciled without reference to 

consideration of which underlying dimensions of 

disparity are emphasized in the measures.
3

be more sensitive to the movement of rates of disease, 

especially those of smaller population groups whose 

rates of disease may be less stable over time.

4. What are the implications for monitoring health 

disparities?

There is currently a strong emphasis in the US public 

health policymaking community on monitoring of 

progress toward eliminating health disparities. The 

results of the case studies presented in this report 

demonstrate that it is easily possible to come to 

fundamentally different conclusions about the extent 

of progress toward eliminating health disparities 

using the same data but different measures of health 

disparity. The naïve use of summary measures of 

health disparity thus has the potential to lead to 

confusion among both policymakers and researchers 

as to whether disparities are increasing or decreasing, 

which cancer-related outcomes show the largest 

disparities, and which health disparities might 

be specifically targeted for increased study. Such 

confusion will be minimized and health disparity 

measurement will be advanced by increased debate 

and discussion of the issues that generate differences 

among measures of health disparity:

• How much weight should we give individuals of 

different social groups when measuring disparity? 

Counting each individual’s health equally implies 

population-weighted measures of disparity among 

social groups. Counting each social group’s health 

the same means using unweighted disparity 

measures (and implies differential weighting of 

individuals from social groups with different 

population sizes).

Figure S1. Graphical Summary of Selected Disparity Trends

Socioeconomic Disparity in

Colorectal Cancer Mortality 1950-2000

Female

Male

Socioeconomic Disparity in

Smoking 1965-2003

Female

Male

Socioeconomic Disparity in Obesity

1960-2000

Female

Male

Mammography Screening 1987-2003

Education Disparity

Income Disparity

Race / ethnic Disparity

Disparity is clearly numerically smaller among 

both males and females, but the RCI and ACI 

indicate an increase in disparity is because 

the socioeconomic gradient reversed.

Large increases in disparity with reversal of

socioeconomic gradient

Large increases in disparity

Large decreases in disparity

Large decreases in disparity

Depends on value position on absolute vs.

relative disparity

Depends on value position on absolute vs.

relative disparity

Depends on value position on absolute vs.

relative disparity

Relative Disparity*

RR IDisp RCI

Absolute Disparity**

RD ACI BGV

Conclusion and Interpretation

Legend

≥30% 11% to 29% 0 to (–)11% (–)11% to(–)29% ≤(–)30%

* Relative Disparity. RR=Rate Ratio; IDisp=Index of Disparity; RCI=Relative Concentration Index

** Absolute Disparity. RD=Rate Difference; ACI=Absolute Concentration Index; BGV=Between Group Variance 
4

relative disparities, while rare conditions can 

generate exceedingly high relative disparities. 

Which of these perspectives is the appropriate scale 

on which to measure disparity trends?

In sum, our recommendations from the previous 

report (1), further clarified here, suggest giving 

priority to disparity measures on the absolute scale 

that weight for population size and, where possible, 

consider the direction of the social gradient in health. 

That recommendation stands but it does not exclude 

consideration of issues of relative disparity or what 

is happening among smaller population groups. For 

those reasons it may always be useful to adopt a “suite” 

of health disparity indicators that make clear which 

aspects of health disparity are changing over time. 

• How much to weight different parts of the health 

distribution? At any given time some social groups 

are on-average healthier than others. Over time 

health changes, and some measures of disparity 

will give equal consideration (i.e., equal weight) 

to a given health change, regardless of in which 

group that change occurs; other measures are more 

sensitive (i.e., give more weight) to changes in 

health among the least healthy or among the poor. 

Which of these perspectives is consistent with our 

concerns about social disparities in health?

• Should we be more concerned about absolute or 

relative disparities? Diseases and conditions that 

exact a large burden on the population, because 

of their high prevalence, often generate smaller 

Figure S2. Trends in lung cancer incidence among males by race and trends in overall absolute and 

relative racial disparity, 1990-2001

Figure S2. Trends in Lung Cancer Incidence among Males by Race and Trends in Overall Absolute and

Relative Racial Disparity, 1990-2001

1990

300

200

100

0

White

1995 2000

Lung Cancer Incidence Trends

Black AI/AN API

1990

140

120

100

80

60

1995 2000

Absolute and Relative Disparity

Between-group variance

(absolute disparity)

Mean log deviation

(relative disparity)

100 = value of index in 1990

Mortality rate per 100k

Relative change
7

Introduction

 There are currently two overarching public 

health goals for the United States, as laid out in the 

Department of Health and Human Services blueprint, 

Healthy People 2010: to increase the span of healthy 

life and to eliminate health disparities across the 

categories of gender, race or ethnicity, education or 

income, disability, geographic location, and sexual 

orientation (2). This report is concerned with the 

practical implementation of the second goal of 

eliminating health disparities. Despite broad consensus 

on the public health importance of social disparities 

in health there is as yet no consensus on how to 

measure and monitor progress toward the goal of 

eliminating health disparities. The lack of consensus 

could potentially make it difficult to communicate 

to policymakers the extent of cancer-related health 

disparities and hinder the ability of public health 

organizations to monitor progress toward the Healthy 

People 2010 cancer objectives. Thus, there is a 

need for a greater understanding of the benefits and 

drawbacks of various strategies for measuring health 

disparities.

 There are a number of ways to conceptualize and 

measure health disparities, and a previous report 

systematically reviewed several potential disparity 

measures on theoretical grounds (1). The purpose 

of this report is to complement that review by 

empirically evaluating several potential measures of 

health disparity for the purposes of measuring progress 

toward reducing social disparities in cancer-related 

health outcomes. It should be emphasized that it is not

the purpose of this report to provide a comprehensive 

assessment across all cancer-related outcomes or 

health disparity measures. Nor is it the goal of this 

report to make substantive conclusions about trends 

in disparities for the selected cancer-related health 

outcomes. The examples contained herein were chosen 

to reflect a variety of types of cancer-related data 

such as incidence, mortality, and health behaviors, 

and do not reflect cancer-related disparities thought 

to be of particular etiologic or policy interest. Thus, 

interpretation of the case studies reported here is 

limited to comparing the performance of the selected 

measures of health disparity.
9

Methods

 The previous review of measures of health disparity 

gave two broad recommendations for the purpose 

of monitoring health disparity trends. First, any 

assessment of health disparity trends should include 

both an absolute and a relative measure of health 

disparity. Second, the review generally recommended 

the use of population-weighted measures of health 

disparity to account for changes in the distribution of 

the population that inevitably occur over time. Here, 

we provide a brief recapitulation of the measures of 

health inequality used in this report. 

Measures of Absolute Disparity 

Rate Difference (RD) 

 The absolute disparity between two health status 

indicators is the simple arithmetic difference. It is 

calculated as: 

[1]

where r

1 and r2 are indicators of health status in two 

social groups. In this case r

2 serves as the reference 

population and the RD is expressed in the same 

units as r

1 and r2. A typical disparity measure that 

uses the absolute difference between two rates for an 

entire population is the range, in which case r

1 above 

corresponds to the least healthy group and r

2 the 

healthiest group. In the context of measuring health 

disparities the RD is often used to compare the health 

of less-advantaged social groups to more-advantaged. 

However, in this we use RD as a summary measure 

of the gap between the best rate and worst rate for a 

given outcome (i.e., the absolute range), regardless of 

which two social groups are being compared.

Between-Group Variance (BGV)

 The variance is a commonly used statistic that 

summarizes all squared deviations from a population 

average. In the case of grouped data this is the 

Between-Group Variance (BGV), and it is simply 

calculated according to the following formula that 

squares the differences in group rates from the 

population average and weights by their population 

sizes: 

[2]

where pj is group j’s population size, yj is group j’s 

average health status, and μ is the average health status 

of the population. One way to interpret the BGV is 

as the variance that would exist in the population if 

each individual had the mean health of their social 

group (i.e., no within-social group variation) (3). The 

Between-Group Variance may be a useful indicator of 

absolute disparity for unordered group data because 

it weights by population group size and is sensitive 

to the magnitude of larger deviations from the 

population average (4).

Absolute Concentration Index (ACI)

 The Absolute Concentration Index (ACI) measures 

the extent to which health or illness is concentrated 

among particular social groups on the absolute scale. 

It may only be used with social groups that have a 

natural ordering, such as income or education groups. 

It is a measure of the covariance between social rank 

and health, and is derived by plotting the cumulative 

share of the population, ranked by social status, 

against the cumulative amount of ill health (i.e., the 

cumulative contribution of each subgroup to the mean 

RD = r1 – r2

BGV = pj (yj – μ)2, j=1

J

Σ
10

weights equal to the population share pj of group j

(6). The coefficient b 1 is the SII, which is interpreted 

as the absolute difference in health status between the 

bottom and top of the social group distribution.

Measures of Relative Disparity

Rate Ratio (RR)

 The RR is virtually identical to the RD described 

above, but is calculated by dividing r`1 by r2 rather 

than subtracting: 

[5]

where, again, r

2 is the reference population. While 

in the context of social group comparisons the RR is 

typically based on comparing, for example, the least 

advantaged group (e.g., the lowest socioeconomic 

group) to the highest group, in the context of 

comparing it to summary measures of health disparity 

we calculate it as one would a range measure. That is, 

at each time point it measures the relative difference in 

the rates of the best and worst group (i.e., the relative 

range), regardless of their social group status.

Index of Disparity (IDisp)

 The Index of Disparity summarizes the difference 

between several group rates and a reference rate, and 

expresses the summed differences as a proportion 

of the reference rate. This measure was formally 

introduced by Pearcy and Keppel (7) and is

calculated as: 

[6]

where rj indicates the measure of health status in the 

jth group, rref is the health status indicator in the 

reference population, and J is the number of groups 

compared. While in principle, any reference group 

may be chosen, the authors recommend the best 

level of health in the population). The absolute version 

of the concentration index is calculated by multiplying 

the relative concentration index (RCI) – described 

below - by the mean rate of the health variable: 

[3]

where RCI is the Relative Concentration Index 

defined below and μ is the mean level of health in the 

population.

Slope Index of Inequality (SII)

 Formally the SII, which was introduced by Preston, 

Haines and Pamuk (5) may be obtained via regression 

of the mean health variable on the mean relative rank 

variable. To calculate relative rank the social groups are 

first ordered from lowest to highest. The population 

of each social group category covers a range in the 

cumulative distribution of the population, and so is 

given a score based on the midpoint of their range in 

the cumulative distribution in the population. The 

regression equation is specified as follows: 

where j indexes social group, y is the average health 

status and Rj the average relative ranking 

[4] 

of social group j in the cumulative distribution of 

the population, b 0 is the estimated health status of 

a hypothetical person at the bottom of the social 

group hierarchy (i.e., a person whose relative rank 

Rj in the social group distribution is zero), and b 1 is 

the difference in average health status between the 

hypothetical person at the bottom of the social group 

distribution and the hypothetical person at the top 

(i.e. Rj=0 vs. Rj=1). Because the relative rank variable is 

based on the cumulative proportions of the population 

(from 0 to 1), a “one-unit” change in relative rank is 

equivalent to moving from the bottom to the top of 

the social group distribution. Because this regression 

is run on grouped data (as opposed to individual data) 

it is estimated via weighted least squares, with the 

ACI = μRCI,

yj = β 0 + β 1Rj

– –

RR = r1 / r2

IDisp = rj – rref /J /rref x 100,

j=1

J–1

Σ
11

group rate as the comparison since that represents the 

rate desirable for all groups to achieve. In this case it 

is not necessary to take the absolute value of the rate 

differences since they will all be positive.

Relative Concentration Index (RCI)

 The Relative Concentration Index (RCI) measures 

the extent to which health or illness is concentrated 

among particular social groups. The RCI may only be 

used with social groups that have an inherent ranking, 

such as income or education groups. The general 

formula for the RCI for grouped data is given by 

Kakwani and colleagues (8) as: 

[7]

where pj is the group’s population share, μj is the 

group’s mean health, and Rj is the relative rank of the 

jth socioeconomic group, which is defined as: 

 [8]

where py is the cumulative share of the population 

up to and including group j and pj is the share of the 

population in group j. Rj essentially indicates the 

cumulative share of the population up to the midpoint 

of each group interval, similar to the categorization 

used for the Slope Index of Inequality above. In fact, 

the RCI has a specific mathematical relationship with 

the SII (6), such that, 

[9]

where b is the slope parameter identified in the 

equation for the SII above. One of the reasons the RCI 

(and, by extension, the SII) is favored by some is that it 

“reflects the socioeconomic dimension to inequalities 

in health” (6, p.548). That is, a downward health 

gradient (such that health worsens with social group 

rank) results in a positive RCI, whereas an upward 

health gradient results in a negative RCI.

Relative Index of Inequality

 The SII discussed above is a measure of absolute 

disparity. However, dividing this estimated slope by 

the mean population health gives a relative disparity 

measure, the Relative Index of Inequality or RII (9):

[10]

where μ is mean population health and the SII is the 

estimate of b 1 from the regression that generates the 

SII. Its interpretation is similar to the SII, but it now 

measures the proportionate (in regard to the average 

population level) rather than absolute increase or 

decrease in health between the highest and lowest 

socioeconomic group.

Theil Index (T) and Mean Log Deviation (MLD)

 The Theil Index and Mean Log Deviation are 

measures of general disproportionality, developed 

by the economist Henri Theil (10). They are both 

summaries of the difference between the natural 

logarithm of shares of health and shares of population. 

They may be written (11) as follows: 

[11]

where pj is the proportion of the population in group 

j and r

j

 is the ratio of the prevalence or rate of health 

in group j relative to the total rate, i.e., r

j

 = yj / μ

where yj is the prevalence of the outcomes in group 

j and μ is the total prevalence. Both measures are 

population-weighted, are more sensitive to health 

differences further from the average rate (by the use 

of the logarithm), and may be used for both ordered 

social groups (education) and unordered groups 

(gender, race).

Rj = p – 2 P j=1 j

J

Σ –

1

RCI = μ pj μj Rj –1 j=1

J – Σ2

RCI = 2 var(x)(β / μ)

RII = SII / μ = β 1 / μ

T = pj rj 1nr j=1 j

J

Σ

MLD = pj – 1nr j=1 j 

J

Σ
12

some of the differences among the measures, such 

as population weighting or which reference group 

is used for comparison, and determine whether 

such factors account for the observed difference in 

disparity change. 

Random Variation

 In the context of evaluating changes in health 

disparities over time it is often of interest to know the 

extent to which a given change in disparity may be 

due to random chance. This is an important issue for 

any substantive analysis of change in health disparity, 

but the focus of this report is not on statistical 

inference about changes in health disparities, nor is it 

our intention to draw substantive conclusions about 

any particular health disparity. For this reason we do 

not typically include estimates of precision for the 

various measures of disparity used in this report. Our 

primary interest is in simply comparing the magnitude 

and direction of estimated trends and changes in 

disparities. Since the various measures of disparity for a 

given case study all use the same underlying data, the 

precision of the underlying estimates will affect all the 

disparity measures and is less relevant for comparing 

of the magnitude and direction of change in disparity. 

However, as an example for Case Study 1 we include 

estimates of precision and hypothesis tests for the 

change in disparity. However, while this report does 

not focus on statistical testing it should be noted that 

methods to calculate indicators of precision (e.g., 95% 

confidence interval) for most of the measures reviewed 

here may be found in the source publications detailed 

in the references. A very brief description of the 

general methods for calculating standard errors for the 

various measures of disparity used in this analysis are 

presented in the Appendix. 

Presentation of Results

 What follows are several case studies that use data 

relevant to the Healthy People 2010 cancer-related 

goals. For each example there is a brief description of 

the data and the measures of health disparity used in 

the example. In presenting each analysis we generally 

follow the series of steps for analyzing health disparity 

trends outlined in the previous review of measures of 

health disparity (1). 

• First, the underlying data are presented in 

graphical and tabular form to give an overall sense 

of the sub-group trends. 

• Second, we estimate the change in health disparity 

for selected time points using the disparity 

measures listed above. As the measures of disparity 

are often measured on different scales, when 

comparing the magnitude of change in disparity 

we focus primarily on the relative or percent 

change in disparity. As many of the disparity 

measures used here have been used relatively 

infrequently in the literature, it is difficult to know 

how meaningful the relative changes in these 

indicators are. Nevertheless, we generally compare 

the relative changes in the measures (i.e., % 

change) to assess their agreement. 

• Third, we present graphs of the trend in disparity 

to compare selected disparity measures over time 

(e.g., Index of Disparity vs. Mean Log Deviation for 

measuring relative disparity). 

• Finally, for selected case studies where there is 

disagreement between either the magnitude or 

the direction of the change in disparity over time, 

we present some diagnostic simulations to help 

understand the nature of the disagreement among 

the measures. In doing so we attempt to minimize 
13

Figure 1. Lung Cancer Incidence by Race among Males 45-74, 1990-2001

1990

350

300

250

200

150

100

50

0

White

1995 2000

Black AI/AN API

Rate per 100,00

0

1991 1992 1993 1994 1996 1997 1998 1999 2001

Results

Case Study 1: Racial Disparities In Lung 

Cancer Incidence, 1990-2001.

 The data source for this analysis come from the 

SEER database called: Incidence - SEER 18 Regs, Nov 

2003 Sub for Expanded Races (1990-2001 varying). 

Individuals for whom race was coded as “Unknown” 

are excluded from this analysis, and Hispanics are not 

identified in this database. The analysis is stratified 

by gender and restricted to ages 45-74. Rates are 

not age-adjusted so as to reflect the existing absolute 

burden of lung cancer.

Males

 Rates of lung cancer incidence by race / ethnicity 

for males 45-74 years of age are shown graphically 

in Figure 1, and the underlying raw data on rates 

and population proportions are shown in Table 1. 

Generally speaking, lung cancer rates are declining for 

all race / ethnic groups, and the relative magnitude of 

the decline is fairly similar for all groups. 
14

Rate per 100,000 Percent of Total Population

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

∆1990 to 2001

%∆

A/PI

130.4

129.2

141.0

126.4

115.8

115.5

116.2

115.6

108.6

99.6

104.4

86.7

-43.6

-33.5%

AI/AN

71.7

62.8

57.9

82.0

87.7

81.5

60.1

57.7

54.4

36.3

42.9

44.0

-27.7

-38.6%

Black

317.1

325.7

317.9

297.6

277.1

292.6

274.3

277.1

260.7

242.2

228.8

215.1

-101.9

-32.1%

White

228.4

219.6

214.5

202.2

196.0

203.7

193.9

188.3

182.7

173.9

166.5

156.9

-71.5

-31.3%

A/PI

0.066

0.068

0.070

0.073

0.076

0.070

0.073

0.075

0.077

0.079

0.081

0.082

0.017

25.3%

AI/AN

0.010

0.010

0.011

0.011

0.011

0.011

0.011

0.011

0.012

0.012

0.012

0.013

0.003

27.8%

Black

0.073

0.074

0.074

0.074

0.075

0.084

0.085

0.086

0.087

0.088

0.088

0.089

0.015

21.2%

White

0.851

0.848

0.845

0.842

0.838

0.835

0.831

0.828

0.825

0.822

0.819

0.816

-0.035

-4.1%

Table 1. Trends in Lung Cancer Incidence and Population Distribution, by Race, among Males 

45-74, 1990-2001

which is used to estimate the standard error. Based on 

the SEboot, Z-statistics were calculated for the change in 

disparity using the general formula: 

 ( )/ ( ), 2

1990

2

2001 1990 2001 Z = D − D SE + SE 

where D indicates the disparity measure and (|Z|>1.96) 

indicates statistical significance at the =0.05 level (12). 

By this measure the approximately 30% increases 

in relative disparity measured by T and MLD are 

statistically significant, while the 10-15% increase 

measured by the RR and IDisp are not statistically 

significant. In terms of absolute disparity, the RD 

decreases by 30% and the BGV decreases by 40%. Both 

of these declines are statistically significant. The trend 

in relative race / ethnic disparity among males, as 

measured by the IDisp and the MLD is shown in

 The change in race / ethnic disparity among males 

is presented in Table 2. Focusing on the shaded line 

at the bottom of the table, the measures of relative 

and absolute disparity seem to be moving in different 

directions (decreasing for absolute and increasing 

for relative). For males, all of the relative measures 

of disparity registered an increase since 1990, but T 

and MLD appear to show a relatively larger increase 

(about twice as large). Table 2 also includes measures 

of precision for each measure of disparity. The first row 

marked ‘SE’ contains standard measures of precision 

for the total rate, the RR and the RD (see the Appendix 

for formulas). The next row marked ‘SEboot’ contains 

standard errors of the each disparity measures based 

on 5000 replications of the underlying rates, assuming 

a random normal distribution (12). This leads to 5000 

estimates of each disparity measure, the distribution of 
15

Raw Data Measures of Relative Disparity Measures of Absolute Disparity

Race

1990

AI/AN

A/PI

Black

White

Total

SEtrad

SEboot

2001

AI/AN

A/PI

Black

White

Total

SEtrad

SEboot

∆1990 to 2001

SE of ∆

 Z-statistic

%∆

Rate

71.7

130.4

317.1

228.4

226.9

1.8

1.9

44.0

86.7

215.1

156.9

154.8

1.2

1.2

-72.1

2.2

-32.4

-31.8%

SE

10.4

5.4

8.0

2.0

1.8

5.9

3.3

1.4

4.9

1.2

% Pop

1.0

6.6

7.3

85.1

1.2

8.1

8.8

81.9

RR*

1.0

1.8

4.4

3.2

4.4

0.7

0.7

1.0

2.0

4.9

3.6

4.9

0.7

0.7

0.5

1.0

0.5

10.6%

IDisp

0.0

19.5

81.8

52.2

214.0

49.2

0

14.2

57

37.6

247.4

50.2

33.4

70.3

0.5

15.6%

T

-3.6

-20.9

34.2

5.6

15.4

1.3

-4.6

-26.7

40.5

10.8

20.1

1.5

4.7

2.0

2.3

30.8%

MLD

11.5

36.3

-24.5

-5.6

17.7

1.7

16

47.6

-29.2

-10.6

23.8

2.0

6.1

2.7

2.3

34.5%

RD*

0

58.6

245.3

156.7

245.3

13.1

13.0

0

42.7

171.1

112.9

171.1

6.0

7.7

-74.2

15.1

-4.9

-30.2%

BGV

239.9

611.1

595

1.9

1447.9

132.0

156.4

381.1

322.4

3.4

863.2

66.2

-584.6

147.7

-4.0

-40.4%

*For the RR and RD the row marked ‘Total’ contains the maximum of RR or RD, a measure of the range

Abbreviations: RR=Rate Ratio; IDisp=Index of Disparity; T=Theil Index; MLD=Mean Log Deviation; RD=Rate Difference;

BGV=Between Group Variance; SEtrad=traditional standard error; SEboot=bootstrap standard error

Table 2. Changes in Racial Disparity in Lung Cancer Incidence between 1990 and 2001 among 

Males 45-74

Females

 Rates of lung cancer incidence among females are 

shown in Figure 3, and the corresponding annual 

rates and population shares are shown in Table 3. The 

incidence of lung cancer has declined among all race / 

ethnic groups, but the absolute and relative decline has 

been larger among blacks and whites than for the A/PI 

and AI/AN groups.

Figure 2. The two measures of relative disparity 

generally give the same picture of the overall trend in 

racial disparity in lung cancer incidence among males. 

From 1991 to 1992 (shown in the box) the IDisp 

showed an increase in disparity but the MLD showed 

a decrease; the opposite was true from 2000 to 2001. 

This latter change seems likely due to the sharp decline 

of the rate among the A/PI group for 2001, which 

moved closer toward the referent group for the IDisp 

(the best rate), but away from the referent group for 

the MLD (the population average).
16

Figure 2. Trends in Racial Disparity in Lung Cancer Incidence among Males 45-74, 1990-2001

Figure 3. Lung Cancer Incidence among Females 45-74, 1990-2001

1990

400

350

300

250

200

150

100

50

0

1995 2000

Index of Disparity

1991 1992 1993 1994 1996 1997 1998 1999 2001

30

25

20

15

10

5

0

Mean Log Deviation

MLD

IDisp

1990

160

140

120

100

80

60

40

20

0

White

1995 2000

Black AI/AN API

Rate per 100,000

1991 1992 1993 1994 1996 1997 1998 1999 2001
17

Rate per 100,000 Percent of Total Population

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

∆1900 to 2001

%∆

A/PI

57.3

52.7

56.6

52.3

52.0

54.6

51.4

49.9

51.3

51.7

50.0

52.5

-4.9

-8.5%

AI/AN

38.0

43.9

46.2

39.5

39.8

37.7

36.8

31.3

24.5

22.4

29.3

32.9

-5.1

-13.5%

Black

141.7

141.0

134.8

135.2

135.8

125.0

127.0

121.4

126.5

123.8

114.1

111.4

-30.3

-21.4%

White

143.4

143.4

141.3

137.0

135.0

139.4

136.4

135.2

131.5

128.7

124.5

117.8

-25.7

-17.9%

A/PI

0.069

0.072

0.075

0.078

0.081

0.075

0.078

0.080

0.083

0.085

0.087

0.089

0.020

29.2%

AI/AN

0.010

0.011

0.011

0.011

0.012

0.011

0.011

0.011

0.012

0.012

0.013

0.013

0.003

26.3%

Black

0.080

0.081

0.081

0.082

0.083

0.094

0.095

0.096

0.097

0.098

0.098

0.099

0.019

23.9%

White

0.841

0.837

0.834

0.829

0.825

0.821

0.816

0.812

0.809

0.805

0.802

0.799

-0.042

-5.0%

Table 3. Trends in Lung Cancer Incidence and Population Distribution, by Race, among Females 

45-74, 1990-2001

larger disparity among females (MLD2001=23.8 for 

males vs. 27.8 for females). This is likely due to the 

fact the RR and IDisp are unweighted indexes and 

use the “best rate” as the reference group, while the 

T and MLD are weighted by population size and use 

the population average as the reference point. Thus, 

despite the fact that all racial groups are relatively 

closer to the best rate among females, incidence in the 

largest population group (whites) is 7% (117.8/110.2) 

higher than the population average in females, 

compared to only 1% higher (156.9/154.8) in males.

 The trend in relative racial disparity among 

females in presented in Figure 4. The trend in relative 

racial disparity generally follows the same trajectory 

whether measured with the Index of Disparity or the 

Mean Log Deviation, despite the former showing 

a modest decline and the latter a modest increase 

in disparity from 1990 to 2001. From 1990 to 1991 

 The overall change in racial disparity among 

females is presented in Table 4. Similar to males, 

absolute racial disparity in lung cancer incidence 

declined, with statistically significant declines of 

20% and 27% in the RD and BGV, respectively. For 

relative disparity, the Rate Ratio and Index of Disparity 

indicate declines in disparity whereas T and MLD 

indicate disparity has increased, but none of these 

changes are statistically significant. Generally speaking 

their was little change in the magnitude of relative 

racial disparity among females, less than 10% change 

for any of the relative measures. In comparing the 

extent of racial disparity in lung cancer across gender 

groups (Table 2 for males, Table 4 for females), the 

Rate Ratio and Index of Disparity indicate that the 

racial disparity in lung cancer incidence is slightly 

larger among males (IDisp2001=247.4) compared to 

females (IDisp2001=185.5), but T and MLD indicate 
18

Raw Data Measures of Relative Disparity Measures of Absolute Disparity

Race

1990

AI/AN

A/PI

Black

White

Total

SEtrad

SEboot

2001

AI/AN

A/PI

Black

White

Total

SEtrad

SEboot

∆1900 to 2001

SE∆

 Z-statistic

%∆

Rate

38.0

57.3

141.7

143.4

136.3

1.4

1.3

32.9

52.5

111.4

117.8

110.2

1.0

1.0

-26.1

1.7

-15.6

-19.1%

SE

7.1

3.3

4.9

1.5

1.4

4.8

2.3

3.2

1.2

1.0

% Pop

1

6.9

8

84

1.3

8.9

9.9

79.9

RR*

1

1.5

3.7

3.8

3.8

0.7

0.8

1

1.6

3.4

3.6

3.6

0.5

0.6

-0.2

1.0

-0.2

-5.1%

IDisp

0

6.4

34.6

35.1

200.4

66.2

0

6.5

26.2

28.3

185.5

46.8

-14.9

81.1

-0.2

-7.4%

T

-3.6

-25.1

3.2

45.2

19.7

1.7

-4.7

-31.4

1.1

56.4

21.4

1.7

1.7

2.4

0.7

8.5%

MLD

13.1

59.6

-3.1

-43

26.6

2.9

15.6

66.1

-1.1

-52.8

27.8

2.6

1.2

3.9

0.3

4.5%

RD*

0

19.3

103.7

105.4

105.4

7.2

7.5

0

19.6

78.6

84.9

84.9

5.0

4.9

-20.5

9.0

-2.3

-19.5%

BGV

98.7

429.4

2.3

43

573.5

44.4

77.2

297.1

0.1

45.4

419.9

29.8

-153.6

53.5

-2.9

-26.8%

*For the RR and RD the row marked ‘Total’ contains the maximum of RR or RD, a measure of the range

Abbreviations: RR=Rate Ratio; IDisp=Index of Disparity; T=Theil Index; MLD=Mean Log Deviation; RD=Rate Difference;

BGV=Between Group Variance; SEtrad=traditional standard error; SEboot=bootstrap standard error

Table 4. Changes in Racial Disparity in Lung Cancer Incidence between 1990 and 2001 among 

Females 45-74

Case Study 2: Area Socioeconomic 

Disparities in Lung Cancer Incidence, 

1988-99

 The data for this analysis come from the SEER 

database, “Incidence - SEER 11 Regs, Nov 2001 Sub 

(1988-1999) with Socio-Economic Attributes by 

County.” The measure of socio-economic position 

(SEP) for each individual case was based on county 

of residence in the 1990 US Census. All counties in 

the SEER database (n=201) were ranked according the 

and 1993 to 1994 the IDisp shows a decline and the 

MLD an increase in racial disparity; the opposite is 

true from 1994 to 1995. This is likely to be due to the 

different referent groups used by the two measures. 

For disagreement between 1990 and 1991, the increase 

in the AI/AN rate brings the rate for the “best group” 

closer to the other rates (and thus the decline in the 

IDisp), but because this group is a small proportion of 

the population this has little effect on the reference 

rate for the T and MLD (the population average rate) 

and so the MLD registers a small increase in disparity. 
19

Males

 Rates of lung cancer mortality from 1988 to 1999 

for males, by area socioeconomic position, are shown 

in Figure 5. Lung cancer incidence has declined for 

all socioeconomic groups, and the magnitude of 

the decline was generally similar (~30%). Table 5

shows the rates for each year and the fraction of the 

male population in each socioeconomic group. It 

is worth noting that between 1988 and 1999 the 

entire population experienced an upward shift in the 

socioeconomic distribution reflecting secular trends 

in increasing education—i.e., the fraction of the 

population in the highest education quintile increased 

by 12% while the fraction in the lowest quintile 

declined by 7%.

Figure 4. Trends in Relative Racial Disparity in Lung Cancer Incidence among Females 45-74, 1990-2001

1990

400

350

300

250

200

150

100

50

0

1995 2000

Index of Disparity

1991 1992 1993 1994 1996 1997 1998 1999 2001

40

35

30

25

20

15

10

5

0

Mean Log Deviation

MLD

IDisp

percentage of the population ages 25 and over with at 

least a high school degree, estimated from the 1990 US 

Census. Educational attainment ranged from 57.8% 

in Guadalupe County, NM to 94.7% in Los Alamos 

County, NM. The 201 counties were classified into five 

categories of an equal number of counties to create 

quintiles of SEP based on educational attainment. The 

unweighted average percent of the population with at 

least a high school education in the five quintiles (low 

to high) was 68.0%, 75.3%, 78.2%, 81.0%, and 86.4%. 

The analysis is restricted to individuals 45-74 years of 

age and rates are not age-adjusted. 
20

Rate per 100,000 Percent of Total Population

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

∆1988 to 1999

%∆

Q1 (low)

231.0

220.6

226.1

213.5

212.9

201.6

192.5

194.2

179.0

170.9

160.1

148.3

-82.8

-35.8%

Q2

254.5

250.5

253.6

259.2

256.3

232.2

226.4

220.3

225.5

213.3

209.3

199.5

-55.0

-21.6%

Q4

207.5

200.1

203.4

194.5

193.9

182.7

176.6

170.3

157.3

152.1

149.1

140.2

-67.2

-32.4%

Q3

270.4

254.5

238.1

256.7

241.9

233.0

220.9

212.4

204.0

199.9

196.9

181.1

-89.3

-33.0%

Q5 (high)

211.3

209.5

207.7

196.8

195.7

180.6

167.9

165.4

157.7

157.6

153.6

141.7

-69.5

-32.9%

Q1 (low)

0.333

0.330

0.326

0.324

0.322

0.319

0.317

0.314

0.313

0.311

0.311

0.311

-0.022

-6.6%

Q3

0.130

0.129

0.128

0.127

0.126

0.125

0.124

0.123

0.122

0.121

0.120

0.120

-0.011

-8.3%

Q2

0.058

0.057

0.057

0.058

0.058

0.058

0.057

0.057

0.058

0.058

0.057

0.057

-0.001

-1.2%

Q4

0.205

0.206

0.207

0.207

0.206

0.206

0.206

0.206

0.206

0.206

0.206

0.206

0.000

0.1%

Q5 (high)

0.274

0.278

0.282

0.285

0.289

0.292

0.296

0.299

0.301

0.304

0.306

0.307

0.033

12.1%

Table 5. Lung Cancer Incidence and Population Distribution among Males 45-74, by Quintile of 

Socioeconomic Position, 1988-1999

Figure 5. Lung Cancer Incidence among Males 45-74, by Area Socioeconomic Position, 1988-1999

1988

300

280

260

240

220

200

180

160

140

120

100

1st Quintile (low)

1993 1998

2nd Quintile 3rd Quintile 4th Quintile

Rate per 100,000

1989 1990 1991 1992 1994 1995 1996 1997 1999

5th Quintile (high)

(Unweighted quintiles of % of county with at least a high school education)
21

 The change in socioeconomic disparity for males 

is shown in Table 6. The relative disparity in lung 

cancer incidence favors the better off (RCI/ACI and 

the RII/SII, which are sensitive to the direction of the 

gradient, are negative). For example, in 1988 the RII 

indicates that moving from the bottom to the top 

of the educational distribution is associated with a 

16.9% decline (RII=-0.169) in lung cancer incidence. 

For males, the Rate Ratio and the Index of Disparity 

show increases in disparity of 9.2% and 18.1%, 

respectively, while the Relative Concentration Index 

and the Relative Index of Inequality both register 

approximately a 20% decline. The increases in the RR 

and IDisp appear to result from the smaller decline 

in incidence among those in the 2nd SEP quintile, 

which, it should be noted, accounts for about 6% of 

the SEER population. The similarity of the change 

in the RCI and the RII should not be surprising as 

they are mathematically related (RCI = 2var(x)*RII), 

where x is relative socioeconomic rank, see Methods 

section). All of the measures of absolute disparity 

indicate that socioeconomic disparity has declined, but 

the magnitude of the decline is much smaller for the 

Rate Difference (-5.7%) than for the other summary 

measures (33-46%). Trends in the Index of Disparity 

and Relative Concentration Index are shown in Figure 

6, and are only moderately consistent over time. The 

IDisp shows a continuous increase in disparity until 

1997, while the RCI begins decreasing in 1995. From 

1989-90 and 1994-97 the IDisp and the RCI move in 

opposite directions (highlighted in boxes in Figure 6), 

with one measure indicating increasing disparity and 

the other indicating decreasing disparity. For absolute 

disparity, the BGV and SII generally agree with respect 

to the magnitude of the change in disparity over 

time, but from 1989-90 and 1994-95 the SII shows an 

Raw Data Measures of Relative Disparity Measures of Absolute Disparity

SEP quintile

1988 

1st quintile 

2nd quintile 

3rd quintile 

4th quintile 

5th quintile 

Total

1999 

1st quintile 

2nd quintile 

3rd quintile 

4th quintile 

5th quintile 

Total

∆1988 to 1999

%∆

Rate

231.0

254.5

270.4

207.5

211.3

227.3

148.3

199.5

181.1

140.2

141.7

151.5

-75.8

-33.3%

% Pop

0.333

0.058

0.130

0.205

0.274

0.311

0.057

0.120

0.206

0.307

RR

1.11

1.23

1.30

1.00

1.02

1.30

1.06

1.42

1.29

1.00

1.01

1.42

0.1

9.2%

IDisp

5.9

11.8

15.7

0.0

1.0

16.6

2.0

14.8

10.2

0.0

0.4

19.6

3.0

18.1%

RCI

-0.2256

-0.0179

-0.0138

0.0463

0.1849

-0.0262

-0.2096

-0.0242

-0.0208

0.0343

0.1991

-0.0211

0.0051

-19.3%

RII*

-0.169

-0.136

0.032

-19.2%

RD

23.6

47.0

62.9

0.0

3.8

62.9

8.1

59.3

40.9

0.0

1.5

59.3

-3.6

-5.7%

ACI

-51.28

-4.07

-3.14

10.51

42.02

-5.95

-31.75

-3.66

-3.14

5.19

30.16

-3.20

2.8

-46.2%

BGV

4.7

42.8

242.8

80.6

70.1

440.9

3.2

131.7

105.0

25.9

29.1

294.8

-146.1

-33.1%

SII*

-38.4

-20.6

17.7

-46.2%

*Based on regression analysis (see methods section).

Abbreviations: RR=Rate Ratio; IDisp=Index of Disparity; RCI=Relative Concentration Index; RII=Relative Index of Inequality; 

RD=Rate Difference; ACI=Absolute Concentration Index; BGV=Between Group Variance; SII=Slope Index of Inequality.

Table 6. Changes in Socioeconomic Disparity in Lung Cancer Incidence between 1988 and 1999 

among Males 45-74
22

increase in disparity while the BGV shows a decrease 

(highlighted in boxes), while the opposite is true from 

1996-97. 

DIAGNOSTICS

Why do the results differ for the RR/IDisp and

RCI/RII?

 Based on the results in Table 6, one would 

conclude that area-socioeconomic disparity in 

lung cancer incidence among males is increasing 

when measured by the RR or IDisp, but decreasing 

when measured by the RCI or RII. Given that each 

of these measures purport to measure “disparity”, 

why do they give different results? To gain some 

leverage on this issue it is worth reconsidering two 

Figure 6. Trends in Relative and Absolute Socioeconomic Disparity in Lung Cancer Incidence among 

Males 45-74, 1988-1999

1988

25

20

15

10

5

0

1990 1992

Relative Disparity

Index of Disparity

0.000

-0.005

-0.010

-0.015

-0.020

-0.025

-0.030

-0.035

-0.040

-0.045

Relative Concentration Index

1994 1996 1998 1988

600

500

400

300

200

100

0

1990 1992

Absolute Disparity

Between Group Variance

0

-10

-20

-30

-40

-50

-60

Slope Index of Inequality

1994 1996 1998

IDisp

RCI

BGV

SII

basic differences between the IDisp and the RCI as 

described in the Methods section. The IDisp uses the 

“best rate” as the reference point and does not weight 

social groups by their population size, while the RCI 

uses the total population rate as the reference group 

and is population-weighted. Thus, the source of the 

difference could potentially be either 1) the use of 

different reference groups; 2) population-weighting; or 

3) changes in population distribution over time. Table 

7 shows a simulation of the change in disparity after 

making some adjustments that attempt to eliminate 

the differences between the IDisp and the RCI. For 

example, one might ask: Is the difference between the 

IDisp and the RCI due to the fact that the IDisp uses 

the “best rate” as the reference group and the RCI uses 

the population average? Apparently not, because if we 
23

1988

1999

∆1988 to 1999

%∆

Observed 

IDisp

16.6

19.6

3.0

18.1%

Observed 

RCI

-0.0262

-0.0211

0.0051

-19.3%

IDisp with 

population average 

as reference group

9.7

13.4

3.8

39.0%

Population 

Weighted IDisp

2.4

2.0

-0.4

-16.2%

Unweighted 

RCI

-0.1881

-0.4270

-0.2390

127.1%

Weighted RCI with 

1988 population 

shares fixed

-0.0262

-0.0191

0.0071

-27.0%

calculate the IDisp using the population average as the 

reference rate, one would still conclude that disparity 

has increased. The most likely answer to why the two 

measures differ is the effect of population weighting 

(See shaded columns 5 and 6 in Table 7). In column 

6, if the RCI is calculated without weighting by 

population size1 it also shows an increase in disparity, 

and if we weight the IDisp by population shares the 

relative change in disparity is quite similar to that for 

the observed changes for the RCI and RII.

Females

 Rates of lung cancer incidence for females 45-74 are 

presented in Figure 7. Similar to the trend for males, 

rates of lung cancer incidence declined from 1988-99 

for all socioeconomic groups, but the magnitude of the 

decline was generally larger for males than for females. 

In addition, lung cancer incidence was slightly 

higher among the worse-off socioeconomic groups, 

as they were for males. However, both absolute and 

relative area-socioeconomic disparities in lung cancer 

incidence were smaller among females compared to 

males in 1988 by all measures of disparity (compare 

the upper panels of Table 6 and Table 9). For example, 

the RCI and ACI for males were, respectively, -0.0262 

and -5.95, while for females the corresponding values 

were -0.0056 and -0.71.

Table 7. Changes in Socioeconomic Disparity in Lung Cancer Incidence between 1988 and 1999 

among Males 45-74 Using Alternative Measures of Disparity

1

It should be pointed out that calculating the RCI without population weights may generate values outside the normal range of the RCI (-1,1). 

Nevertheless, for our purposes it could be thought of as a potential disparity measure that summarizes the ratio of each group’s health relative 

to the total population and attaches higher weight to the health of lower-ranked social groups.
24

Rate per 100,000 Percent of Total Population

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

∆1988 to 1999

%∆

Q1 (low)

128.9

126.2

128.8

130.4

121.4

125.4

118.0

115.6

113.8

106.6

109.9

101.6

-27.3

-21.2%

Q2

141.4

138.5

146.1

137.7

140.0

133.6

137.9

133.9

146.6

129.8

137.3

127.9

-13.6

-9.6%

Q4

121.2

115.7

125.3

121.1

123.5

113.2

111.5

114.6

111.3

109.5

107.1

101.7

-19.5

-16.1%

Q3

127.2

129.3

132.1

133.2

146.5

137.3

139.1

133.3

138.2

131.5

127.6

123.1

-4.1

-3.2%

Q5 (high)

128.2

123.5

125.2

133.8

125.4

118.3

124.2

120.4

118.1

124.2

116.0

112.9

-15.3

-12.0%

Q1 (low)

0.339

0.336

0.332

0.331

0.329

0.327

0.324

0.322

0.320

0.319

0.319

0.319

-0.019

-5.7%

Q3

0.133

0.132

0.130

0.129

0.128

0.127

0.126

0.125

0.124

0.123

0.123

0.122

-0.012

-8.8%

Q2

0.058

0.058

0.058

0.058

0.058

0.058

0.057

0.058

0.058

0.057

0.057

0.057

-0.001

-1.6%

Q4

0.203

0.204

0.204

0.204

0.204

0.204

0.204

0.204

0.204

0.205

0.205

0.204

0.001

0.6%

Q5 (high)

0.267

0.271

0.275

0.278

0.281

0.285

0.288

0.291

0.293

0.295

0.297

0.298

0.031

11.6%

Table 8. Lung Cancer Incidence and Population Distribution among Females 45-74, by Quintile of 

Socioeconomic Position, 1988-1999

Figure 7. Lung Cancer Incidence among Females 45-74, by Area Socioeconomic Position, 1988-1999

1988

150

140

130

120

110

100

90

1st Quintile (low)

1993 1998

2nd Quintile 3rd Quintile 4th Quintile

Rate per 100,000

1989 1990 1991 1992 1994 1995 1996 1997 1999

5th Quintile (high)

(Unweighted quintiles of % of county with at least a high school education)
25

Raw Data Measures of Relative Disparity Measures of Absolute Disparity

SEP quintile

1988 

1st quintile 

2nd quintile 

3rd quintile 

4th quintile 

5th quintile 

Total

1999 

1st quintile 

2nd quintile 

3rd quintile 

4th quintile 

5th quintile 

Total

∆1988 to 1999

%∆

Rate

128.9

141.4

127.2

121.2

128.2

127.7

101.6

127.9

123.1

101.7

112.9

109.1

-18.6

-14.6%

% Pop

0.339

0.058

0.133

0.203

0.267

0.319

0.057

0.122

0.204

0.298

RR

1.06

1.17

1.05

1.00

1.06

1.17

1.00

1.26

1.21

1.00

1.11

1.26

0.09

7.7%

IDisp

1.9

5.1

1.5

0.0

1.8

8.4

0.0

6.6

5.4

0.0

2.8

14.6

6.2

73.8%

RCI

0.0000

-0.0019

0.0014

0.0072

-0.0012

-0.0056

0.0000

-0.0044

-0.0074

0.0076

-0.0104

0.0146

0.020

-360.7

RII*

-0.036

-0.094

0.130

-360.9%

RD

7.7

20.2

6.0

0.0

7.0

20.2

0.0

26.3

21.5

0.1

11.3

26.3

6.1

30.2

ACI

-28.86

-2.17

-1.25

6.46

25.11

-0.71

-22.07

-2.22

-1.89

4.15

23.62

1.59

2.3

-322.8%

BGV

0.5

11.0

0.0

8.4

0.1

19.9

18.1

20.1

23.9

11.0

4.3

77.4

57.5

288.2%

SII*

-4.6

10.2

14.8

-323.0%

 Changes in socioeconomic disparities in lung 

cancer incidence for females are presented in Table 

9. From 1988 to 1999 the disparity measures that are 

sensitive to the direction of the gradient (RCI/ACI 

and RII/SII) indicate that the gradient changed from 

favoring the better off socioeconomic groups (higher 

incidence generally among the lower SEP groups) to 

favoring the worse off (higher incidence generally 

among the higher SEP groups). This reversal is likely 

due to the faster decline in incidence among those 

in the low-SEP quintile 1 (21.2% decline) compared 

to the high-SEP quintile 5 (12.0% decline). Generally 

speaking, all the measures of relative disparity appear 

consistent in showing that socioeconomic disparities 

are increasing (the change in sign makes this difficult 

to see with the RCI and RII). The RR, IDisp, and RD 

all show increases over this time period, and the 

absolute value of the RCI, RII, ACI, and SII increased 

as well. However, the magnitude of the increase in 

disparity differed across the summary indicators. The 

size of the positive gradient in 1999 as measured by 

the population-weighted measures—the RCI, RII, ACI, 

BGV, and SII—is nearly three times as large as the 

negative gradient observed in 1988 (~300% change) 

while the RR, IDisp, and RD show more moderate 

increases.

 Figure 8 shows the trends in absolute and relative 

socioeconomic disparity in lung cancer incidence 

among females. For relative disparity (left panel) 

both the Relative Concentration Index and the Index 

of Disparity generally show increases in lung cancer 

disparity over time, but during the periods 1990-91 

and 1993-94 the RCI shows disparity decreasing (i.e., 

moving towards zero) while the IDisp shows disparity 

increasing (boxed areas on right panel of Figure 8). 

A similar situation is seen for the trend in absolute 

Table 9. Changes in Socioeconomic Disparity in Lung Cancer Incidence between 1988 and 1999 

among Females 45-74

*Based on regression analysis (see methods section).

Abbreviations: RR=Rate Ratio; IDisp=Index of Disparity; RCI=Relative Concentration Index; RII=Relative Index of Inequality; 

RD=Rate Difference; ACI=Absolute Concentration Index; BGV=Between Group Variance; SII=Slope Index of Inequality.
26

Figure 8. Trends in Relative and Absolute Socioeconomic Disparity in Lung Cancer Incidence among 

Females 45-74, 1988-1999

1988

18

16

14

12

10

8

6

4

2

0

1990 1992

Relative Disparity

Index of Disparity

0.030

0.025

0.020

0.015

0.010

0.005

0.000

-0.005

-0.010

-0.015

-0.020

-0.025

Relative Concentration Index

1994 1996 1998 1988

120

100

80

60

40

20

0

1990 1992

Absolute Disparity

Between Group Variance

25

20

15

10

5

0

-5

-10

-15

-20

Slope Index of Inequality

1994 1996 1998

IDisp

RCI

BGV

SII

disparity. The Between Group Variance and the Slope 

Index of Inequality show similar increases in absolute 

disparity over time, but from 1990-91 the SII shows 

little change while the BGV shows an increase (boxed 

areas on graph); the opposite is true from 1994-95.

Case Study 3: Area Socioeconomic 

Disparities In Colorectal Cancer Mortality, 

1950-2000

 The data for this analysis come from the following 

two SEER databases, “Mortality - Cancer, Total U.S. 

(1950-2000)” and “Socio-Economic Attributes - Total 

U.S. (1969+ county definitions).” The measure of 

socio-economic position for each individual case, 

derived from the SEER variable “SES Index 1990 

unweighted quintile,” was based on state and county 

of residence in the 1990 US Census. An index of 

socioeconomic position, based on 11 aspects of 

material, social, and economic environment (e.g., 

education, employment, income, housing, etc.) 

reported in the 1990 US Census was created for each 

county. The detailed methods for the construction 

of the index are given in Singh GP et al., “Changing 

Area Socioeconomic Patterns in U.S. Cancer Mortality, 

1950-1998: Part I—All Cancers Among Men.” JNCI

2002;94:904-15 (13). All counties in the United States 

were classified into five categories of equal number of 
27

Figure 9. Trends in Colorectal Cancer Mortality among Males 45-74, by Area Socioeconomic Position, 

1950-2000

1950

70

60

50

40

30

20

10

0

1st Quintile (low)

1975 2000

2nd Quintile 3rd Quintile 4th Quintile

Rate per 100,000

1955 1960 1965 1970 1980 1985 1990 1995

5th Quintile (high)

counties to create quintiles of socioeconomic position 

based on the value of the SEP index. Similar to Singh 

et al., the categorization of counties in 1990 was used 

in all years, as Singh et al. found the 1990 ranking to 

be very reliable over time. The detailed analyses are 

presented for individuals ages 45-74, but a summary 

of results for those ages 75 and over are also presented. 

Rates are not age-adjusted.

Males

 Rates of colorectal cancer mortality among 

socioeconomic groups are plotted in Figure 9. It is 

clear that in 1950 mortality rates were higher among 

residents of higher-SEP areas, but while rates among 

the higher-SEP 4th and 5th quintiles declined slowly 

over the next 40 years rates among the lower-SEP 1st-3rd

quintiles increased. It also appears that since 1990 the 

rates have been declining for all socioeconomic groups. 

Table 10 shows the underlying data documenting 

two major changes in the distribution of colorectal 

cancer mortality from 1950-2000: 1) colorectal cancer 

mortality rates declined among higher area-SEP groups 

and increased among lower area-SEP groups, and 2) 

the fraction of the population living in the highest 

SEP area (5th quintile) increased while decreasing in all 

other quintiles.
28

1st Quintile (low) 2nd Quintile 3rd Quintile 4th Quintile 5th Quintile (high)

Year

1950

1960

1970

1980

1990

2000

∆1950 to

2000

%∆

Rate

27.0

28.3

37.9

43.5

45.5

44.2

17.2

63.5

% Pop

6.2

5.5

4.9

4.8

4.5

4.5

-1.7

-27.2

% Pop

8.0

7.3

6.6

6.5

6.1

6.1

-1.9

-24.0

Rate

38.1

40.7

45.1

49.2

50.6

44.2

6.0

15.8

Rate

44.7

42.9

52.1

55.2

53.0

44.0

-0.7

-1.5

% Pop

10.3

9.6

9.0

9.3

8.9

8.7

-1.6

-15.9

% Pop

25.1

23.8

22.3

18.6

17.9

17.2

-7.9

-31.4

Rate

63.3

62.4

59.9

58.1

55.7

42.6

-20.7

-32.8

Rate

61.7

58.7

55.1

57.5

51.8

36.1

-25.6

-41.5

% Pop

50.4

53.9

57.2

60.8

62.6

63.5

13.1

26.0

Measures of Relative Disparity Measures of Absolute Disparity

Year

1950

1960

1970

1980

1990

2000

∆1950 to 2000

%∆1950 to 2000

%Change in Disparity

%∆1950 to 1960

%∆1960 to 1970

%∆1970 to 1980

%∆1980 to 1990

%∆1990 to 2000

RR

2.34

2.20

1.58

1.34

1.22

1.23

-1.12

-83.2%

-10.3%

-51.6%

-42.3%

-33.2%

0.7%

IDisp

92.1

80.6

40.0

26.5

16.0

21.3

-70.9

-76.9%

-12.5%

-50.3%

-33.9%

-39.4%

32.7%

RCI

0.0792

0.0607

0.0213

0.0213

-0.0010

-0.0451

-0.1243

-156.9%

-23.3%

-64.9%

-0.2%

-104.6%

4494.0%

RII*

0.557

0.440

0.160

0.166

-0.008

-0.367

-0.924

-165.9%

-21.0%

-63.7%

4.1%

-104.7%

4562.6%

RD

36.25

34.08

22.02

14.58

10.19

8.13

-28.12

-77.6%

-6.0%

-35.4%

-33.8%

-30.1%

-20.2%

ACI

4.5

3.3

1.2

1.2

-0.1

-1.7

-6.2

-139.1%

-25.0%

-65.3%

3.1%

-104.3%

3307.9%

BGV

119.6

88.3

26.7

12.8

4.5

12.6

-107.0

-89.5%

-26.2%

-69.7%

-52.3%

-64.5%

178.4%

SII*

31.3

24.2

8.7

9.3

-0.4

-14.2

-45.6

-145.3%

-22.7%

-64.1%

7.5%

-104.4%

3358.8%

Table 10. Rates of Colorectal Cancer Mortality and Population Share by Area Socioeconomi 

Quintile among Males 45-74, 1950-2000

Table 11. Measures of Disparity in Each Decade and Percent Change in Disparity by Decade 

among Males 45-74, 1950-2000

*Based on regression analysis (see methods section).

Abbreviations: RR=Rate Ratio; IDisp=Index of Disparity; RCI=Relative Concentration Index; RII=Relative Index of Inequality; 

RD=Rate Difference; ACI=Absolute Concentration Index; BGV=Between Group Variance; SII=Slope Index of Inequality.
29

 All of the measures of relative disparity indicate 

that the magnitude of disparity is lower in 2000 than 

in 1950 (Table 11, shaded cells). However, note that 

the Index of Disparity does not distinguish between 

positive gradients (i.e. higher mortality among higher 

area-SEP individuals from 1950-1980) and negative 

gradients (i.e. higher mortality among lower area-SEP 

individuals), while the RCI and RII capture the reversal 

of the gradient that occurs between 1980 and 1990. 

During the 1970s both the Rate Ratio and the Index 

of Disparity registered strong declines (-42.3% and 

-33.9%, respectively), whereas the RCI and RII showed 

virtually no decline. This seems likely due to the 

worsening of the mortality rate in the reference group 

for these measures (i.e., the 1st quintile). All of the 

Figure 10. Trends in Relative Socioeconomic Disparity in Colorectal Cancer among Males 45-74, 

1950-2000 (3-Year Moving Average)

1950

120

110

100

90

80

70

60

50

40

30

20

10

0

1960 1970

Index of Disparity

0.120

0.100

0.080

0.060

0.040

0.020

0.000

-0.020

-0.040

-0.060

Relative Concentration Index

1980 1990 2000

120

110

100

90

80

70

60

50

40

30

20

10

0

Between Group Variance

5.0

4.0

3.0

2.0

1.0

0.0

-1.0

-2.0

-3.0

Absolute Concentration Index

1950 1960 1970 1980 1990 2000

IDisp avg

RCI avg

BGV avg

ACI avg

measures of absolute disparity also registered lower 

values in 2000 than in 1950, strongly suggesting that 

absolute area-socioeconomic disparities in colorectal 

cancer have declined. However, the magnitude of the 

decline was greater when measured with the SII, which 

additionally captures the reversal of the gradient over 

time. For absolute disparity, between 1970 and 1980 

both the ACI and the Slope Index show very small 

increases in disparity, but the RD and the BGV show 

declines.

 Trends for the absolute and relative disparity 

measures among males are presented in Figure 10. 

Generally, for relative disparity the IDisp and the RCI 

show similar trends, but note that the RCI crosses the 

zero line around 1990, demonstrating the reversal 
30

of the socioeconomic gradient in colorectal cancer 

mortality. The boxed area in the left panel of Figure 

10 shows that during the approximate period of 

1970-80 the IDisp declines but the RCI shows little 

change. It seems likely that this is due to the increase 

in the rate among the 1st quintile, which is the 

referent group for the IDisp, but only accounts for 

about 5% of the total population, which would have 

less effect on a population-weighted measure such as 

the RCI. In terms of absolute disparity, the BGV and 

the ACI tend to follow similar trajectories, but again, 

the period of the 1970s shows continued declines in 

the BGV but little change in the ACI, as the movement 

of the group furthest away from the population rate 

(the 1st quintile), which receives additional weight in 

the calculation of the BGV, contributes to declines in 

the BGV.

Females

Rates of colorectal cancer mortality by socioeconomic 

groups are plotted for females in Figure 11. Overall 

the rates are slightly lower than for males, but the 

general temporal pattern is the same. In 1950 

colorectal cancer mortality rates were substantially 

higher among women living in higher-SEP areas, but 

over the next 50 years rates declined fastest for this 

group and slowest for women living in lower-SEP 

areas. In fact, rates of colorectal cancer mortality 

were relatively flat for women living in the bottom 3 

quintiles until they began sustained declines around 

1990.

Figure 11. Trends in Colorectal Cancer Mortality among Females 45-74, by Area Socioeconomic Position, 

1950-2000

1950

70

60

50

40

30

20

10

0

1st Quintile (low)

1975 2000

2nd Quintile 3rd Quintile 4th Quintile

Rate per 100,000

1955 1960 1965 1970 1980 1985 1990 1995

5th Quintile (high)
31

1st Quintile (low) 2nd Quintile 3rd Quintile 4th Quintile 5th Quintile (high)

Year

1950

1960

1970

1980

1990

2000

∆1950 to

2000

%∆

Rate

32.9

32.1

35.1

35.9

31.0

29.1

-3.8

-11.6

% Pop

5.9

5.3

4.9

4.9

4.6

4.4

-1.4

-24.3

% Pop

7.8

7.2

6.6

6.5

6.2

6.0

-1.9

-23.8

Rate

42.5

38.4

39.7

38.2

38.1

30.9

-11.5

-27.1

Rate

47.2

43.8

43.7

43.7

36.5

30.7

-16.6

-35.1

% Pop

10.0

9.3

8.8

9.4

8.9

8.6

-1.5

-14.5

% Pop

24.8

23.8

22.7

18.9

18.2

17.4

-7.4

-29.8

Rate

59.5

54.1

51.1

43.5

39.1

29.0

-30.5

-51.3

Rate

56.8

53.7

47.6

44.2

36.0

25.7

-31.0

-54.7

% Pop

51.5

54.4

57.1

60.4

62.1

63.6

12.1

23.6

Measures of Relative Disparity Measures of Absolute Disparity

Year

1950

1960

1970

1980

1990

2000

∆1950 to 2000

%∆1950 to 2000

%Change in Disparity

%∆1950 to 1960

%∆1960 to 1970

%∆1970 to 1980

%∆1980 to 1990

%∆1990 to 2000

RR

1.81

1.69

1.45

1.23

1.26

1.20

-0.61

-74.9%

-14.9%

-34.0%

-49.1%

13.4%

-22.6%

IDisp

56.6

48.2

29.7

18.2

20.8

16.4

-40.2

-71.1%

-14.8%

-38.5%

-38.5%

13.9%

-21.2%

RCI

0.0487

0.0508

0.0201

0.0182

-0.0046

-0.0354

-0.0841

-172.7%

4.2%

-60.4%

-9.2%

-125.3%

668.8%

RII*

0.345

0.369

0.151

0.142

-0.037

-0.289

-0.634

-183.6%

7.0%

-59.3%

-5.8%

-125.9%

686.7%

RD

26.63

22.07

15.97

8.31

8.14

5.23

-21.40

-80.4%

-17.1%

-27.7%

-48.0%

-2.0%

-35.8%

ACI

2.63

2.57

0.94

0.79

-0.17

-0.96

-3.59

-136.6%

-2.2%

-63.3%

-16.3%

-121.3%

472.2%

BGV

52.7

41.6

15.2

4.9

3.0

4.0

-48.7

-92.4%

-21.1%

-63.4%

-68.0%

-38.8%

33.6%

SII*

18.6

18.7

7.1

6.1

-1.3

-7.8

-26.5

-142.1%

0.4%

-62.3%

-13.2%

-121.9%

485.5%

Table 12. Rates of Colorectal Cancer Mortality and Population Share by Area Socioeconomic 

Quintile among Females 45-74, 1950-2000

Table 13. Measures of Disparity in Each Decade and Percent Change in Disparity by Decade 

among Females 45-74, 1950-2000

*Based on regression analysis (see methods section).

Abbreviations: RR=Rate Ratio; IDisp=Index of Disparity; RCI=Relative Concentration Index; RII=Relative Index of Inequality; 

RD=Rate Difference; ACI=Absolute Concentration Index; BGV=Between Group Variance; SII=Slope Index of Inequality.
32

 Changes in area-socioeconomic disparities for 

females are presented in Table 13. All of the relative 

measures show declines in socioeconomic disparity 

(shaded cells), but the RCI and RII register larger 

declines and demonstrate that the gradient in 2000 

favors the better off while the gradient in 1950 favored 

the worse off. Between 1950 and 1960 both the RR 

and the IDisp showed approximately 15% declines 

in relative disparity, while the RCI and RII showed 

increases of 5-7%. In terms of absolute disparity, 

all measures showed declines in the magnitude of 

disparity over time, but the change was slightly larger 

for the ACI and SII as for the RD and BGV. Similar to 

the results for relative disparity, from 1950-60 the SII 

increased slightly while all three other measures of 

disparity declined. In 2000 the relative gradient, as 

measured by the RII, was almost as large in favoring 

the better off (-0.29) as it was in 1950 (0.35) when it 

favored the worse off. In absolute terms, however, the 

SII in 2000 is less than half the magnitude (-7.8) it was 

in 1950 (18.6). This reflects the fact that rates for all 

socioeconomic groups have generally been declining 

among women 45-74.

 Trends in disparity for females 45-74 are presented 

in Figure 12. For relative disparity, the IDisp and 

the RCI generally show similar trends. However, 

from 1951-55 there is a sharp increase in the RCI 

but a sharp decrease in the IDisp. Additionally, from 

the late 1960s to the late 1970s the RCI remained 

approximately constant while the IDisp continued to 

decline. In terms of absolute disparity, the BGV and 

the SII generally show similar trends, except for the 

Figure 12. Trends in Relative Socioeconomic Disparity in Colorectal Cancer among Females 45-74, 

1950-2000 (3-Year Moving Average) 

1950

120

110

100

90

80

70

60

50

40

30

20

10

0

1960 1970

Index of Disparity

0.120

0.100

0.080

0.060

0.040

0.020

0.000

-0.020

-0.040

-0.060

Relative Concentration Index

1980 1990 2000

120

110

100

90

80

70

60

50

40

30

20

10

0

Between Group Variance

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

-0.5

-1.0

-1.5

Absolute Concentration Index

1950 1960 1970 1980 1990 2000

IDisp avg

RCI avg

BGV avg

ACI avg
33

period from the late 1980s to the late 1990s, which 

the BGV remained approximately constant but the 

SII continued to decline (boxed area on right panel 

graph).

DIAGNOSTICS

Why do some results differ for the IDisp and

the RCI?

 For three different decades among females 45-74 

(1950-60, 1980-90, 1990-2000) the RR and IDisp 

suggest that the change in disparity is in the opposite 

direction than the RCI and RII. For example, the 

percent change in socioeconomic disparity from 

1990 to 2000, shown in the last row of Table 13

indicates disagreement for both relative and absolute 

measures of disparity. The RR and IDisp indicate a 20% 

reduction in disparity, while the RII and RCI indicate 

a substantial increase in disparity. In absolute terms 

the RD also shows a decline in absolute disparity, but 

the BGV indicates a 34% increase and the SII and 

ACI indicate a much larger increase. Is it possible to 

reconcile these observed differences?

 Again, recall that the RCI and IDisp differ by 

both the reference group they use and the weights 

attached to each group’s health. Table 14 shows 

results of a simulation for hypothetical disparity 

measures that attempt to minimize these differences. 

Simply weighting the standard IDisp (column 4) by 

population size actually increases the magnitude of the 

decrease in disparity (-68% change compared to -21% 

for unweighted). Using the population average as the 

reference group for the IDisp generates an increase 

in disparity (63%), while population weighting 

plus using the population average as the reference 

group further magnifies the disparity increase (115% 

change). Nevertheless, even in this case the relative 

change in the IDisp is quite a bit lower than the near 

700% increase shown by the RCI. Thus, unlike the 

hypothetical results given for lung cancer incidence in 

Table 7, simply weighting the IDisp does not provide 

results similar to the observed values of the RCI

and RII. 

 The reason that the RCI and the IDisp may not 

be reconcilable in this example has to do with the 

fact that, in addition to weighting each subgroup by 

its population fraction, the RCI (and RII) also gives 

additional weight to the health of the worst-off social 

groups. This is what makes such measures sensitive 

to the direction of the socioeconomic gradient (6). 

Figure 13 shows the observed mortality change by 

area-socioeconomic quintile from 1990 to 2000, and 

Table 15 below demonstrates the sensitivity of the RII/

RCI to different orderings of socioeconomic groups. 

For example, the worst off group (quintile 1) had the 

lowest mortality rate in 1990 but the slowest decline in 

mortality from 1990-2000. Table 15 shows that, if the 

position of quintile 1 and quintile 4 are reversed (i.e., 

1990

2000

∆1990 to 2000

%∆

Observed 

IDisp

20.8

16.4

-4.4

-21.2%

Observed 

RCI

-0.0046

-0.0354

-0.0308

668.8%

Population 

Weighted IDisp

4.44

1.42

-3.03

-68.1%

Unweighted IDisp with 

population average as

reference group

5.64

9.16

3.53

62.6%

Weighted IDisp with 

population average 

as reference group

0.63

1.36

0.73

115.2%

Unweighted

RCI

-2.47

-2.92

-0.45

18.2%

Table 14. Changes in Socioeconomic Disparity in Colorectal Cancer Incidence between 1990 and 

2000 among Females 45-74 Using Alternative Measures of Disparity
34

if “Q1” and “Q4” are exchanged in Figure 13), the 

estimated RCI in 1990 increases (-0.0137 vs. -0.0046 

observed) and the magnitude of the increase the RCI 

over time is reduced (140.8% vs. 668.8% observed). 

The change in the ACI is similar, but note that both 

the IDisp and the BGV are insensitive to the ordering 

of the socioeconomic groups. Thus, because measures 

like the RCI/RII and ACI/SII are sensitive to which

groups are changing, there may be cases for which 

it is impossible to reconcile their results with that of 

disparity measures (e.g., the Index of Disparity) that 

are not sensitive to which groups change.

1990

45

40

35

30

25

20

Rate per 100,000

2000

Total

Q4

Q2

Q3

Q5

Q1

Relative Disparity Absolute Disparity

Scenario*

Observed change

Exchange 1st and 

2nd quintiles

Exchange 1st and 

3rd quintiles

Exchange 1st and 

4th quintiles

Exchange 1st and 

5th quintiles

Year

1990

2000

%∆

1990

2000

%∆

1990

2000

%∆

1990

2000

%∆

1990

2000

%∆

IDisp

20.8

16.4

-21.2%

20.8

16.4

-21.2%

20.8

16.4

-21.2%

20.8

16.4

-21.2%

20.8

16.4

-21.2%

RCI

-0.0046

-0.0354

668.8%

-0.0057

-0.0358

526.6%

-0.0065

-0.0361

459.9%

-0.0137

-0.0329

140.8%

0.0071

0.0320

350.4%

BGV

2.98

3.98

33.6%

2.98

3.98

33.6%

2.98

3.98

33.6%

2.98

3.98

33.6%

2.98

3.98

33.6%

ACI

-0.17

-0.96

472.2%

-0.21

-0.97

366.4%

-0.24

-0.98

316.7%

-0.50

-0.89

79.2%

0.26

0.87

235.2%

Case Study 4: Area Socioeconomic 

Disparities in Prostate Cancer Mortality, 

1950-2000

 The data for this analysis come from the following 

two SEER databases, “Mortality - Cancer, Total U.S. 

(1950-2000)” and “Socio-Economic Attributes - Total 

U.S. (1969+ county definitions).” The measure of 

socio-economic position for each individual case, 

derived from the SEER variable “SES Index 1990 

unweighted quintile,” was based on state and county 

*Alternative scenarios exchange the rate and population size of different 

socioeconomic quintiles and recalculate mortality disparity.

Figure 13. Observed Change in Colorectal 

Cancer Mortality among Females 45-74, 

by Area Socioeconomic Position, 1990 

and 2000

Table 15. Changes in Socioeconomic Disparity in 

Colorectal Cancer Incidence between 1990 and 2000 

among Females 45-74 for Different Orderings of 

Socioeconomic Groups
35

of residence in the 1990 US Census. An index of 

socioeconomic position, based on 11 aspects of 

material, social, and economic environment (e.g., 

education, employment, income, housing, etc.) 

reported in the 1990 US Census was created for each 

county. The detailed methods for the construction 

of the index are given in Singh GP et al., “Changing 

Area Socioeconomic Patterns in U.S. Cancer Mortality, 

1950-1998: Part I—All Cancers Among Men.” JNCI

2002;94:904-15 (13). All counties in the United States 

were classified into five categories of equal number of 

counties to create quintiles of socioeconomic position 

based on the value of the SEP index. The analyses are 

stratified by age (45-74, 75 and over) and rates are not 

age-adjusted.

Ages 45-74 

 Rates of prostate cancer mortality from 1950-2000 

among those ages 45-74 by area-socioeconomic 

position are shown in Figure 14. In 1950 mortality 

rates were relatively equal across income quintiles, 

but began to diverge around 1960, as the highest 

income quintile experienced a decline in mortality as 

rates began rising for lower-income quintiles. Rates 

for all groups began increasing rather steeply after 

1970 or so but have declined dramatically since the 

early 1990s. Over the entire period from 1950 to 2000, 

Table 16 shows that the 5th quintile (high area-SEP) 

experienced the largest decline in prostate cancer 

mortality rates (-9.1 deaths per 100,000), while the 

1st quintile (low area-SEP) experienced the smallest 

(-1.3 deaths per 100,000). In addition, a considerable 

shift is noticeable in the distribution of the population 

over time, with the 5th quintile increasing its share of 

the population from 50.4% to 63.5% while all other 

quintiles lost population.

Figure 14. Trends in Prostate Cancer Mortality among Males 45-64, by Quintile of Area Socioeconomic 

Position, 1950-2000

1950

50

45

40

35

30

25

20

15

1st Quintile (low)

1975 2000

2nd Quintile 3rd Quintile 4th Quintile

Rate per 100,000

1955 1960 1965 1970 1980 1985 1990 1995

5th Quintile (high)
36

 Relative and absolute disparities in prostate 

cancer mortality and the percentage change in each 

decade from 1950-2000 are presented in Table 17. In 

general, all of the relative measures show increases in 

socioeconomic disparity in prostate cancer mortality. 

However, the magnitude of the increase in relative 

disparity is considerably larger for the RCI and RII (on 

the order of 1000% increase) than for the RR or the 

IDisp (~150% increase). This is likely due to both the 

steeper decline in the mortality rate among the highest 

(5th) quintile, which is the most populous and is the 

only quintile that gained population over this time 

period. 

1st Quintile 2nd Quintile 3rd Quintile 4th Quintile 5th Quintile

Year

1950

1960

1970

1980

1990

2000

∆1950 to

2000

%∆

Rate

31.1

34.1

35.7

42.4

46.3

29.9

-1.3

-4.2%

% Pop

6.2

5.5

4.9

4.8

4.5

4.5

-1.7

-27.2%

% Pop

8.0

7.3

6.6

6.5

6.1

6.1

-1.9

-24.0%

Rate

32.6

32.1

37.2

39.5

43.9

27.4

-5.2

-15.9%

Rate

33.2

32.4

32.3

37.8

44.9

25.8

-7.4

-22.3%

% Pop

10.3

9.6

9.0

9.3

8.9

8.7

-1.6

-15.9%

% Pop

25.1

23.8

22.3

18.6

17.9

17.2

-7.9

-31.4%

Rate

29.0

32.3

32.6

36.7

45.4

26.3

-2.7

-9.3%

Rate

31.0

29.6

27.6

32.9

40.4

21.9

-9.1

-29.3%

% Pop

50.4

53.9

57.2

60.8

62.6

63.5

13.1

26.0%

Table 16. Rates of Prostate Cancer Mortality and Population Share by Area Socioeconomic 

Quintile among Males 45-74, 1950-2000

 The long term trends in relative and absolute 

disparity for males 45-74 are shown in Figure 15. In 

general, the IDisp and the RCI show similar trends for 

relative socioeconomic disparity in prostate cancer 

mortality from 1950-2000. However, during the period 

from the early to the late 1950s (highlighted in boxed 

area in left panel of Figure 4-2), the RCI was increasing 

while the IDisp was decreasing. This may have 

occurred because the referent group in the early 1950s 

(1st quartile) experienced a sharp increase in prostate 

cancer mortality. In terms of absolute disparity among 

males 45-74, the BGV and the SII give very similar 

pictures of the disparity trend.
37

Measures of Relative Disparity Measures of Absolute Disparity

Year

1950

1960

1970

1980

1990

2000

∆1950 to 2000

%∆1950 to 2000

%Change in Disparity

%∆1950 to 1960

%∆1960 to 1970

%∆1970 to 1980

%∆1980 to 1990

%∆1990 to 2000

RR

1.14

1.15

1.35

1.29

1.15

1.36

0.22

154.2%

6.7%

128.0%

-17.7%

-48.7%

147.4%

IDisp

10.1

10.6

24.9

18.7

11.8

24.9

14.8

146.4%

4.7%

136.1%

-24.9%

-37.1%

111.2%

RCI

-0.004

-0.025

-0.053

-0.040

-0.026

-0.051

-0.05

1120.6%

491.1%

117.8%

-26.1%

-33.5%

92.9%

RII*

-0.029

-0.178

-0.400

-0.309

-0.211

-0.412

-0.38

1313.7%

509.3%

125.4%

-22.9%

-31.8%

95.8%

RD

4.2

4.5

9.6

9.4

5.9

8.0

3.8

91.7%

8.8%

112.3%

-1.7%

-37.1%

34.3%

ACI

-0.13

-0.76

-1.61

-1.38

-1.11

-1.20

-6.1

837.5%

493.0%

112.3%

-14.2%

-19.9%

8.5%

BGV

1.6

2.3

10.3

7.8

5.5

6.2

4.5

277.2%

38.8%

354.7%

-24.4%

-29.7%

12.4%

SII*

-0.90

-5.50

-12.07

-10.80

-8.87

-9.77

-8.9

986.0%

511.4%

119.6%

-10.5%

-17.8%

10.1%

Table 17. Measures of Socioeconomic Disparity in Prostate Cancer Mortality in Each Decade and 

Percent Change in Disparity by Decade among Males 45-74, 1950-2000

*Based on regression analysis (see methods section).

Abbreviations: RR=Rate Ratio; IDisp=Index of Disparity; RCI=Relative Concentration Index; RII=Relative Index of Inequality; 

RD=Rate Difference; ACI=Absolute Concentration Index; BGV=Between Group Variance; SII=Slope Index of Inequality.
38

Ages 75 and Over

 Rates of prostate for those 75 and over are shown 

in Figure 16. Rates are notably higher than for males 

ages 45-74, but the broad trend appears similar, with 

moderate rise in mortality until the early 1990s, after 

which rates have steeply declined. Mortality was 

Figure 15. Trends in Area Socioeconomic Disparity in Prostate Cancer Mortality among Males 45-74, 

1950-2000

1950

30

20

10

0

1960 1970

Index of Disparity

0.04

0.03

0.02

0.01

0.00

-0.01

-0.02

-0.03

-0.04

-0.05

-0.06

Relative Concentration Index

1980 1990 2000

16

14

12

10

8

6

4

2

0

Between Group Variance

0.5

0.0

-0.5

-1.0

-1.5

-.02

Absolute Concentration Index

1950 1960 1970 1980 1990 2000

IDisp avg

RCI avg

BGV avg

ACI avg

Relative Socioeconomic Disparity Absolute Socioeconomic Disparity

lower among those living the lower income quintiles 

in 1950, but lower among those living in the highest 

income quintiles in 2000. This is a consequence of the 

overall increase in mortality rates from 1950 to 2000, 

during which the increase over time was largest among 

the lowest income quintiles.
39

Figure 16. Trends in Prostate Cancer Mortality by Area Socioeconomic Position among Males 75 and 

Over, 1950-2000

1950

500

450

400

350

300

250

200

1st Quintile (low)

1975 2000

2nd Quintile 3rd Quintile 4th Quintile

Rate per 100,000

1955 1960 1965 1970 1980 1985 1990 1995

5th Quintile (high)

1st Quintile 2nd Quintile 3rd Quintile 4th Quintile 5th Quintile

Year

1950

1960

1970

1980

1990

2000

∆1950 to

2000

%∆

Rate

253.2

284.1

337.2

359.7

422.9

424.6

171.4

67.7

% Pop

7.0

6.7

6.0

5.8

5.3

4.5

-2.6

-36.9

% Pop

9.3

8.8

7.9

7.6

7.2

6.3

-3.0

-31.9

Rate

284.3

320.1

311.4

337.3

425.0

385.0

100.7

35.4

Rate

302.6

314.3

347.6

376.1

444.8

380.6

77.9

25.8

% Pop

12.3

11.4

10.3

10.5

10.0

9.1

-3.1

-25.5

% Pop

23.4

23.1

23.2

19.6

19.2

18.6

-4.8

-20.6

Rate

295.2

301.2

311.9

360.2

428.7

371.9

76.7

26.0

Rate

318.1

310.2

324.6

370.3

419.0

345.1

26.9

8.5

% Pop

48.0

50.0

52.6

56.5

58.4

61.5

13.5

28.2

Table 18. Rates of Prostate Cancer Mortality and Population Share by Area Socioeconomic 

Quintile among Males 75 and Over, 1950-2000
40

 In general, Table 19 shows that all of the measures 

of relative disparity indicate that socioeconomic 

inequality in prostate cancer among those 75 and over 

has declined. However, the magnitude of the decline 

is substantially greater when measured by the RCI 

and RII (~200%) than when measured by either the 

RR or the IDisp (10-30% decline). Additionally, both 

the RCI and RII show that the gradient has changed 

from favoring the worse off area-socioeconomic groups 

to favoring the better off, whereas the RR and IDisp 

do not indicate the direction of the gradient. In each 

decade from 1960 to 1990 the magnitude of decline 

in disparity is considerably greater when measured by 

the RCI and RII than with the RR and IDisp. For the 

1990s the magnitude of increase is similar. All of the 

measures of absolute disparity show increases, but only 

the SII indicates that the absolute gradient changed 

direction over the past 50 years.

 Trends in relative and absolute disparities in 

prostate cancer mortality are shown in Figure 17. 

The IDisp and RCI generally agree with respect to 

the trend in relative socioeconomic disparity, but 

the boxed area suggests that during the 1950 the RCI 

remained approximately constant while the IDisp 

continued to decline. Overall the BGV and the ACI 

demonstrate that disparity fell steadily from 1950 to 

1970, then remained approximately constant until 

the mid 1980s, after which absolute disparity has been 

increasing. However, during the mid 1970s the ACI 

indicated rising disparity that favored the worse off 

socioeconomic groups, while the BGV stayed about the 

same. 

Measures of Relative Disparity Measures of Absolute Disparity

Year

1950

1960

1970

1980

1990

2000

∆1950 to 2000

%∆1950 to 2000

%Change in Disparity

%∆1950 to 1960

%∆1960 to 1970

%∆1970 to 1980

%∆1980 to 1990

%∆1990 to 2000

RR

1.26

1.13

1.12

1.11

1.06

1.23

-0.03

-10%

-50.5%

-8.5%

-1.0%

-46.2%

273.0%

IDisp

18.5

9.6

6.1

8.7

2.7

13.2

-5.34

-28.9%

-48.0%

-36.9%

42.8%

-68.7%

384.4%

RCI

0.029

0.004

-0.002

0.008

-0.007

-0.027

-0.06

-195.0%

-87.1%

-140.6%

-618.4%

-187.1%

300.3%

RII*

0.1982

0.0261

-0.0108

0.0582

-0.0519

-0.2164

-0.41

-209.2%

-86.8%

-141.6%

-637.1%

-189.1%

317.3%

RD

64.9

36.1

36.2

38.8

25.9

79.5

14.6

22.4%

-44.5%

0.3%

7.2%

-33.2%

207.2%

ACI

8.75

1.15

-0.49

2.88

-2.90

-9.85

-18.6

-212.6%

-86.9%

-142.7%

-685.7%

-201.0%

239.2%

BGV

331.4

68.7

114.1

92.6

62.5

427.1

95.6

28.9%

-79.3%

66.0%

-18.8%

-32.6%

583.7%

SII*

60.1

8.0

-3.5

21.3

-22.0

-77.8

-137.8

-229.4%

-86.6%

-143.7%

-706.9%

-203.3%

253.6%

Table 19. Measures of Disparity in Each Decade and Percent Change in Disparity by Decade 

among Males 75 and Over, 1950-2000

*Based on regression analysis (see methods section).

Abbreviations: RR=Rate Ratio; IDisp=Index of Disparity; RCI=Relative Concentration Index; RII=Relative Index of Inequality; 

RD=Rate Difference; ACI=Absolute Concentration Index; BGV=Between Group Variance; SII=Slope Index of Inequality.
41

Case Study 5: Socioeconomic Disparities in 

Smoking, 1965-2003

 Trends in current smoking were investigated using 

smoking supplements to the National Health Interview 

Survey (NHIS), beginning in 1965 and ending in 

2003 (n=876,280). Sample weights were used in each 

survey to account for unequal sampling probabilities 

and nonresponse. Individuals missing information 

on age, gender, race, and education were excluded 

(2.0%), leaving an analytic sample of 859,014. 

Individuals who reported ever smoking 100 or more 

cigarettes in their lifetime and who currently smoke 

were considered “current smokers.” Education was 

categorized as <12 years, 12 years, 13-15 years, 16 years 

or more, and the analysis was restricted to individuals 

ages 25 and over.

Figure 17. Trends in Area Socioeconomic Disparity in Prostate Cancer Mortality among Males 75 and 

Over, 1950-2000

1950

500

450

400

350

300

250

200

1st Quintile (low)

1975 2000

2nd Quintile 3rd Quintile 4th Quintile

Rate per 100,000

1955 1960 1965 1970 1980 1985 1990 1995

5th Quintile (high)

Males 

 Rates of current smoking among males 25 and 

over, by educational status, are presented in Figure 

18. In 1965 rates of smoking were clearly lowest 

among those with 16 or more years of education but 

relatively similar among other education groups. 

Since 1965 smoking has declined among all groups, 

but the decline in smoking appears to have been 

strongest among those with more education. Table 

20 shows that the proportionate decline in smoking 

from 1965 to 2003 is graded by education, with the 

least educated group declining by 36.8% and the 

most educated group by 71.2%. In addition, Table 20

shows the proportion of the male population in each 

education group from 1965 to 2003 and demonstrates 

the important secular shifts in education over time. In 

1965 roughly 78% of the male population had a high 

school education or below, but by 2003 this proportion 

had declined to only 47%.
42

Figure 18. Trends in the Prevalence of Smoking by Years of Education among Males, 1965-2003

1965

0.60

0.50

0.40

0.30

0.20

0.10

0.00

<12 Years

1985

12 Years 13-15 Years 16+ Years

Prevalence Rate

1970 1975 1980 1990 1995 2000

Prevalence of Current Smoking Percent of Total Population

Year

1965

1970

1976

1980

1985

1990

1995

2000

2001

2002

2003

∆1965 to 2003

%∆

<12y

0.511

0.476

0.407

0.461

0.386

0.366

0.360

0.326

0.332

0.330

0.323

-0.188

-36.8%

12y

0.564

0.467

0.393

0.421

0.359

0.331

0.323

0.328

0.317

0.325

0.304

-0.260

-46.1%

13-15y

0.498

0.399

0.371

0.353

0.294

0.259

0.248

0.245

0.248

0.251

0.231

-0.267

-53.6%

16+y

0.404

0.300

0.265

0.293

0.198

0.143

0.146

0.124

0.119

0.114

0.116

-0.288

-71.2%

<12y

0.296

0.418

0.339

0.283

0.238

0.210

0.182

0.184

0.179

0.166

0.172

-0.124

-42.0%

12y

0.479

0.309

0.319

0.346

0.357

0.357

0.343

0.299

0.285

0.291

0.292

-0.187

-39.0%

13-15y

0.089

0.141

0.163

0.182

0.194

0.203

0.220

0.274

0.282

0.280

0.280

0.191

214.9%

16+y

0.136

0.132

0.178

0.189

0.211

0.230

0.255

0.244

0.254

0.263

0.256

0.120

88.4%

Table 20. Prevalence of Current Smoking and Population Distribution among Males 25 and Over, 

by Years of Education, NHIS Selected Years 1965-2003
43

 Changes in relative and absolute disparity in 

smoking are given in Table 21. All relative measures 

show increases in educational disparity over this time 

period (based on the % change from 1965-2003), 

but the magnitude of the increase is approximately 

twice as large for the RCI and RII (~700%) as for 

the RR or IDisp (~400%). This is likely to be related 

to both larger declines in smoking among the 

population with greater than 12 years of education 

and the substantial increases in the share of the 

population in these groups, to which the RCI and 

RII, as population-weighted measures, would be more 

sensitive. The RCI and RII indicate that educational 

disparities in smoking among men increased most 

Measures of Relative Disparity Measures of Absolute Disparity

Year

1965

1976

1985

1995

2003

∆1965 to 2003

%∆

∆1965 to 1976

∆1976 to 1985

∆1985 to 1995

∆1995 to 2003

 RR

1.40

1.54

1.95

2.47

2.78

1.38

346.6%

35.6%

76.5%

54.5%

20.7%

 IDisp

29.8

47.5

75.2

112.9

146.1

116.3

390.1%

59.3%

58.3%

50.2%

29.5%

 RCI

-0.022

-0.064

-0.115

-0.163

-0.179

-0.157

715.5%

192.0%

79.2%

42.2%

9.6%

 RII*

-0.153

-0.418

-0.744

-1.056

-1.152

-0.999

653.8%

173.7%

78.0%

41.9%

9.0%

 RD

0.161

0.143

0.188

0.214

0.206

0.046

28.5%

-11.1%

31.8%

13.9%

-3.7%

 ACI

-0.0114

-0.0238

-0.0366

-0.0437

-0.0427

-0.0313

274.0%

108.2%

53.8%

19.5%

-2.3%

 BGV

28.5

26.2

48.6

64.9

63.3

34.8

121.9%

-8.1%

85.6%

33.5%

-2.5%

 SII*

-0.080

-0.155

-0.237

-0.283

-0.275

-0.196

245.7%

95.2%

52.8%

19.2%

-2.8%

Table 21. Measures of Educational Disparity in Current Smoking in Selected Years and Percent 

Change in Disparity by Year among Males 25 and Over, 1965-2003

*Based on regression analysis (see methods section).

Abbreviations: RR=Rate Ratio; IDisp=Index of Disparity; RCI=Relative Concentration Index; RII=Relative Index of Inequality; 

RD=Rate Difference; ACI=Absolute Concentration Index; BGV=Between Group Variance; SII=Slope Index of Inequality.

during the early part of this period, from 1965-76 

and the pace of increase has slowed considerably by 

2003. On the other hand, the RR and the IDisp show 

approximately the same relative increase in all periods, 

with the exception of 1995-2003. In terms of absolute 

disparity, all of the measures indicate that disparity has 

increased, but the magnitude of the increase varies. 

The magnitude of the increase is about twice as large 

for the ACI and SII (~250%) as for the BGV (122%), 

while the RD shows only a marginal increase (29%). 

While the BGV and SII are both population-weighted 

measures of absolute disparity, the BGV indicates 

a decrease in disparity from 1965-76, while the SII 

indicates a strong increase.
44

Figure 19. Trends in Educational Disparity in Smoking among Males 25 and Over, 1965-2003

1965

200

180

160

140

120

100

80

60

40

20

0

1975

Relative Education Disparity

Index of Disparity

0.00

-0.02

-0.04

-0.06

-0.08

-0.10

-0.12

-0.14

-0.16

-0.18

-0.20

Relative Concentration Index

1985 1995

90

80

70

60

50

40

30

20

10

0

Absolute Education Disparity

Between Group Variance

0.00

-0.01

-0.02

-0.03

-0.04

-0.05

-0.06

IDisp

RCI

BGV

ACI

Absolute Concentration Index

1965 1975 1985 1995

 Disparity trends among males are shown in Figure 

19. Overall, the graphs for relative disparity (left 

panel) show that the IDisp and the RCI are relatively 

consistent with respect to the trend in relative 

educational disparity in smoking, though there are 

some particular years (e.g., 1979-80) when they 

indicate disparity is moving in opposite directions. 

With respect to absolute educational disparity in 

smoking, Figure 19 generally indicates that absolute 

disparity increased rather sharply from the mid 1960s 

to the late 1980s, and has shown minimal increase 

as the rate of decline in current smoking among all 

groups has slowed.

Females 

 Rates of current smoking among females from 

1965-2003 are shown in Figure 20 and are given, along 

with the population distribution, in Table 22. While 

in 1965 differences in smoking appear considerably 

smaller than for men, the overall pattern of smoking 

looks similar over time, with the strongest declines in 

smoking occurring among women with 16 or more 

years of education. The population distribution of 

women across educational groups also demonstrates 

a trend similar to that for men, with substantial 

increases in the proportion of women with greater 

than 12 years of education.
45

Figure 20. Trends in the Prevalence of Smoking by Years of Education among Females, 1965-2003

1965

0.45

0.40

0.35

0.30

0.25

0.20

0.15

0.10

0.05

0.00

<12 Years

1985

12 Years 13-15 Years 16+ Years

Prevalence Rate

1970 1975 1980 1990 1995 2000

Prevalence of Current Smoking Percent of Total Population

Year

1965

1970

1976

1980

1985

1990

1995

2000

2001

2002

2003

∆1965 to 2003

%∆

<12y

0.238

0.304

0.309

0.325

0.307

0.267

0.261

0.244

0.230

0.246

0.217

-0.021

-9.0%

12y

0.387

0.338

0.334

0.324

0.306

0.263

0.257

0.260

0.253

0.242

0.240

-0.147

-37.9%

13-15y

0.371

0.314

0.299

0.280

0.247

0.200

0.219

0.208

0.217

0.205

0.202

-0.169

-45.5%

16+y

0.350

0.261

0.228

0.236

0.160

0.122

0.135

0.108

0.109

0.097

0.095

-0.255

-72.8%

<12y

0.267

0.412

0.337

0.291

0.247

0.214

0.189

0.177

0.173

0.166

0.165

-0.101

-38.0%

12y

0.568

0.384

0.404

0.420

0.420

0.407

0.388

0.313

0.304

0.306

0.299

-0.270

-47.5%

13-15y

0.079

0.123

0.144

0.165

0.188

0.207

0.224

0.293

0.301

0.301

0.304

0.225

284.9%

16+y

0.086

0.081

0.115

0.124

0.145

0.172

0.199

0.217

0.222

0.227

0.232

 0.146

170.1%

Table 22. Prevalence of Current Smoking and Population Distribution among Females Age 25 

and Over, by Years of Education, NHIS Selected Years 1965-2003
46

 Measures of relative and absolute disparity for 

females are presented in Table 23. All of the relative 

measures show increases in educational disparity 

over this time period (based on the % change from 

1965-2003), but similar to the results for males, the 

magnitude of the increase is approximately twice 

as large for the RCI and RII as for the RR or IDisp. 

However, only the RCI and RII indicate that the 

direction of the gradient changed over this time period 

(in fact the sign of the % change for the RCI and RII is 

negative because the gradient changed direction over 

time, but there is clearly an increase in socioeconomic 

disparity over time). The RCI and RII indicate that 

educational disparities in smoking among females (as 

for males) increased most during the early part of this 

period, from 1965-85, and the pace of increase has 

slowed considerably by 2003. On the other hand, the 

RR and the IDisp show a large increase in disparity 

from 1995-2003, most likely because the rate in the 

reference group (16+ years) has continued to decline. 

In terms of absolute disparity, both the RD and the 

BGV indicate that educational disparity has declined 

among females. On the other hand, the ACI and SII 

show that in 1965 smoking was more concentrated 

among the better educated but over time this gradient 

reversed and by 2003 the gradient was similar in 

magnitude but smoking was more concentrated among 

the less educated. While the BGV and SII are both 

population-weighted measures of absolute disparity, 

the BGV indicates a greater increase (33%) in disparity 

from 1995-2003 than does the SII (7%). This seems 

likely due to the fact that the BGV squares deviations 

further from the population average, and the rate for 

the 16 and over group declined strongly over this 

period.

 Overall, the graphs for relative disparity (left 

panel of Figure 21) show that the IDisp and the RCI 

are generally consistent with respect to the trend in 

relative educational disparity in smoking. With respect 

to absolute educational disparity in smoking, Figure 

Measures of Relative Disparity Measures of Absolute Disparity

Year

1965

1976

1985

1995

2003

∆1965 to 2003

%∆

%∆1965 to 1976

%∆1976 to 1985

%∆1985 to 1995

%∆1995 to 2003

 RR

1.63

1.47

1.92

1.93

2.52

0.90

143.1%

-25.6%

98.6%

0.8%

63.2%

 IDisp

55.2

37.7

79.6

81.9

130.5

75.3

136.3%

-31.7%

111.0%

2.9%

59.4%

 RCI

0.074

-0.027

-0.088

-0.103

-0.132

-0.206

-279.0%

-136.6%

227.3%

16.7%

28.2%

 RII*

0.556

-0.182

-0.588

-0.675

-0.854

-1.410

-253.5%

-132.7%

223.7%

14.8%

26.5%

 RD

0.15

0.11

0.15

0.13

0.14

0.0

-2.6%

-28.7%

39.0%

-14.6%

15.1%

 ACI

0.0253

-0.0083

-0.0242

-0.0232

-0.0252

-0.051

-199.8%

-132.9%

190.8%

-4.1%

8.9%

 BGV

41.0

10.2

27.4

22.5

30.0

-11.054

-27.0%

-75.0%

167.6%

-17.9%

33.2%

 SII*

0.191

-0.056

-0.161

-0.152

-0.163

-0.354

-185.6%

-129.4%

187.6%

-5.6%

7.4%

Table 23. Measures of Educational Disparity in Current Smoking in Selected Years and Percent 

Change in Disparity by Year among Females 25 and Over, 1965-2003

*Based on regression analysis (see methods section).

Abbreviations: RR=Rate Ratio; IDisp=Index of Disparity; RCI=Relative Concentration Index; RII=Relative Index of Inequality; 

RD=Rate Difference; ACI=Absolute Concentration Index; BGV=Between Group Variance; SII=Slope Index of Inequality.
47

Figure 21. Trends in Educational Disparity in Smoking among Females 25 and Over, 1965-2003

1965

160

140

120

100

80

60

40

20

0

1975

Relative Educational Disparity

Index of Disparity

0.10

-0.05

0.00

-0.05

-0.10

-0.15

-0.20

Relative Concentration Index

1985 1995

45

40

35

30

25

20

15

10

5

0

Absolute Educational Disparity

Between Group Variance

0.03

0.02

0.01

0.00

-0.01

-0.02

-0.03

-0.04

IDisp

RCI

BGV

ACI

Absolute Concentration Index

1965 1975 1985 1995

21 generally indicates that absolute disparity increased 

rather sharply from the mid-70s and early 80s to the 

mid-1990s, and has shown smaller increases as the rate 

of decline in current smoking among all groups has 

slowed. The BGV appears to exhibit substantially more 

variation around the mid-1990s than does the SII, but 

given the differences in scale it is difficult to judge 

whether or not this is of any consequence.

DIAGNOSTICS

Why is the increase in disparity larger for the RCI 

and RII than for the IDisp?

 For both males and females, the proportionate 

increase in relative disparity is approximately twice as 

large when measured by the RCI/RII as when measured 

by the IDisp or RR. Given that one of the differences 

between these sets of measures is that the RCI and RII 

are population-weighted and there were dramatic shifts 

in the distribution of education over this period, it is 

worth investigating the potential impact of population 

shifts on the disparity measures. Table 24 below shows 

measures of relative and absolute disparity for males 

and females assuming no change in the distribution of 

education groups over time (i.e., the level of disparity 

was recalculated in 2003 using the 1965 population 

distribution). Holding population distribution 

constant, there is now far more agreement between the 

IDisp and RCI. For males the proportionate change 

in the RCI is now 163%, compared to 716% increase 

when the actual 2003 educational distribution is used. 

For women the RCI increase is now 136% compared to 

the 280% using the 2003 distribution. Naturally, the 

values for the RR, IDisp, and RD are exactly the same 
48

since these measures ignore population distribution. 

Holding constant the distribution of education in this 

case serves to reduce the magnitude of the increase in 

the RCI and ACI because this gives less weight in 2003 

to the larger-than-average decline in smoking among 

those with >16 years of education.

Case Study 6: Race and Ethnic Disparities 

in Breast Cancer Incidence, 1990-2001

Data and Methods

 The data source for this analysis come from the 

SEER database called: Incidence - SEER 18 Regs, 

Nov 2003 Sub for Expanded Races (1990-2001 

varying). Individuals for whom race was coded as 

“Unknown” are excluded from this analysis. Because 

of the difficulties in obtaining accurate estimates for 

Hispanics, the analysis was confined to the following 

12 registries, consistent with the categorization 

used in the Annual Report to the Nation on the 

Status of Cancer (14): SEER 12 registries = ‘San 

Francisco-Oakland SMSA - 1990+’,’Connecticut 

Measures of Relative Disparity Measures of Absolute Disparity

Males

1965

2003

∆1965 to 2003

%∆

Females

1965

2003

∆1965 to 2003

%∆

 Total Rate

0.52

0.26

-0.26

-50.3%

0.34

0.22

-0.12

-36.0%

 RR

1.40

2.78

1.38

346.6%

1.63

2.52

0.90

143.1%

IDisp

29.8

146.1

116.3

390.1%

55.2

130.5

75.3

136.3%

 RCI

-0.0219

-0.0577

-0.0358

163.4%

0.0738

-0.0263

-0.1001

-135.6%

 RD

0.16

0.21

0.05

28.5%

0.15

0.14

0.00

-2.6%

 ACI

-0.0114

-0.0149

-0.0035

30.9%

0.0253

-0.0058

-0.0311

-122.8%

 BGV

28.5

50.3

21.8

76.5%

41.0

16.0

-25.1

-61.1%

Table 24. Changes in Educational Disparity between 1965 to 2003 in Current Smoking Holding 

Constant the Population Distribution Equal to That Observed in 1965

*Based on regression analysis (see methods section).

Abbreviations: RR=Rate Ratio; IDisp=Index of Disparity; RCI=Relative Concentration Index; RII=Relative Index of Inequality; 

RD=Rate Difference; ACI=Absolute Concentration Index; BGV=Between Group Variance; SII=Slope Index of Inequality.

- 1990+’,’Detroit (Metropolitan) - 1990+’,’Hawaii - 

1990+’,’Iowa - 1990+’,’New Mexico - 1990+’,’Seattle 

(Puget Sound) - 1990+’,’Utah - 1990+’,’Atlanta 

(Metropolitan) - 1990+’,’San Jose-Monterey - 

1990+’,’Los Angeles - 1990+’,’Alaska Natives - 1990+’. 

The analysis is stratified by age and rates are not 

age-adjusted.

Ages 45-74 

 Trends in the incidence of breast cancer among 

females 45-74, by race / ethnicity are shown in 

Figure 22, and the underlying data and population 

distribution are given in Table 25. White females 

have the highest incidence rates across all years, and 

rates are lowest among Asian/Pacific Islanders. Overall 

incidence rates generally remained constant over 

the period from 1990-2001, though there is some 

suggestion that rates have begun to decline since the 

late 1990s.

 The changes in race / ethnic disparity in breast 

cancer incidence are given in Table 26. Overall, the 

measures of both relative and absolute disparity 
49

Incidence Rate per 100,000 Percent of Total Population

Year

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

∆1990 to 2001

%∆

A/PI

111.4

126.1

165.9

149.7

149.7

138.8

164.1

147.3

129.9

127.7

122.7

109.1

-2.3

-2.1%

AI/AN

193.4

189.3

200.9

201.3

194.6

206.0

211.9

232.9

235.4

235.2

214.1

225.7

32.3

16.7%

Black

254.6

262.5

269.7

274.0

279.1

272.6

274.0

271.8

273.6

280.7

261.7

244.7

-9.9

-3.9%

White

320.5

327.2

320.5

312.9

319.9

322.8

324.0

333.3

337.7

336.0

327.7

322.1

1.6

0.5%

Hispanic

190.6

196.9

194.3

181.1

196.8

196.3

204.7

193.7

209.2

199.6

209.8

193.6

3.0

1.6%

A/PI

0.008

0.008

0.008

0.009

0.009

0.009

0.009

0.010

0.010

0.010

0.011

0.011

0.003

37.0%

AI/AN

0.078

0.081

0.084

0.087

0.090

0.093

0.095

0.098

0.100

0.101

0.104

0.105

0.027

34.2%

Black

0.083

0.083

0.083

0.084

0.085

0.085

0.086

0.087

0.087

0.088

0.088

0.089

0.006

6.8%

Hispanic

0.086

0.088

0.089

0.091

0.092

0.094

0.096

0.098

0.101

0.103

0.106

0.109

0.022

25.7%

White

0.744

0.739

0.735

0.730

0.725

0.719

0.714

0.708

0.703

0.697

0.691

0.686

-0.058

-7.7%

Table 25. Incidence of Female Breast Cancer and Population Distribution among Females 45-74, 

by Race and Ethnicity, 1990-2001

Figure 22. Trends in Breast Cancer Incidence by Race and Ethnicity among Women 45-74, 1990-2001

1990

400

350

300

250

200

150

100

50

0

White

1995 2000

Black AI/AN API

Rate per 100,000

1991 1992 1993 1994 1996 1997 1998 1999 2001

Hispanic
50

Underlying Data Measures of Relative Disparity

Measures of

Absolute Disparity

Race

1990

A/PI

AI/AN

Black

Hispanic

White

Total

1995

A/PI

AI/AN

Black

Hispanic

White

Total

∆1990 to 1995

%∆

2001

A/PI

AI/AN

Black

Hispanic

White

Total

∆1995 to 2001

%∆

∆1990 to 2001

%∆

Rate

111.4

193.4

254.6

190.6

320.5

292.1

138.8

206.0

272.6

196.3

322.8

294.2

2.1

0.7%

109.1

225.7

244.7

193.6

322.1

288.8

-5.4

-1.8%

-3.4

-1.2%

% Pop

0.008

0.078

0.083

0.086

0.744

0.009

0.093

0.085

0.094

0.719

0.011

0.105

0.089

0.109

0.686

RR*

1.0

1.7

2.3

1.7

2.9

2.9

1.0

1.5

2.0

1.4

2.3

2.3

-0.6

-29.4%

1.0

2.1

2.2

1.8

3.0

3.0

0.6

47.3%

0.1

4.0%

IDisp

0

18.4

32.1

17.8

46.9

115.3

0

12.1

24.1

10.3

33.1

79.7

-35.6

-30.9%

0

26.7

31.1

19.4

48.8

126.0

46.3

58.1%

10.8

9.3%

T

-2.9

-21.4

-10.0

-24.1

75.6

17.2

-3.2

-23.1

-6.0

-25.3

73.3

15.7

-1.5

-8.8%

-4.0

-20.3

-12.5

-29.1

83.5

17.7

2.0

12.8%

0.5

2.9%

MLD

7.7

32.4

11.4

36.9

-68.9

19.5

6.9

33.0

6.5

37.9

-66.8

17.5

-2.1

-10.5%

10.6

26.0

14.7

43.4

-74.9

19.8

2.4

13.5%

0.3

1.6%

RD*

0

82.0

143.2

79.2

209.1

209.1

0.0

67.2

133.8

57.5

184.0

184.0

-25.1

-12.0%

0.0

116.6

135.6

84.5

213.0

213.0

29.0

15.7%

3.9

1.9%

BGV

260.0

765.2

117.3

891.0

597.5

2631.0

220.5

720.3

39.7

898.3

589.7

2468.4

-162.7

-6.2%

352.0

419.1

172.5

984.4

760.9

2688.9

220.6

8.9%

57.9

2.2%

Table 26. Changes in Race and Ethnic Disparity in Female Breast Cancer Incidence among 

Females 45-74, 1990-2001

*For the RR and RD the row marked ‘Total’ contains the maximum of RR or RD, a measure of the range

Abbreviations: RR=Rate Ratio; IDisp=Index of Disparity; T=Theil Index; MLD=Mean Log Deviation; RD=Rate Difference; 

BGV=Between Group Variance.
51

generally agree with respect to the overall change in 

race / ethnic disparity (bottom shaded row of Table 

26). All four measures of relative disparity register 

an increase, as do the two measures of absolute 

disparity. The magnitude of the change in disparity 

differs, primarily for measures of relative disparity. 

For example, from 1995-2001 the Rate Ratio increases 

from 2.3 to 3.0, a relative increase of 47%, the Index of 

Disparity registers nearly a 60% increase, but both the 

Theil Index and the Mean Log Deviation show only 

modest increases, on the order of 15%. In general, the 

Index of Disparity appears to be more variable than 

either T or MLD, which likely reflects the fact that it is 

not weighted by population size.

 The analysis of changes in race / ethnic disparities 

in breast cancer above showed general agreement 

in the overall change in disparity from 1990-2001. 

Figure 23 shows trends in relative and absolute 

disparity and also suggests broad agreement with 

Figure 23. Trends in Race and Ethnic Disparity in Breast Cancer Incidence among Women 45-74, 

1990-2001

1990

140

120

100

80

60

40

20

0

1995 2000

Index of Disparity

1991 1992 1993 1994 1996 1997 1998 1999 2001

25

20

15

10

5

0

Mean Log Deviation

MLD

IDisp

respect to the trends in disparity. Both the MLD and 

the IDisp show declines from 1990 to 1996, and 

rising disparity thereafter. Despite broad agreement 

with respect to the trend in race / ethnic disparity, 

the plot of the trends also shows disagreement for 

specific periods. For three specific periods (boxed areas 

on Figure 23), 1990-1, 1994-5, and 1997-8, the MLD 

and the IDisp move in opposite directions, with one 

measure indicating an increase in disparity and one 

suggesting a decrease.

Ages 75 and Over 

Trends in breast cancer incidence among those 75 and 

over are shown in Figure 24. The overall patterning 

by race / ethnicity is similar to that seen for women 

45-74, with Whites having the highest mortality rates 

and Asian/Pacific Islanders the lowest. On the whole, 

incidence rates appear to be roughly constant over the 

period 1990-2001
52

Figure 24. Trends in Breast Cancer Incidence by Race and Ethnicity among Women 75 and Over, 

1990-2001

1990

600

500

400

300

200

100

0

White

1995 2000

Black AI/AN API

Rate per 100,000

1991 1992 1993 1994 1996 1997 1998 1999 2001

Hispanic

Incidence Rate per 100,000 Percent of Total Population

Year

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

∆1990 to 2001

%∆

A/PI

85.1

40.6

216.0

209.4

128.6

229.1

286.2

112.6

201.8

177.0

137.4

130.0

44.9

52.8%

AI/AN

245.5

204.0

289.2

231.1

192.7

215.5

231.2

286.1

249.3

248.3

230.0

252.1

6.6

2.7%

Black

406.1

334.8

402.6

344.2

346.9

419.0

387.6

423.2

398.4

389.2

379.1

375.1

-31.0

-7.6%

White

480.7

482.8

478.4

471.5

458.6

471.9

464.7

486.5

498.8

492.5

466.4

463.9

-16.7

-3.5%

Hispanic

267.7

313.3

280.7

231.0

253.6

301.4

304.6

298.5

294.0

323.2

284.1

258.6

-9.1

-3.4%

A/PI

0.004

0.004

0.004

0.004

0.005

0.005

0.005

0.005

0.005

0.005

0.005

0.005

0.001

26.1%

AI/AN

0.046

0.048

0.050

0.053

0.056

0.059

0.062

0.065

0.069

0.072

0.076

0.080

0.034

75.5%

Black

0.064

0.064

0.064

0.065

0.065

0.065

0.065

0.066

0.066

0.066

0.066

0.066

0.002

2.8%

Hispanic

0.053

0.053

0.054

0.054

0.054

0.055

0.056

0.056

0.057

0.059

0.061

0.065

0.012

22.0%

White

0.833

0.830

0.827

0.824

0.820

0.816

0.812

0.808

0.803

0.798

0.791

0.784

-0.049

-5.9%

Table 27. Incidence of Female Breast Cancer and Population Distribution among Females 75 and 

Over, by Race and Ethnicity, 1990-2001
53

Underlying Data Measures of Relative Disparity

Measures of

Absolute Disparity

Race

1990

A/PI

AI/AN

Black

Hispanic

White

Total

1995

A/PI

AI/AN

Black

Hispanic

White

Total

∆1990 to 1995

%∆

2001

A/PI

AI/AN

Black

Hispanic

White

Total

∆1995 to 2001

%∆

∆1990 to 2001

%∆

Rate

85.1

245.5

406.1

267.7

480.7

452.2

229.1

215.5

419.0

301.4

471.9

442.9

-9.3

-2.1%

130.0

252.1

375.1

258.6

463.9

426.0

-16.8

-3.8%

-26.1

-5.8%

% Pop

0.004

0.046

0.064

0.053

0.833

0.005

0.059

0.065

0.055

0.816

0.005

0.080

0.066

0.065

0.784

RR*

1.0

2.9

4.8

3.1

5.7

5.7

1.1

1.0

1.9

1.4

2.2

2.2

-3.5

-74.4%

1.0

1.9

2.9

2.0

3.6

3.6

1.4

116.0%

-2.1

-36.8%

IDisp

0

47.1

94.3

53.7

116.3

311.4

1.6

0

23.6

10.0

29.7

64.9

-246.5

-79.2%

0

23.5

47.1

24.7

64.2

159.6

94.7

146.0%

-151.9

-48.8%

T

-1.4

-15.1

-6.2

-16.4

54.1

15.0

-1.6

-20.6

-3.4

-14.4

55.2

15.2

0.2

1.2%

-2.0

-24.9

-7.4

-19.6

72.7

19.0

3.8

24.8%

4.0

26.3%

MLD

7.3

27.9

6.9

27.7

-50.9

18.8

3.1

42.3

3.6

21.2

-51.8

18.3

-0.5

-2.7%

6.5

42.0

8.4

32.2

-66.8

22.3

4.0

21.7%

3.5

18.5%

RD*

0.0

160.4

321.0

182.6

395.6

395.6

19.0

0.0

17.2

6.2

75.8

75.8

-319.8

-80.8%

0.0

122.1

245.1

128.6

333.9

333.9

258.1

340.7%

-61.7

-15.6%

BGV

586.6

1949.5

135.5

1800.2

675.7

5147.4

211.9

3035.7

37.1

1101.0

687.1

5072.8

-74.6

-1.4%

481.0

2423.4

170.3

1810.3

1125.6

6010.7

937.9

18.5%

863.3

16.8%

Table 28. Changes in Race and Ethnic Disparity in Female Breast Cancer Incidence among 

Females 75 and Over, 1990-2001

*For the RR and RD the row marked ‘Total’ contains the maximum of RR or RD, a measure of the range

Abbreviations: RR=Rate Ratio; IDisp=Index of Disparity; T=Theil Index; MLD=Mean Log Deviation; RD=Rate Difference; 

BGV=Between Group Variance.
54

 Changes in race / ethnic relative and absolute 

disparity are shown in Table 28. In contrast to the 

overall pattern of results for women 45-74, there 

is generally disagreement among both relative and 

absolute measures for the change in race / ethnic 

disparity from 1990-2001 (bottom row of Table 28). 

Both the Rate Ratio and the Index of Disparity suggest 

that race / ethnic disparity has declined, by 37% and 

49%, respectively, while the Theil Index and Mean 

Log Deviation indicate increases in relative disparity 

of around 20-25%. With respect to absolute disparity, 

from 1990-2001 the Rate Difference declined from 

395.6 to 333.9, a 16% decline, while the Between 

Group Variance showed an increase of nearly 17%. 

Given that the referent group for the RR, IDisp, and RD 

is the group with the lowest rate (A/PI), the increase in 

the rate reported among this group from 1990 to 2001 

seems the likeliest explanation for why each of these 

measures registered a decline over this period.

 Trends in relative disparity among those 75 and 

over are shown in Figure 25. The MLD and the IDisp 

generally follow similar patterns over time, but the 

boxed regions show that from 1992-3 and 1997-8 the 

MLD suggests an increase in disparity while the IDisp 

suggests a decrease; the opposite is true for 1996-7. 

The steep decline in the IDisp from 1991-2 is likely to 

be related to the sharp increase in the rate for the A/PI 

group that year, which became the reference group for 

the IDisp.

Case Study 7: Socioeconomic Disparities in 

Obesity, 1960-2000

 Trends in obesity were assessed using data from 

five adult samples of the National Health Examination 

Surveys (NHANES): the Health Examination Survey 

Figure 25. Race and Ethnic Trends in Relative Disparity in Female Breast Cancer Incidence among Those 

75 and Over, 1990-2001

1990

800

700

600

500

400

300

200

100

0

1995 2000

Index of Disparity

1991 1992 1993 1994 1996 1997 1998 1999 2001

30

25

20

15

10

5

0

Mean Log Deviation

MLD

IDisp
55

Figure 26. Trends in Obesity by Years of Education among Males, 1960-2000

1960

0.30

0.25

0.20

0.15

0.10

0.05

0.00

<12 Years

1980

12 Years >12 Years

Prevalence Rate

1965 1970 1975 1985 1990 1995

(1959-62), NHANES I (1971-74), NHANES II (1976-80), 

NHANES III (1988-94), and NHANES 1999-2002 

(n=56,311). Sample weights were used in each survey 

to account for unequal sampling probabilities and 

nonresponse. For ease of presentation, the midpoint 

of data collection years for each survey was used as 

the survey year (1961, 1973, 1978, 1991, and 2000). 

While the examination surveys are not conducted 

as frequently as the NHIS, they have the advantage 

of obtaining measured, rather than self-reported, 

height and weight. Self-reported height and weight 

are subject to bias and the extent of bias differs with 

social group characteristics (15), which makes using 

self-reported data for assessing disparities difficult. 

Pregnant women were excluded, and individuals were 

categorized as obese if they had a body mass index 

(BMI) of 30 or greater. The analysis was restricted to 

individuals 18-74 years of age with no missing data on 

age, gender, race, or education. In order to minimize 

the effect of extreme or implausible values of BMI 

individuals falling outside the 1st and 99th percentiles 

of the BMI distribution in each survey year were 

excluded. The above exclusions yielded an analytic 

sample of 54,066 individuals. In order to maintain 

a consistent grouping across surveys, education was 

categorized as <12 years, 12 years, or greater than 12 

years (NHANES 1999-2002 did not disaggregate those 

with >12 years of education).

Males 

Obesity trends by education among males are 

shown in Figure 26. Rates of obesity have increased 

substantially in all educational groups, particularly 

since 1978. 
56

1961

1973

1978

1991

2000

∆1961 to 2000

%∆

<12 years

0.138

0.121

0.128

0.212

0.256

0.118

85.4%

12 years

0.099

0.129

0.124

0.196

0.286

0.187

189.1%

>12 years

0.094

0.076

0.083

0.165

0.256

0.162

172.2%

<12 years

0.311

0.356

0.306

0.246

0.220

-0.091

-29.2%

12 years

0.473

0.297

0.297

0.318

0.259

-0.214

-45.3%

>12 years

0.216

0.347

0.398

0.436

0.521

0.305

141.3%

Prevalence of obesity Percent of Total Population

Raw Data Measures of Relative Disparity Measures of Absolute Disparity

Education

1960 

<12 years 

12 years 

>12 years 

Total

2000 

<12 years 

12 years 

>12 years 

Total

∆1960 to 2000

%∆

Rate

0.138

0.099

0.094

0.110

0.256

0.286

0.256

0.264

0.154

139.6%

% Pop

0.311

0.473

0.216

0.220

0.259

0.521

RR*

1.47

1.05

1.00

1.47

1.00

1.12

1.00

1.12

-0.36

-75.4%

IDisp

23.6

2.5

0.0

26.1

0.1

5.8

0.0

5.9

-20.2

-77.3%

RCI

-0.269

0.040

0.145

-0.084

-0.167

-0.085

0.242

-0.009

0.075

-89.1%

RII†

-0.593

-0.067

0.526

-88.8%

RD*

0.044

0.005

0.000

0.044

0.001

0.030

0.000

0.030

-0.015

-33.0%

ACI

-0.0296

0.0044

0.0159

-0.0093

-0.0440

-0.0223

0.0638

-0.0024

0.007

-73.8%

BGV

2.49

0.60

0.56

3.65

0.11

1.24

0.32

1.67

-1.97

-54.1%

SII†

-0.065

-0.018

0.048

-73.0%

Table 29. Prevalence of Obesity and Population Distribution by Education among Males, 

1960-2000

Table 30. Changes in Educational Disparity in Obesity among Males, 1960-2000

*For the RR and RD the row marked ‘Total’ contains the maximum of RR or RD, a measure of the range

†Based on regression analysis (see methods section).

Abbreviations: RR=Rate Ratio; IDisp=Index of Disparity; RCI=Relative Concentration Index; RII=Relative Index of Inequality; 

RD=Rate Difference; ACI=Absolute Concentration Index; BGV=Between Group Variance; SII=Slope Index of Inequality.

 Changes in disparity are shown in Table 30. 

Relative disparity among education groups in the 

prevalence of obesity has declined according to all four 

measures, and the magnitude of the decline is similar 

across all the measures. For absolute disparity all the 

measures suggest that disparity has declined, but the 

magnitude of the increase is slightly larger for the ACI 

and SII.

 Disparity trends are shown in Figure 27. For relative 

disparity both the IDisp and the RII show educational 
57

Figure 27. Trends in Educational Disparity in Obesity among Males, 1960-2000

1960

70

60

50

40

30

20

10

0

1970 1980

Relative Educational Disparity

Index of Disparity

0

-0.1

-0.2

-0.3

-0.4

-0.5

-0.6

-0.7

Relative Concentration Index

1990 2000

6

5

4

3

2

1

0

Absolute Educational Disparity

Between Group Variance

0

-0.01

-0.02

-0.03

-0.04

-0.05

-0.06

-0.07

-0.08

Slope Index of Inequality

IDisp

RII

BGV

SII

1960 1970 1980 1990 2000

disparity in obesity among males first increasing then 

decreasing from 1961 to 2000. However, from 1973 

to 1978 the IDisp shows a decline in disparity while 

the RII shows a small increase. This may be due to 

the small increase in obesity during this period among 

those with >12 years of education, the referent group 

for the IDisp. For absolute disparity, both the BGV and 

the SII indicate that educational disparity among males 

increased from 1960 but decreased thereafter, but 

between 1973 and 1978 the BGV shows a decline while 

the SII shows an increase (i.e., the SII becomes more 

negative, indicating the gap in obesity rates between 

the most and least educated has grown). Additionally, 

the magnitude of the increase in absolute disparity 

from 1961 to 1973 appears much larger for the BGV 

than for the SII.

Females 

 Trends in the prevalence of obesity among female 

education groups are shown in Figure 28. Similar to 

the pattern for males, rates of obesity have increased 

dramatically since 1978. However, among females 

the rates of obesity clearly increase with decreasing 

education.

 Relative disparity among education groups in the 

prevalence of obesity has declined according to all 

four measures, and the magnitude of the decline is 

very similar across all the measures (Table 32). For 

absolute disparity the RD, ACI, and SII all indicate 

that educational disparity has declined by around 

40%, with the BGV indicating a slightly larger 

decline (67%).
58

Figure 28. Trends in Obesity by Years of Education among Females, 1960-2000

1960

0.45

0.40

0.35

0.30

0.25

0.20

0.15

0.10

0.05

0.00

<12 Years

1980

12 Years >12 Years

Prevalence Rate

1965 1970 1975 1985 1990 1995

1961

1973

1978

1991

2000

∆1961 to 2000

%∆

<12 years

0.262

0.228

0.233

0.312

0.390

0.128

48.9%

12 years

0.129

0.133

0.144

0.267

0.355

0.226

176.0%

>12 years

0.080

0.080

0.087

0.178

0.296

0.216

268.4%

<12 years

0.288

0.357

0.308

0.225

0.209

-0.079

-27.5%

12 years

0.536

0.380

0.375

0.377

0.258

-0.278

-51.9%

>12 years

0.176

0.263

0.317

0.398

0.533

0.358

203.3%

Prevalence of obesity Percent of Total Population

Table 31. Prevalence of Obesity and Population Distribution by Education among Females, 

1960-2000
59

Raw Data Measures of Relative Disparity Measures of Absolute Disparity

Education

1960 

<12 years 

12 years 

>12 years 

Total

2000 

<12 years 

12 years 

>12 years 

Total

∆1960 to 2000

%∆

Rate

0.262

0.129

0.080

0.159

0.390

0.355

0.296

0.331

0.172

108.8%

% Pop

0.288

0.536

0.176

0.209

0.258

0.533

RR*

3.26

1.60

1.00

3.26

1.32

1.20

1.00

1.32

-1.94

-86.0%

IDisp

112.8

30.0

0.0

142.8

15.8

9.9

0.0

25.7

-117.0

-82.0%

RCI

-0.339

0.049

0.074

-0.217

-0.195

-0.090

0.223

-0.062

0.155

-71.6%

RII†

-1.590

-0.449

1.141

-71.7%

RD*

0.181

0.048

0.000

0.181

0.094

0.059

0.000

0.094

-0.088

-48.4%

ACI

-0.0537

0.0077

0.0117

-0.0343

-0.0645

-0.0297

0.0738

-0.0204

0.014

-40.6%

BGV

30.79

4.81

10.74

46.33

7.26

1.49

6.42

15.16

-31.17

-67.3%

SII†

-0.252

-0.149

0.103

-41.0%

Table 32. Changes in Educational Disparity in Obesity among Females, 1960-2000

*For the RR and RD the row marked ‘Total’ contains the maximum of RR or RD, a measure of the range

†Based on regression analysis (see methods section).

Abbreviations: RR=Rate Ratio; IDisp=Index of Disparity; RCI=Relative Concentration Index; RII=Relative Index of Inequality; 

RD=Rate Difference; ACI=Absolute Concentration Index; BGV=Between Group Variance; SII=Slope Index of Inequality.

 The overall trends in absolute and relative disparity 

for females are shown in Figure 29. For relative 

disparity both the IDisp and the RCI give similar 

pictures of the trend in relative educational disparity, 

while for absolute disparity both the BGV and the 

SII give similar pictures of the trend in absolute 

educational disparity in obesity among females.
60

Figure 29. Trends in Educational Disparity in Obesity among Females, 1960-2000

1960

160

140

120

100

80

60

40

20

0

1970 1980

Relative Educational Disparity

Index of Disparity

0

-0.2

-0.4

-0.6

-0.8

-1

-1.2

-1.4

-1.6

-1.8

Relative Concentration Index

1990 2000

50

45

40

35

30

25

20

15

10

5

0

Absolute Educational Disparity

Between Group Variance

0

-0.05

-0.1

-0.15

-0.2

-0.25

-0.3

Slope Index of Inequality

IDisp

RII

BGV

SII

1960 1970 1980 1990 2000

Case Study 8: Race and Ethnic Disparities 

in Cervical Cancer Incidence, 1990-2001

 The data source for this analysis come from the 

SEER database called: Incidence - SEER 18 Regs, 

Nov 2003 Sub for Expanded Races (1990-2001 

varying). Individuals for whom race was coded as 

“Unknown” are excluded from this analysis. Because 

of the difficulties in obtaining accurate estimates for 

Hispanics, the analysis was confined to the following 

12 registries, consistent with the categorization 

used in the Annual Report to the Nation on the 

Status of Cancer (14): SEER 12 registries = ‘San 

Francisco-Oakland SMSA - 1990+’,’Connecticut 

- 1990+’,’Detroit (Metropolitan) - 1990+’,’Hawaii - 

1990+’,’Iowa - 1990+’,’New Mexico - 1990+’,’Seattle 

(Puget Sound) - 1990+’,’Utah - 1990+’,’Atlanta 

(Metropolitan) - 1990+’,’San Jose-Monterey - 

1990+’,’Los Angeles - 1990+’,’Alaska Natives - 1990+’. 

The analysis is stratified by age and rates are not 

age-adjusted.

Ages <45 

 Trends the incidence of cervical cancer among 

women less than 45 according to race / ethnicity are 

shown in Figure 30, and the underlying rates and 

population distribution are given in Table 33. Rates 

of cervical cancer incidence appear to be declining 

among most race / ethnic groups. Hispanics have 

higher rates of incidence than other groups for the 

entire period from 1990-2001.
61

Figure 30. Trends in Cervical Cancer Incidence by Race and Ethnicity among Women <45 Years of Age, 

1990-2001

1990

10

9

8

7

6

5

4

3

2

1

0

White

1995 2000

Black AI/AN API

Rate per 100,000

1991 1992 1993 1994 1996 1997 1998 1999 2001

Hispanic

Incidence Rate per 100,000 Percent of Total Population

Year

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

∆1990 to 2001

%∆

A/PI

5.5

5.9

3.6

3.0

2.9

3.4

4.2

2.7

3.1

3.4

1.2

3.3

-2.2

-40.5%

AI/AN

5.1

4.3

5.5

4.1

4.8

4.6

5.0

5.0

4.6

3.7

3.1

5.5

0.4

8.0%

Black

8.1

6.0

6.5

7.4

5.6

6.7

5.1

5.7

6.5

6.1

5.6

4.0

-4.1

-50.6%

White

7.6

6.8

7.3

6.7

6.9

6.6

6.7

6.6

6.7

6.5

6.0

5.8

-1.8

-23.8%

Hispanic

9.3

9.0

9.1

7.9

8.4

7.4

7.6

7.4

7.6

7.2

7.7

6.8

-2.5

-27.2%

A/PI

0.013

0.013

0.013

0.013

0.014

0.014

0.014

0.014

0.015

0.015

0.015

0.015

0.003

20.8%

AI/AN

0.081

0.083

0.085

0.087

0.088

0.090

0.091

0.092

0.093

0.094

0.095

0.096

0.015

18.4%

Black

0.103

0.104

0.104

0.104

0.104

0.104

0.104

0.104

0.104

0.104

0.104

0.104

0.001

0.8%

Hispanic

0.152

0.154

0.158

0.161

0.164

0.167

0.171

0.174

0.178

0.182

0.186

0.189

0.037

24.6%

White

0.651

0.646

0.641

0.635

0.630

0.625

0.620

0.615

0.610

0.605

0.600

0.595

-0.056

-8.6%

Table 33. Incidence of Cervical Cancer and Population Distribution among Females <45 Years of 

Age, by Race and Ethnicity, 1990-2001
62

Underlying Data Measures of Relative Disparity

Measures of

Absolute Disparity

Race 

1990 

A/PI 

AI/AN 

Black 

Hispanic

White 

Total

1995 

A/PI

AI/AN 

Black 

Hispanic 

White 

Total

∆1990 to 1995 

%∆ 

2001 

A/PI 

AI/AN 

Black 

Hispanic 

White 

Total

∆1995 to 2001 

%∆ 

∆1990 to 2001 

%∆ 

Rate

5.5

5.1

8.1

9.3

7.6

7.7

3.4

4.6

6.7

7.4

6.6

6.5

-1.2

-15.4%

3.3

5.5

4.0

6.8

5.8

5.7

-0.8

-11.9%

-2.0

-25.5%

% Pop

0.013

0.081

0.103

0.152

0.651

0.014

0.090

0.104

0.167

0.625

0.015

0.096

0.104

0.189

0.595

RR*

1.09

1.00

1.59

1.84

1.51

1.84

1.00

1.36

2.00

2.20

1.97

2.20

0.4

43.1%

1.00

1.67

1.21

2.07

1.78

2.07

-0.14

-11.3%

0.23

12.3%

IDisp

2.2

0.0

14.8

21.0

12.8

50.8

0.0

9.0

25.1

30.1

24.2

88.3

37.6

73.9%

0

16.7

5.3

26.7

19.4

68.1

-20.3

-23.0%

17.3

34.0%

T

-3.0

-22.4

4.8

34.9

-5.0

9.2

-4.7

-22.5

3.2

23.7

8.4

8.1

-1.2

-12.9%

-4.9

-4.5

-26.4

37.1

8.9

10.2

2.2

27.0%

1.0

10.6%

MLD

4.2

34.1

-4.6

-28.9

5.0

9.9

9.2

32.2

-3.1

-20.9

-8.3

9.1

-0.9

-8.8%

8.5

4.8

38.0

-31.4

-8.8

11.1

2.1

22.7%

1.2

11.9%

RD*

0.45

0.00

2.99

4.26

2.58

4.26

0.00

1.20

3.36

4.04

3.26

4.04

-0.2

-5.2%

0.00

2.19

0.70

3.50

2.55

3.50

-0.54

-13.3%

-0.75

-17.7%

BGV

0.06

0.57

0.01

0.40

0.00

1.04

0.14

0.35

0.00

0.13

0.00

0.62

-0.4

-40.3%

0.09

0.01

0.32

0.20

0.00

0.63

0.01

1.7%

-0.41

-39.3%

Table 34. Changes in Race and Ethnic Disparity in Cervical Cancer Incidence among Females 

<45 Years of Age, 1990-2001

*For the RR and RD the row marked ‘Total’ contains the maximum of RR or RD, a measure of the range

Abbreviations: RR=Rate Ratio; IDisp=Index of Disparity; T=Theil Index; MLD=Mean Log Deviation; 

RD=Rate Difference; BGV=Between Group Variance.
63

Changes in the relative and absolute race-ethnic 

disparity in cervical cancer incidence are shown 

in Table 34. In terms of the overall change in 

race-ethnic disparity from 1990 to 2001 for women 

<45, there is generally agreement among the set of 

relative measures and among the absolute measures, 

but there is disagreement between the absolute 

and relative measures. All of the relative measures 

indicate an increase in disparity whereas and both 

absolute measures show a decrease. Between 1990 

and 1995 both the RR and the IDisp suggest that 

race-ethnic disparity has increased by >40% but T 

and MLD suggest a moderate decrease (~ -10%). The 

opposite is true between 1995 and 2001, with RR and 

IDisp suggesting a 10-20% decrease but T and MLD 

suggesting a 23-27% increase in relative disparity. For 

absolute disparity the magnitude of the overall decline 

is greater for the BGV (-39%) than for the RD (-18%). 

Figure 31. Trends in Relative Race and Ethnic Disparity in Cervical Cancer Incidence among Women <45 

Years of Age, 1990-2001

1990

400

350

300

250

200

150

100

50

0

1995 2000

Index of Disparity

1991 1992 1993 1994 1996 1997 1998 1999 2001

40

35

30

25

20

15

10

5

0

MLD

Mean Log Deviation

IDisp

Between 1995 and 2001 the RD suggest a decline in 

disparity while the BGV suggest minimal change. The 

disagreement between the RD and BGV is likely due 

to the fact that, while Hispanics consistently had the 

highest rate, the lowest rate shifted from AI/AN to the 

A/PI group, which would affect the RD more than the 

BGV.

 The trends in relative disparity for the IDisp and the 

MLD are plotted in Figure 31. Overall both the MLD 

and the IDisp show that race / ethnic relative disparity 

increased marginally between 1990 and 2001, but this 

hides considerable year-to-year variation during this 

period. Specifically, for several periods (highlighted by 

boxes in Figure 31), specifically 1991-2, 1993-4, and 

1995-99, the MLD and the IDisp moved in opposite 

directions, with one measure indicating an increase in 

relative disparity and the other indicating a decrease.
64

Figure 32. Trends in Cervical Cancer Incidence by Race and Ethnicity among Women 45-74, 1990-2001

1990

45

40

35

30

25

20

15

10

5

0

White

1995 2000

Black AI/AN API

Rate per 100,000

1991 1992 1993 1994 1996 1997 1998 1999 2001

Hispanic

Ages 45-74 

 Trends in cervical cancer incidence among females 

45-74 are shown in Figure 32, and the rates and 

population distribution over time are given in Table 

35. Rates are substantially higher among women in 

this age group compared to those <45 years of age, but 

the general trend for this group is also one of declining 

incidence. Hispanics also have higher rates than other 

race / ethnic groups at ages 45-74

 Changes in race / ethnic disparity in cervical 

cancer incidence among those 45-74 are shown in 

Table 36. Overall, the measures of relative disparity 

generally agree with respect to the overall change in 

race / ethnic disparity: relative disparity has increased. 
65

However, the magnitude of the increase is considerably 

larger for the RR and IDisp (~120%) than for the T or 

MLD (~17%). Between 1995 and 2001 both the RR 

and the IDisp indicate increases in relative disparity of 

70-110%, while the T and MLD suggest a decrease in 

relative disparity of approximately 20%. This would 

appear to be the result of a strong decline in cervical 

cancer incidence among the A/PI group, which after 

Incidence Rate per 100,000 Percent of Total Population

Year

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

∆1990 to 2001

%∆

A/PI

23.3

12.4

11.7

15.6

8.6

16.3

21.2

12.9

6.9

6.5

12.1

8.7

-14.6

-62.6%

AI/AN

27.1

25.1

24.6

26.0

31.8

21.7

28.0

21.8

22.5

17.1

16.3

17.5

-9.6

-35.5%

Black

28.0

30.3

24.0

25.7

24.5

24.3

25.6

25.4

21.2

21.4

17.0

19.6

-8.4

-29.9%

White

18.7

17.7

17.1

17.4

17.0

15.2

16.9

15.2

15.1

14.7

15.0

13.5

-5.2

-27.7%

Hispanic

37.7

37.8

35.3

38.3

41.1

34.4

36.8

29.2

27.5

33.9

32.9

27.9

-9.8

-26.0%

A/PI

0.008

0.008

0.008

0.009

0.009

0.009

0.009

0.010

0.010

0.010

0.011

0.011

0.003

37.0%

AI/AN

0.078

0.081

0.084

0.087

0.090

0.093

0.095

0.098

0.100

0.101

0.104

0.105

0.027

34.2%

Black

0.083

0.083

0.083

0.084

0.085

0.085

0.086

0.087

0.087

0.088

0.088

0.089

0.006

6.8%

Hispanic

0.086

0.088

0.089

0.091

0.092

0.094

0.096

0.098

0.101

0.103

0.106

0.109

0.022

25.7%

White

0.744

0.739

0.735

0.730

0.725

0.719

0.714

0.708

0.703

0.697

0.691

0.686

-0.058

-7.7%

Table 35. Incidence of Cervical Cancer and Population Distribution among Females 45-74, 

by Race and Ethnicity, 1990-2001

1996 becomes the referent group for the IDisp. The 

strong change in this group has less impact on the T 

and MLD because it accounts for only about 1% of the 

SEER population in this database.

 Trends in relative disparity are shown in Figure 33. 

The analysis of changes in race / ethnic disparities in 

cervical cancer above showed general agreement in the 

overall change in disparity from 1990-2001
66

Underlying Data Measures of Relative Disparity

Measures of

Absolute Disparity

Race 

1990 

A/PI 

AI/AN 

Black 

Hispanic

White 

Total

1995 

A/PI

AI/AN 

Black 

Hispanic 

White 

Total

∆1990 to 1995 

%∆ 

2001 

A/PI 

AI/AN 

Black 

Hispanic 

White 

Total

∆1995 to 2001 

%∆ 

∆1990 to 2001 

%∆ 

Rate

23.3

27.1

28.0

37.7

18.7

21.8

16.3

21.7

24.3

34.4

15.2

18.4

-3.5

-15.9%

8.7

17.5

19.6

27.9

13.5

16.0

-2.4

-12.9%

-5.8

-26.7%

% Pop

0.008

0.078

0.083

0.086

0.744

0.009

0.093

0.085

0.094

0.719

0.011

0.105

0.089

0.109

0.686

RR*

1.2

1.4

1.5

2.0

1.0

2.0

1.1

1.4

1.6

2.3

1.0

2.3

0.3

25.6%

1.0

2.0

2.3

3.2

1.6

3.2

0.9

72.6%

1.2

116.9%

IDisp

6.1

11.1

12.4

25.3

0.0

54.9

1.9

10.9

15.0

31.8

0.0

59.6

4.7

8.5%

0

25.0

31.3

54.9

13.8

125.0

65.3

109.6%

70.0

127.4%

T

0.6

20.9

26.6

81.5

-97.8

31.8

-1.0

18.6

31.4

110.5

-113.8

45.7

13.9

43.8%

-3.6

10.1

22.4

105.2

-97.1

37.0

-8.7

-18.9%

5.2

16.5%

MLD

-0.5

-16.9

-20.7

-47.2

114.0

28.7

1.1

-15.7

-23.8

-58.9

137.9

40.6

11.9

41.7%

6.6

-9.3

-18.2

-60.3

114.7

33.5

-7.1

-17.5%

4.8

16.9%

RD*

4.6

8.3

9.3

19.0

0.0

19.0

1.2

6.6

9.1

19.3

0.0

19.3

0.3

1.7%

0.0

8.7

10.9

19.2

4.8

19.2

-0.1

-0.6%

0.2

1.0%

BGV

0.0

2.1

3.2

21.7

7.2

34.2

0.0

1.1

3.0

24.2

7.4

35.6

1.4

4.1%

0.6

0.2

1.2

15.3

4.2

21.5

-14.2

-39.7%

-12.7

-37.2%

Table 36. Changes in Race and Ethnic Disparity in Cervical Cancer Incidence among Females 

45-74, 1990-2001

*For the RR and RD the row marked ‘Total’ contains the maximum of RR or RD, a measure of the range

Abbreviations: RR=Rate Ratio; IDisp=Index of Disparity; T=Theil Index; MLD=Mean Log Deviation; 

RD=Rate Difference; BGV=Between Group Variance.
67

Figure 33. Trends in Relative Race and Ethnic Disparity in Cervical Cancer Incidence among Those 45-74, 

1990-2001

1990

250

200

150

100

50

0

1995 2000

Index of Disparity

1991 1992 1993 1994 1996 1997 1998 1999 2001

60

50

40

30

20

10

0

Mean Log Deviation

MLD

IDisp

(increasing), but the figure above suggests more 

limited agreement with respect to the annual changes 

in disparity. For a number of periods (highlighted by 

boxes in Figure 8-4), specifically 1992-3, 1995-98, and 

2000-01, the MLD and the IDisp give different answers 

with respect to the change in disparity. In particular, 

from 1995-98 the IDisp shows a striking rise in 

disparity, likely due to the large decline in the rates for 

the A/PI groups (referent group), while the MLD shows 

a moderate decline.

Case Study 9: Social Disparities in 

Mammography Screening, 1987-2003

The data for this analysis come from screening 

supplements to the National Health Interview 

Survey. Supplements asked about mammography 

screening in 1987, 1992, 1995-8, 2000, and 2003. In 

addition, to facilitate comparison with the results 

for education, household income was collapsed into 

four groups, generally similar to quartiles of the 

weighted population distribution of income for the 

entire sample over the period 1987-96. The analysis is 

restricted to individuals 45-74 years of age, and rates 

are not age-adjusted. 

Prevalence Trends

 Trends in the proportion of women 40 and over not 

reporting not receiving a mammogram within the past 

2 years by education and income are shown in 

Figure 34, and by race / ethnicity in Figure 35. There 

have clearly been sharp declines in the proportion of 

women not receiving a mammogram, but it appears 

that the bulk of the decline occurred between 1987 

and 2000 and rates appear to have changed little from 

2000 to 2003. For virtually all years Non-Hispanic 

whites, those with 16 or more years of education, and 

those in the top income quartile are more likely to 

report having received a mammogram during the past 

two years.
68

Figure 34. Trends in the Proportion of Women 40 and Over Not Receiving a Mammogram in the Past 2 

Years, by Education and Income, 1987-2003 National Health Interview Surveys

1987 1987

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

1992 1997

Education

Prevalence Rare

2002

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

Income

Prevalence Rare

<12 Years 12 Years 13-15 Years 16+ Years

1992 1997 2002

1st Q (lowest) 2nd Q 3rd Q 4th Q (highest)

 Generally speaking, there is broad agreement 

among all of the disparity measures with respect 

to which social group demonstrates the largest 

disparity in mammography screening (Table 37). 

Both relative and absolute disparities are clearly 

larger across socioeconomic groups than across race 

/ ethnic groups regardless of which measure is used, 

and disparities appear to be marginally larger across 

income than education groups. However, it might 

also be pointed out that the degree to which relative 

socioeconomic disparities are larger than relative race 

/ ethnic disparities differs across disparity measures. 

Income-related disparities in 2003 are roughly 3.7 

(70.33/19.00) times larger than race / ethnic disparities 

when measured by the Index of Disparity, but nearly 

13.6 times larger when measured by the Mean 
69

Figure 35. Trends in the Proportion of Women 40 and Over Not Receiving a Mammogram in the Past 2 

Years, by Race and Ethnicity, 1987-2003 National Health Interview Surveys

1990

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

White

1995 2000

Black Non-Hispanic Other

Prevalence Rare

1991 1992 1993 1994 1996 1997 1998 1999 2001

Hispanic

Log Deviation. This reflects the fact that the MLD 

weights social group deviations by their population 

size while the IDisp does not. Since the population 

distribution of race / ethnicity is heavily dominated by 

Non-Hispanic whites (77% of the 2003 population), 

the deviations of other race / ethnic groups receive 

relatively less weight than do deviations among 

income groups that roughly correspond to quartiles. 

 Table 37 also includes two additional 

modifications of the Index of Disparity, one which 

simply weights the Index by population size (wIDisp) 

and another that weights by population size and uses 

the population average as the referent group (wIDispP). 

We can see that even using these modified versions 

of the IDisp do not lead to results that are similar 

to the change observed by the MLD, so this is not 

simply a function of using a weighted vs. unweighted 

measure of disparity. It seems more likely that the 

difference is attributable to the fact that the MLD uses 

the natural logarithm, which gives additional weight 

to observations further from the population average, 

while the IDisp weights all deviations from the referent 

group equally.
70

1987

Education

Income

Race / ethnicity

2003

Education

Income

Race / ethnicity

%∆1987 to 2003

Education

Income

Race / ethnicity

RR

1.36

1.39

1.20

2.04

2.09

1.38

191.4%

178.4%

91.8%

IDisp

18.09

23.38

15.54

65.51

70.33

19.00

262.1%

200.7%

22.3%

wIDisp

5.89

7.59

0.76

15.53

18.12

0.98

163.6%

138.7%

29.5%

wIDispP

2.44

2.54

0.92

4.49

5.74

1.20

84.2%

126.5%

30.6%

MLD

6.10

7.04

1.25

26.38

38.27

2.81

332.7%

443.4%

125.4%

RD

0.216

0.226

0.139

0.213

0.208

0.112

-1.4%

-8.0%

-19.4%

BGV

63.04

69.02

13.59

46.88

62.73

5.91

-25.6%

-9.1%

-56.5%

Relative Disparity Absolute Disparity

Table 37. Changes in Education, Income, and Race and Ethnic Disparity in the Proportion of 

Women 40 and Over Not Receiving a Mammogram in the Past 2 Years, 1987 and 2003 NHIS

Abbreviations: RR=Rate Ratio; IDisp=Index of Disparity; wIDisp=Population-weighted IDisp; wIDispP=Population-weighted IDisp 

with population mean as referent group; MLD=Mean Log Deviation; RD=Rate Difference; BGV=Between Group Variance.

Case Study 10: Geographic Disparities in 

Stomach Cancer Mortality, 1950-2001

 The data for this analysis come from the come 

from the SEER database, “Mortality - Cancer, Total 

U.S. (1950-2001).” Rates of stomach cancer for those 

ages 60 years and over were calculated for each state 

from 1950-2001 to determine the relative and absolute 

disparity across geographic areas. Two similar analyses 

were carried out after aggregating total deaths and 

population in each state by US Division and US Region 

as defined by the US Census Bureau (16). Overall rates 

and trends were similar among males and females, 

and were combined for all analyses. Rates are not 

age-adjusted.

Stomach cancer mortality rates for the three levels 

of geographic aggregation (region, division, state) 

are presented in Figure 36. Mortality from stomach 

cancer has declined impressively over the past 

half-century, but note that there is considerable 

variation in mortality across US states that is hidden 

by looking only at differences between the four US 

regions.
71

Figure 36. Mortality from Stomach Cancer among Those 60 and Over, US Geographic Areas, 1950-2001

1950

25

20

15

10

5

0

Rate per 100,000

1990

Mortality by US Region

1955 1960 1965 1970 1975 1980 1985 1995 2000

1950

25

20

15

10

5

0

Rate per 100,000

1990

Mortality by US Division

1955 1960 1965 1970 1975 1980 1985 1995 2000

1950

25

20

15

10

5

0

Rate per 100,000

1990

Mortality by US State

1955 1960 1965 1970 1975 1980 1985 1995 2000
72

 Table 38 shows the trends in relative and absolute 

geographic disparity for the three levels of aggregation. 

At the regional level (n=4) there is substantial 

agreement between the measures of disparity. The RR, 

IDisp, and measures of entropy (T/MLD) indicate that 

Measures of Relative Disparity Measures of Absolute Disparity

Geographic Area

Region

 (n=4)

Division

 (n=9)

State

 (n=51)

Year

1950

1960

1970

1980

1990

2000

∆1950 to 2000

%∆

1950

1960

1970

1980

1990

2000

∆1950 to 2000

%∆

1950

1960

1970

1980

1990

2000

∆1950 to 2000

%∆

RR

1.7

1.6

1.4

1.5

1.4

1.4

-0.3

-38.9%

1.7

1.7

1.8

1.8

1.7

1.7

0.0

-2.8%

2.8

4.6

4.3

4.3

3.8

4.3

1.4

77.8%

IDisp

47.5

34.4

19.7

20.4

16.3

18.9

-28.6

-60.2%

40.7

31.8

44.2

35.7

30.5

37.9

-2.8

-6.8%

102.6

219.0

158.5

125.5

98.4

98.4

-4.2

-4.1%

T

20.5

16.6

11.0

11.9

8.6

7.2

-13.3

-64.8%

21.0

17.3

12.1

13.3

9.8

9.7

-11.3

-53.8%

28.0

24.4

20.7

23.6

19.1

18.6

-9.4

-33.7%

MLD

21.3

16.5

10.7

11.3

8.2

6.9

-14.4

-67.6%

21.8

17.4

12.0

12.9

9.4

9.7

-12.2

-55.8%

29.9

25.5

21.1

23.7

19.1

18.6

-11.3

-37.8%

RD

7.8

5.5

3.0

2.6

2.0

1.6

-6.2

-79.5%

8.3

6.3

4.4

3.6

2.9

2.3

-5.9

-71.7%

14.2

12.9

9.7

8.8

7.7

7.3

-7.0

-49.0%

BGV

10.3

4.5

1.4

1.0

0.6

0.3

-10.0

-97.1%

10.5

4.7

1.5

1.1

0.7

0.4

-10.1

-96.3%

13.9

6.5

2.5

1.9

1.2

0.8

-13.1

-94.5%

Table 38. Changes in Geographic Disparity in Stomach Cancer Mortality by Different Levels of

Geographic Aggregation, 1950-2000

RR=Rate Ratio; IDisp=Index of Disparity; T=Theil Index; MLD=Mean Log Deviation; RD=Rate Difference; BGV=Between Group Variance.

relative geographic disparity has declined by around 

60% from 1950-2000, and the RD and BGV suggest 

slightly larger absolute declines, on the order of 

80-100%. Disaggregating regions down to US divisions 

(n=9) gives a slightly different picture, especially for 
73

measures of relative inequality. The RR shows virtually 

no change in disparity (-2.8%) and the IDisp shows 

only a 7% decline, but the T and MLD both continue 

to suggest a 50-60% decline in disparity. Finally, if 

states are used as the unit of analysis (n=51), the RR 

suggests that disparity has increased by 78%, the IDisp 

shows virtually no change (4% decline), and the T and 

MLD still suggest that disparity has declined, but by 

approximately 40% as opposed to 60% when measured 

across divisions or regions. In terms of absolute 

disparity among states, the RD shows a 50% decline 

and the BGV a 95% decline from 1950-2000. In fact, 

the change in the BGV over time is virtually identical 

for all three levels of aggregation.

 Because the RR, IDisp, and RD do not weight social 

groups (in this case geographic areas) by population 

size, it might be expected that they would be more 

sensitive to the unit of aggregation in this analysis. 

Overall, the results in Table 38 tend to confirm this 

assertion. For example, in 1970 the IDisp is about 8 

times higher when measured across states (19.7) than 

when measured across regions (158.5). In contrast, 

the MLD changes by about 2-fold, from 10.7 to 21.1. 

Table 39 shows for each year and disparity measure 

the ratio of disparity measured across states to disparity 

measured across regions, and it is clear that this has 

a more dramatic effect on the RR, IDisp, and RD. It 

is worth pointing out that for all three analyses the 

total number of deaths, population, and the total 

mortality rate are exactly the same—only the method 

of aggregation changes.

 Trends in relative disparity for the three levels 

of aggregation are presented in Figure 37, with 

three-year moving averages plotted for the IDisp 

and the MLD. The general trend is very similar 

across regions whether measured by the IDisp or the 

MLD, but further aggregating the data leads to some 

inconsistencies. Across divisions, both measures 

indicate a decline in disparity until about 1960, after 

which the IDisp remains approximately constant and 

the MLD shows a moderate decline. The difference is 

more pronounced among US states, as the IDisp shows 

a steep increase in disparity from the late-1950s to the 

late-1960s while the MLD declines.

Measures of Relative Disparity Measures of Absolute Disparity

1950

1960

1970

1980

1990

2000

RR

1.71

2.91

2.98

2.89

2.70

3.03

IDisp

2.16

6.37

8.06

6.15

6.03

5.21

T

1.37

1.48

1.87

1.97

2.21

2.58

MLD

1.40

1.54

1.97

2.09

2.33

2.70

RD

1.83

2.34

3.24

3.35

3.78

4.55

BGV

1.35

1.43

1.80

1.90

2.13

2.52

Table 39. Ratio of Level of Disparity in Stomach Cancer Mortality Calculated Across US States 

Relative to Disparity Calculated Across US Regions, 1950-2000

Abbreviations: RR=Rate Ratio; IDisp=Index of Disparity; T=Theil Index; MLD=Mean Log Deviation; RD=Rate Difference; 

BGV=Between Group Variance.
74

Figure 37. Geographic Disparities in Stomach Cancer Mortality among Those 60 and Over, 1950-2001

(3-Year Moving Average)

30

25

20

15

10

5

0

1950

70

60

50

40

30

20

10

0

Index of Disparity

1990

Mean Log Deviation

30

25

20

15

10

5

0

Relative Geographic Disparity by US Region 

1960 1970 1980 2000

60

50

40

30

20

10

0

Index of Disparity

Mean Log Deviation

Relative Geographic Disparity by US Division

450

400

350

300

250

200

150

100

50

0

Index of Disparity

Mean Log Deviation

40

35

30

25

20

15

10

5

0

Relative Geographic Disparity by US State

MLD

IDisp

MLD

IDisp

MLD

IDisp

1955 1965 1975 1985 1995

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
77

Results Summary

 The purpose of this report was to empirically 

evaluate different methods for measuring social 

disparities in cancer-related health outcomes, 

primarily with respect to evaluating disparity trends. 

The purpose was to determine whether the choice of 

disparity measure makes a difference for answering the 

question of whether social disparities in cancer-related 

outcomes are increasing or decreasing. 

With that purpose in mind it is useful to summarize 

whether interpretations of the trend in disparity are 

consistent across selected measures of disparity for the 

10 case studies used in this report. Figure 38 provides a 

graphical comparative summary of the 10 case studies. 

In each case we have classified the percent change in 

the magnitude of each disparity measure as either large 

(≥30%), moderate (10-29%), or small (<10%), with 

increases in disparity shaded red and decreases shaded 

green. This categorization is admittedly arbitrary, but it 

seems reasonable to classify relative changes of 30% or 

greater as more than moderate. We also give an overall 

substantive interpretation of the change in disparity 

based on the (in)consistency of the different measures.

Socioeconomic Disparity Trends

Lung Cancer Incidence 

 The first two rows of Figure 38 show the summary 

for area-socioeconomic disparities in lung cancer 

incidence for males and females. For females there is 

broad agreement among almost all of the measures 

that both relative and absolute area-socioeconomic 

disparities have substantially increased from 1988 

to 1999. In this case, the general conclusion about 

the disparity trend (i.e., is disparity increasing or 

decreasing?) does not depend on which measure of 

disparity is used. Of course, the magnitude of the 

change varies across measures but this is simply 

because of the different mathematical properties 

inherent in each measures calculation. 

For lung cancer incidence in males, however, the 

results across measures are inconsistent. This is a clear 

example of the importance of choosing a disparity 

measure based on apriori principles because the 

empirical result cannot inform the reader about which 

measure is “right”. Any substantive conclusion is 

therefore entirely dependent on which measure is 

chosen. 

In this case, the value position rests on whether 

or not disparity measures should be weighted by 

population size. The unweighted disparity measures 

(RR, IDisp, RD) would generally suggest that the 

area-socioeconomic disparity situation is worse in 

1999 than 1988 (little change in absolute disparity 

and increasing relative disparity). On the other hand, 

population weighted disparity measures (RCI, ACI) 

suggest improvement: moderate decrease in relative 

disparity and strong decreases in absolute disparity. 

This happened because the incidence rate declined 

more slowly in the 2nd area-socioeconomic quintile 

(see Figure 5), which only contained about 5% of 

the SEER population. This smaller-population group 

had less influence on population-weighted disparity 

measures and greater influence on the unweighted 

disparity measures.
78

Colorectal Cancer Mortality

 For area-socioeconomic disparities in both female 

and male colorectal cancer mortality, the results are 

consistent despite the contradictory red and green 

shading in cells, which we explain below. By going 

back to the plots of the raw data shown in Figure 

9 and Figure 11 it is obvious to the naked eye that 

both absolute and relative disparities have decreased. 

Thus, all of the disparity measures register numerical 

declines. Note however, that the cells for the RCI and 

ACI are shaded dark red, and the magnitude of change 

is greater than for the other measures. This is because 

these measures are sensitive to the direction of the 

socioeconomic gradient, and only these measures 

indicate that the socioeconomic gradient shifted 

from favoring the poor in 1950 to favoring the rich 

in 2000. The other measures of disparity indicate a 

reduction but only the RCI and ACI tell us that the 

social gradient in colorectal cancer mortality actually 

reversed over this time period and they show that, 

according to the way they are calculated, disparity 

worsened, hence the red shading.

This highlights the value of understanding the 

difference between asking whether disparity “has 

increased or decreased” and asking whether disparity 

has become “worse or better.” Answers to these 

seemingly innocuous questions are not straightforward 

and are often dependent on prior principles of what 

is important to know about disparity. In this case, 

even though disparity is smaller in magnitude, for 

both the RCI and ACI it could be argued that the 

disparity situation is now “worse” since it is the poor 

who now have the highest rates of mortality. But, 

according to a strict interpretation of the Healthy 

People 2010 disparity goals it could also be argued 

that this situation represents progress towards 

eliminating disparity. Such alternative interpretations 

beg the question whether we care more about health 

disparities where the burden is on the disadvantaged 

than when the burden is on the advantaged. 

Prostate Cancer Mortality

 Another interesting example in Figure 38 is the 

trend in area-socioeconomic disparity for prostate 

cancer mortality. Among men 45-74 years of age there 

is consistency among all the measures that disparity 

has increased (though they differ with respect to the 

magnitude of the change). But for men 75 and over 

it is more difficult to come to a firm conclusion. The 

measures of relative disparity suggest a moderate 

decline but the measures of absolute disparity suggest 

a moderate increase. Thus, the conclusion about the 

trend in socioeconomic disparity in prostate cancer in 

this age group in this case depends on an apriori value 

position concerning relative and absolute disparity. 

Is it more important that we see improvements in 

relative or absolute disparity? Only when that question 

is answered can we reach a substantive conclusion 

about prostate cancer mortality trends in those 

over 75.

Smoking and Obesity

 For some outcomes there is a great deal of 

consistency among all the measures. For example, 

it seems clear that socioeconomic disparities in 

current smoking are increasing among both men and 

women, while socioeconomic disparities in obesity 

are decreasing. Given the magnitude of the changes 

in the prevalence of these two outcomes for virtually 

all social groups—declining for smoking and rising for 

obesity—this result may not be surprising.

Race and Ethnic Disparity Trends

Lung Cancer Incidence

 For female lung cancer incidence among race and 

ethnic groups, most relative measures suggest little or 

no change (though note that the IDisp and MLD move 

in different directions because the most populous 

group, whites, moved away from the population 

average), but rates have moderately declined for most 

groups, leading to moderate declines in absolute 
79

disparity. For males, however, it is a bit more difficult 

to judge whether the disparity situation is better 

or worse in 2001 than in 1999. Relative disparity 

increased according to all three measures, but, as rates 

of lung cancer incidence have been declining among 

males, absolute disparity among race and ethnic 

groups has also declined. Thus, the overall conclusion 

about this disparity again depends on whether 

absolute or relative disparity is thought to be more 

important.

Breast Cancer Incidence

 For racial disparities in the incidence of breast 

cancer there is virtually no change in disparity among 

women ages 45-74, regardless of how it is measured. 

On the contrary, among women ages 75 and over, 

there is substantive divergence among the measures 

and it appears that one’s interpretation of the disparity 

trend will depend on the value position with respect 

to population weighting. The unweighted relative 

disparity measures (RR, IDisp) indicated strong declines 

in relative disparity, while the RD declines moderately. 

Using unweighted measures would therefore lead 

one to conclude that there has been considerable 

improvement in race/ethnic disparities in breast 

cancer incidence. On the contrary, the population 

weighted measures (MLD, BGV) both indicate that 

disparity actually increased by around 20% during 

the 1990s. This difference is very likely due to the 

fact that the initially low rate among Asian/Pacific 

Islander women in 1990 increased over the decade. 

This group represents 0.4% of the population and so 

had less impact on the population-weighted measures. 

Thus, the issue of population-weighting is central to 

interpreting the disparity trend in this case. 

Cervical Cancer Incidence

 For trends in race and ethnic disparity in cervical 

cancer incidence disparity trends for both younger 

and older women are difficult to interpret without 

specifying whether one thinks absolute or relative 

disparities are more important. As cervical cancer 

incidence has been generally declining but declining 

faster among those with lower rates, relative disparity 

is increasing, but absolute disparities are decreasing. 

Geographic Disparity Trends

Stomach Cancer Mortality

 Geographic disparities in stomach cancer mortality 

appear to have declined, but the unit of geographic 

aggregation affects the degree of consistency across 

the measures of relative disparity. For the 4 U.S. 

regions (Midwest, Northeast, South, West) there 

has been considerable reduction of disparity across 

regions, whether measured on the relative or absolute 

scale. However, as the unit of aggregation moves 

from regions to divisions to states the extent of 

disagreement across the measures increases. Among 

the 50 states, the unweighted disparity measures 

suggest either a strong increase or no change in 

disparity, while the population-weighted MLD 

consistently suggests that relative disparity has 

declined. Thus, at the level of US states, the issue 

of whether disparity measures should be weighted 

by population size has important implications for 

interpreting the disparity trend.

Comparing Socioeconomic and Race and 

Ethnic Disparity Trends

Mammography Screening

 Finally, the last rows of Figure 38 show a direct 

comparison of income, education, and race/ethnic 

disparity for the same outcome: the proportion of 

women not receiving a mammogram in the past two 

years. For all of these cases we find that interpreting 

the trend in disparity depends on how much emphasis 

is place on relative or absolute disparities. Generally 

speaking, as the rates of not receiving a mammogram 

have declined, they tend to have declined faster 

among those with initially lower rates in 1987 (see 
80

Figure 34 and Figure 35). Relative disparities have 

thus increased but absolute disparities have declined. 

With respect to the direct comparison between race/

ethnic disparity and socioeconomic disparity, there 

is general agreement among all of the measures 

that relative disparities have increased more among 

socioeconomic than among race/ethnic groups. 

Similarly, absolute disparity has declined more across 

race/ethnic groups than across socioeconomic groups, 

regardless of which disparity measure is used.
81

Figure 38. Graphical summary of disparity trends

Lung Cancer Incidence 1988-1999

Female

Male

Colorectal Cancer Mortality 1950-2000

Female

Male

Prostate Cancer Mortality 1950-2000

Ages 45-74

Ages 75+

Smoking 1965-2003

Female

Male

Obesity 1960-2000

Female

Male

Race and Ethnic Disparity

Lung Cancer Incidence 1990-2001

Female

Male

Breast Cancer Incidence 1990-2001

Ages 45-74

Ages 75+

Cervical Cancer Incidence 1990-2001

Ages < 45

Ages 45-74

Geographic Disparity

Stomach Cancer Mortality 1950-2001

Region

Division

State

Comparing Socioeconomic and 

Race/ethnic Disparity

Mammography Screening 1987-2003

Education Disparity

Income Disparity

Race / ethnic Disparity

Legend

Increasing disparity to the detriment of those 

living in poorer areas

Depends on value position on population

weighting

Disparity is clearly numerically smaller among 

both males and females, but the RCI and ACI 

indicate an increase in disparity is because 

the socioeconomic gradient reversed.

Increasing disparity to the detriment of those 

living in poorer areas

Depends on value position on absolute vs.

relative disparity

Large increases in disparity with reversal of

socioeconomic gradient

Large increases in disparity

Large decreases in disparity

Large decreases in disparity

No change

Depends on value position on population

weighting

Depends on value position on absolute vs.

relative disparity

Depends on value position on absolute vs.

relative disparity

Large decrease in disparity

Large decrease in disparity

Large decrease in absolute disparity but 

relative disparity goes up, down or is stable 

and depends on your value position on 

population weighting

Depends on value position on absolute vs.

relative disparity

Depends on value position on absolute vs.

relative disparity

Depends on value position on absolute vs.

relative disparity

Small change in relative and moderate 

decrease in absolute disparity

Depends on value position on absolute 

vs. relative disparity

Relative Disparity

 RR IDisp RCI

Absolute Disparity

RD ACI BGV

Socioeconomic Disparity Conclusion and Interpretation

?

?

?

?

?

?

Relative Disparity

 RR IDisp MLD RD BGV

Absolute Disparity

Relative Disparity

 RR IDisp MLD RD BGV

Absolute Disparity

Disparity Increasing Disparity Decreasing

?

?

?

≥30% 11% to 29% 0 to (–)11% (–)11% to(–)29% ≤(–)30%
83

Conclusions

 We will briefly attempt to summarize the analyses 

of the case studies in this report by answering the 

series of questions that framed its development:

1. Does the choice of a measure of disparity matter for 

assessing disparity trends? 

Yes. The 10 case studies showed a number of situations 

where substantively different interpretations 

concerning the level and trend in disparity resulted 

from using different measures of health disparity. 

Such differences in interpretation could not be 

reconciled without reference to consideration of which 

underlying dimensions of disparity are emphasized in 

the measures. That is, absolute vs. relative disparity; 

whether or not disparity measures are weighted by 

population size, and whether measures are sensitive to 

the direction of the social gradient in health.

2. How often does the choice of disparity measure 

matter? 

Of the 22 separate analyses summarized in Figure 38, 9 

(41%) revealed situations where the overall substantive 

conclusion about the trend in disparity was difficult 

to make without some apriori judgment about what 

dimensions of disparity are important. It is impossible 

to know what this percentage would be across all 

relevant cancer-related outcomes, but it is clear from 

these analyses that the issue is likely to be reasonably 

common.

3. Why does the choice of disparity measure matter? 

It is crucial to reiterate the conclusions of our 

theoretical review of disparity measures, that 

different disparity measures often contain implicit or 

explicit value judgments about what dimensions of 

disparity are important. These value judgments play 

an important role in understanding why different 

measures of disparity may give different answers to 

questions about disparity trends. 

In particular, most of the cases of disagreement 

between measures of disparity depended on two 

issues. One is the scale on which disparity should 

be evaluated. In many cases relative measures of 

disparity moved in one direction, while absolute 

measures moved in the opposite direction. Thus, 

specifying whether absolute or relative disparities are 

more important prior to undertaking any analyses 

will assist in minimizing disagreement about disparity 

trends. The second issue is whether to weight social 

groups by population size. In several cases we found 

that population-weighted disparity measures differed 

in either magnitude or direction from unweighted 

disparity measures. In particular, and as might be 

expected, unweighted measures of disparity appear to 

be more sensitive to the movement of rates of disease, 

especially those of smaller population groups whose 

rates of disease may be less stable over time.

4. The Index of Disparity

As the Index of Disparity has been proposed as a 

measure of progress toward relative disparity goals 

for Healthy People 2010, we thought it important to 

comment specifically on its performance in the case 

studies. In general terms, the Index of Disparity was 

more volatile in cases where there are large differences 

in the population size of sub-groups across which 

disparity is being measured, such as in comparisons 

across different race/ethnic groups. When the size 

of the social groups is fairly similar, as in the case of 

socioeconomic disparities in smoking and obesity 
84

(see Figure 38 for a summary), the Index of Disparity 

is usually consistent with other relative disparity 

indicators. The instability of the Index of Disparity 

in cases where social groups differ substantially in 

population size is most easily seen in the example of 

stomach cancer mortality disparities across differing 

aggregations of geographic areas (see Figure 37). 

Among the four US regions, which are all relatively 

populous, there is generally agreement between the 

Index of Disparity and population-weighted measures. 

But using the same data measured across US states, 

which vary dramatically in population size, the 

Index of Disparity becomes much less stable and is 

inconsistent with population-weighted measures.

5. What are the limitations of applying measures of 

economic disparity to health disparity?

Part of the reason for this evaluation of measures of 

health disparity was the notion that the quantification 

of disparity is a phenomenon that has a long history 

in other disciplines, particularly in economics. We 

have thus attempted to evaluate some traditional 

measures of economic disparity (e.g., measures of 

entropy, the concentration index) as measures of 

health disparity. While these measures have much 

that is to be recommended, one potential limitation 

is that most measures of economic disparity use the 

population average as the reference point from which 

to measure disparity. This makes sense in economics 

because income is a fungible good, and disparity may 

decline through the transfer of income from the rich 

to the poor, bringing the incomes of the rich closer 

to the population average. But health (i.e., health 

status) is not a transferable good. The analogous 

situation for health disparity, where declines in 

disparity come about by worsening health among 

the healthiest groups, is difficult to cast in a positive 

light. Other things being equal, it is hard to imagine 

policymakers viewing declines in health among the 

healthiest groups as positive, even if it reduces health 

disparity. Thus, applying traditional measures of 

economic disparity to health requires acknowledging 

and understanding this limitation. It should be noted 

that another proposed measure of health disparity, the 

Index of Disparity, overcomes this specific limitation 

by using the healthiest group as the reference group, 

but has other limitations as well. At present it appears 

that no currently-used measure of disparity is entirely 

free from limitations for monitoring disparity trends, 

as we pointed out in our previous review. While 

additional research on alternative measures of health 

disparity may bear fruit, we can, in fact, apply 

measures of economic disparity to health but should 

remember that, as the Healthy People 2010 dual goals 

make clear, disparity is not our only health concern.

6. What are the implications for monitoring health 

disparities?

There is currently a strong emphasis in the US public 

health policymaking community on monitoring of 
85

progress toward eliminating health disparities. The 

results of the case studies presented in this report 

demonstrate that it is easily possible to come to 

fundamentally different conclusions about the extent 

of progress toward eliminating health disparities 

using the same data but different measures of health 

disparity. The naïve use of summary measures of 

health disparity thus has the potential to lead to 

confusion among both policymakers and researchers 

as to whether disparities are increasing or decreasing, 

which cancer-related outcomes show the largest 

disparities, and which health disparities might 

be specifically targeted for increased study. Such 

confusion will be minimized and health disparity 

measurement will be advanced by increased debate 

and discussion of the issues that generate differences 

among measures of health disparity:

• How much weight should we give individuals of 

different social groups when measuring disparity? 

Counting each individual’s health equally implies 

population-weighted measures of disparity among 

social groups. Counting each social group’s health 

the same means using unweighted disparity 

measures (and implies differential weighting of 

individuals from social groups with different 

population sizes).

• How much to weight different parts of the health 

distribution? At any given time some social 

groups are healthier than others. Over time health 

changes, and some measures of disparity weight 

health improvements among all groups the 

same, while others are more sensitive to health 

improvements among the least healthy or among 

the poor. Which of these perspectives is consistent 

with our concerns about social disparities in 

health?

• Should we be more concerned about absolute or 

relative disparities? Diseases and conditions that 

exact a large burden on the population, because 

of their high prevalence, often generate smaller 

relative disparities, while rare conditions can 

generate exceedingly high relative disparities. 

Which of these perspectives is the appropriate scale 

on which to measure disparity trends?

In sum, our recommendations from the original 

report, further clarified here, suggest giving priority to 

disparity measures on the absolute scale, that weight 

for population size and where possible consider 

the direction of the social gradient in health. That 

recommendation stands but it does not exclude 

consideration of issues of relative disparity or what 

is happening among smaller population groups. For 

those reasons it may always be useful to adopt a “suite” 

of health disparity indicators that make clear which 

aspects of health disparity are changing over time.
87

 While this report did not focus on statistical 

inference about changes in health disparities, it 

is likely to be of interest to many researchers and 

policymakers. Therefore, in this Appendix we provide 

a very basic introduction to the various methods 

used for calculating standard errors for the summary 

measures of disparity discussed in this report. We 

would encourage those interested to consult the source 

publications referenced here for more details.

Underlying Rates

 Most of the underlying data in this report are based 

on either incidence or mortality rates and are assumed 

to come from a Poisson distribution (17). The general 

formula for the standard error for crude or age-specific 

mortality and incidence rates is: 

[12]

where SE is standard error, d is the number of 

incident cases or deaths, and n is the estimated 

population size (17). 

Rate Ratio and Rate Difference

 The general formula for calculating the standard 

error of the Rate Difference (RD) is (12): 

[13]

where r

0

 is the reference rate and SE indicates 

the standard error of the rates being compared. 

Similarly, the standard error for the rate ratio, 

assuming the two estimates are independent, may be 

written as (18): 

though it is often more convenient to work with 

the natural log of the RR for generating confidence 

intervals: 

[15]

Slope and Relative Index of Inequality

Recall that the SII can be easily obtained via regression 

on grouped data: 

[16]

where j indexes social group, yj is the average 

health status and Rj the average relative ranking 

of social group j in the cumulative distribution of 

the population, b 0 is the estimated health status of 

a hypothetical person at the bottom of the social 

group hierarchy (i.e., a person whose relative rank 

Rj in the social group distribution is zero), andb 1

is the difference in average health status between 

the hypothetical person at the bottom of the social 

group distribution and the hypothetical person at 

the top (i.e. Rj=0 vs. Rj=1). However, because this 

regression is on grouped data, the standard errors are 

heteroskedastic, and Kakwani and colleagues, and Low 

and Low (19), among others, note that the following 

transformation should be used, which is equivalent to 

running weighted ordinary least squares:

Appendix: Random Variation

SEr = d

n

SERD = 

SEr1 + 

2 SEr02

r1 – r0

SERR = SEr1

2

+

r1

r1 r0

SEr0

2

r0

SERR = SEr1

2

+

r1

r1 r0

SEr0

2

r0

SElog(RR) = 1 

– + d1

1 

n1

1 – d01 

n0

[14]
88

where s2is the variance of m, 

 [21]

where n is the sample size, J is the number of groups, 

pj is the proportion of the total population in group j, 

mj is the mean value of the health variable in group j, 

and RCI is the Relative Concentration Index. A very 

useful guide for calculating the RCI and its standard 

error is available at the World Bank’s ‘Poverty and 

Health’ website (Accessed 30 July 2007) at:

http://web.worldbank.org/WBSITE/EXTERNAL/

TOPICS/EXTHEALTHNUTRITIONANDPOPULATION/E

XTPAH/0,,contentMDK:20216933~menuPK:400482~pa

gePK:148956~piPK:216618~theSitePK:400476,00.html

Other Summary Measures

To our knowledge, details of standard formulas 

for calculating standard errors for other summary 

measures used in this report, including the Index of 

Disparity, the Theil Index and Mean Log Deviation, 

and the Between-Group Variance, are not widely 

available. Cowell (22, 23) gives an overview for various 

measures of income inequality, and Biewen and 

Jenkins (24) recently derived methods for estimating 

various inequality measures with complex survey data. 

However, recent developments in computing power 

and resampling methods (25) have led many authors 

to suggest calculating standard errors via bootstrapping 

(12, 26-29), which was the approach we took in Case 

Study 1.

where pj is the proportion of the population in the jth 

group. However, this transformation does not account 

for the correlation of error terms induced by the 

relative ranking variable. See Kakwani and colleagues 

(8), Wagstaff (20), or Low and Low (19) for additional 

details.

Hayes and Berry (21) have also developed a formula for 

the standard error for the Relative Index of Inequality 

(RII) for grouped data, using the Kunst-Mackenbach 

version of the RII, which may, via substitution, be 

re-written as: 

[18] 

in which case the estimated variance and standard 

error of g are: 

[19]

Relative Concentration Index

The formula for the standard error for the Relative 

Concentration Index (RCI) for grouped data is given 

by Kakwani and colleagues, which accounts for the 

autocorrelation in error terms, as: 

 [20]

yj Pj = Pj + β 1Rj Pj 

– –

y –βR – –

y –β(1 – R)

RIIKM = – – γ = = α + β

α

var(RCI) = pj a2 – (1+ RCI)2 [ ]+ 

n

1

nμ2

1

j=1

J

j=1

J

Σ Σ

pj �2

j (2Rj

 – 1 – RCI)2

j

aj = (2Rj – 1 – C)+2 – qj–1 – qj μ

μj

qj = γ =1 μγ pγ μ

1 j

– var( – γ ) =

SE(γ ) =

[y + β(1 – R)]4

β2 var(y ) + y 2 var(β )

–

var(γ )

–

[17]
91

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NIH Publication No. 07-6281

Printed September 2007