CDC Health Disparities and Inequalities Report — 

United States, 2013

Supplement / Vol. 62 / No. 3 November 22, 2013 

U.S. Department of Health and Human Services

Centers for Disease Control and Prevention

Morbidity and Mortality Weekly Report
Supplement

The MMWR series of publications is published by the Center for Surveillance, Epidemiology, and Laboratory Services, Centers for Disease Control and Prevention (CDC), 

U.S. Department of Health and Human Services, Atlanta, GA 30333.

Suggested Citation: Centers for Disease Control and Prevention. [Title]. MMWR 2013;62(Suppl 3):[inclusive page numbers].

Centers for Disease Control and Prevention

Thomas R. Frieden, MD, MPH, Director

Harold W. Jaffe, MD, MA, Associate Director for Science

Joanne Cono, MD, ScM, Acting Director, Office of Science Quality 

Chesley L. Richards, MD, MPH, Deputy Director for Public Health Scientific Services

MMWR Editorial and Production Staff

Ronald L. Moolenaar, MD, MPH, Editor, MMWR Series

Christine G. Casey, MD, Deputy Editor, MMWR Series

Teresa F. Rutledge, Managing Editor, MMWR Series

David C. Johnson, Lead Technical Writer-Editor

Jeffrey D. Sokolow, MA, Catherine B. Lansdowne, MS, 

Denise Williams, MBA, Project Editors

Martha F. Boyd, Lead Visual Information Specialist

Maureen A. Leahy, Julia C. Martinroe, 

Stephen R. Spriggs, Terraye M. Starr

Visual Information Specialists

Quang M. Doan, MBA, Phyllis H. King

Information Technology Specialists

MMWR Editorial Board

William L. Roper, MD, MPH, Chapel Hill, NC, Chairman

Matthew L. Boulton, MD, MPH, Ann Arbor, MI

Virginia A. Caine, MD, Indianapolis, IN

Barbara A. Ellis, PhD, MS, Atlanta, GA

Jonathan E. Fielding, MD, MPH, MBA, Los Angeles, CA

David W. Fleming, MD, Seattle, WA

William E. Halperin, MD, DrPH, MPH, Newark, NJ

King K. Holmes, MD, PhD, Seattle, WA

Timothy F. Jones, MD, Nashville, TN

Rima F. Khabbaz, MD, Atlanta, GA

Dennis G. Maki, MD, Madison, WI

Patricia Quinlisk, MD, MPH, Des Moines, IA

Patrick L. Remington, MD, MPH, Madison, WI

William Schaffner, MD, Nashville, TN

Asthma Attacks Among Persons with Current Asthma — 

United States, 2001–2010.......................................................................... 93

Diabetes — United States, 2006 and 2010............................................ 99

Health-Related Quality of Life — United States, 2006 and 2010....105

HIV Infection — United States, 2008 and 2010..................................112

Obesity — United States, 1999–2010....................................................120

Periodontitis Among Adults Aged ≥30 Years — 

United States, 2009–2010........................................................................129

Preterm Births — United States, 2006 and 2010...............................136

Potentially Preventable Hospitalizations — United States, 

2001–2009 ....................................................................................................139

Prevalence of Hypertension and Controlled Hypertension — 

United States, 2007–2010........................................................................144

Tuberculosis — United States, 1993–2010..........................................149

Health Outcomes: Mortality..............................................................155

Coronary Heart Disease and Stroke Deaths — 

United States, 2009....................................................................................157

Drug-Induced Deaths — United States, 1999–2010........................161

Homicides — United States, 2007 and 2009 ......................................164

Infant Deaths — United States, 2005–2008........................................171

Motor Vehicle–Related Deaths — United States, 2005 and 2009..176

Suicides — United States, 2005–2009...................................................179

Conclusion and Future Directions: CDC Health Disparities and 

Inequalities Report — United States, 2013 ....................................184

Foreword................................................................................................... 1

Introduction: CDC Health Disparities and Inequalities Report 

— United States, 2013............................................................................ 3

Social Determinants of Health.............................................................. 7

Education and Income — United States, 2009 and 2011....................9

Access to Healthier Food Retailers — United States, 2011.............. 20

Unemployment — United States, 2006 and 2010.............................. 27

Environmental Hazards........................................................................33

Nonfatal Work-Related Injuries and Illnesses — 

United States, 2010...................................................................................... 35

Fatal Work-Related Injuries — United States, 2005–2009 ............... 41

Residential Proximity to Major Highways — United States, 2010.... 46

Health-Care Access and Preventive Services ...................................51

Colorectal Cancer Incidence and Screening — 

United States, 2008 and 2010 .................................................................. 53

Health Insurance Coverage — United States, 2008 and 2010........ 61

Seasonal Influenza Vaccination Coverage — 

United States, 2009–10 and 2010–11 ................................................... 65

Behavioral Risk Factors.........................................................................69

Pregnancy and Childbirth Among Females Aged 10–19 Years — 

United States, 2007–2010.......................................................................... 71

Binge Drinking — United States, 2011 ................................................... 77

Cigarette Smoking — United States, 2006-2008 and 2009-2010 .... 81

Health Outcomes: Morbidity...............................................................85

Expected Years of Life Free of Chronic Condition–Induced 

Activity Limitations — United States, 1999–2008............................ 87

CONTENTS
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MMWR / November 22, 2013 / Vol. 62 / No. 3 1

CDC works 24 hours a day, seven days a week protecting 

people in the United States from health threats in order to 

save lives, promote health, and reduce costs. Achieving health 

equity, eliminating health disparities, and improving health in 

the United States are overarching goals to improve and protect 

our nation’s health.

Over the past 50 years, the United States has made significant 

progress toward these important goals. Life expectancy 

increased from just under 70 years in 1960 to approximately 

79 years in 2011 (1,2). People are living longer, healthier, and 

more productive lives. However, this upward trend is neither 

as rapid as it should be — we lag behind dozens of other 

nations (3) – nor is it uniformly experienced by people in the 

United States.

In fact, these two shortcomings of our health system 

are distinct but related. Our overall health status does not 

achieve our potential. An important part of this — even 

though preventable illness, injury, disability, and death affect 

all segments of society — is that life expectancy and other 

key health outcomes vary greatly by race, sex, socioeconomic 

status, and geographic location. In the United States, whites 

have a longer healthy life expectancy than blacks, and women 

live longer than men (4). There are also marked regional 

differences, with much lower life expectancy among both white 

and black Americans who live in the Southeast (4).

CDC Health Disparities and Inequalities Report — United 

States, 2013 is the second agency report examining some of 

the key factors that affect health and lead to health disparities 

in the United States. Four findings bring home the enormous 

personal tragedy of health disparities:

•	 Cardiovascular disease is the leading cause of death in the 

United States. Non-Hispanic black adults are at least 50% 

more likely to die of heart disease or stroke prematurely 

(i.e., before age 75 years) than their non-Hispanic white 

counterparts (5).

•	 The prevalence of adult diabetes is higher among 

Hispanics, non-Hispanic blacks, and those of other or 

mixed races than among Asians and non-Hispanic whites. 

Prevalence is also higher among adults without college 

degrees and those with lower household incomes (6).

•	 The infant mortality rate for non-Hispanic blacks is more 

than double the rate for non-Hispanic whites. Rates also 

vary geographically, with higher rates in the South and 

Midwest than in other parts of the country (7).

Foreword

Thomas R. Frieden, MD, MPH

Director, CDC 

•	 Men are far more likely to commit suicide than women, 

regardless of age or race/ethnicity, with overall rates nearly 

four times those of women. For both men and women, 

suicide rates are highest among American Indians/Alaska 

Natives and non-Hispanic whites (8).

CDC and its partners work to identify and address the 

factors that lead to health disparities among racial, ethnic, 

geographic, socioeconomic, and other groups so that barriers 

to health equity can be removed. The first step in this process 

is to shine a bright light on the problem to be solved. Providing 

accurate, useful data on the leading causes of illness and death 

in the United States and across the world is a foundation of 

CDC’s mission and work. 

In 1966, Martin Luther King said that “Of all the forms of 

inequality, injustice in health care is the most shocking and 

inhumane” (9). Nearly a half century after Reverend King made 

this observation, we have made some but not nearly enough 

progress in reducing the barriers to equitable health care and to 

health equity. We should work with what he called “the fierce 

urgency of now” to eliminate this form of inequality wherever 

and whoever it affects.

As Secretary of Health and Human Services Kathleen G. 

Sebelius has said, “Health equity benefits everyone” (10). Every 

person who dies young, is avoidably disabled, or is unable to 

function at their optimal level represents not only a personal 

and family tragedy but also impoverishes our communities and 

our country. We are all deprived of the creativity, contributions, 

and participation that result from disparities in health status.

Eliminating the burden of racial and ethnic health disparities 

is not easy, but it can be done. For example, 20 years ago the 

Vaccines for Children (VFC) program was created to provide 

vaccines at no cost to eligible children. It is now one of our 

country’s most successful public health initiatives (11). By 

removing cost barriers associated with vaccines, the VFC 

program has protected millions of children from diseases 

— both those who participated directly in the program and 

others — and has helped to virtually eliminate disparities in 

childhood vaccination rates. More recently, the Affordable 

Care Act (12), with its provisions to require insurer coverage 

of preventive services without cost to patients and to increase 

health insurance access for millions of previously uninsured 

Americans (13,14), provides a powerful opportunity to further 

reduce health disparities. 
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Achieving health equity requires the hard work of many 

people and organizations. CDC’s many partners can use 

the information in this report to stimulate action to further 

reduce health disparities. The future health of our nation will 

be determined, to a large extent, by how effectively federal, 

state, and local agencies and private organizations work with 

communities to eliminate health disparities among populations 

that continue to experience a disproportionate burden of 

disease, disability, injury, and death.

References

1. Arias E. United States life tables, 2008. Natl Vital Stat Rep 2012;61(3). 

2. Hoyert DL, Xu J. Deaths: preliminary data for 2011. Natl Vital Stat 

Rep 2012;61(6). 

3. World Health Organization. World health statistics 2013. Geneva, 

Switzerland: World Health Organization; 2013. 

4. CDC. State-specific healthy life expectancy at age 65 years—United 

States, 2007–2009. MMWR 2013;62:561–6. 

5. CDC. Coronary heart disease and stroke deaths—United States, 2009. 

In: CDC health disparities and inequalities report—United States, 2013. 

MMWR 2013;62(No. Suppl 3):155-8.

6. CDC. Diabetes—United States, 2006 and 2010. In: CDC health 

disparities and inequalities report—United States, 2013. MMWR 

2013;62(No. Suppl 3):97-102.

7. CDC. Infant deaths—United States, 2005-2008. In: CDC health 

disparities and inequalities report—United States, 2013. MMWR 

2013;62(No. Suppl 3):169-72.

8. CDC. Suicides—United States, 2005-2009. In: CDC health disparities 

and inequalities report—United States, 2013. MMWR 2013;

62(No. Suppl 3):177-81.

9. Families USA. Martin Luther King Jr.: A civil rights icon’s thoughts on 

health care. Available at http://www.standupforhealthcare.org/blog/

martin-luther-king-jr-a-civil-rights-icon-s-thoughts-on-health-care.

10. U.S. Department of Health and Human Services. HHS action plan to 

reduce racial and ethnic health disparities. Washington, DC: US 

Department of Health and Human Services; 2012. Available at http://

minorityhealth.hhs.gov/npa/files/Plans/HHS/HHS_Plan_complete.pdf. 

11. CDC. Vaccines for children program (VFC). Atlanta, GA: US 

Department of Health and Human Services, CDC; 2012. Available at 

http://www.cdc.gov/vaccines/programs/vfc/index.html. 

12. US Department of Health and Human Services. Read the Law: The 

Affordable Care Act, Section by Section. Washington, DC: US 

Department of Health and Human Services; 2012. Available at http://

www.hhs.gov/healthcare/rights/law/index.html. 

13. Congressional Budget Office.  CBO’s February 2013 estimate of the 

effects of the Affordable Care Act on health insurance coverage. 

Washington, DC: Congressional Budget Office; 2013. http://cbo.gov/

sites/default/files/cbofiles/attachments/43900_ACAInsurance

CoverageEffects.pdf. 

14. US Centers for Medicare and Medicaid Services. How does the healthcare law protect me? Baltimore, MD: US Centers for Medicare and 

Medicaid Services; 2012. Available at https://www.healthcare.gov/howdoes-the-health-care-law-protect-me/#part8=undefined/part=1. 
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MMWR / November 22, 2013 / Vol. 62 / No. 3 3

Public Health Importance of 

Health Disparities

The burden of illness, premature death, and disability 

disproportionately affects certain populations. During the 

past decade, documented disparities have persisted for 

approximately 80% of the Healthy People 2010 objectives and 

have increased for an additional 13% of the objectives (3). Data 

from the REACH U.S. Risk Factor Survey of approximately 

30 communities in the United States indicate that residents 

in mostly minority communities continue to have lower 

socioeconomic status, greater barriers to health-care access, 

and greater risks for, and burden of, disease compared with 

the general population living in the same county or state (4). 

Both the 2012 National Healthcare Disparities Report (5) and 

the 2012 National Healthcare Quality Report (6) found that 

almost none of the disparities in access to care are improving. 

In addition, quality of care varies not only across types of 

care but also across parts of the country (5,6). Disparities in 

health care access and quality can result in unnecessary direct 

and indirect costs. According to a 2009 study by the Joint 

Center for Political and Economic Studies, eliminating health 

disparities for minorities would have reduced direct medical 

care expenditures by $229.4 billion and reduced indirect costs 

associated with illness and premature death by approximately 

$1 trillion during 2003–2006 (7).

Introduction: CDC Health Disparities and Inequalities Report — 

United States, 2013

Pamela A. Meyer, PhD1

Paula W. Yoon, ScD2

Rachel B. Kaufmann, PhD2

1Office for State, Tribal, Local and Territorial Support, CDC

2Center for Surveillance, Epidemiology, and Laboratory Services, CDC

Disparities in Health Outcomes and 

Health Determinants

Health is influenced by many factors. Poor health status, 

disease risk factors, and limited access to health care are often 

interrelated and have been reported among persons with social, 

economic, and environmental disadvantages. The conditions 

and social context in which persons live can explain, in part, 

why certain populations in the United States are healthier than 

others and why some are not as healthy as they could be (1). 

The World Health Organization (WHO) defines the social 

determinants of health as the conditions in which persons 

are born, grow, live, work, and age, including the health-care 

system (2). According to WHO, “the social determinants 

of health are mostly responsible for health inequities—the 

unfair and avoidable differences in health status seen within 

and between countries” (2). The social determinants of health 

as well as race and ethnicity, sex, sexual orientation, age, and 

disability all influence health. Identification and awareness of 

the differences among populations regarding health outcomes 

and health determinants are essential steps towards reducing 

disparities in communities at greatest risk.

Disparities exist when differences in health outcomes or 

health determinants are observed between populations. The 

terms health disparities and health inequalities are often 

used interchangeably. This supplement uses the terms health 

disparities and inequalities to refer to gaps in health between 

segments of the population. 

Summary

This supplement is the second CDC Health Disparities and Inequalities Report (CHDIR). The 2011 CHDIR was the first CDC 

report to assess disparities across a wide range of diseases, behavioral risk factors, environmental exposures, social determinants, and 

health-care access (CDC. CDC Health Disparities and Inequalities Report—United States, 2011. MMWR 2011;60[Suppl; 

January 14, 2011]). The 2013 CHDIR provides new data for 19 of the topics published in 2011 and 10 new topics. When data 

were available and suitable analyses were possible for the topic area, disparities were examined for population characteristics that 

included race and ethnicity, sex, sexual orientation, age, disability, socioeconomic status, and geographic location. The purpose of 

this supplement is to raise awareness of differences among groups regarding selected health outcomes and health determinants and 

to prompt actions to reduce disparities. The findings in this supplement can be used by practitioners in public health, academia 

and clinical medicine; the media; the general public; policymakers; program managers; and researchers to address disparities and 

help all persons in the United States live longer, healthier, and more productive lives.
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About This Report

This supplement is the second in a series of reports that 

address health disparities. The 2011 CHDIR was the first 

CDC report to assess health disparities across a wide range 

of diseases, behavioral risk factors, environmental exposures, 

social determinants, and health-care access (8). The 2013 

CHDIR includes more current data for 19 of the topics 

published in 2011. Two 2011 topics, housing and air quality, 

are not included in 2013 because there were no new data 

to report. There are 10 new topics. The new topics include: 

access to healthier food retailers, unemployment, nonfatal 

work-related injuries and illnesses, fatal work-related injuries, 

residential proximity to major highways, activity limitations 

due to chronic diseases, asthma attacks, health-related quality 

of life, periodontitis in adults, and tuberculosis. In the 2011 

CHDIR, the prevalence of asthma (i.e., the percentage of 

persons who have ever been diagnosed with asthma and 

still have asthma) was reported, whereas in this report, the 

characteristics of persons who experienced an asthma attack 

during the preceding 12 months are discussed. Although 

the focus of these reports is on the measurement of health 

disparities, most also mentioned existing evidence-based 

interventions or strategies.

Criteria for Topic Selection

Selection of new topics for this supplement was done in 

consultation with CDC’s Associate Directors for Science. The 

primary prerequisites for selection of topics were that data be of 

high quality and appropriate for developing national estimates. 

In addition, the topic had to meet one or more of the following 

criteria: 1) leading cause of premature death, higher disease 

burden, or lower life expectancy at birth for certain segments of 

the U.S. population as defined by sex, race/ethnicity, income or 

education, geography, sexual orientation, and disability status; 

2) known determinant of health (e.g., social, demographic, and 

environmental) where disparities have been identified; and 3) 

health outcome for which effective and feasible interventions 

exist where disparities have been identified.

Analysis

Most of the analyses in this supplement are descriptive and 

did not control for potential confounders or adjust for age; 

therefore, caution should be used in comparing these findings 

to findings from studies with different analytical approaches. 

When data were available and suitable for analysis, disparities 

were examined for characteristics that included race and 

ethnicity, sex, age, household income, educational attainment, 

and geographic location. Other characteristics that were 

analyzed included place of birth, language spoken at home, 

disability status, and sexual orientation. Consistent definitions 

were used as a guide to promote standardization of analyses 

across the reports. However, readers should be attentive to the 

definitions used in each report. There are some similarities and 

some differences in definitions across reports because there are 

multiple ways to categorize these variables. For certain variables, 

the most appropriate categorization depends on the topic being 

studied (e.g., age groups). For other variables, the Office of 

Management and Budget (OMB) and the U.S. Department of 

Health and Human Services have set rules that are to be used 

in federal surveys (i.e., race, ethnicity). To the extent possible, 

OMB standards were used in the analyses. However, some 

data sources did not collect or report information with the 

granularity recommended by OMB because the numbers of 

some racial and ethnic groups were small and their estimates 

would not be meaningful. Subject matter experts across CDC 

participated in identifying appropriate definitions.

Analyses focused on the estimated prevalence of a risk factor or 

health outcome or on the estimated rate of a health outcome in 

the population. Also, in certain reports, change in the estimated 

prevalence or rate over time in recent years was calculated. 

Analytic methods used in the reports varied; therefore, it is 

important to read the methods description for each report. Most 

authors calculated absolute or relative difference in prevalence 

or rate, or both, between segments of the U.S. population. The 

absolute difference is the arithmetic difference between two 

groups. For example, if the prevalence of a certain condition is 

1% among women and 5% among men, the absolute difference 

is 4 percentage points. The relative difference is the absolute 

difference divided by the value for the referent group; the result 

is multiplied by 100% to create a percentage. In the above 

example, the relative difference for men compared with women 

is 400% ([4%/1%]*100%). In other words, men have an excess 

prevalence that is four times the prevalence of what occurs among 

women. This example illustrates that the relative difference can 

be far larger than the absolute difference, especially when the 

overall prevalence of the condition is low. Conversely, the relative 

difference can be smaller when the overall prevalence is high. 

For example, if the prevalence is 91% among women and 95% 

among men, the absolute difference is still 4 percentage points 

but the relative difference is only 4%. To gain a more complete 

understanding of the population’s health status and the impact 

of disparities, it is instructive to look at both measures.

In most analyses, the statistical significance of observed 

differences was assessed using formal significance testing with 

alpha=0.05. If statistical testing was not done, differences were 
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MMWR / November 22, 2013 / Vol. 62 / No. 3 5

assessed by calculating and comparing 95% confidence intervals 

(CIs) around the estimated prevalence or rate. In this approach, 

CIs were used as a measure of variability, and nonoverlapping 

CIs were considered statistically different. While using CIs in 

this way is a conservative evaluation of significance differences, 

infrequently this approach might lead to a conclusion that 

estimates are similar when the point estimates do differ. Because 

of analytical constraints, neither statistical significance nor 95% 

CIs were calculated for three reports (9–11). 

Use of This Report

The findings and conclusions in this supplement are intended 

for practitioners in public health, academia and clinical 

medicine; the media; general public; policymakers; program 

managers; and researchers to address disparities and help all 

persons in the United States live longer, healthier, and more 

productive lives. The information on disparities can be used to 

help select interventions for specific subgroups or populations 

and support community actions to address disparities.

References

1. U.S. Department of Health and Human Services. Healthy People 2020: 

Disparities. Available at http://healthypeople.gov/2020/about/

DisparitiesAbout.aspx.

2. World Health Organization. Social determinants of health. Available at 

http://www.who.int/social_determinants/sdh_definition/en/index.html.

3. US Department of Health and Human Services. Healthy people 2010 

final review. Washington, DC.: U.S. Government Printing Office. 2011.

4. CDC. Surveillance of health status in minority communities—Racial and 

Ethnic Approaches to Community Health Across the U.S. (REACH U.S.) 

Risk Factor Survey, United States, 2009. MMWR 2011;60 (No. SS-6).

5. US Department of Health and Human Services. National healthcare 

disparities report, 2012. AHRQ Publication No. 12-0006. March 2012, 

Rockville, MD. Available at http://www.ahrq.gov/research/findings/

nhqrdr/nhdr11/key.html. 

6. US Department of Health and Human Services. National healthcare 

quality report, 2012. AHRQ Publication No. 13-0003. May 2013, 

Rockville, MD. Available at http://www.ahrq.gov/research/findings/

nhqrdr/nhdr12/nhdr12_prov.pdf.

7. Joint Center for Political and Economic Studies. The economic burden 

of health inequalities in the United States. 2009. Washington, DC. 

Available at http://www.jointcenter.org/research/the-economic

-burden-of-health-inequalities-in-the-united-states.

8. CDC. CDC Health disparities and inequalities report—United States, 

2011. MMWR 2011;60(Suppl; January 14, 2011).

9. CDC. Residential proximity to major highways—United States, 2010. 

In: CDC Health disparities and inequalities report—United States, 

2013. MMWR 2013;62(No. Suppl 3).

10. CDC. Potentially preventable hospitalizations—United States, 2001–

2009. In: CDC Health disparities and inequalities report—United States, 

2013. MMWR 2013;62(No. Suppl 3).

11. CDC. Tuberculosis—United States, 1993–2010. In: CDC Health 

disparities and inequalities report—United States, 2013. MMWR 2013;

62(No. Suppl 3).
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Social Determinants of Health
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MMWR / November 22, 2013 / Vol. 62 / No. 3 9

Education and Income — United States, 2009 and 2011

Gloria L. Beckles, MD1

Benedict I. Truman, MD2

1National Center for Chronic Disease Prevention and Health Promotion, CDC

2National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, CDC

Corresponding author: Gloria L. Beckles, Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, CDC. 

Telephone: 770-488-1272; E-mail: glb4@cdc.gov.

Introduction

The factors that influence the socioeconomic position of 

individuals and groups within industrial societies also influence 

their health (1,2). Socioeconomic position has continuous and 

graded effects on health that are cumulative over a lifetime. 

The socioeconomic conditions of the places where persons 

live and work have an even more substantial influence on 

health than personal socioeconomic position (3,4). In the 

United States, educational attainment and income are the 

indicators that are most commonly used to measure the effect 

of socioeconomic position on health. Research indicates 

that substantial educational and income disparities exist 

across many measures of health (1,5–8). A previous report 

described the magnitude and patterns of absolute and relative 

measures of disparity in noncompletion of high school and 

poverty in 2005 and 2009 (9). Notable disparities defined by 

race/ethnicity, socioeconomic factors, disability status, and 

geographic location were identified for 2005 and 2009, with 

no evidence of a temporal decrease in racial/ethnic disparities, 

whereas socioeconomic and disability disparities increased 

from 2005 to 2009.

The analysis and discussion of educational attainment 

and income that follow are part of the second CDC Health 

Disparities and Inequalities Report (CHDIR) and update 

information on disparities in the prevalence of noncompletion 

of high school and poverty presented in the first CHDIR (8). 

The 2011 CHDIR (9) was the first CDC report to describe 

disparities across a wide range of diseases, behavioral risk 

factors, environmental exposures, social determinants, and 

health-care access. The topic presented in this report is based 

on criteria that are described in the 2013 CHDIR Introduction 

(10). The purposes of this analysis are to discuss and raise 

awareness about group differences in levels of noncompletion 

of high school and poverty and to motivate actions to reduce 

these disparities.

Methods

To monitor progress toward eliminating health disparities 

in the prevalence of noncompletion of high school and 

poverty, CDC analyzed 2009 and 2011 data from the Current 

Population Survey (CPS), using methods described previously 

(8). The CPS is a cross-sectional monthly household survey 

of a representative sample of the civilian, noninstitutionalized 

U.S. household population that is conducted jointly by the 

U.S. Census Bureau and the Bureau of Labor Statistics (11). 

Data on the continuous income-to-poverty ratio (IPR) in 

the 2009 and 2011 National Bureau of Economic Research 

(NBER) data sets based on the March CPS were merged with 

the March supplement files from the 2009 and 2011 Integrated 

Public Use Microdata Series — Current Population Surveys 

(IPUMS-CPS) (12,13).

Self-reported data were collected on various characteristics, 

including demographic, socioeconomic, and geographic 

characteristics and place of birth. Group disparities in agestandardized prevalence of noncompletion of high school and 

poverty were assessed according to sex, race/ethnicity, age, 

educational attainment, poverty status, disability status, place 

of birth, world region (country) of birth, U.S. census region 

of residence, and metropolitan area of residence.

Race/ethnicity categories included non-Hispanic white, nonHispanic black, American Indian/ Alaska Native, Asian/Pacific 

Islander, Hispanic, and multiple races. Age groups included 

25–44, 45–64, 65–79, and ≥80 years. Educational attainment 

categories included less than high school, high school graduate 

or equivalent, some college, and college graduate. Poverty 

status was derived from the IPR, which is based on family 

income relative to federally established poverty thresholds that 

are revised annually to reflect changes in the cost of living as 

measured by the Consumer Price Index (14).

 Disability status was defined by the national data collection 

standards released by the U.S. Department of Health and 

Human Services (HHS) in 2011 (15). World region of birth 

was aggregated to approximate the regions of the world from 

which the foreign born now originate (16). Absolute and 

relative disparities in noncompletion of high school were 

assessed separately for adults aged ≥25 years and 18–24 years; 

for poverty, disparities were assessed for the total population 

aged ≥18 years.

Disparities between groups were measured as deviations from 

a referent category rate. Referent categories were usually those 
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10 MMWR / November 22, 2013 / Vol. 62 / No. 3

that had the most favorable group estimates for most variables; 

for racial/ethnic comparisons, white males and females were 

selected because they were the largest group (17,18). Absolute 

difference was measured as the simple difference between a 

group estimate and the estimate for its respective reference 

category, or referent group. Relative difference, a percentage, 

was obtained by dividing the absolute difference by the value 

in the referent category and multiplying by 100. To evaluate 

changes in disparity over time, relative differences for the 

groups in 2009 were subtracted from relative differences in 

2011 (17,18). The z statistic and a two-tailed test at p<0.05 

with Bonferroni correction for multiple comparisons were 

used to test for the statistical significance of the observed 

absolute and relative differences and for changes over time. 

To calculate the standard errors for testing the change over 

time, a previously described method was used (19), modified 

to account for the parameter being compared (i.e., relative 

difference). Statistically significant increases and decreases in 

relative differences from 2009 to 2011 were interpreted as 

increases and decreases in disparity, respectively. CDC used 

statistical software to account for the complex sample design 

of the CPS and to produce point estimates, standard errors, 

and 95% confidence intervals. Estimates were age standardized 

by the direct method to the year 2000 age distribution of the 

U.S. population (20). Estimates with relative standard error 

≥30% were not reported.

Results

 In the 2011 population aged ≥25 years, statistically 

significant absolute disparities in noncompletion of high school 

were identified for all the characteristics studied (Table 1). 

Noncompletion of high school increased with age; the absolute 

differences between the age-specific percentages in the referent 

group (45–64 years) and the age groups 65–79 years and 

≥80 years were 6.6 and 14.8 percentage points, respectively. 

The absolute racial/ethnic difference between non-Hispanic 

whites and each of the other racial ethnic groups was highest 

for Hispanics (30.4 percentage points), lowest for the multiple 

races group (4.0 percentage points), and intermediate for nonHispanic American Indian/Alaska Natives (11.6 percentage 

points), and non-Hispanic blacks (8.8 percentage points). This 

pattern was similar in both sexes, except that among women, 

the absolute difference for the multiple races group (3.1 

percentage points) was not statistically significant. Absolute 

differences between the age-standardized percentages of adults 

who had not completed high school in each poverty status 

group and the referent group (high income, IPR ≥4) were 

statistically significant overall and in both men and women. 

Noncompletion of high school increased with increasing 

poverty; the absolute difference for the poorest group was 

approximately three times the absolute difference for the 

middle-income group (6.4 versus 1.7 percentage points). 

Significant absolute differences between adults with and 

without a disability in noncompletion of high school also 

were found (total: 9.8 percentage points; men: 9.5 percentage 

points; women: 10.1 percentage points).

Among adults aged ≥25 years in 2011, noncompletion of 

high school was generally more common among foreignborn than U.S.-born adults (Table 1). Significant absolute 

differences from the U.S. born were observed in the total 

population (24.9 percentage points), among non-Hispanic 

whites (3.1 percentage points), A/PIs (9.0 percentage points), 

and Hispanics (27.7 percentage points). Disparities in 

noncompletion of high school also were found according to 

world region (countries) of birth. In 2011, significant absolute 

differences were found between persons born in the United 

States (referent group) and those born in Latin American and 

Caribbean countries (46.1 percentage points) or in countries 

in Asia and the Pacific (6.1 percentage points). In 2011, 

significant absolute differences were also found between 

residents of the U.S. census regions of the Midwest, South, 

or West and the referent group (the Northeast). The absolute 

difference in age-standardized noncompletion of high school 

between residents who lived inside metropolitan areas and 

those who lived outside metropolitan areas (referent group) 

also was significant. In 2009 and 2011, the magnitude and 

pattern of age, poverty status, and disability differences were 

similar in men and women. No significant differences were 

identified in the relative differences of any these characteristics 

from 2009 to 2011.

Among younger adults aged 18–24 years in 2011, 

significant disparities in place of birth and in demographic, 

socioeconomic, disability, and geographic characteristics were 

found in the age-standardized percentages of adults who did 

not complete high school (Table 2). Unlike adults aged ≥25 

years, the absolute difference between the percentages of young 

adults who did not complete high school in the younger age 

group (18–19 years) and older referent group (20–24 years) 

was significant (33.1 percentage points). The relative difference

between persons aged 18–19 years and the referent group 

increased significantly by 61.6 percentage points from 2009 

to 2011, whereas no change occurred from 2009 to 2011 in 

age-specific disparities in the older population (≥25 years) 

(Table 1). Among racial/ethnic groups, absolute differences 

from non-Hispanic whites were only significant among 

non-Hispanic blacks (7.2 percentage points) and Hispanics 

(12.4 percentage points), with the magnitude and pattern 
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 11

TABLE 1. Age-standardized* percentage of adults aged ≥25 years who did not complete high school, by selected characteristics — Integrated 

Public Use Microdata Series, Current Population Survey, United States, 2009 and 2011

Characteristic

2009 2011

Change in 

relative 

difference 

from 2009 to 

2011

(percentage 

points)

% who did 

not complete 

high school (SE)

Absolute 

difference 

(percentage 

points)

Relative 

difference

(%)

% who did 

not complete 

high school (SE)

Absolute 

difference 

(percentage 

points)

Relative 

difference

(%)

Sex

Male 14.1 (0.2) 1.4† 11.0 13.2 (0.2) 1.4† 11.9 0.9

Female 12.7 (0.2) Ref. Ref. 11.8 (0.2) Ref. Ref. Ref.

Age group (yrs)§

Both sexes

25–44 11.7 (0.2) 0.7 6.2 11.0 (0.2) 0.4 4.1 -2.1

45–64 11.0 (0.2) Ref. Ref. 10.5 (0.2) Ref. Ref. Ref.

65–79 19.5 (0.4) 8.5† 77.3 17.2 (0.4) 6.6† 63.1 -14.2

≥80 27.6 (0.8) 16.6† 150.7 25.3 (0.7) 14.8† 140.6 -10.0

Male

25–44 13.0 (0.3) 1.3† 11.5 12.1 (0.2) 1.0† 9.0 -2.5

45–64 11.7 (0.3) Ref. Ref. 11.1 (0.3) Ref. Ref. Ref.

65–79 18.5 (0.6) 6.8† 58.0 16.6 (0.5) 5.4† 48.7 -9.4

≥80 27.2 (1.1) 15.5† 132.1 26.0 (1.1) 14.9† 133.5 1.4

Female

25–44 10.3 (0.2) 0 -0.3 9.8 (0.2) -0.2 -1.7 -1.4

45–64 10.4 (0.2) Ref. Ref. 9.9 (0.2) Ref. Ref. Ref.

65–79 20.4 (0.5) 10.0† 96.7 17.7 (0.5) 7.7† 77.8 19.0

≥80 27.9 (0.9) 17.5† 169.0 24.9 (0.8) 15.0† 150.6 -18.4

Race/Ethnicity

Both sexes

White, non-Hispanic 8.0 (0.1) Ref. Ref. 7.3 (0.1) Ref. Ref. Ref.

Black, non-Hispanic 17.0 (0.4) 9.0† 112.6 16.1 (0.4) 8.8† 121.2 8.9

Asian/Pacific Islander 12.7 (0.7) 4.7† 59.1 12.1 (0.6) 4.9† 66.8 7.7

American Indian/Alaska Native 20.0 (1.8) 12.0† 149.8 18.8 (1.9) 11.5† 158.5 8.8

Multiple races 13.4 (1.0) 5.4† 67.8 11.3 (1.0) 4.0† 55.4 -12.4

Hispanic¶ 40.1 (0.6) 32.1† 400.8 37.7 (0.5) 30.4† 419.3 18.5

Male

White, non-Hispanic 8.6 (0.2) Ref. Ref. 7.9 (0.2) Ref. Ref. Ref.

Black, non-Hispanic 17.6 (0.6) 9.1† 106.0 17.3 (0.6) 9.4† 120.1 14.1

Asian/Pacific Islander 10.3 (0.8) 1.8 20.9 10.2 (0.7) 2.3† 29.5 8.6

American Indian/Alaska Native 21.0 (2.2) 12.4† 145.0 20.8 (2.4) 13.0† 165.1 20.1

Multiple races 13.3 (1.4) 4.7† 55.1 12.7 (1.5) 4.8† 61.6 6.5

Hispanic 41.1 (0.7) 32.5† 380.4 38.4 (0.7) 30.5† 388.2 7.8

Female

White, non-Hispanic 7.4 (0.2) Ref. Ref. 6.7 (0.2) Ref. Ref. Ref.

Black, non-Hispanic 16.6 (0.5) 9.2† 123.3 15.2 (0.5) 8.5† 127.1 3.8

Asian/Pacific Islander 14.6 (0.8) 7.2† 96.1 13.6 (0.7) 7.0† 104.2 8.1

American Indian/Alaska Native 19.6 (2.1) 12.2† 163.8 16.9 (2.1) 10.2† 152.8 -11.0

Multiple races 13.1 (1.2) 5.7† 76.3 9.8 (1.2) 3.1 46.3 -30.1

Hispanic 38.7 (0.6) 31.3† 420.0 36.8 (0.6) 30.1† 450.9 30.9

Income-to-poverty ratio**

Both sexes

Poor, <1.00 18.4 (0.5) 7.4† 66.6 16.6 (0.4) 6.4† 61.9 -4.7

Near poor, 1.00–1.9 15.6 (0.3) 4.6† 41.7 14.7 (0.3) 4.4† 42.9 1.2

Middle income, 2.00–3.9 13.2 (0.2) 2.1† 19.4 12.0 (0.2) 1.7† 16.9 -2.5

High income, ≥4.0 11.0 (0.2) Ref. Ref. 10.3 (0.2) Ref. Ref. Ref.

Male

Poor, <1.00 18.9 (0.6) 7.5† 66.5 17.3 (0.6) 6.5† 60.1 -6.4

Near poor, 1.00–1.9 17.0 (0.5) 5.7† 50.2 16.0 (0.5) 5.2† 48.1 -2.1

Middle income, 2.00–3.9 14.1 (0.3) 2.8† 24.3 12.7 (0.3) 1.9† 17.6 -6.7

High income, ≥4.0 11.3 (0.3) Ref. Ref. 10.8 (0.3) Ref. Ref. Ref.

Female

Poor, <1.00 17.9 (0.5) 7.2† 67.1 15.9 (0.5) 6.2† 63.0 -4.1

Near poor, 1.00–1.9 14.3 (0.3) 3.6† 34.1 13.5 (0.4) 3.7† 37.9 3.8

Middle income, 2.00–3.9 12.3 (0.3) 1.6† 14.9 11.3 (0.3) 1.5† 15.8 0.9

High income, ≥4.0 10.7 (0.2) Ref. Ref. 9.8 (0.2) Ref. Ref. Ref.

See table footnotes on the next page.
Supplement

12 MMWR / November 22, 2013 / Vol. 62 / No. 3

TABLE 1. (Continued) Age-standardized* percentage of adults aged ≥25 years who did not complete high school, by selected characteristics 

— Integrated Public Use Microdata Series, Current Population Survey, United States, 2009 and 2011

Characteristic

Change in 

relative 

difference 

from 2009 to 

2011

(percentage 

points)

2009 2011

% who did 

not complete 

high school (SE)

Absolute 

difference 

(percentage 

points)

Relative 

difference

(%)

% who did 

not complete 

high school (SE)

Absolute 

difference 

(percentage 

points)

Relative 

difference

(%)

Disability status

Both sexes

Disability 23.3 (0.5) 11.4† 95.5 21.1 (0.5) 9.8† 87.3 -8.3

No disability 11.9 (0.2) Ref. Ref. 11.2 (0.1) Ref. Ref. Ref.

Male

Disability 23.3 (0.8) 10.4† 80.8 21.5 (0.7) 9.5† 78.9 -1.9

No disability 12.9 (0.2) Ref. Ref. 12.0 (0.2) Ref. Ref. Ref.

Female

Disability 23.2 (0.6) 12.2† 111.2 20.6 (0.7) 10.1† 96.3 -14.9

No disability 11.0 (0.2) Ref. Ref. 10.5 (0.2) Ref. Ref. Ref.

Place of birth

All racial/ethnic groups

United States or U.S. territory 9.6 (0.2) Ref. Ref. 8.7 (0.1) Ref. Ref. Ref.

Foreign country 35.6 (0.6) 26.0† 270.0 33.7 (0.6) 24.9† 286.0 16.0

White, non-Hispanic

United States or U.S. territory 8.0 (0.2) Ref. Ref. 7.2 (0.1) Ref. Ref. Ref.

Foreign country 10.6 (0.7) 2.6† 33.1 10.3 (0.7) 3.1† 43.3 10.3

Black, non-Hispanic

United States or U.S. territory 17.3 (0.5) Ref. Ref. 16.1 (0.4) Ref. Ref. Ref.

Foreign country 15.7 (1.3) -1.6 -9.2 16.3 (1.4) 0.2 1.1 10.3

Asian/Pacific Islander

United States or U.S. territory 4.7 (1.1) Ref. Ref. 4.7 (1.0) Ref. Ref. Ref.

Foreign country 14.2 (0.8) 9.5† 199.8 13.7 (0.7) 9.0† 191.3 -8.5

American Indian/Alaska Native

United States or U.S. territory 20.1 (1.8) Ref. Ref. 19.0 (2.0) Ref. Ref. Ref.

Foreign country —†† — NA NA 20.0 (5.7) 1.0 5.2 NA

Multiple races

United States or U.S. territory 14.4 (1.2) Ref. Ref. 12.3 (1.2) Ref. Ref. Ref.

Foreign country — — NA NA — — NA NA NA

Hispanic

United States or U.S. territory 22.3 (1.0) Ref. Ref. 20.5 (0.9) Ref. Ref. Ref.

Foreign country 50.5 (0.7) 28.1† 125.9 48.2 (0.7) 27.7† 135.3 9.3

World region (country) of birth

United States 9.8 (0.2) Ref. Ref. 8.8 (0.1) Ref. Ref. Ref.

Canada, Europe, Australia, or 

New Zealand

5.3 (1.0) -4.5† -45.9 6.0 (1.3) -2.8 -32.2 13.7

Mexico, South America, Central 

America, or Caribbean

57.2 (0.9) 47.4† 485.8 54.9 (0.9) 46.1† 522.9 37.1

Africa or the Middle East 11.6 (2.3) 1.8 18.5 9.1 (2.3) 0.3 2.9 -15.6

Asia or the Pacific Islands 17.3 (1.7) 7.5† 77.3 14.9 (1.6) 6.1† 69.3 -8.0

U.S. census region§§

Northeast 9.4 (0.4) Ref. Ref. 8.7 (0.4) Ref. Ref. Ref.

Midwest 10.7 (0.4) 1.3 13.9 10.2 (0.4) 1.5† 17.4 3.5

South 12.2 (0.4) 2.8† 30.4 11.3 (0.3) 2.6† 29.5 -0.8

West 12.6 (0.6) 3.3† 34.8 11.0 (0.5) 2.3† 26.8 -8.0

Area of residence

Inside metropolitan area 16.4 (0.4) 1.4† 9.1 15.7 (0.4) 1.4† 9.5 0.4

Outside metropolitan area 15.0 (0.5) Ref. Ref. 14.3 (0.5) Ref. Ref. Ref.

Abbreviations: FPL = federal poverty level; NA = not applicable; Ref. = referent; SE = standard error.

* Age standardized to the 2000 U.S. standard population.

† Difference between a group estimate and the estimate for its respective referent group is significant (p<0.05, two-tailed z test with Bonferroni correction for multiple 

comparisons).

§ Age-specific estimates are not age standardized.

¶ Persons of Hispanic ethnicity might be of any race or combination of races. 

** On the basis of the U.S. FPL. FPL was based on U.S. Census Bureau poverty thresholds (available at http://www.census.gov/hhes/www/poverty.html).

†† Estimate is statistically unreliable because relative SE ≥30%.

§§ Northeast: Connecticut, Maine, Massachusetts, New Jersey, New Hampshire, New York, Pennsylvania, Rhode Island, and Vermont; Midwest: Illinois, Indiana, Iowa, 

Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin; South: Alabama, Arkansas, Delaware, District of Columbia, 

Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West Virginia; West: Alaska, 

Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 13

See table footnotes on the next page.

TABLE 2. Age-standardized* percentage of adults aged 18–24 years who did not complete high school, by selected characteristics — Integrated 

Public Use Microdata Series, Current Population Survey, United States, 2009 and 2011

Characteristic

2009 2011

Change in 

relative 

difference 

from 2009 to 

2011 

(percentage 

points)

% who 

did not 

complete 

high school (SE)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%)

% who 

did not 

complete 

high school (SE)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%)

Sex

Male 22.4 (0.5) 4.6† 25.8 22.4 (0.5) 3.6† 18.9 -6.9

Female 17.8 (0.4) Ref. Ref. 18.8 (0.5) Ref. Ref. Ref.

Age group (yrs)§

Both sexes

18–19 40.1 (0.7) 28.7† 252.8 43.6 (0.8) 33.1† 314.4 61.6§§

20–24 11.4 (0.4) Ref. Ref. 10.5 (0.4) Ref. Ref. Ref.

Male

18–19 43.9 (1.0) 30.9† 238.9 47.0 (1.1) 35.4† 305.2 66.3

20–24 13.0 (0.5) Ref. Ref. 11.6 (0.5) Ref. Ref. Ref.

Female

18–19 36.2 (1.0) 26.4† 271.3 40.2 (1.1) 30.8† 327.1 55.8

20–24 9.7 (0.4) Ref. Ref. 9.4 (0.4) Ref. Ref. Ref.

Race/Ethnicity

Both sexes

White, non-Hispanic 16.3 (0.4) Ref. Ref. 17.2 (0.5) Ref. Ref. Ref.

Black, non-Hispanic 24.4 (1.1) 8.2† 50.2 24.3 (1.0) 7.2† 41.7 -8.5

Asian/Pacific Islander 13.8 (1.3) -2.5 -15.3 16.2 (2.0) -1.0 -5.9 9.4

American Indian/Alaska Native 25.1 (3.2) 8.9 54.4 26.0 (3.7) 8.9 51.7 -2.7

Multiple races 19.9 (2.2) 3.6 22.2 18.8 (2.2) 1.6 9.4 -12.8

Hispanic¶ 31.5 (1.0) 15.2† 93.7 29.5 (0.9) 12.4† 72.0 -21.7

Male

White, non-Hispanic 18.2 (0.6) Ref. Ref. 18.4 (0.6) Ref. Ref. Ref.

Black, non-Hispanic 26.7 (1.8) 8.6† 47.2 26.7 (1.5) 8.3† 45.0 -2.2

Asian/Pacific Islander 14.6 (1.8) -3.6 -19.7 17.1 (2.5) -1.3 -7.2 12.5

American Indian/Alaska Native 28.6 (4.7) 10.4 57.4 31.0 (5.9) 12.6 68.4 11.0

Multiple races 22.5 (3.1) 4.3 23.6 16.0 (2.5) -2.4 -13.0 -36.6

Hispanic 35.1 (1.4) 17.0† 93.3 32.0 (1.2) 13.6† 73.8 -19.5

Female

White, non-Hispanic 14.3 (0.5) Ref. Ref. 15.9 (0.6) Ref. Ref. Ref.

Black, non-Hispanic 22.2 (1.3) 7.9† 55.4 22.0 (1.5) 6.1 38.5 -16.9

Asian/Pacific Islander 12.7 (1.6) -1.6 -11.3 15.2 (2.3) -0.7 -4.5 6.9

American Indian/Alaska Native 21.9 (4.1) 7.6 53.4 23.2 (5.0) 7.4 46.3 -7.0

Multiple races 17.0 (2.8) 2.7 18.9 21.3 (3.4) 5.4 34.1 15.2

Hispanic 27.6 (1.2) 13.3† 92.8 26.6 (1.1 10.7† 67.4 -25.4

Income-to-poverty ratio**

Both sexes

Poor, <1.00 23.7 (1.1) 5.9† 32.9 23.5 (0.9) 4.7† 25.1 -7.8

Near poor, 1.00–1.9 22.8 (0.8) 5.0† 27.8 22.3 (0.9) 3.6† 19.0 -8.7

Middle income, 2.00–3.9 19.8 (0.6) 1.9 10.8 20.4 (0.6) 1.6 8.5 -2.3

High income, ≥4.0 17.9 (0.5) Ref. Ref. 18.8 (0.5) Ref. Ref. Ref.

Male

Poor, <1.00 25.0 (1.4) 5.4† 27.4 24.9 (1.3) 4.4 21.3 -6.1

Near poor, 1.00–1.9 26.0 (1.2) 6.3† 32.0 24.6 (1.3) 4.1† 19.9 -12.1

Middle income, 2.00–3.9 22.5 (0.9) 2.9 14.6 22.0 (0.9) 1.5 7.2 -7.4

High income, ≥4.0 19.7 (0.8) Ref. Ref. 20.5 (0.7) Ref. Ref. Ref.

Female

Poor, <1.00 22.5 (1.4) 6.5† 40.6 22.0 (1.1) 5.0† 29.6 -11.0

Near poor, 1.00–1.9 19.5 (1.0) 3.5† 21.6 20.0 (1.1) 3.0 17.9 -3.7

Middle income, 2.00–3.9 17.0 (0.7) 1.0 6.3 18.6 (0.8) 1.6 9.2 3.0

High income, ≥4.0 16.0 (0.7) Ref. Ref. 17.0 (0.7) Ref. Ref. Ref.

Disability status

Both sexes

Disability 32.4 (2.0) 12.7† 64.5 35.5 (2.3) 15.4† 76.3 11.8

No disability 19.7 (0.4) Ref. Ref. 20.1 (0.4) Ref. Ref. Ref.
Supplement

14 MMWR / November 22, 2013 / Vol. 62 / No. 3

TABLE 2. (Continued) Age-standardized* percentage of adults aged 18–24 years who did not complete high school, by selected characteristics 

— Integrated Public Use Microdata Series — Current Population Survey, United States, 2009 and 2011

Characteristic

2009 2011

Change in 

relative 

difference 

from 2009 to 

2011 

(percentage 

points)

% who 

did not 

complete 

high school (SE)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%)

% who 

did not 

complete 

high school (SE)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%)

Male

Disability 29.5 (2.4) 7.3† 32.7 38.0 (2.8) 16.1† 73.8 41.1§§

No disability 22.2 (0.5) Ref. Ref. 21.8 (0.5) Ref. Ref. Ref.

Female

Disability 36.0 (3.4) 18.8† 109.4 32.0 (3.3) 13.6† 73.7 -35.7

No disability 17.2 (0.4) Ref. Ref. 18.4 (0.5) Ref. Ref. Ref.

Place of birth

All racial/ethnic groups

United States or U.S. territory 18.5 (0.4) Ref. Ref. 19.1 (0.4) Ref. Ref. Ref.

Foreign country 32.7 (1.4) 14.2† 77.1 31.4 (1.4) 12.0† 63.0 -14.1

White, non-Hispanic

United States or U.S. territory 16.5 (0.4) Ref. Ref. 17.3 (0.5) Ref. Ref. Ref.

Foreign country 13.2 (2.3) -3.4 -20.3 16.6 (3.2) -0.7 -4.1 16.2

Black, non-Hispanic

United States or U.S. territory 24.9 (1.2) Ref. Ref. 24.7 (1.1) Ref. Ref. Ref.

Foreign country 22.1 (4.1) -2.8 -11.4 23.3 (4.4) -1.4 -5.6 5.7

Asian/Pacific Islander

United States or U.S. territory —†† — Ref. Ref. — — Ref. Ref. Ref.

Foreign country 18.1 (2.3) NA NA 22.1 (3.6) NA NA NA

American Indian/Alaska Native

United States or U.S. territory 24.7 (3.2) Ref. Ref. 26.8 (3.9) Ref. Ref. Ref.

Foreign country 0 (0) -24.7 -100.0 — — NA NA NA

Multiple races

United States or U.S. territory 21.1 (2.7) Ref. Ref. 20.4 (2.6) Ref. Ref. Ref.

Foreign country — — NA NA — — NA NA NA

Hispanic

United States or U.S. territory 24.6 (1.5) Ref. Ref. 24.5 (1.4) Ref. Ref. Ref.

Foreign country 44.9 (1.9) 20.4† 82.9 40.6 (1.8) 16.0† 65.4 -17.4

World region (country) of birth

United States 18.4 (0.3) Ref. Ref. 19.2 (0.4) Ref. Ref. Ref.

Canada, Europe, Australia, or New 

Zealand

— — NA NA — — NA NA NA

Mexico, South America, Central 

America, or the Caribbean

46.5 (2.1) 28.1† 153.1 42.5 (2.1) 23.4† 121.9 -31.3

Africa or the Middle East — — NA NA 30.5 (0) 11.3 58.9 NA

Asia or the Pacific Islands 20.1 (4.0) 1.7 9.3 24.8 (3.8) 5.6 29.2 19.9

U.S. census region¶¶

Northeast 15.5 (1.1) Ref. Ref. 18.0 (1.2) Ref. Ref. Ref.

Midwest 18.6 (0.9) 3.1 20.0 19.2 (0.8) 1.2 6.8 -13.2

South 20.8 (0.9) 5.3† 34.1 21.1 (0.9) 3.1 17.3 -16.8

West 22.6 (1.2) 7.1† 46.0 20.4 (1.0) 2.4 13.4 -32.6

Residence in metropolitan area

Inside metropolitan area 21.3 (0.8) 0.1 0.3 22.1 (0.7) -1.3 -5.3 -5.6

Outside metropolitan area 21.3 (1.0) Ref. Ref. 23.4 (1.0) Ref. Ref. Ref.

Abbreviations: FPL = federal poverty level; NA = not applicable; Ref. = referent; SE = standard error.

* Age standardized to the 2000 U.S. standard population.

† Difference between a group estimate and the estimate for its respective referent group is significant (p<0.05, two-tailed z test with Bonferroni correction for multiple 

comparisons).

§ Age-specific estimates are not age standardized.

¶ Persons of Hispanic ethnicity might be of any race or combination of races. 

** On the basis of the U.S. FPL. FPL was based on U.S. Census Bureau poverty thresholds (available at http://www.census.gov/hhes/www/poverty.html).

†† Estimate is statistically unreliable because relative SE ≥30%.

§§ Difference between the relative differences in 2011 and 2009 is significant (p<0.05, two-tailed z test with Bonferroni correction for multiple comparisons). 

¶¶ Northeast: Connecticut, Maine, Massachusetts, New Jersey, New Hampshire, New York, Pennsylvania, Rhode Island, and Vermont; Midwest: Illinois, Indiana, Iowa, 

Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin; South: Alabama, Arkansas, Delaware, District of Columbia, 

Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West Virginia; West: Alaska, 

Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 15

similar in men and women. Overall, absolute differences in 

noncompletion of high school between the referent group 

(high income) and those who lived in poor (4.7 percentage 

points) or near-poor families (3.6 percentage points) were 

significant; however, absolute differences were only significant 

for men in middle-income families and women in poor 

families. Significant absolute differences in noncompletion 

of high school also were found among young adults with a 

disability (15.4 percentage points); however, unlike men aged 

≥25 years, the disparity among younger adult men worsened 

from 2009 to 2011 by 41.1 percentage points. No temporal 

change in disability disparity was observed among young 

adult females (Table 2). In 2011, absolute differences in the 

age-standardized percentage of persons who did not complete 

high school among those who were foreign born and U.S. born 

(referent group) were significant in the total population (12.0 

percentage points) and among Hispanics (16.0 percentage 

points). In addition, absolute differences were only significant 

between U.S.-born young adults and young adults born in 

Latin American and Caribbean countries (23.4 percentage 

points). No significant differences were found by U.S. census 

region or metropolitan area. No significant changes in the 

U.S. census region disparities occurred from 2009 to 2011.

In 2011, overall and for men and women, significant absolute 

differences in the age-standardized percentages of adults in poor 

families (IPR <1.00) were found among the youngest adults, 

non-Hispanic blacks, and Hispanics; all groups that had not 

completed college; and adults with disabilities (Table 3). In 2009 

and 2011, disparities in poverty increased with decreasing level of 

educational attainment, with the greatest disparity experienced 

by the group with the lowest level of educational attainment. 

Significant absolute differences in the age-standardized 

percentages in poor families were found between persons of 

either sex with a disability and those with no disability (referent 

group) (men: 3.2 percentage points; women 3.5 percentage 

points). In 2009 and 2011, the absolute differences between 

persons who were foreign born and U.S. born (referent group) 

in age-standardized percentages of adults in poor families were 

significant in the total population (1.7 and 1.6 percentage 

points, respectively) but not by race/ethnicity. In addition, 

significant absolute differences also were found between adults 

born in Latin American and Caribbean countries and those born 

in the United States. In 2009 and 2011, significant absolute 

differences in the percentages of adults who lived in poverty were 

found between residents of the U.S. census regions of the West, 

South, or Midwest and the referent group (Northeast region) 

but not between residents who lived inside compared with 

outside metropolitan areas. From 2009 to 2011, no statistically 

significant changes in the relative differences in poverty by any 

characteristic were found (Table 3).

Discussion

The findings in this report indicate that racial/ethnic, 

socioeconomic, and geographic disparities in noncompletion 

of high school and poverty persist in the U.S. adult population; 

little evidence of improvement from 2009 to 2011 was 

identified. Within each year studied to date, significant 

absolute and relative differences were found; however, between 

years, these differences were not statistically different. The 

pattern of disparities is consistent with sociodemographic and 

geographic differences reported by several national surveys 

(6–8,16,21–25). The findings also reveal that young racial/

ethnic, foreign-born, and poor adults might be especially 

vulnerable to early onset and progression of poor health as 

evidenced by marked disparities in noncompletion of high 

school among these subgroups.

Educational attainment and income provide psychosocial 

and material resources that protect against exposure to health 

risks in early and adult life (1–3). Persons with low levels of 

education and income generally experience increased rates of 

mortality, morbidity, and risk-taking behaviors and decreased 

access to and quality of health care (1,6–8). This report 

confirms that the lowest levels of education and income 

are most common and persistent among subgroups that 

systematically exhibit the poorest health. For example, two 

out of five Hispanics and nearly one out of five non-Hispanic 

blacks or American Indian/Alaska Natives had not completed 

high school, and at least one out of 10 of these racial/ethnic 

groups had incomes less than the official poverty threshold. 

However, substantial empirical evidence from the United 

States and elsewhere consistently shows no thresholds in the 

relationships between education or income and health. Among 

children and adults in the overall population and within racial/

ethnic groups, rates of mortality, morbidity, and poor health 

behaviors decrease in a continuous and graded manner with 

increasing levels of education and income (6,7,23–25).

Health-promotion efforts have emphasized racial/ethnic 

disparities in health as part of an approach to risk reduction that 

focuses on groups at high risk, with little or no improvement 

in disparities (24,26). The patterns described in this report 

suggest that interventions and policies that are also designed to 

take account of the influence of educational attainment, family 

income, and other socioeconomic conditions on health risks 

in the entire population might prove to be more effective in 

reducing health disparities (27,28).
Supplement

16 MMWR / November 22, 2013 / Vol. 62 / No. 3

See table footnotes on the next page.

TABLE 3. Age-standardized* percentage of adults aged ≥18 years with incomes less than the federal poverty level, by selected characteristics 

— Integrated Public Use Microdata Series — Current Population Survey, United States, 2009 and 2011

Characteristic

2009 2011

Change in relative 

difference from 

2009 to 2011 

(percentage points)

% with IPR 

<1.00 (SE)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%)

Percentage 

with IPR 

<1.00 (SE)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%)

Sex

Male 11.4 (0.2) Ref. Ref. 13.2 (0.2) Ref. Ref. Ref.

Female 11.9 (0.1) 0.5† 4.0 13.5 (0.2) 0.4 2.9 -1.1

Age group (yrs)§

Both sexes

18–24 12.8 (0.3) 1.9† 17.1 15.1 (0.4) 2.4† 18.6 1.5

25–44 11.9 (0.2) 0.9 8.5 13.5 (0.2) 0.8 6.2 -2.3

45–64 11.0 (0.2) Ref. Ref. 12.8 (0.2) Ref. Ref. —

65–79 11.1 (0.3) 0.1 1.1 12.5 (0.3) -0.2 -1.6 -2.7

≥80 12.1 (0.6) 1.1 10.0 12.8 (0.5) 0 0 -10

Male

18–24 12.3 (0.4) 1.7† 15.8 14.8 (0.5) 2.1† 16.2 0.4

25–44 11.7 (0.2) 1.1† 10.2 13.5 (0.3) 0.8 6.1 -4.1

45–64 10.6 (0.3) Ref. Ref. 12.7 (0.3) Ref. Ref. Ref.

65–79 11.1 (0.5) 0.4 4.2 11.6 (0.4) -1.1 -8.7 -12.9

≥80 11.4 (0.9) 0.8 7.5 13.0 (0.9) 0.3 2.1 -5.4

Female

18–24 13.3 (0.4) 2.1† 18.6 15.5 (0.5) 2.7† 21 2.4

25–44 12.0 (0.2) 0.8 6.9 13.6 (0.2) 0.8 6.3 -0.6

45–64 11.2 (0.2) Ref. Ref. 12.8 (0.3) Ref. Ref. Ref.

65–79 11.1 (0.4) -0.2 -1.7 13.3 (0.4) 0.5 4.0 5.7

≥80 12.4 (0.6) 1.2 10.4 12.6 (0.7) -0.2 -1.5 -11.9

Race/Ethnicity

Both sexes

White, non-Hispanic 10.7 (0.2) Ref. Ref. 12.4 (0.2) Ref. Ref. Ref.

Black, non-Hispanic 14.4 (0.4) 3.7† 34.8 16.4 (0.4) 4.1† 32.8 -2.0

Asian/Pacific Islander 10.7 (0.5) 0 0.1 11.5 (0.5) -0.8 -6.8 -6.9

American Indian/Alaska Native 15.3 (1.9) 4.7 43.6 18.9 (3.5) 6.6 53.4 9.7

Multiple races 11.2 (0.9) 0.5 4.6 12.4 (1.0) 0 0.2 -4.4

Hispanic¶ 14.5 (0.4) 3.9† 36.3 16.0 (0.4) 3.7† 29.7 -6.6

Male

White, non-Hispanic 10.5 (0.2) Ref. Ref. 12.2 (0.2) Ref. Ref. Ref.

Black, non-Hispanic 14.0 (0.5) 3.5† 33.4 15.6 (0.5) 3.4† 28.1 -5.3

Asian/Pacific Islander 10.8 (0.6) 0.3 3.0 12.0 (0.7) -0.2 -1.6 -4.6

American Indian/Alaska Native 12.9 (1.9) 2.4 22.5 18.6 (3.1) 6.4 52.1 29.6

Multiple races 9.4 (1.2) -1.1 -10.3 12.6 (1.4) 0.4 3.3 13.6

Hispanic 14.3 (0.5) 3.8† 36.2 15.8 (0.5) 3.5† 29.0 -7.2

Female

White, non-Hispanic 10.8 (0.2) Ref. Ref. 12.5 (0.2) Ref. Ref. Ref.

Black, non-Hispanic 14.7 (0.5) 3.9† 35.6 17.0 (0.6) 4.5† 36.1 0.5

Asian/Pacific Islander 10.5 (0.6) -0.3 -3.0 11.1 (0.5) -1.4 -11.1 -8.1

American Indian/Alaska Native 17.7 (2.4) 6.9 63.8 19.3 (4.3) 6.8 54.2 -9.5

Multiple races 12.6 (1.2) 1.8 16.9 12.1 (1.2) -0.4 -3.4 -20.4

Hispanic 14.9 (0.4) 4.1† 37.6 16.2 (0.4) 3.8† 30.1 -7.5

Educational attainment

Both sexes

Less than high school 15.8 (0.4) 5.6† 55.0 17.6 (0.4) 6.1† 53.1 -1.8

High school graduate or equivalent 11.8 (0.2) 1.7† 16.2 13.8 (0.3) 2.3† 20.3 4.2

Some college 10.9 (0.2) 0.7 6.8 12.9 (0.3) 1.4† 12.5 5.8

College graduate 10.2 (0.2) Ref. Ref. 11.5 (0.3) Ref. Ref. Ref.

Male

Less than high school 15.1 (0.5) 4.6† 43.5 17.1 (0.5) 5.6† 48.4 4.9

High school graduate or equivalent 11.2 (0.3) 0.7 7.1 13.2 (0.3) 1.6† 14.1 7.1

Some college 10.8 (0.3) 0.3 2.5 12.8 (0.3) 1.3† 11.5 9.0

College graduate 10.5 (0.3) Ref. Ref. 11.5 (0.4) Ref. Ref. Ref.

Female

Less than high school 16.6 (0.5) 6.7† 66.9 18.0 (0.5) 6.7† 58.4 -8.5

High school graduate or equivalent 12.5 (0.3) 2.6† 26.0 14.6 (0.3) 3.2† 27.6 1.6

Some college 11 (0.2) 1.0† 10.5 13.0 (0.3) 1.6† 13.7 3.3

College graduate 10 (0.3) Ref. Ref. 11.5 (0.3) Ref. Ref. Ref.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 17

TABLE 3. (Continued) Age-standardized* percentage of adults aged ≥18 years with incomes less than the federal poverty level, by selected 

characteristics — Integrated Public Use Microdata Series — Current Population Survey, United States, 2009 and 2011

Characteristic

2009 2011

Change in relative 

difference from 

2009 to 2011 

(percentage points)

% with IPR 

<1.00 (SE)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%)

Percentage 

with IPR 

<1.00 (SE)

Absolute 

difference 

(percentage 

points)

Relative 

difference (%)

Disability status

Both sexes

Disability 14.7 (0.5) 3.4† 29.8 16.4 (0.6) 3.4† 25.8 -4.0

No disability 11.4 (0.1) Ref. Ref. 13.0 (0.2) Ref. Ref. Ref.

Male

Disability 14.1 (0.7) 2.9† 26.0 16.1 (0.7) 3.2† 24.9 -1.1

No disability 11.2 (0.2) Ref. Ref. 12.9 (0.2) Ref. Ref. Ref.

Female

Disability 15.4 (0.7) 3.8† 33.2 16.7 (0.8) 3.5† 26.8 -6.4

No disability 11.5 (0.2) Ref. Ref. 13.2 (0.2) Ref. Ref. Ref.

Place of birth

All racial/ethnic groups

United States or U.S. territory 11.4 (0.2) Ref. Ref. 13.1 (0.2) Ref. Ref. Ref.

Foreign country 13 (0.3) 1.7† 15.1 14.6 (0.3) 1.6† 12.4 -2.7

White, non-Hispanic

United States or U.S. territory 10.6 (0.2) Ref. Ref. 12.3 (0.2) Ref. Ref. Ref.

Foreign country 11.1 (0.6) 0.5 4.7 13.4 (0.7) 1.1 8.7 3.8

Black, non-Hispanic

United States or U.S. territory 14.4 (0.4) Ref. Ref. 16.5 (0.5) Ref. Ref. Ref.

Foreign country 13.3 (1.0) -1.1 -7.8 16.8 (1.3) 0.3 1.5 9.3

Asian/Pacific Islander

United States or U.S. territory 10.2 (1.4) Ref. Ref. 12.6 (1.8) Ref. Ref. Ref.

Foreign country 10.8 (0.6) 0.7 6.7 12.0 (0.6) -0.6 -4.8 -11.5

American Indian/Alaska Native

United States or U.S. territory 15.4 (1.9) Ref. Ref. 19.7 (3.7) Ref. Ref. Ref.

Foreign country —** — NA NA — — NA NA NA

Multiple races

United States or U.S. territory 11.8 (1.0) Ref. Ref. 12.9 (1.1) Ref. Ref. Ref.

Foreign country — — NA NA — — NA NA NA

Hispanic

United States or U.S. territory 13.7 (0.7) Ref. Ref. 16.0 (0.8) Ref. Ref. Ref.

Foreign country 14.8 (0.5) 1.1 7.8 16.3 (0.5) 0.2 1.5 -6.3

World region (country) of birth

United States 11.4 (0.1) Ref. Ref. 13.1 (0.2) Ref. Ref. Ref.

Canada, Europe, Australia, or 

New Zealand

12.2 (1.8) 0.8 7.0 11.7 (1.7) -1.4 -10.6 -17.6

Mexico, South America, Central 

America, or the Caribbean

15.6 (0.6) 4.2† 37.0 16.5 (0.6) 3.4† 26.0 -11.0

Africa or the Middle East 7.1 (1.2) -4.3 -37.5 14.0 (3.1) 0.9 6.6 44.1

Asia or the Pacific Islands 8.9 (1.1) -2.4 -21.4 12.6 (1.3) -0.5 -4.1 17.3

U.S. census region††

Northeast 9.4 (0.5) Ref. Ref. 9.8 (0.5) Ref. Ref. Ref.

Midwest 11.1 (0.4) 1.7† 18.2 12.7 (0.4) 2.9† 29.1 10.9

South 11.3 (0.3) 1.9† 20.3 13.8 (0.4) 4.0† 40.8 20.5

West 11.7 (0.5) 2.3† 24.5 12.9 (0.6) 3.1† 31.6 7.1

Residence in metropolitan area

Inside metropolitan area 12.7 (0.3) 0.2 1.2 14.4 (0.3) 0.4 2.5 1.3

Outside metropolitan area 12.6 (0.4) Ref. Ref. 14.1 (0.4) Ref. Ref. Ref.

Abbreviations: IPR = income-to-poverty ratio; NA = not applicable; Ref. = referent; SE = standard error.

* Age standardized to the 2000 U.S. standard population.

† Difference between a group estimate and the estimate for its respective referent group is significant.

§ Age-specific estimates are not age standardized.

¶ Persons of Hispanic ethnicity might be of any race or combination of races. 

** Estimate is statistically unreliable because relative SE ≥30%.

†† Northeast: Connecticut, Maine, Massachusetts, New Jersey, New Hampshire, New York, Pennsylvania, Rhode Island, and Vermont; Midwest: Illinois, Indiana, Iowa, 

Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin; South: Alabama, Arkansas, Delaware, District of Columbia, 

Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West Virginia; West: Alaska, 

Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming.
Supplement

18 MMWR / November 22, 2013 / Vol. 62 / No. 3

Limitations

The findings in this report are subject to at least two 

limitations. First, all data were self-reported and therefore are 

subject to recall and social desirability bias. Second, CDC 

used cross-sectional data for the analyses; therefore, no causal 

inferences can be drawn from the findings. The limited 

findings for disparities in place of birth among racial/ethnic 

groups might reflect small sample sizes in single years of data, 

as suggested by unstable estimates in the foreign-born strata 

of several racial/ethnic groups.

Conclusion

The U.S. Department of Education’s Institute of Education 

Sciences recommends effective evidence-based interventions 

to prevent or reduce the dropout rates among middle school 

and high school students (29). The U.S. Task Force on 

Community Preventive Services recommends interventions 

that promote healthy social environments for low-income 

children and families and to reduce risk-taking behaviors 

among adolescents (30). Since 2011, HHS has released several 

complementary initiatives to eliminate health disparities 

(26,31). The 2011 HHS action plan focuses specifically on 

reduction of racial/ethnic disparities but includes education 

and social and economic conditions among its major strategic 

areas (26). The 2012 National Prevention Council action 

plan will implement strategies of the National Prevention 

Strategy by targeting communities at greatest risk for health 

disparities, disparities in access to care, and the capacity of the 

prevention workforce; research to identify effective strategies; 

and standardization and collection of data to better identify 

and address disparities. CDC proposes increasing its efforts 

to eliminate health disparities by focusing on surveillance, 

analysis, and reporting of disparities and identifying and 

applying evidence-based strategies to achieve health equity 

(31). Integration of these efforts across federal departments; 

among federal, state, and local levels of government; and with 

nongovernment organizations could increase understanding 

of how socioeconomic disparities in health arise and persist 

and provide information on how best to design effective 

interventions for populationwide and targeted approaches.

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20 MMWR / November 22, 2013 / Vol. 62 / No. 3

Introduction

According to the Dietary Guidelines for Americans, persons 

in the United States aged ≥2 years should increase their intake 

of certain nutrient-rich foods, including fruits and vegetables 

(1). Fruits and vegetables contribute important nutrients that 

are underconsumed in the United States (1). Higher intake of 

fruits and vegetables might reduce the risk for many chronic 

diseases including heart disease (2), stroke (3), diabetes (4), and 

some types of cancer (5). In addition, replacing high-calorie 

foods with fruits and vegetables can aid in weight management 

(1,6,7). However, most persons in the United States do not 

consume the recommended amounts of fruits and vegetables 

and other healthier food groups (e.g., whole grains or fat-free 

or low-fat dairy foods) (1,8).

Persons who live in neighborhoods with better access to 

retailers such as supermarkets and large grocery stores that 

typically offer fruits and vegetables and other healthy foods 

might have healthier diets (9,10). However, in 2009, the 

U.S. Department of Agriculture estimated that 40% of all 

U.S. households do not have easy access (i.e., access within 

1 mile of residence) to supermarkets and large grocery stores 

(11). Although few national studies examining disparities in 

access exist (11–13), research suggests that access is often lower 

among residents of rural, lower-income, and predominantly 

minority communities than among residents of other 

communities (9,12). Because of positive associations between 

the retail environment and diet (9,10), a Healthy People 2020

developmental objective (14) is to increase the percentage of 

persons in the United States who have access to a retailer that 

sells the various foods recommended in the Dietary Guidelines 

for Americans, including fruits and vegetables, whole-grain 

foods, and low-fat milk, which are referred to as healthier foods 

in this report. Improving access to healthier food retailers has 

also been adopted as a promising strategy to improve dietary 

quality by philanthropic and governmental entities (11,15,16).

Access to healthier foods includes not only proximity to 

retail locations that offer these types of foods but also the 

variety, cost, and quality of foods (17). However, in this report 

and in most other studies, access refers to the proximity of 

food retailers because of the inherent challenges and resource 

needs in measuring variety, cost and quality of food. Access 

to supermarkets, supercenters, and large grocery stores is 

frequently measured because these types of stores tend to offer 

a wider selection and larger quantity of fruits and vegetables 

and other healthy foods at affordable prices than other retailers, 

such as convenience stores and small grocery stores (18).

This report is part of the second CDC Health Disparities 

and Inequalities Report (CHDIR). The 2011 CHDIR (19) was 

the first CDC report to assess disparities across a wide range 

of diseases, behavioral risk factors, environmental exposures, 

social determinants, and health-care access. The topic presented 

in this report is based on criteria that are described in the 2013 

CHDIR Introduction (20). This report provides information 

concerning disparities in access to healthier food retailers, a 

topic that was not discussed in the 2011 CHDIR (19). The 

purposes of this report on access to healthier food retailers are to 

discuss and raise awareness of differences in the characteristics 

of areas with access to healthier food retailers across census 

tracts and to prompt actions to reduce disparities.

Methods

To estimate access to healthier food retailers across the United 

States and regionally (i.e., places persons live and might shop), 

CDC analyzed 2011 data from various sources using census 

tracts as the unit of analysis. In this report, the term access refers 

to potential access to healthier food retailers, which is where 

consumers can shop, rather than actual access, which is where 

consumers actually do shop. Access to healthier food retailers 

by area demographics of the census tracts also was compared. 

Access to a retailer was estimated by calculating the percentage 

of census tracts that did not have at least one healthier food 

retailer located within the tract or within 1⁄2 mile of the tract 

boundary (21). Census tracts are small, relatively permanent 

subdivisions of counties designed to be similar in population 

characteristics, economic status, and living conditions. The 

median tract area size and population was 1.9 square miles 

and 4,022 people. 

A list of 54,666 healthier food retailers was developed from 

two national directories of retail food stores. One directory 

was purchased in June 2011 from the commercial data 

provider InfoUSA (available at http://www.infousa.com). 

Access to Healthier Food Retailers — United States, 2011

Kirsten A. Grimm, MPH

Latetia V. Moore, PhD

Kelley S. Scanlon, PhD

National Center for Chronic Disease Prevention and Health Promotion, CDC

Corresponding author: Kirsten A. Grimm, National Center for Chronic Disease Prevention and Health Promotion, CDC. Telephone: 770-488-5041; 

E-mail: KGrimm@cdc.gov.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 21

The other directory was from a list of authorized stores 

that accept Supplemental Nutrition Assistance Program 

(SNAP) benefits as of January 2012 (available at http://www.

snapretailerlocator.com). Two independent data sources were 

used to reduce inaccuracies in store operational status and store 

misclassification (22–28). Evidence suggests that secondary 

data might only capture 55%–68% of food outlets that truly 

exist in an area (24,26,27), and store misclassification is 

common (24).

Healthier food retailers are defined as supermarkets, large 

grocery stores, supercenters and warehouse clubs, and fruit and 

vegetable specialty stores (21). These retailers were identified 

from the InfoUSA directory by using several criteria, including 

2007 North American Industry Classification System (NAICS) 

codes (available at http://www.census.gov/eos/www/naics/), 

annual sales volume, annual employees on payroll, and chain 

store name lists. Large grocery stores and supermarkets were 

defined as retailers with the appropriate NAICS code (NAICS 

445110: grocery stores/supermarkets) with either ≥10 annual 

payroll employees or ≥$2 million in annual sales or whose 

company name matches a chain name list (21). This list of 228 

national and regional supermarket, supercenter, and warehouse 

club chain stores was developed from 2000 and 2005 data from 

the commercial data provider Nielsen TDLinx (29) and 2011 

InfoUSA data and includes stores that have at least eight to 

10 locations nationwide and were verified as having a full line 

of groceries. Supercenters and warehouse clubs were defined 

as retailers with the appropriate NAICS codes (NAICS 445, 

452112, 452910: supercenters and warehouse clubs) or included 

if their company name matched the national chain name list. 

Fruit and vegetable specialty food stores were defined as retailers 

with the appropriate NAICS codes (NAICS 445230: fruit and 

vegetable specialty food stores).

The second directory of stores included retailers who had 

actively processed SNAP benefits as recently as January 3, 2012, 

and had store classifications through the SNAP application 

process consistent with the definition of healthier food retailers 

as described in this report (30). The healthier food retailers 

included from SNAP were those categorized as supermarkets, 

supercenters/warehouse clubs, large grocery stores, or fruit and 

vegetable specialty stores (30).

To estimate national and regional percentages of census 

tracts that had at least one healthier food retailer, stores from 

the two directories were assigned to one or more tracts if they 

were located within the tract’s boundaries or within 1⁄2 mile of 

the boundary using geocodes provided by InfoUSA or SNAP 

and ArcGIS 10 (available at http://www.esri.com/software/

arcgis/index.html). Boundaries for the 72,531 census tracts in 

the 50 U.S. states and the District of Columbia (DC) with a 

population of >0 were obtained from 2010 U.S. census TIGER/

Line shapefiles (available at http://www.census.gov/geo/mapsdata/data/tiger-line.html). Sixty-three percent (n = 22,359) of 

the healthier food retailers identified in InfoUSA were also in 

SNAP. Name, address, location, and store classification type 

matched in these two sources for this subset of stores (referred 

to as verified retailers). The remaining 32,307 stores appeared 

only in one data source (7,549 InfoUSA stores and 19,418 

SNAP stores) or appeared in both but store classification types 

were inconsistent (n = 5,340). Previous evidence indicates that 

if a store is open, the probability that a secondary data source 

lists it as operational ranges from 55% to 89% (24,27,31,32). 

The use of secondary data to accurately classify store type (e.g., 

grocery store, supermarket, or supercenter) has been estimated 

to be 49%–85% (24). One study estimates that if a store is in 

the InfoUSA list, the likelihood that the store is operational 

and correctly classified as a supermarket, grocery store, or 

specialty store is 34.4%–44.5% (32). Because the operational 

status, store presence, and store type of the retailers that only 

appeared in one directory could not be verified by a second 

data source, tracts that only contained two or more of these 

stores were counted as having a healthier food retailer. If a 

tract has two or more unverified stores, evidence indicates that 

it is reasonable to assume that at least one is operational and 

appropriately classified (24,27). Nine percent of tracts (n = 

6,563) were counted as having a healthier food retailer because 

two or more unverified stores were present. Twelve percent of 

tracts (n = 8,343) had only one unverified store from either 

source and therefore were counted as not having any verifiable 

healthier food retailers. Nineteen percent of tracts did not have 

stores from either directory present (n = 13,761 tracts).

To estimate percentages of access to healthier food retailers by 

area demographics, CDC obtained demographic information 

on educational attainment and per capita income at the 

census tract level from the 2006–2010 American Community 

Survey. Information on age and race/ethnicity were obtained 

from the 2010 U.S. census. Tracts were categorized into two 

groups (low and high) for each demographic characteristic by 

dichotomizing at the mean of the distribution. A census tract 

was considered urban if the geographic centroid of that tract 

was located in an area designated by the 2010 U.S. census as 

an urbanized area or urban cluster (available at http://www.

census.gov/geo/www/ua/2010urbanruralclass.html). All other 

tracts were classified as rural. Median tract size and population 

density for urban tracts was 1 square mile and 3,852 persons 

per square mile versus 42 square miles and 100 persons per 

square mile in rural tracts. 

Comparisons of percentages by demographics among national 

and U.S. Census regions (available at http://www.census.gov/

geo/maps-data/maps/pdfs/reference/us_regdiv.pdf) were 

assessed using chi-square tests, with significance set at p<0.05. 
Supplement

22 MMWR / November 22, 2013 / Vol. 62 / No. 3

Odds ratios and 95% confidence intervals (CIs) were estimated 

using logistic regression to characterize national and regionspecific odds of not having access to a healthier food retailer 

by each demographic characteristic separately. Significant 

differences in access to healthier food retailers described in this 

report are those in which the 95% CIs do not include 1.0; thus, 

the odds of access are significantly higher or lower. Tracts that 

had either no sample observations or too few sample observations 

for computing demographic estimates were excluded (n = 404; 

0.6%). 

Disparities were measured as the deviations from a referent 

category rate or prevalence. Referent groups in all analyses were 

as follows: tracts with a low proportion of youths (≤23.4% of 

the population aged ≤18 years), a low proportion of seniors 

(≤13.6% of the population aged ≥65 years), a high per capita 

income (>$27,269 per capita income adjusted to 2010 

dollars), a high proportion of non-Hispanic whites (>63.9% 

non-Hispanic white population), and a high proportion of 

college-educated persons (>27.0% of the population with a 

college degree or higher). Absolute difference was measured as 

the simple difference between a population subgroup estimate 

and the estimate for its respective reference group. 

Results

In 2011, 30.3% of census tracts did not have at least one 

healthier food retailer within the tract or within 1⁄2 mile of tract 

boundaries. This represents 83.6 million persons, representing 

approximately 27% of the 2010 continental U.S. population. 

The percentage of census tracts without at least one healthier 

food retailer ranged from 24.1% in the West to 36.6% in the 

Midwest. Overall, access to healthier food retailers varied by 

each of the demographic characteristics examined, although 

these disparities were not always consistent by region (Tables 1, 

2, and 3). Persons in rural census tracts were approximately 4 

times as likely to lack access to a healthier food retailer than 

persons in urban tracts. This pattern was consistent across 

regions. Sensitivity analyses using national models stratified 

by urban status found similar relationships only for race/

ethnicity. Other associations were mixed. For example, persons 

in urban areas with a youth population of >23.4% had a higher 

odds of lacking access than those in rural areas with the same 

proportion of youth. Education was significantly associated 

with access in rural areas but not in urban areas. 

Overall, tracts where seniors comprised >13.6% of the 

population were 1.3 times as likely not to have a healthier food 

retailer than tracts with a lower proportion of seniors, a pattern 

that was similar across regions. Nationwide, tracts with <64% 

of non-Hispanic whites were about half as likely to lack access 

to a healthier food retailer than tracts with a higher percentage 

of non-Hispanic whites. This pattern was also similar across 

regions, with up to an approximately 75% reduction in the 

odds of no access among tracts in the Northeast with a low 

versus high percentage of non-Hispanic whites.

Other associations were not as consistent across regions. 

Nationwide, persons in tracts with an income of ≤$27,269 were 

1.2 times as likely to lack access to a healthier food retailer than 

tracts with higher income. This association differed by region, 

with no association in the Midwest and a stronger association 

in the South. However, in the Northeast and West, persons 

in low-income tracts had a lower odds of lacking access to a 

healthier food retailer (OR: 0.91 [95% CI: 0.85–0.98]) and 

0.88 [95% CI: 0.82–0.94], respectively). Similarly, nationwide, 

persons in tracts where ≤27.0% had a college education were 

significantly more likely to lack access to a healthier food 

retailer than persons in a tract with a higher proportion of 

college-educated persons; the association was not significant 

in the Northeast and West.

Nationwide, persons living in tracts where youths comprised 

>23.4% of the population had slightly higher odds of lacking 

access to a healthier food retailer than persons living in tracts 

with low proportions of youths (OR: 1.06 [95% CI: 1.03–

1.09]). Regionally, persons living in tracts in the Midwest with 

a higher proportion of youths were 1.2 times as likely to lack 

access as persons in tracts with a low proportion of youths, 

with no additional associations by region.

TABLE 1. Percentage of census tracts* without at least one healthier food retailer within the tract or within 1⁄2 mile of the tract, by geographic 

region† — United States, 2011

United States Northeast Midwest South West

Total no. of tracts 72,127 13,333 16,924 25,948 15,922

Tracts without at least one healthier food retailer (%) 30.3 27.3 36.6 31.6 24.1

* N = 72,531 census tracts in the 50 U.S. states and the District of Columbia per the 2010 U.S. census. A total of 404 (0.6%) census tracts were excluded because either 

no sample observations or too few sample observations were available to calculate demographic estimates.

† Northeast: Connecticut, Maine, Massachusetts, New Jersey, New Hampshire, New York, Pennsylvania, Rhode Island, and Vermont; Midwest: Illinois, Indiana, Iowa, 

Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin; South: Alabama, Arkansas, Delaware, District of Columbia, 

Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West Virginia; West: Alaska, 

Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 23

Discussion

The analyses in this report reveal that persons in 30.3% 

of census tracts in the U.S. do not have access to at least one 

healthier food retailer. The most substantial disparities were 

associated with urbanization; persons in rural tracts were four 

times as likely to lack access than persons in more urban tracts. 

Persons living in tracts with a high percentage of non-Hispanic 

whites and those with a high percentage of seniors also had 

consistently worse access across regions. Access to healthier food 

retailers among youths and by income and education varied by 

region. Some of the findings in this study are similar to those 

of other national studies, including those that assess urban and 

rural areas, whereas other findings, such as those that assess 

access to food retailers according to income, are not consistent 

with previous studies (11,12). However, findings related to 

race/ethnicity and access vary substantially among studies. 

One national study found no differential access to healthy 

food retailers among racial/ethnic groups (11), whereas another 

national study found a lack of access in minority neighborhoods 

(12). After controlling for demographic characteristics, one 

study found fewer chain supermarkets in non-Hispanic black 

neighborhoods than in non-Hispanic white neighborhoods and 

fewer chain supermarkets in Hispanic neighborhoods than in 

non-Hispanic white neighborhoods. However, non-Hispanic 

black neighborhoods were found to have more nonchain 

supermarkets and grocery stores than white neighborhoods 

(12). The definition of healthier food retailers in this particular 

study was chain vs. nonchain supermarkets. This distinction 

was used because chain supermarkets tend to have more 

healthy, affordable foods than nonchain supermarkets. CDC 

conducted a sensitivity analysis of the data in this report to 

explore access to chain supermarkets only among tracts with 

predominantly (>50%) non-Hispanic black residents compared 

with predominantly non-Hispanic white residents, adjusting 

for region and urbanization. This sensitivity analysis revealed 

that access to chain supermarkets was lower in census tracts 

with predominantly non-Hispanic black residents than in tracts 

with predominantly non-Hispanic white residents, results that 

are similar to those of another study (12).

Limitations

The findings in this report are subject to at least four 

limitations. First, the estimates of access to food retailers reflect 

potential access, which indicates retailers where consumers 

are able to shop, but do not reflect actual access, which is 

where consumers actually decide to shop, or 

other aspects of access, such as affordability, 

selection, and quality of foods within 

stores or modes of transportation to stores. 

Neighborhoods identified as not having at 

least one healthier food retailer might still 

have access to healthier foods if their local 

convenience stores and corner stores provide 

a wide selection and adequate quantity of 

affordable produce and other items. Although 

some studies have shown these types of retailers 

typically do not stock healthier foods (9,18), 

others have reported improved food selection 

because of recent changes implemented in the 

Special Supplemental Nutrition Program for 

Women, Infants, and Children (WIC) that 

require that healthy foods be stocked at stores 

that accept vouchers (33). However, because 

no systematic way exists at a national level 

to identify small retailers offering healthier 

foods, they are not counted as a healthier 

food retailer. In addition, although residents 

might have additional access to produce 

in their neighborhoods through farmers 

markets and farm stands, these venues are 

not included in this analysis. Second, only 

TABLE 2.  Percentage of census tracts* without at least one healthier food retailer within 

the tract or within 1⁄2 mile of the tract, by census tract demographic characteristics — 

United States, 2011

Demographic characteristics† %

Absolute difference 

(percentage points) OR§ (95% CI)§

Urbanization

Rural 51.5 30.9 4.10 (3.96–4.24)

Urban¶ 20.6 Ref. — —

Youths aged ≤18 yrs (%)

High: >23.4% of population 30.9 1.2 1.06 (1.03–1.09)

Low: ≤23.4% of population 29.7 Ref. — —

Adults aged ≥65 yrs (%)

High: >13.6% of population 33.6 6.0 1.33 (1.29–1.37)

Low: ≤13.6% of population 27.6 Ref. — —

Whites, non-Hispanic (%)

Low: ≤63.9% of population 21.2 15.0 0.48 (0.46–0.49)

High: >63.9% of population 36.2 Ref. — —

Per capita income in 2010 dollars (%)

Low: ≤$27,269 31.4 2.9 1.15 (1.11–1.18)

High: >$27,269 28.5 Ref. — —

Persons with college degree (%)

Low: ≤27.0% of population 33.3 7.5 1.43 (1.38–1.48)

High: >27.0% of population 25.8 Ref. — —

Abbreviations: 95% CI = 95% confidence interval; OR = odds ratio; Ref. = referent. 

* N = 72,531 census tracts in the 50 U.S. states and the District of Columbia per the 2010 U.S. census. A 

total of 404 (0.6%) were excluded because either no or too few sample observations were available 

to calculate demographic estimates.

† Tracts were categorized into low and high groups for each demographic characteristic by 

dichotomizing at the mean of the distribution.

§ ORs and 95% CIs were estimated using logistic regression.

¶ A census tract was considered urban if the centroid of that tract was located in a 2010 U.S. census– 

designated urbanized area or urban cluster. All other tracts were considered rural.
Supplement

24 MMWR / November 22, 2013 / Vol. 62 / No. 3

tracts that had at least one store that was verified by two 

independent data sources (60% tracts) or at least two stores 

that appeared in either directory of stores (9% of tracts) were 

counted as having a healthier food retailer. Not including tracts 

with only a single store listed in only one source might have 

overestimated lack of access if that one store was operational 

and appropriately classified. A sensitivity analysis showed 

that demographic estimates using stores identified in either 

source (not just those that were verified by two sources and 

those where two or more unverified stores were present) were 

similar to results shown in this report, with the exception of 

urbanization. In general, odds ratios were attenuated, although 

the direction of the associations remained unchanged. Third, 

only secondary data were available for this national and regional 

analysis. Secondary data sources have been show to misclassify 

store type and operational status and both undercount and 

overcount stores in comparison with direct field assessments 

(22–28). However, the analyses in this report included two 

sources of secondary data to reduce these inaccuracies. Finally, 

a national and regional analysis might mask various local and 

state disparities in access.

Conclusion

This report describes one of the few national studies assessing 

disparities in access to healthier food retailers by demographic 

characteristics nationwide and by region. Because the data cannot 

fully account for the heterogeneity of the U.S. food environment, 

a more in-depth evaluation is required to determine whether 

interventions are needed in specific neighborhoods.

TABLE 3. Percentage of census tracts* without at least one healthier food retailer within the tract or within 1⁄2 mile of the tract, by census tract 

demographic characteristics and region†— United States, 2011

Demographic 

characteristics§

No. of 

tracts 

Northeast Midwest South West

%

Absolute 

difference 

(percentage 

points) OR¶ (95% CI)¶ %

Absolute 

difference 

(percentage 

points) OR (95% CI) %

Absolute 

difference 

(percentage 

points) OR (95% CI) %

Absolute 

difference 

(percentage 

points) OR (95% CI)

Urbanization

Rural 11,675 52.1 32.2 4.37 (4.01–4.76) 53.4 26.4 3.10 (2.90–3.31) 50.6 30.5 4.06 (3.84–4.29) 50.3 34.1 5.26 (4.85–5.70)

Urban**,†† 10,186 19.9 Ref. — — 27.0 Ref. — — 20.1 Ref. — — 16.2 Ref. — —

Youths aged ≤18 yrs (%)

High: >23.4% of 

population

11,535 26.7 0.9 0.95 (0.88–1.03) 38.4 3.9 1.18 (1.11–1.26) 31.8 0.4 1.02 (0.97–1.07) 24.1 0.1 1.00 (0.93–1.08)

Low: ≤23.4% of 

population††

10,326 27.6 Ref. — — 34.5 Ref. — — 31.4 Ref. — — 24 Ref. — —

Adults aged ≥65 yrs (%)

High: <13.6% of 

population

10,879 30.8 7.2 1.44 (1.33–1.56) 38.2 3.2 1.15 (1.08–1.22) 34.4 5.1 1.27 (1.20–1.34) 28.5 6.8 1.44 (1.33–1.55)

Low: ≤13.6% of 

population††

10,982 23.6 Ref. — — 35..0 Ref. — — 29.3 Ref. — — 21.7 Ref. — —

Whites, non-Hispanic (%)

Low: ≤63.9% of 

population

6,029 11.3 23.6 0.24 (0.22–0.26) 27.7 11.4 0.60 (0.55–0.65) 25.2 11.7 0.58 (0.55–0.61) 18.0 13.2 0.48 (0.45–0.52)

High: >63.9% of 

population††

15,832 34.9 Ref. — — 39.1 Ref. — — 36.9 Ref. — — 31.2 Ref. — —

Per capita income in 

2010 dollars (%)

Low: ≤$27,269 of 

population

13,990 26.4 1.8 0.91 (0.85–0.98) 37.5 2.7 1.13 (1.05–1.20) 33.6 6.2 1.34 (1.27–1.42) 23.0 2.4 0.88 (0.82–0.94)

High: >$27,269 of 

population††

7,871 28.2 Ref. — — 34.8 Ref. — — 27.4 Ref. — — 25.4 Ref. — —

Persons with college

 degree (%)

Low: ≤27.0% of 

population

14,471 27.8 1.1 1.05 (0.98–1.14) 40.0 9.9 1.55 (1.45–1.66) 35.5 11.2 1.72 (1.62–1.82) 24.5 0.9 1.05 (0.98–1.13)

High: >27.0% of 

population††

7390 26.7 Ref. — — 30.1 Ref. — — 24.3 Ref. — — 23.6 Ref. — —

Abbreviations: 95% CI = 95% confidence interval; OR= odds ratio; Ref. = referent. 

*N = 72,531 census tracts in the 50 U.S. states and the District of Columbia per the 2010 U.S. census. A total of 404 (0.6%) were excluded because either no or too few sample observations 

were available to calculate demographic estimates.

†Northeast: Connecticut, Maine, Massachusetts, New Jersey, New Hampshire, New York, Pennsylvania, Rhode Island, and Vermont; Midwest: Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, 

Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin; South: Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, 

North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West Virginia; West: Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, 

Utah, Washington, and Wyoming.

§ Tracts were categorized into low and high groups for each demographic characteristic by dichotomizing at the mean of the distribution.

¶ORs and 95% CIs were estimated using logistic regression.

**A census tract was considered urban if the centroid of that tract was located in a 2010 U.S. census designated urbanized area or urban cluster. All other tracts were considered rural. †† Significant difference in percentage across regions using chi-square tests (p<0.001)
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 25

Several strategies might improve community access to 

retailers that sell healthier foods. Such strategies include 

incentives to bring healthier food retailers into underserved 

areas, transportation improvements so that residents in 

underserved areas can reach the food retailers, and upgrading 

facilities to enable stocking of all forms of fruits and vegetables 

and to increase shelf space dedicated to fruits and vegetables, 

ultimately increasing the availability of high-quality, affordable 

fruits and vegetables in existing venues (15).

An example of efforts at the national level to bring healthier 

food retailers into underserved areas is a collaboration 

among the U.S. Department of Agriculture (USDA), U.S. 

Department of Health and Human Services (HHS), and 

the U.S. Department of Treasury to support projects that 

increase access to healthier, affordable food and encourage 

the purchase and consumption of healthier food (available at 

http://apps.ams.usda.gov/fooddeserts). The state-level pioneer 

effort called the Pennsylvania Fresh Food Financing Initiative 

has provided funding for 88 fresh-food retail projects in 34 

Pennsylvania counties and improved access to healthier food 

for approximately 500,000 persons (34). Similar efforts have 

been expanding rapidly across states. 

Changes in WIC-authorized stores improve access to 

healthy food in existing stores. Stores authorized to accept 

WIC benefits must maintain on their shelves at all times a 

minimum variety of healthy foods, including fruits, vegetables, 

and whole grains that align with the 2005 Dietary Guidelines 

for Americans and the American Academy of Pediatrics infant 

feeding practice guidelines (35). Studies have demonstrated 

that WIC-authorized stores are providing more healthy 

foods than stores that are not WIC authorized (33,35,36). 

Additional ways to bring healthier foods to persons living in 

underserved areas without changing existing retailers include 

establishing farmers markets, farm stands, and green carts 

(15). For example, in New York, the New York City Green 

Cart Initiative provides fruits and vegetables to underserved 

neighborhoods (information available at http://www.nyc.gov/

html/doh/html/diseases/green-carts.shtml), and the Veggie 

Mobile delivers fruits and vegetables to low-income seniors in 

upstate New York (information available at http://www.cdcg.

org/programs/veggie/veggie). Fruits and vegetables also can 

be delivered through drop-off boxes to churches, community 

centers, and other central locations (15).

Although the precise number of healthy food retailers that 

need to be in a particular area to allow adequate access to fruits 

and vegetables and other healthy foods is not known, ensuring 

that all persons in the United States have access to at least one 

retail venue that offers healthier foods is an important step 

toward supporting healthy choices and diets in communities. 

Improving access to healthy food retailers is important but 

unlikely to be sufficient to improve overall diet quality. Even in 

communities that have sufficient access, strategies such as store 

promotions and shelf labeling that help consumers identify 

healthy options, education on health benefits of particular 

foods, and information about preparation, storage, and cooking 

skills can encourage persons to purchase healthy foods in retail 

venues and might improve diet quality. The combined efforts 

of interventions that improve knowledge and skills, as well 

as increase the affordability, selection, and quality of foods in 

many settings are needed to encourage healthier choices among 

persons in the United States.

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local food environment: a comparison of existing data sources. Am J 

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neighbourhood food environment accurate? Case-study in Glasgow, 

UK. Prev Med 2009;49:527–8.

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FJ. Classification bias in commercial business lists for retail food stores 

in the U.S. Int J Behav Nutr Phys Act 2012;9:46.

25. Hoehner CM, Schootman M. Concordance of commercial data sources 

for neighborhood—effects studies. J Urban Health 2010;87:713–25.

26. Lake AA, Burgoine T, Greenhalgh F, Stamp E, Tyrrell R. The foodscape: 

classification and field validation of secondary data sources. Health Place 

2010;16:666–73.

27. Liese AD, Colabianchi N, Lamichhane AP, et al. Validation of 3 food 

outlet databases: completeness and geospatial accuracy in rural and urban 

food environments. Am J Epidemiol 2010;172:1324–33.

28. Paquet C, Daniel M, Kestens Y, Leger K, Gauvin L. Field validation of 

listings of food stores and commercial physical activity establishments 

from secondary data. Int J Behav Nutr Phys Act 2008;5:58.

29. Auchincloss AH, Moore K, Moore L, Diez Roux AV. Improving 

characterization of the food environment for a large region in the United 

States during a historic time period. Health Place 2012;18:1341–7.

30. US Department of Agriculture. Benefit redemption patterns in the 

Supplemental Nutrition Assistance Program. Washington, DC: US 

Department of Agriculture; 2011. 

31. Fleischhacker SE, Rodriguez DA, Evenson KR, et al. Evidence for validity 

of five secondary data sources for enumerating retail food outlets in seven 

American Indian communities in North Carolina. Int J Behav Nutr 

Phys Act 2012;9:137.

32. Powell LM, Han E, Zenk SN, et al. Field validation of secondary 

commercial data sources on the retail food outlet environment in the 

U.S. Health Place. 2011;17:1122–31.

33. Gleason S, Morgan R, Bell L. Impact of the revised WIC food package 

on small WIC vendors: insight from a four-state evaluation: Altarum 

Institute: Systems Research for Better Health; 2011.

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36. Tester JM, Yen IH, Pallis LC, Laraia BA. Healthy food availability and 

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Women, Infants, and Children) in food stores around lower- and higherincome elementary schools. Public Health Nutr 2011;14:960–4.
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MMWR / November 22, 2013 / Vol. 62 / No. 3 27

Introduction

The association between unemployment and poor physical 

and mental health is well established (1–7). Unemployed 

persons tend to have higher annual illness rates, lack health 

insurance and access to health care, and have an increased risk 

for death (1,2,8,9). Several studies indicate that employment 

status influences a person’s health; however, poor health also 

affects a person’s ability to obtain and retain employment (10). 

Poor health predisposes persons to a more uncertain position in 

the labor market and increases the risk for unemployment (5,6).

According to the Bureau of Labor Statistics (BLS), the 

unemployment prevalence in the United States increased from 

4.7% in 2006 to 9.4% in 2010, yielding an estimated 14.5 million 

unemployed persons (11). Both the prevalence of unemployment 

and the health status of populations vary widely among and 

within communities by age, sex, and race/ethnicity. In 2010, the 

unemployment prevalence both for males and females was twice 

as high in the black and Hispanic populations as in the white 

population (11). The disparities in unemployment prevalence 

extend across the country and have increased from January 2008 

to December 2010 (12). Because unemployment has historically 

been substantially higher in black and Hispanic populations 

during past decades and because unemployment has increased 

substantially from the start of the recession in December 2007 

(13,14), associations between unemployment and health and 

between unemployment and minority status need to be further 

studied.

This report is part of the second CDC Health Disparities 

and Inequalities Report (CHDIR). The 2011 CHDIR (15) was 

the first CDC report to assess disparities across a wide range 

of diseases, behavioral risk factors, environmental exposures, 

social determinants, and health-care access. The topic presented 

in this report is based on criteria that are described in the 2013 

CHDIR Introduction (16). This report is the first assessment of 

unemployment and health status in a CHDIR. The purposes of 

this unemployment and health analysis are to discuss and raise 

awareness of differences in the characteristics of persons who 

are unemployed and differences in health status by employment 

status and to prompt actions to reduce these disparities.

Methods

To assess changes in unemployment rates by several 

population characteristics, CDC analyzed 2006 and 2010 data 

from the Behavioral Risk Factor Surveillance System (BRFSS). 

The association between unemployment and self-reported 

health status, physical health, and mental health in 2010 

also was examined. The 2010 state-specific unemployment 

prevalences were calculated and shown on a U.S. map using 

statistical software; prevalences were shown for men and 

women, non-Hispanic blacks, and non-Hispanic whites (17). 

All analyses were limited to persons aged 18–64 years.

BRFSS is a state population-based, telephone survey of 

noninstitutionalized U.S. adults aged ≥18 years collected in 

all states and selected territories. The BRFSS median response 

rate* for 2006 was 51.4% and for 2010 was 54.6%; the median 

cooperation rate† for 2006 was 74.5% and for 2010 was 76.9% 

(18,19). The same question from the BRFSS survey was used 

to assess employment status and unemployment status by 

asking participants whether they are currently 1) employed for 

wages; 2) self-employed; 3) out of work for >1 year; 4) out of 

work for <1 year; 5) a homemaker; 6) a student; 7) retired; or 

8) unable to work. Persons who did not respond to this question 

were excluded from the analysis. The employment question 

responses were recategorized into the following groups: 1) 

employed (including employed for wages and self-employed), 

2) unemployed (out of work for <1 year and out of work for 

>1 year), and 3) other (homemaker, student, retired, or unable 

to work).

Data were analyzed to assess disparities in unemployment 

prevalence for 2006 and 2010. To examine the association 

between unemployment and health status, data for the 

following three health outcomes were collected from the 2010 

BRFSS data set: 1) health status, 2) number of physically 

unhealthy days, and 3) number of mentally unhealthy days. 

Unemployment — United States, 2006 and 2010

Heba M. Athar, MD

Man-Huei Chang, MPH

Robert A. Hahn, PhD

Eric Walker, MPH

Paula Yoon, ScD

Center for Surveillance, Epidemiology, and Laboratory Services, CDC

Corresponding author: Heba M. Athar, Division of Epidemiology, Analysis and Library Services, Center for Surveillance, Epidemiology, and Laboratory 

Services, CDC. Telephone: 404-498-2216; E-mail: viv8@cdc.gov.

*The perentage who completed interviews among all eligible persons, including 

those who were not contacted successfully.

†The perentage who completed interviews among all eligible persons who were 

contacted.
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28 MMWR / November 22, 2013 / Vol. 62 / No. 3

The related BRFSS questions were as follows: 1) “Would you 

say that in general your health is excellent, very good, good, 

fair, or poor?” 2) “Thinking about your physical health, which 

includes physical illness and injury, for how many days during 

the past 30 days was your physical health not good?” and 3) 

“Thinking about your mental health, which includes stress, 

depression, and problems with emotions, for how many days 

during the past 30 days was your mental health not good?” In 

this analysis, responses to the BRFSS health status question 

were recategorized into the following groups: excellent/very 

good, good, and fair/poor. Participants with the responses 

“do not know/not sure” or “refused to respond” to both the 

physical health and mental health questions were categorized 

as having missing values and were excluded from the analysis. 

Physically unhealthy days and mentally unhealthy days were 

categorized separately as 0 days, 1–15 days, and 16–30 days.

Disparities were measured as the deviations from a referent 

group, which was the group that had the most favorable 

estimate for the variables used to assess disparities during 

the time reported. Absolute difference was calculated by 

subtracting the unemployment prevalence for the group of 

interest from the referent group. The relative difference, a 

percentage, was calculated by dividing the absolute difference 

by the value in the referent category and multiplying by 100. 

All state and national estimates were weighted by BRFSS 

sample weights using statistical software to account for the 

complex design. For unemployment prevalence and health 

status prevalence, 95% confidence intervals (CIs) were 

calculated for the point estimates. CIs were used as measure of 

variability, and nonoverlapping CIs were considered statistically 

different. Using CIs in this way is a conservative evaluation 

of significance differences; infrequently, this might lead to a 

conclusion that estimates are similar when the point estimates 

do differ. All reported differences in this report are significant 

based on the CI comparison.

Results

Unemployment prevalence increased from 2006 to 2010 for 

all adults aged 18–64 years, particularly among adults aged 

25–44 years (Table 1). In general, unemployment prevalence 

increased among both males and females (referent group); 

however, males reported higher unemployment prevalence than 

females in both 2006 and 2010, and this difference gradually 

increased to 2010. The highest unemployment prevalence 

among racial/ethnic groups was among non-Hispanic blacks 

(10.4% in 2006 and 16.5% in 2010), which was almost 

twofold that of non-Hispanic whites (4.7% in 2006 and 

8.3% in 2010). The unemployment prevalence for American 

Indians/Alaska Natives increased substantially from 8.8% in 

2006 to 15.8% in 2010. In both years, the unemployment 

prevalence among persons with no health insurance was 

approximately 4 times higher than that for persons with health 

insurance. The unemployment prevalence decreased as levels of 

education and income increased in both 2006 and 2010. The 

greatest change in unemployment prevalence for education and 

income from 2006 to 2010 occurred among those who did not 

graduate from high school and in households with an annual 

income of <$25,000 per year. In 2006 and 2010, persons with 

a disability had an unemployment prevalence (60%) that was 

higher than that of persons without a disability (40%).

In 2010, the highest prevalence of unemployment among 

men was in the Northeast and West (Figure 1) and among 

women was in the South and West (Figure 2). The Midwest 

region had the lowest unemployment prevalence for both sexes. 

The West region (e.g., Nevada, California, and Oregon) had 

the highest prevalence of unemployment both among nonHispanic blacks and non-Hispanic whites (Figures 3, and 4), 

and the Midwest region (e.g., North Dakota and South 

Dakota) had the lowest prevalence of unemployment for both 

these groups.

In 2010, unemployed persons were less likely than employed 

persons to report their health as excellent or very good 

(Table 2). A higher percentage of employed persons reported 

that they were in excellent or very good health (62.7%) than 

did persons who were unemployed for <1 year (49.2%) or 

unemployed for >1 year (39.7%). Persons who were employed 

were more likely to report no physically unhealthy days 

(70.3%) and no mentally unhealthy days (67.3%) in the past 

30 days than were persons who were unemployed for <1 year 

(no physically unhealthy days: 63.1%; no mentally unhealthy 

days: 54.2%). Persons who were unemployed for >1 year were 

even less likely to report having had no physically or mentally 

unhealthy days in the past 30 days.

Discussion

BRFSS defines unemployment differently from BLS, the 

agency that monitors unemployment in the United States. BLS 

defines an unemployed person as someone who does not have a 

job, has been actively looking for work in the past 4 weeks, and 

is currently available for work (12,20). This might contribute 

to the slight difference in unemployment estimates between 

BLS and BRFSS. BRFSS was selected for this analysis because 

the data set includes variables of interest that enable health 

status assessment and report on the disparities by employment.
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MMWR / November 22, 2013 / Vol. 62 / No. 3 29

The analysis in this report found an association between 

unemployment and overall health status and between 

unemployment and number of physically and mentally 

unhealthy days and also found that disparities by employment 

increased from 2006 to 2010 for certain population groups, 

including persons with less than a high school education, 

household income <$25,000, and no health insurance 

coverage, as well as for American Indians/Alaska Natives. The 

disparities in unemployment among demographic groups 

reported in this study are consistent with findings from several 

other national surveys (11,14,21,22).

Limitations

The findings in this report are subject to at least six limitations. 

First, the 2006 and 2010 surveys excluded certain populations, 

such as persons without landlines, persons in institutions, and 

homeless persons; therefore, the results of this study might not 

be generalizable to the entire U.S. adult population. Second, 

the low BRFSS median state response rates for 2006 and 

2010 (18,19) increase the possibility of nonresponse bias in 

the results. Third, BRFSS health status data are self-reported 

and are therefore subject to recall bias and measurement error 

(23,24). Fourth, BRFSS is cross-sectional, and the timeframe 

TABLE 1. Unemployment* prevalence among adults aged 18–64 years, by selected demographic characteristics — Behavioral Risk Factor 

Surveillance System, United States, 2006 and 2010

Characteristic

2006 2010

% (95% CI) 

Absolute 

difference

Relative 

difference (%) % (95% CI) 

Absolute 

difference

Relative 

difference (%)

Age group (yrs)

18–24 10.5 (9.6–11.6) 6.5 162.5 14.4 (13.5–15.5) 6.7 87.0

25–34 5.8 (5.3–6.3) 1.8 45.0 11.4 (10.8–12.1) 3.7 48.1

35–44 4.7 (4.4–5.1) 0.7 17.5 9.5 (9.0–10.0) 1.8 23.4

45–54 5.2 (4.8–5.5) 1.2 30.0 9.2 (8.8–9.5) 1.5 19.5

55–64 4.0 (3.7–4.3) Ref. Ref. 7.7 (7.4–8.0) Ref. Ref.

Sex†

Male 5.9 (5.5–6.2) 0.1 1.7 11.3 (10.9–11.6) 2.5 28.4

Female 5.8 (5.5–6.1) Ref. Ref. 8.8 (8.6–9.1) Ref. Ref.

Race/Ethnicity§

White, non-Hispanic 4.7 (4.5–4.9) Ref. Ref. 8.3 (8.1–8.6) Ref. Ref.

Black, non-Hispanic 10.4 (9.5–11.3) 5.7 121.3 16.5 (15.6–17.4) 8.2 98.8

Hispanic 7.2 (6.4–8.1) 2.5 53.2 12.4 (11.7–13.3) 4.1 49.4

Asian American/Pacific Islander 6.2 (4.8–8.1) 1.5 31.9 9.7 (8.5–11.1) 1.4 16.9

American Indian/Alaska Native 8.8 (6.9–11.2) 4.1 87.2 15.8 (13.2–18.9) 7.5 90.4

Health insurance¶

Yes 4.0 (3.8–4.2) Ref. Ref. 6.7 (6.5–6.9) Ref. Ref.

No 13.8 (13.0–14.7) 9.8 245.0 25.1 (24.3–26.0) 18.4 274.6

Educational attainment

Some high school 9.7 (8.8–10.6) 6.4 193.9 16.9 (15.9–18.0) 11.0 186.4

High school graduate or equivalent 8.0 (7.5–8.5) 4.7 142.4 13.7 (13.2–14.3) 7.8 132.2

Some college 5.2 (4.8–5.7) 1.9 57.6 9.8 (9.4–10.2) 3.9 66.1

College graduate 3.3 (3.0–3.6) Ref. Ref. 5.9 (5.6–6.2) Ref. Ref.

Income**

<$25,000 12.7 (12.0–13.4) 10.9 605.6 21.6 (20.9–22.3) 18.3 554.5

$25,000–$50,000 5.0 (4.6–5.5) 3.2 177.8 9.8 (9.3–10.3) 6.5 197.0

$50,000–$75,000 2.8 (2.4–3.2) 1.0 55.6 5.7 (5.3–6.2) 2.4 72.7

>$75,000 1.8 (1.6–2.1) Ref. Ref. 3.3 (3.0–3.5) Ref. Ref.

Disability†† 

Yes 8.4 (7.9–9.0) 3.2 61.5 12.8 (12.3–13.3) 3.5 37.6

No 5.2 (5.0–5.5) Ref. Ref. 9.3 (9.1–9.6) Ref. Ref.

Abbreviations: 95% CI = 95% confidence interval.

* Includes persons unemployed for <1 year and unemployed for >1 year. 

† Sex is assessed by the interviewer, and the question is only asked if necessary

§ Race and ethnicity are two separate questions, and the data from both was merged. Persons of Hispanic ethnicity might be of any race or combination of races.

¶ Health insurance includes health insurance plans, prepaid plans such as health maintenance organizations, or government plans such as Medicare or Indian Health Services.

** Income is annual household income from all sources at intervals of $25,000.

†† Respondents were asked if they were limited in any activities because of physical, mental, or emotional problems and if they had any health problems that require 

use of special equipment, such as a cane, a wheelchair, a special bed, or a special telephone. If they answered “no” to one question and either said “don’t know” or 

didn’t repond to the other question, their disability status was coded as missing.
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30 MMWR / November 22, 2013 / Vol. 62 / No. 3

used to define unemployment is limited to two categories 

(<1 year and >1 year); therefore, the directionality of the 

relations between short-term unemployment, longer-term 

unemployment, and health outcomes cannot be assessed. Fifth, 

the categorization of the BRFSS health status, physical health, 

and mental health responses is subjective. Finally, this analysis 

could neither address the reason that unemployment and health 

status are related nor determine whether these pathways altered 

substantially between 2006 and 2010. 

Conclusion

This study supports existing findings on unemployment and 

health status (1–7). The relation between unemployment and 

health status is multifactorial and complex. Studies of health 

and unemployment disparities have identified associated 

intermediary factors, including health insurance coverage 

and access to health care (14,25–27). Similarly, the National 

Prevention Strategy notes that health disparities are often 

FIGURE 1. Unemployment prevalence among men aged 18–64 

years, by state — Behavioral Risk Factor Surveillance System, United 

States, 2010

12.2%–15.1%

8.1%–10.3%

10.3%–12.2%

5.9%–8.1%

FIGURE 2. Unemployment prevalence among women aged 18–64 

years, by state — Behavioral Risk Factor Surveillance System, United 

States, 2010

8.1%–9.9%

6.1%–8.1%

9.9%–12.6%

4.1%–6.1%

FIGURE 3. Unemployment prevalence among non-Hispanic white 

adults aged 18–64 years, by state — Behavioral Risk Factor 

Surveillance System, United States, 2010

7.5%–9.0%

5.7%–7.5%

9.0%–11.5%

3.7%–5.7%

FIGURE 4. Unemployment prevalence among non-Hispanic black 

adults aged 18–64 years, by state — Behavioral Risk Factor 

Surveillance System, United States, 2010

18.3%–23.4%

13.5%–18.3%

23.4%–38.1%

0.0%–13.5%

Insucient data
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 31

linked to social, economic, and environmental disadvantages 

(28). The federal government is implementing the Affordable 

Care Act and, beginning in 2014, the law will expand access to 

health insurance coverage for millions of previously uninsured 

persons in the United States (29).

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TABLE 2. Overall health status and number of physically and mentally unhealthy days in past 30 days among adults aged 18–64 years, by 

employment status — Behavioral Risk Factor Surveillance System, United States, 2010

Health status

Unemployed

>1 year <1 year Employed*

% (95% CI) % (95% CI) % (95% CI)

Overall health status

Excellent or very good† 39.7 (38.1–41.4) 49.2 (47.7–51.2) 62.7 (62.2–63.1)

Good† 35.1 (33.5–36.7) 33.9 (32.3–35.6) 29.1 (28.7–29.5)

Fair or poor† 25.2 (23.8–26.7 16.6 (15.4–18.0) 8.2 (8.0–8.5)

No. of physically unhealthy days in past 30 days 

0† 55.9 (54.2–57.5) 63.1 (61.4–64.9) 70.3 (69.9–70.7)

1–15† 31.4 (29.8–33.0) 28.6 (27.0–30.2) 26.1 (25.7–26.5)

16–30† 12.8 (11.7–13.9) 8.3 (7.3–9.4) 3.6 (3.5–3.8)

No. of mentally unhealthy days in past 30 days 

0† 50.6 (48.9–52.3) 54.2 (52.4–56.0) 67.3 (66.9–67.8)

1–15† 32.6 (31.0–34.3) 32.9 (31.2–34.6) 27.3 (26.8–27.7)

16–30† 16.8 (15.6–18.1) 12.9 (11.9–14.1) 5.4 (5.2–5.6)

Abbreviation: 95% CI = 95% confidence interval.

* Employed for wages or self-employed.

† Statistically significant (p<0.05).
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what-is-the-health-insurance-marketplace.
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Environmental Hazards
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MMWR / November 22, 2013 / Vol. 62 / No. 3 35

Introduction

In 2012, the U.S. civilian labor force comprised an estimated 

155 million workers (1). Although employment can contribute 

positively to a worker’s physical and psychological health, each 

year, many U.S. workers experience a work-related injury or 

illness. In 2011, approximately 3 million workers in private 

industry and 821,000 workers in state and local government 

experienced a nonfatal occupational injury or illness (2). 

Nonfatal workplace injuries and illnesses are estimated to cost 

the U.S. economy approximately $200 billion annually (3). 

Identifying disparities in work-related injury and illness rates can 

help public health authorities focus prevention efforts. Because 

work-related health disparities also are associated with social 

disadvantage, a comprehensive program to improve health equity 

can include improving workplace safety and health.

This report and a similar study (4) are part of the second 

CDC Health Disparities and Inequalities Report (CHDIR). 

The 2011 CHDIR (5) was the first CDC report to assess 

disparities across a wide range of diseases, behavior risk factors, 

environmental exposures, social determinants, and health-care 

access. The topic presented in this report is based on criteria 

that are described in the 2013 CHDIR Introduction (6). This 

report provides information concerning disparities in nonfatal 

work-related injury and illness, a topic that was not discussed 

in the 2011 CHDIR. A separate report providing information 

on disparities in fatal work-related injuries and homicides 

across industry and occupation categories also is included in 

this second CHDIR (4). The purposes of this report are to 

discuss and raise awareness of differences in the characteristics 

of workers employed in high-risk occupations and to prompt 

actions to reduce these disparities.

Methods

To examine disparities in nonfatal work-related injury and 

illness by selected characteristics, CDC used two sources of 

data. Health outcomes were identified by using the Bureau 

of Labor Statistics (BLS) Survey of Occupational Injuries and 

Illnesses (SOII) (available at http://www.bls.gov/iif). Data 

on selected worker characteristics (i.e., race/ethnicity, place 

of birth, sex, age, educational attainment, income level, and 

geographic region of residence) were derived from the 2010 

Current Population Survey (CPS) microdata files (available 

at http://thedataweb.rm.census.gov/ftp/cps_ftp.html). CPS 

(available at http://www.census.gov/cps) is the primary source 

of U.S. workforce statistics and is based on monthly household 

surveys conducted by the U.S. Census Bureau.

Race and ethnicity were combined into seven groups: 

Hispanic, non-Hispanic white, non-Hispanic black, American 

Indian/Alaska Native, Asian, Hawaiian or Pacific Islander, 

or multiple races. Persons of Hispanic ethnicity can be of 

any race or combination of races. Educational attainment 

was defined as either 1) no education beyond high school, 

including those with less than a first-grade education to 

those who received a high school diploma or its equivalent or 

2) education beyond high school, including enrollment in an 

occupational/vocational program, completion of some college, 

or receipt of a college degree or an advanced degree. Place of 

birth was defined as the United States, a U.S. territory, or a 

foreign country. Persons born in a foreign country include 

U.S. citizens born abroad (one or both of whose parents were 

U.S. citizens), naturalized citizens, and noncitizens. Income 

level was defined as low wage or nonlow wage; a low wage was 

defined as an income of ≤$435 per week (which is equivalent 

to the wage earned by a person working 40 hours a week at 

or less than 1.5 times the minimum wage of $7.25 per hour). 

Geographic region of residence was defined using the four U.S. 

Census Bureau regions: Northeast, Midwest, South and West.

SOII is a collaborative federal/state survey program 

administered by BLS that includes reports from a nationally 

representative sample of approximately 220,000 privatesector employers. The survey excludes workers on farms with 

<11 employees, private household workers, self-employed 

persons, and federal government workers. Data for employees 

covered by certain specific federal safety and health legislation 

are provided to BLS to be included in SOII by the Mine 

Safety and Health Administration of the U.S. Department of 

Labor and the Federal Railroad Administration of the U.S. 

Department of Transportation. Employers are required to 

report workplace injuries and illnesses that meet recordkeeping 

Nonfatal Work-Related Injuries and Illnesses — United States, 2010

Sherry L. Baron, MD

Andrea L. Steege, PhD

Suzanne M. Marsh, MPA

Cammie Chaumont Menéndez, PhD

John R. Myers, MS

National Institute for Occupational Safety and Health

Corresponding author: Sherry L. Baron, National Institute for Occupational Safety and Health, CDC. Telephone: 513-458-7159; E-mail: slb8@cdc.gov.
Supplement

36 MMWR / November 22, 2013 / Vol. 62 / No. 3

requirements established by the Occupational Safety and 

Health Administration (OSHA), including those that result 

in loss of consciousness, restriction of work or motion, 

transfer to another job, or medical treatment other than first 

aid. Information about the survey methodology is available 

at http://www.bls.gov/opub/hom/pdf/homch9.pdf. In 2008, 

BLS began including state and local government employees, 

with that portion of the survey conducted separately from the 

private-sector survey. SOII data presented in this report are 

limited to private-sector workers.

In SOII, for those persons whose cases result in ≥1 day 

away from work (DAFW), employers provide additional 

information, including the affected worker’s occupation coded 

according to the Standard Occupational Classification Manual 

(SOC) (7). For each of approximately 800 occupations, BLS 

then estimates the rate of DAFW cases per 10,000 full-time 

equivalents (FTEs) using the formula one FTE = 2,000 hours 

worked per year. BLS derives occupation-specific denominator 

data from the Occupational Employment Statistics program, 

which produces employment and wage estimates for 

approximately 800 occupations at the state and national level 

(available at http://www.bls.gov/oes). In addition to detailed 

SOC occupation rates, BLS also provides injury and illness 

rates for all higher-level SOC categories (i.e., injury and illness 

rates at the two- through six-digit level of SOC). 

CDC used the SOII occupation-specific DAFW injury and 

illness rates from 2008 to categorize all private-sector occupations 

into two groups: high-risk occupations and all other occupations. 

The list of high-risk occupations was obtained from the Council 

of State and Territorial Epidemiologists (CSTE) Occupational 

Health Indicator activity, indicator no. 15, workers employed 

in occupations with a high risk for occupational morbidity 

(8). A high-risk occupation was defined as one with a DAFW 

rate of at least twice the national DAFW rate of 113.3 cases of 

injury and illness per 10,000 FTEs. The CSTE Occupational 

Health Indicator activity used Census Bureau occupation 

codes, which are a condensed version of the SOC code set 

that includes approximately 500 occupation codes (9). Injury 

and illness rates for Census Bureau occupation codes were 

determined by matching to the corresponding hierarchical 

SOC occupation code injury and illness rates released by BLS 

in SOII. Of all Census Bureau occupation codes, 61 were 

classified as high risk (i.e., having at least twice the national 

average DAFW injury and illness rate). Employment estimates 

and demographic characteristics were obtained from the 2010 

CPS for private-sector wage and salary workers aged ≥16 years 

who were employed in the group of 61 high-risk occupations 

and for all occupations.

Disparities within high-risk occupations presented in this 

report were measured as the absolute differences from a referent 

prevalence within each demographic category examined. The 

relative difference was calculated by dividing the absolute 

difference by the value of the referent category and multiplying 

by 100. Statistical significance was assessed based on whether 

the 95% confidence intervals (CIs) for each absolute or relative 

measure overlapped with the comparison value selected for 

each demographic variable. Of the 61 high-risk occupations, 

six occupations in which more than 1 million workers were 

employed (health aides; janitors and cleaners; maids and 

housekeepers; miscellaneous production workers; drivers: sales 

and trucks; and hand laborers: freight, stock, material movers) 

were examined more closely. Demographic characteristics 

were calculated for each specific occupation. Differences were 

assessed by calculating and comparing 95% CIs around the 

percentage of workers experiencing a nonfatal work-related 

injury or illness. In this approach, CIs were used as a measure 

of variability and nonoverlapping CIs were considered 

statistically different. Using CIs in this way is a conservative 

way to evaluate significance differences; infrequently this might 

lead to a conclusion that estimates are similar when the point 

estimates do differ.

Results

In 2010, approximately 16,679,000 wage and salary workers, 

or 16% of all private-sector workers in the United States, were 

employed in high-risk occupations. The proportion of workers 

employed in high-risk occupations differed significantly by 

demographic category, with 21% of males, 24% of Hispanics, 

21% of non-Hispanic blacks, 20% of American Indians/Alaska 

Natives, 22% of foreign-born workers, and 26% of workers with 

no more than a high school education employed in high-risk 

occupations, compared with 9% of women, 9% of Asians, 13% 

of non-Hispanic whites, and 14% of persons born in the United 

States. A higher percentage of workers receiving low wages 

worked in high-risk occupations compared with those receiving 

higher wages (18% vs. 14%), and the proportion of workers 

employed in high-risk occupations was higher in the Midwest 

and the South than in the West (16% versus 14%) (Table 1).

In 2010, the six high-risk occupations in which more 

than 1 million workers were employed (in each occupation) 

accounted for 61% of private-sector wage and salary workers 

employed in a high-risk job (Table 2). When the demographic 

profiles of each of these six occupations were compared with 

those of all U.S. private-sector wage and salary workers, two 

demographic characteristics were found consistently to be 

statistically elevated in all six occupations: the proportion of 

non-Hispanic black workers and that of workers with a high 

school education or less. More than half of the workers in four 
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 37

of the six occupations earned low wages, a proportion that 

exceeded the national average: health aides (64%), janitors 

and cleaners (64%), maids and housekeepers (78%), and hand 

laborers (58%) (Table 2).

Whereas overall almost three quarters of those employed 

in any high-risk occupation were males, two of the six largest 

high-risk occupations employed predominately females: maids 

and housekeepers (89%) and health aides (88%). Maids 

and housekeepers had the highest proportion of Hispanics 

(42%) among the six high-risk occupations but a much lower 

proportion of non-Hispanic black workers (16%). A reverse 

pattern is apparent among health aides, who had the highest 

percentage of non-Hispanic black workers (34%) and the 

lowest percentage of Hispanics (15%). Foreign-born workers 

make up a significantly higher proportion of the workforce 

compared with all private-sector workers in four of the six 

high-risk occupations: maids and housekeepers (52%), janitors 

and cleaners (36%), miscellaneous production workers (25%), 

and health aides (25%). With the exception of miscellaneous 

production workers, these occupations also had the highest 

proportions of low-wage workers found among the six highrisk/high employment occupations. Compared with all private 

sector workers, a higher proportion of maids and housekeepers 

(40%) and drivers (40%) were employed in the South, and 

a higher proportion of health aides (26%), miscellaneous 

TABLE 1. Estimated number and percentage of workers employed in high-risk* occupations, by selected characteristics — United States, 2010

Characteristic

Workers employed in high-risk occupations Absolute difference Relative 

difference† 

No. % (95% CI) %

Percentage 

points (95% CI)

Sex

Male 12,240,312 21.1 (20.7–21.5) 12.2§ (11.7–12.7) 137.1§

Female 4,438,820 8.9 (8.6–9.2) Ref. —¶ Ref.

Race/Ethnicity

Hispanic** 4,009,024 24.4 (23.6–25.2) 15.2§ (14.1–16.3) 165.2§

White, non-Hispanic 9,584,598 13.0 (12.7–13.3) 3.8§ (3.0–4.6) 41.3§

Black, non-Hispanic 2,277,643 20.8 (19.9–21.7) 11.6§ (10.4–12.8) 126.1§

American Indian/Alaska Native 97,197 20.2 (15.9–24.5) 11.0§ (6.7–15.3) 119.6§

Asian 494,505 9.2 (8.4–10.0) Ref. — Ref.

Hawaiian or Pacific Islander 47,318 17.8 (13.0–22.6) 8.6§ (3.8–13.4) 93.5§

Multiple races 168,847 15.1 (12.7–17.5) 6.0§ (3.4–8.6) 64.1§

Educational attainment

No education beyond high school†† 11,095,990 25.6 (25.2–26.0) 16.9§ (16.4–17.4) 197.7§

Education beyond high school§§ 5,583,142 8.6 (8.4–8.8) Ref. — Ref.

Place of birth

United States 12,253,418 13.9 (13.6–14.2) Ref. — Ref.

U.S. territory 110,365 19.9 (16.1–23.7) 6.1§ (2.3–9.9) 43.2§

Foreign country¶¶ 4,315,349 22.1 (21.4–22.8) 8.2§ (7.5–8.9) 59.0§

Income level

Low-wage earner *** 7,275,060 18.3 (17.7–18.9) 4.5§ (3.7–5.3) 32.6§

Nonlow-wage earner 9,421,506 13.8 (13.4–14.2) Ref. — Ref.

Geographic region†††

Northeast 3,034,789 14.8 (14.2–15.4) 0.6 (-0.2–1.4) 4.1

Midwest 3,941,498 15.8 (15.3–16.3) 1.6§ (0.9–2.3) 11.2§

South 6,272,961 16.2 (15.8–16.6) 2.0§ (1.3–2.7) 14.3§

West 3,429,884 14.2 (13.7–14.7) Ref. — Ref.

Abbreviations: 95% CI = 95% confidence interval; Ref. = referent.

Source: U.S. Department of Labor, Bureau of Labor Statistics, Current Population Survey microdata files (available at http://thedataweb.rm.census.gov/ftp/cps_ftp.html).

* Occupations for which having a “days away from work” nonfatal injury and illness rate of ≥226.6 cases per 10,000 full-time equivalents based on U.S. Department 

of Labor, Bureau of Labor Statistics 2008 Survey of Occupational Injuries and Illnesses (available at http://www.cste.org/resource/resmgr/OccupationalHealth/

OHIGuidanceMarch2013.pdf?hhSearchTerms=%22Occupational+and+Health+and+Indicator%22). 

† Compared with the lowest category.

§ Significantly different when assessed by comparison of nonoverlapping 95% CIs.

¶ Confidence intervals are not provided for the reference category.

** Persons of Hispanic ethnicity might be of any race or combination of races.

†† Includes those with less than a first-grade education to those who received a high school diploma or its equivalent. 

§§ Includes enrollment in an occupational/vocational program, completion of some college, or receipt of a college degree or an advanced degree. 

¶¶ Includes U.S. citizens born abroad (one or both of whose parents were U.S. citizens), naturalized citizens, and noncitizens.

*** Worker whose wage is ≤$435 per week.

††† Northeast: Connecticut, Maine, Massachusetts, New Jersey, New Hampshire, New York, Pennsylvania, Rhode Island, and Vermont; Midwest: Illinois, Indiana, Iowa, 

Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin; South: Alabama, Arkansas, Delaware, District of Columbia, 

Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West Virginia; West: Alaska, 

Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming.
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38 MMWR / November 22, 2013 / Vol. 62 / No. 3

TABLE 2. Estimated percentage of private sector wage and salary workers employed in six high-risk* injury and illness occupations† (each with 

>1 million workers), by selected characteristics— United States, 2010

Characteristic

All occupations 

(N = 108,216,000; 

rate§: 113.3)

Health aides 

(n = 1,656,000; 

rate: 320.7)

Janitors and 

cleaners 

(n = 1,561,000; 

rate: 243.0)

Maids and 

housekeepers 

(n = 1,198,000; 

rate: 277.7)

Misc. production 

workers 

(n = 1,047,000; 

rate: 462.4)

Drivers: sales 

and trucks 

(n = 2,721,000; 

rate: 329.4)

Hand laborers: 

freight, stock, and 

material movers 

(n = 1,616,000 

rate: 440.3)

% (95% CI) % (95% CI) % (95% CI) % (95% CI) % (95% CI) % (95% CI) % (95% CI)

Sex

Male 53.7 (53.4–54.0) 11.7¶ (9.9–13.5) 67.5¶ (64.8–70.2) 11.1¶ (9.1–13.1) 72.9¶ (69.8–76.0) 95.6¶ (94.7–96.5) 82.5¶ (80.4–84.6)

Female 46.3 (46.0–46.6) 88.3¶ (86.6–90.0) 32.5¶ (29.9–35.1) 88.9¶ (86.9–90.9) 27.1¶ (24.1–30.1) 4.4¶ (3.5–5.3) 17.6¶ (15.5–19.7)

Race/Ethnicity

Hispanic** 15.2 (14.9–15.5) 14.8 (12.8–16.8) 34.0¶ (31.2–36.8) 41.7¶ (38.4–45.0) 23.7¶ (20.6–26.8) 17.8¶ (16.1–19.5) 21.2¶ (18.8–23.6)

White, 

non-Hispanic

68.0 (67.7–68.3) 45.4¶ (42.7–48.1) 46.2¶ (43.4–49.0) 35.4¶ (32.3–38.5) 56.7¶ (53.3–60.1) 66.4 (64.4–68.4) 57.6¶ (54.8–60.4)

Black, 

non-Hispanic

10.1 (9.9–10.3) 33.5¶ (30.7–36.3) 14.9¶ (12.7–17.1) 16.3¶ (13.7–18.9) 14.0¶ (11.4–16.6) 12.6¶ (11.1–14.1) 16.3¶ (14.1–18.5)

American 

Indian/Alaska 

Native

0.4 (0.4–0.4) 1.0 (0.4–1.6) 0.7 (0.2–1.2) 0.5 (0.0–1.0) 0.6 (0.0–1.2) 0.4 (0.1–0.7) 0.6 (0.1–1.1)

Asian 5.0 (4.9–5.1) 4.0 (3.0–5.0) 3.4¶ (2.5–4.3) 5.2 (3.9–6.5) 3.9 (2.7–5.1) 1.5¶ (1.0–2.0) 2.8¶ (2.0–3.6)

Hawaiian or 

other Pacific 

Islander

0.3 (0.3–0.3) 0.3 (0.0–0.6) 0.2 (0.0–0.4) 0.1 (-0.1–0.3) 0.4 (0.0–0.8) 0.2 (0.0–0.4) 0.4 (0.1–0.7)

Multiple races 1.0 (0.9–1.1) 1.1 (0.5–1.7) 0.7 (0.2–1.2) 0.9 (0.3–1.5) 0.9 (0.2–1.6) 1.1 (0.6–1.6) 1.1 (0.5–1.7)

Educational attainment

No education 

beyond high 

school††

40.1 (39.8–40.4) 53.8¶ (51.3–56.3) 75.4¶ (73.2–77.6) 81.4¶ (79.1–83.7) 72.2¶ (69.4–75.0) 68.9¶ (67.1–70.7) 69.3¶ (67.0–71.6)

Education 

beyond high 

school§§

59.9 (59.6–60.2) 46.2¶ (43.7–48.7) 24.6¶ (22.4–26.8) 18.6¶ (16.5–20.7) 27.8¶ (25.0–30.6) 31.1¶ (29.3–32.9) 30.7¶ (28.4–33.0)

Place of birth

United States 81.5 (81.2–81.8) 74.4¶ (72.0–76.8) 62.7¶ (59.9–65.5) 47.6¶ (44.3–50.9) 74.3¶ (71.2–77.4) 82.9 (81.3–84.5) 81.3 (79.1–83.5)

U.S. territory 0.5 (0.5–0.5) 0.8 (0.3–1.3) 1.7¶ (1.0–2.4) 0.6 (0.1–1.1) 0.8 (0.2–1.4) 0.5 (0.2–0.8) 0.6 (0.2–1.0)

Foreign 

country¶¶

18.0 (17.7–18.3) 24.8¶ (22.4–27.2) 35.6¶ (32.8–38.4) 51.8¶ (48.5–55.1) 24.9¶ (21.9–27.9) 16.6 (15.0–18.2) 18.1 (15.9–20.3)

Income level

Low-wage 

earner***

36.8 (36.3–37.3) 63.7¶ (59.9–67.5) 64.1¶ (60.2–68.0) 78.0¶ (74.1–81.9) 34.7 (30.0–39.4) 28.5¶ (25.7–31.3) 57.5¶ (53.5–61.5)

Nonlow-wage 

earner

63.2 (62.7–63.7) 36.4¶ (32.6–40.2) 35.9¶ (32.0–39.8) 22.0¶ (18.1–25.9) 65.3 (60.6–70.0) 71.5¶ (68.7–74.3) 42.5¶ (38.5–46.5)

Geographic region†††

Northeast 19.0 (18.7–19.3) 25.1¶ (22.7–27.5) 19.5 (17.2–21.8) 16.8 (14.3–19.3) 16.2 (13.6–18.8) 15.2¶ (13.6–16.8) 16.6 (14.5–18.7)

Midwest 23.1 (22.8–23.4) 26.2¶ (23.7–28.7) 23.8 (21.3–26.3) 17.6¶ (15.1–20.1) 31.2¶ (27.9–34.5) 24.7 (22.8–26.6) 27.9¶ (25.4–30.4)

South 34.7 (34.4–35.0) 34.7 (32.0–37.4) 34.0 (31.3–36.7) 40.2¶ (37.0–43.4) 35.7 (32.3–39.1) 40.4¶ (38.3–42.5) 35.7 (33.0–38.4)

West 22.3 (22.0–22.6) 14.0¶ (12.1–15.9) 22.7 (20.3–25.1) 25.4 (22.5–28.3) 17.0¶ (14.4–19.6) 19.7¶ (18.0–21.4) 19.8 (17.5–22.1)

Abbreviation: 95% CI = 95% confidence interval.

Source: U.S. Department of Labor, Bureau of Labor Statistics, Current Population Survey microdata files (available at http://thedataweb.rm.census.gov/ftp/cps_ftp.html).

* Occupations for which having a “days away for work” nonfatal injury and illness rate of 226.6 cases per 10,000 full time equivalents or greater based on U.S. 

Department of Labor, Bureau of Labor Statistics 2008 Survey of Occupational Injuries and Illnesses (available at http://www.cste.org/resource/resmgr/

OccupationalHealth/OHIGuidanceMarch2013.pdf?hhSearchTerms=%22Occupational+and+Health+and+Indicator%22). 

† 2002 Census Occupation codes are as follows: health aides (3600); janitors and cleaners (4220); maids and housekeepers (4230); miscellaneous production workers 

(8850–8960); drivers: sales and trucks (9130); and hand laborers: freight, stock, and material movers (9620).

§ Injury and illness rate/10,000 full-time equivalents.

¶ Significantly different than all occupations percentage when assessed by comparison of nonoverlapping 95% CIs.

** Persons of Hispanic ethnicity can be of any race or combination of races.

†† Includes those with less than a first-grade education to those who received a high school diploma or its equivalent. 

§§ Includes enrollment in an occupational/vocational program, completion of some college, or receipt of a college degree or an advanced degree. 

¶¶ Includes U.S. citizens born abroad (one or both of whose parents were U.S. citizens), naturalized citizens, and noncitizens.

*** Worker whose wage is ≤$435 per week.

††† Northeast: Connecticut, Maine, Massachusetts, New Jersey, New Hampshire, New York, Pennsylvania, Rhode Island, and Vermont; Midwest: Illinois, Indiana, Iowa, 

Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin; South: Alabama, Arkansas, Delaware, District of Columbia, 

Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West Virginia; West: Alaska, 

Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 39

production workers (31%), and hand laborers (28%) were 

employed in the Midwest. 

Discussion

Work-related injuries and illnesses are common and 

preventable. On average each year, one of every 100 workers 

suffers a work-related injury or illness that is severe enough to 

result in a missed day of work. These injuries and illnesses are 

costly to workers, their families, and society at large. Compared 

with U.S.-born white non-Hispanic workers, in 2010, a 

higher percentage of workers of all other races, ethnicities, 

and places of birth (other than Asian workers) worked in a 

job that had at least twice the national average DAFW injury 

and illness rate. Overall, a higher percentage of males worked 

in a high-risk occupation, but in certain high-risk and highemployment occupations, workers are predominately female. 

A higher percentage of workers who had no more than a high 

school education or who earned a weekly wage of ≤$435 

worked in a higher-risk job compared with workers who had 

a higher education level or who earned more. The burden of 

a work-related injury or illness for these workers might be 

compounded further by other sources of health inequalities. 

For example, in 2010, among working-age adults with an 

income of 100%–200% of the federal poverty level, 43% 

did not have access to health insurance for at least part of the 

previous year (10). In addition, because a greater proportion 

of workers in high-risk occupations are foreign-born and have 

lower levels of educational attainment than other workers, as 

one element of a comprehensive workplace safety and health 

program, training and education materials are needed that 

focus on addressing the needs of persons with low English 

proficiency and literacy levels (11). Intervention priorities 

can be informed by employment patterns (i.e., geographic 

concentration of workers employed in high-risk occupations) 

such as programs to promote better workplace safety for maids 

and housekeepers employed in the southern states. 

Limitations

The findings in this report are subject to at least three 

limitations. First, the private-sector SOII data exclude workers 

on farms with <11 employees, private household workers, 

and persons who are self-employed (12). If these excluded 

workers have higher or lower injury and illness rates than 

other private-sector wage and salary workers employed in the 

same occupation, then the workplace injury and illness rates 

for that occupation might be under- or overestimated. Second, 

inclusion of cases in SOII is dependent on identifying cases as 

work-related; such determinations can be difficult for certain 

types of incidents for which the work relationship might not 

be clear or recordkeeping requirements are misinterpreted 

(13). Also, the work relationship might be underreported 

by some workers, especially those who perceive their jobs 

as being insecure, which might affect minority and lowerincome workers differentially (14). Finally, underreporting of 

work-related illnesses is especially problematic because many 

work-related illnesses (e.g., cancer and chronic obstructive 

lung diseases) take years to develop and might be difficult to 

attribute to the workplace (15). 

Conclusion

The findings provided in this report highlight the importance 

of preventing work-related injuries and illnesses. The 

Occupational Safety and Health Act affords equal protection 

to all workers, regardless of race, ethnicity, or immigrant 

status. Furthering a culture in which occupational safety and 

health is recognized and valued as a fundamental component 

of economic growth and prosperity can play an important 

role in promoting health equity. Identifying disparities in 

work-related injury and illness rates can help public health 

authorities focus prevention efforts. Because work-related 

health disparities also are associated with social disadvantage 

(i.e., workers with low socioeconomic status are those workers 

who had no more than a high school education or who earned 

a weekly wage of ≤$435), a comprehensive program to improve 

health equity should include improving workplace safety and 

health. The data presented in this report can be used to help 

focus prevention efforts on those workers in the highest-risk 

jobs. This information can be used to improve intervention 

efforts by developing programs that better meet the needs of 

the increasing diversity of the U.S. workforce. The National 

Institute for Occupational Safety and Health’s Occupational 

Health Disparities program has prioritized research projects to 

improve outreach to eliminate health disparities. Prevention 

recommendations and publications that discuss common 

injury and illness concerns for these workers are available 

in English and Spanish; topics include safe patient lifting, 

chemical use, eye protection, motor vehicle safety, and manual 

materials handling (available at http://www.cdc.gov/NIOSH).

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8. Council of State and Territorial Epidemiologists. Occupational health 

indicators: a guide for tracking occupational health conditions and their 

determinants; Atlanta, GA: Council of State and Territorial 

Epidemiologists; 2012. 

9. US Bureau of Labor Statistics. Occupational and industry classification 

systems used in the Current Population Survey. Available at http://www.

bls.gov/cps/cpsoccind.htm.

10. Cohen RA, Ward BW, Schiller JS. Health insurance coverage: early 

release of estimates from the National Health Interview Survey, 2010. 

Hyattsville, MD: US Department of Health and Human Services, CDC, 

National Center for Health Statistics; 2010. Available at http://www.

cdc.gov/nchs/data/nhis/earlyrelease/insur201106.htm#Footnotes4.

11. O’Connor T. White paper on reaching Spanish-speaking workers and 

employers with occupational safety and health information. In: 

Committee on Communicating Occupational Safety and Health 

Information to Spanish-speaking Workers; Committee on Earth 

Resources; Board on Earth Sciences and Resources (BESR); Division on 

Earth and Life Studies (DELS); National Research Council. Safety is 

seguridad: a workshop summary. Washington, DC: National Academies 

Press; 2003.

12. US Bureau of Labor Statistics. Occupational safety and health statistics. 

In: US Bureau of Labor Statistics. BLS handbook of methods. 

Washington, DC: US Bureau of Labor Statistics; 2009. Available at 

http://www.bls.gov/opub/hom/pdf/homch9.pdf.

13. US Government Accounting Office. Workplace safety and health: 

enhancing OSHA’s records audit process could improve the accuracy of 

worker injury and illness data. Washington, DC: US Government 

Accounting Office; 2009. 

14. McGreevy K, Lefkowitz D, Valiante D, Lipsitz S. Utilizing hospital 

discharge data (HD) to compare fatal and non-fatal work-related injuries 

among Hispanic workers in New Jersey. Am J Ind Med 2010;

53:146–52.

15. Ruser J. Examining evidence on whether BLS undercounts workplace 

injuries and illnesses. Monthly Labor Review 2008;131:20–32.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 41

Introduction

In 2012, the U.S. civilian labor force comprised an estimated 

155 million workers (1). Although employment can contribute 

positively to a worker’s physical and psychological health, each 

year, many U.S. workers are fatally injured at work. In 2011, 

a total of 4,700 U.S. workers died from occupational injuries 

(2). Workplace deaths are estimated to cost the U.S. economy 

approximately $6 billion annually (3). Identifying disparities 

in work-related fatality rates can help public health authorities 

focus prevention efforts. Because work-related health disparities 

also are associated with social disadvantage, a comprehensive 

program to improve health equity should include improving 

workplace safety and health.

This report and a similar study (4) are part of the second 

CDC Health Disparities and Inequalities Report (CHDIR). 

The 2011 CHDIR (5) was the first CDC report to assess 

disparities across a wide range of diseases, behavior risk factors, 

environmental exposures, social determinants, and health-care 

access. The topic presented in this report is based on criteria 

that are described in the 2013 CHDIR Introduction (6). This 

report provides information on disparities in work-related 

death and homicide rates across industry and occupation 

categories, a topic that was not discussed in the 2011 CHDIR. 

A separate report providing information on disparities in 

nonfatal work-related injuries and illnesses also is included 

in this second CHDIR (4). The purposes of this report are to 

discuss and raise awareness of differences in the characteristics 

of work-related fatal injuries and to prompt actions to reduce 

these disparities.

Methods

To characterize work-related death and homicide rates by 

selected characteristics, CDC used two sources of data. Fatalities 

were identified by using the Census of Fatal Occupational 

Injuries (CFOI),* and employment data were derived from 

the Current Population Survey (CPS) microdata files.

For CFOI, BLS collects data on occupational injury deaths 

from multiple federal, state, and local sources, including 

death certificates, police reports, and workers’ compensation 

reports. To be included in CFOI, the decedent must have been 

employed at the time of the incident, working as a volunteer 

in the same functions as a paid employee, or present at a site 

as a job requirement (7). Public- and private-sector civilian 

workers are included. CFOI excludes deaths that occurred 

during a worker’s normal commute to and from work and 

deaths related to occupational illnesses (e.g., lung disease or 

cancer). CFOI uses its fatality source documents to extract 

and code demographic information and place of birth as well 

as information related to the event or exposure that directly 

caused the death and the occupation and industrial sector in 

which the decedent was employed. 

Race and ethnicity were combined into four broad groups: 

non-Hispanic white, non-Hispanic black, American Indian/

Alaska Native/Asian/Pacific Islander (AI/AN/A/PI), and 

Hispanic. Persons of Hispanic ethnicity might be of any race 

or combination of races. Place of birth was defined as either the 

United States or its territories (including Puerto Rico, Guam, 

and the U.S. Virgin Islands) or a foreign country. Persons born 

in a foreign country include U.S. citizens born abroad (one or 

both of whose parents were U.S. citizens), naturalized citizens, 

and noncitizens. Legal immigrants, legal nonimmigrants, and 

undocumented workers were included in the foreign-born 

population if their deaths were confirmed as work-related. 

Information on educational attainment was not available 

from the CFOI data. Information on geographic region, while 

available in the data, were not included in the analysis.

To calculate injury-related fatality rates, CDC derived labor 

force denominator estimates from the CPS microdata files (8).

CPS is the primary source of U.S. labor force statistics and is 

based on monthly household surveys conducted by the U.S. 

Census Bureau. Demographic and employment characteristics 

in CPS were grouped to match categories in CFOI. CPS 

uses the Census Bureau definition of “foreign-born,” which 

is slightly different than the definition used by CFOI. Along 

with including persons who were born in the United States and 

its territories, CPS, unlike CFOI, also identifies persons born 

Fatal Work-Related Injuries — United States, 2005–2009 

Suzanne M. Marsh, MPA

Cammie Chaumont Menéndez, PhD

Sherry L. Baron, MD

Andrea L. Steege, PhD

John R. Myers, MS

National Institute for Occupational Safety and Health, CDC

Corresponding author: Suzanne M. Marsh, National Institute for Occupational Safety and Health, CDC. Telephone: 304-285-6009; E-mail: smm2@cdc.gov.

*Analysis was conducted using restricted CFOI data that the National Institute for 

Occupational Safety and Health receives through a Memorandum of Understanding. 

Results might differ from those released by the Bureau of Labor Statistics.
Supplement

42 MMWR / November 22, 2013 / Vol. 62 / No. 3

abroad to a U.S. citizen as “native-born.” Rates were calculated 

per 100,000 workers aged ≥15 years.

Poisson regression was used to estimate injury-fatality rates 

and 95% confidence intervals (CIs) for selected categorical 

groups (sex, age group, selected events, industry division, and 

occupation) stratified by demographic variables (race/ethnicity 

and place of birth). The injury-fatality category rate was 

considered elevated if it was >1.5 times the U.S. rate (3.7 per 

100,000 workers for all fatalities and 0.4 per 100,000 workers 

for homicides) and also was considered significantly different 

if it did not contain the U.S. rate (3.7 for all fatalities and 

0.4 for homicides). The injury-fatality rate for each category 

was further stratified by certain demographic variables (race/

ethnicity and place of birth). The demographic-specific rate was 

considered elevated if it also was >1.5 times the corresponding 

U.S. rate for that particular category (i.e., sex, age group, 

selected events, industry division, and occupation) and also 

was considered significantly different if its confidence interval 

did not contain the overall category rate. No statistical testing 

was done for this analysis.

Results

During 2005–2009, U.S. workers died from an injury while 

at work at a rate of 3.7 per 100,000 workers. Hispanics and 

foreign-born workers had the highest work-related fatal injury 

rates (4.4 and 4.0 per 100,000 workers, respectively) (Table 1). 

For all races, ethnicities, and places of birth, males had workrelated fatality rates that were 9 to 14 times higher than the rates 

for females. Fatal injury rates increased with age for all races, 

ethnicities, and nativities, with non-Hispanic whites, nonHispanic blacks, and workers born in the United States or its 

territories having the most dramatic increases. Hispanics of all 

age groups <65 years had the highest fatality rates, particularly 

Hispanics aged 15–24 years. Similarly, foreign-born workers of 

all age groups <65 years had higher fatality rates than workers 

who were born in the United States or its territories.

The greatest differences in work-related injury fatality rates 

were across industry sectors, with the rates in agriculture, 

mining, construction, and transportation/warehousing/utilities 

being three to almost eight times higher than the overall U.S. 

rate (Table 1). Although fatality rates by industry sector were 

similar across most races/ethnicities, non-Hispanic blacks 

had either the highest or second highest fatality rate for every 

industry sector, and in agriculture, forestry, and fishing, their 

rate was just over 1.5 times the U.S. rate for that industry. AI/

AN/A/PI and foreign-born workers in the trade sector had rates 

that were 1.5 to 2.0 times the U.S. rate.

Transportation incidents at work resulted in the highest 

work-related fatality rates for workers of all races, ethnicities, 

and nativities (Table 1). Rates for assaults and violent acts, 

particularly homicides, showed the greatest disparity across 

race, ethnicity, and place of birth and were highest among 

non-Hispanic blacks, AI/AN/A/PIs, and foreign-born workers.

During 2005–2009, a total of 2,803 workers were homicide 

victims (rate: 0.4 per 100,000 workers) (Table 2). Homicide 

rates for non-Hispanic black and AI/AN/A/PI workers were 

three times those of non-Hispanic white workers. The homicide 

rate for foreign-born workers was more than twice that of all 

other workers. The majority of workplace homicide victims 

among non-Hispanic blacks were not foreign-born (83%), 

whereas the majority of such victims among Hispanic workers 

(61%) and AI/AN/A/PI workers (89%) were foreign-born.

Male workers experienced at least triple the homicide rate 

that women experienced regardless of race/ethnicity or place of 

birth (Table 2). Most notably, non-Hispanic black, AI/AN/A/

PI, and foreign-born men experienced the highest homicide 

rates. Hispanic women had the highest rate among women. 

Overall, workers aged 15–19 years experienced the lowest 

rates, and workers aged ≥65 years experienced the highest rates. 

Non-Hispanic black and AI/AN/A/PI workers experienced 

significantly higher rates for every age group.

Sales and related occupations (e.g., store managers, clerks, 

and cashiers) and transport and material moving occupations 

(e.g., taxi drivers and truck drivers) had the highest work-related 

homicide rates (Table 2). AI/AN/A/PI workers in sales and 

transportation occupations experienced the highest homiciderelated fatality rates. Non-Hispanic blacks consistently had at 

least double the work-related homicide rates compared with 

non-Hispanic whites for every industry and occupation group.

To further understand the circumstances of these workplace 

homicides, CDC explored specific characteristics of the victims 

(data not presented). Among the 1,483 (55%) homicides for 

which the type of perpetrator was specified, 1,039 (70%) were 

committed by suspected robbers, 292 (20%) by a coworker or 

former coworker, 109 (7%) by a relative of the homicide victim, 

and 43 (3%) by miscellaneous “others.” Men and women were 

generally victims of different types of workplace violence. Of 

those homicides that occurred during a suspected robbery or that 

were perpetrated by a coworker/former coworker, 1,119 (84%) 

victims were men, whereas of the 109 homicides perpetrated by 

a relative of the victim, 84 (77%) victims were women.
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MMWR / November 22, 2013 / Vol. 62 / No. 3 43

TABLE 1. Number and rate* of fatal occupational injuries — Census of Fatal Occupational Injuries, United States, 2005–2009

Characteristic

Total

Race/Ethnicity Place of birth†

White, 

non-Hispanic

Black, 

non-Hispanic AI/AN/A/PI Hispanic§ Foreign born

U.S. or U.S. 

territories

No. Rate No. Rate No. Rate No. Rate No. Rate No. Rate No. Rate

Total¶ 26,996 3.7 18,682 3.7 2,707 3.5 982 2.6 4,367 4.4 4,665 4.0 22,331 3.6

Sex

Male 24,995 6.4** 17,227 6.4 2,485 7.0 893 4.4 4,151 6.9 4,416 6.3 20,579 6.4

Female 2,001 0.6 1,455 0.6 222 0.5 89 0.5 216 0.5 249 0.5 1,752 0.6

Age group (yrs)

15–19 572 1.8 342 1.5 45 1.5 20 2.3 163 3.6†† 141 6.1†† 431 1.4

20–24 1,845 2.6 1,049 2.2 183 2.3 69 2.2 524 4.3†† 445 4.8†† 1,400 2.2

25–34 4,603 2.9 2,744 2.8 493 2.7 153 1.6 1,162 3.9 1,054 3.4 3,549 2.8

35–44 5,720 3.4 3,660 3.3 689 3.6 212 2.0 1,089 4.2 1,179 3.5 4,541 3.3

45–54 6,696 3.9 4,802 3.8 722 4.1 284 3.4 836 4.8 1,034 4.0 5,662 3.9

55–64 4,603 4.6 3,569 4.5 395 4.7 182 4.0 430 5.7 610 4.8 3,993 4.6

≥65 2,882 10.0** 2,472 10.5 179 8.8 56 5.0 159 8.4 198 5.8 2,684 10.6

Selected events§§

Contact with object and equipment¶¶ 4,596 0.6 3,210 0.6 375 0.5 108 0.3 881 0.9†† 809 0.7 3,787 0.6

Falls 3,789 0.5 2,561 0.5 219 0.3 107 0.3 877 0.9†† 879 0.7 2,910 0.5

 Fall to lower level*** 3,279 0.5 2,175 0.4 172 0.2 85 0.2 826 0.8†† 817 0.7 2,462 0.4

Exposure to harmful substances/

environs†††

2,388 0.3 1,593 0.3 220 0.3 56 0.1 495 0.5†† 446 0.4 1,942 0.3

Transportation incidents 11,228 1.5 8,263 1.6 1,134 1.5 309 0.8 1,394 1.4 1,358 1.1 9,870 1.6

 Highway incident 6,407 0.9 4,669 0.9 728 1.0 176 0.5 780 0.8 730 0.6 5,677 0.9

Fires and explosions 800 0.1 588 0.1 79 0.1 12 <0.1 118 0.1 104 0.1 696 0.1

Assaults and violent acts 4,097 0.6 2,403 0.5 666 0.9†† 390 1.0†† 582 0.6 1,052 0.9†† 3,045 0.5

 Assaults and violent acts by person 2,803 0.4 1,354 0.3 605 0.8†† 334 0.9†† 459 0.5 876 0.7†† 1,927 0.3

Industry division§§§

Agriculture/Forestry/Fishing 3,236 29.2** 2,576 31.0 130 46.9†† 64 31.4 426 19.7 399 18.7 2,837 31.7

Mining 810 22.6** 612 22.1 38 22.3 16 16.8 140 27.1 57 19.1 753 23.0

Construction 5,674 10.2** 3,661 9.8 390 13.6 117 9.9 1,473 10.9 1,338 10.1 4,336 10.2

Manufacturing 1,984 2.5 1,373 2.5 214 2.9 65 1.4 327 2.8 321 2.1 1,663 2.6

Trade 2,725 2.6 1,813 2.4 275 2.8 260 5.0†† 346 2.5 644 4.3†† 2,081 2.3

Transportation/Warehousing/Utilities 4,484 11.9** 3,125 12.8 679 11.5 151 9.0 469 9.2 663 11.6 3,821 12.0

Services, excluding health care 7,388 2.1 4,995 2.0 889 2.5 283 1.4 1,141 2.6 1,189 2.2 6,199 2.1

Health care and social services 664 0.7 509 0.9 90 0.6 23 0.4 39 0.4 49 0.4 615 0.8

Occupation group¶¶¶

Management, business, and finance 2,896 2.7 2,614 3.1 84 1.1 81 1.4 106 1.4 192 1.5 2,704 2.9

Professional and related 1,274 0.8 1,031 0.9 93 0.7 57 0.5 75 0.7 144 0.7 1,130 0.9

Service 3,456 2.8 2,153 3.0 504 2.8 104 1.6 673 2.8 607 2.3 2,849 3.0

Sales and related 1,532 1.9 982 1.6 154 2.1 214 5.0 158 1.7 448 4.1†† 1,084 1.5

Office and administrative support 516 0.5 359 0.5 66 0.5 20 0.5 63 0.5 71 0.7 445 0.5

Farming, fishing, and forestry 1,405 27.5** 844 31.0 98 43.6 54 44.0 376 19.3 351 18.6 1,054 32.9

Construction and extraction 5,445 12.3** 3,412 12.4 380 14.2 113 12.7 1,502 11.7 1,338 11.0 4,107 12.8

Installation, maintenance, and repair 1,896 7.2 1,466 7.6 145 7.2 39 4.1 234 6.4 198 5.5 1,698 7.5

Production 1,289 2.9 824 3.0 143 2.7 51 2.0 265 2.8 260 2.4 1,029 3.0

Transport and material moving 7,057 16.0** 4,837 18.0 1,020 14.8 238 17.2 889 10.6 1,039 12.6 6,018 16.8

Abbreviation: AI/AN/A/PI = American Indian/Alaska Native/Asian/Pacific Islander. 

* Per 100,000 workers aged ≥15 years. Rates were calculated by CDC based on the number of fatalities from restricted data from the Bureau of Labor Statistics (BLS) Census of Fatal 

Occupational Injuries during 2005–2009 and might differ from estimates published by the BLS; the estimated number of employed workers was obtained from the BLS Current Population 

Survey, 2005–2009. Per BLS publication requirements, numbers of deaths are reported for workers of all ages whereas rates are for workers aged ≥15 years. 

† For CFOI, persons born in a foreign country include U.S. citizens born abroad (one or both of whose parents were U.S. citizens), naturalized citizens, and noncitizens. For CPS, persons 

born in the U.S. or its territories include U.S. citizens born abroad (one or both of whose parents were U.S. citizens). 

§ Persons of Hispanic ethnicity might be of any race or combination of races. 

¶ Totals include workers of other/unknown race and ethnicity. 

** Indicates that the overall rate for the certain categories (sex, age, selected events, industry division, and occupation group) is >1.5 times the U.S. injury-fatality rate of 3.7 per 100,000 

workers. Also indicates that the overall category rate is significantly different from the U.S. rate because the confidence interval for the category rate does not contain 3.7 (the U.S. rate). †† Indicates that demographic-specific (race/ethnicity and place of birth) rate is considered elevated because it is >1.5 times the corresponding U.S. rate for that particular category (i.e., 

sex, age group, selected events, industry division and occupation) rate. Also indicates that this demographic-specific rate is considered significantly different because its confidence 

interval does not contain the corresponding overall category rate. §§ Event or exposure according to the BLS Occupational Injury and Illness Classification System (available at http://www.bls.gov/iif/oiics_manual_2007.pdf). Totals for major events or 

exposures include subcategories not shown separately. ¶¶ Examples include being struck by a falling object such as a tree, being crushed during a cave-in while digging ditches, or getting caught in running machinery. 

*** Examples include falling from a ladder, roof, or scaffold; falling down stairs or steps; or falling through a floor or roof. ††† Examples include heat stroke or hypothermia, poisoning through inhalation or ingestion of harmful substances, insect stings and animal bites, and non-transportation-related drownings. §§§ Industry in which the decedent worked was coded according to the 2002 North American Industry Classification System (NAICS) (available at http://www.census.gov/eos/www/naics). 

The detailed codes from the 20 NAICS sectors were combined into eight industry sectors according to the similarity of their occupational safety and health risks. ¶¶¶ Occupation in which the decedent worked was coded according to the 2000 Standard Occupational Classification Manual (SOC) (available at http://www.bls.gov/soc). The detailed codes 

from the 22 civilian SOC groups were combined into ten occupation groups according to the similarity of their work and their occupational safety and health risks.
Supplement

44 MMWR / November 22, 2013 / Vol. 62 / No. 3

Discussion

On average, each day, 12–13 workers in the United States 

die from injuries sustained at work. Hispanic and foreignborn workers are at higher risk compared with other workers, 

primarily because of the type of work that they do. Workers of 

all races, ethnicities, and places of birth working in construction, 

agriculture, mining, and transportation face a similar and 

higher risk for a work-related fatal injury than workers in other 

industries. Approximately 10% of injury-related fatalities at 

TABLE 2. Number and rate* of homicide deaths — Census of Fatal Occupational Injuries, United States, 2005–2009

Characteristic

Total

Race/Ethnicity Place of birth†

White, 

non-Hispanic

Black, 

non-Hispanic AI/AN/A/PI Hispanic§ Foreign born

U.S. or U.S. 

territories

No. Rate No. Rate No. Rate No. Rate No. Rate No. Rate No. Rate

Total¶ 2,803 0.4 1,354 0.3 605 0.8** 334 0.9** 459 0.5 876 0.74** 1,927 0.3

Sex

Male 2,291 0.6†† 1,054 0.4 523 1.5** 299 1.5** 368 0.6 772 1.1** 1,519 0.5

Female 512 0.2 300 0.1 82 0.2 35 0.2 91 0.2** 104 0.2 408 0.1

Age group (yrs)

15–19 62 0.2 23 0.1 14 0.5** 8 0.9** 17 0.4** 23 1.0** 39 0.1

20–24 207 0.3 81 0.2 51 0.7** 23 0.8** 50 0.4 60 0.6** 147 0.2

25–34 589 0.4 247 0.3 163 0.9** 44 0.5 123 0.4 157 0.5 432 0.4

35–44 714 0.4 324 0.3 155 0.8** 80 0.8** 136 0.5 251 0.8** 463 0.3

45–54 633 0.4 326 0.3 125 0.7** 89 1.1** 82 0.5 209 0.8** 424 0.3

55–64 415 0.4 243 0.3 60 0.7** 72 1.6** 34 0.5 137 1.1** 278 0.3

≥65 178 0.6†† 110 0.5 36 1.8** 16 1.4** 16 0.9 38 1.1** 140 0.6

Industry division§§

Agriculture/Forestry/Fishing 40 0.4 24 0.3 —¶¶ — — — 13 0.6** 15 0.7** 25 0.3

Mining 5 0.1 — — — — — — — — — — — —

Construction 99 0.2 51 0.1 13 0.5** — — 28 0.2 26 0.2 73 0.2

Manufacturing 80 0.1 — — — — — — 23 0.2** 24 0.2** 56 0.1

Trade 790 0.8†† 342 0.5 141 1.5** 190 3.7** 98 0.7 389 2.6** 401 0.5

Transportation/Warehousing/

Utilities

267 0.7†† 99 0.4 96 1.6** 18 1.0 39 0.8 91 1.6** 176 0.6

Services, excluding health care 1,412 0.4 731 0.3 312 0.9** 107 0.6 247 0.6** 314 0.6 1,098 0.4

Health care and social services 109 0.1 58 0.1 32 0.2** 9 0.2 — — 16 0.1 93 0.1

Occupation group***

Management, business, and finance 267 0.3 154 0.2 42 0.6** 39 0.7** 31 0.4** 73 0.6** 194 0.2

Professional and related 162 0.1 111 0.1 29 0.2** 11 0.1 8 0.1 29 0.1 133 0.1

Service 841 0.7†† 419 0.6 216 1.2** 37 0.6 161 0.7 157 0.6 684 0.7

Sales and related 773 0.9†† 344 0.6 129 1.7** 179 4.2** 103 1.1 373 3.4** 400 0.6

Office and administrative support 133 0.1 74 0.1 26 0.2** — — 21 0.2 27 0.3** 106 0.1

Farming, fishing, and forestry 26 0.5 10 0.4 — — — — 12 0.6 13 0.7 13 0.4

Construction and extraction 82 0.2 36 0.1 — — — — 31 0.2 23 0.2 59 0.2

Installation, maintenance, and repair 74 0.3 37 0.2 19 0.9** 5 0.5** 13 0.4 19 0.5** 55 0.3

Production 66 0.2 26 0.1 12 0.2** — — 23 0.3** 26 0.2** 40 0.1

Transport and material moving 365 0.8†† 135 0.5 120 1.7** 40 2.9** 54 0.6 134 1.6** 231 0.7

Abbreviation: AI/AN/A/PI = American Indian/Alaska Native/Asian/Pacific Islander.

* Per 100,000 workers aged ≥15 years. Rates were calculated by CDC based on the number of fatalities from restricted data from the Bureau of Labor Statistics (BLS) Census of Fatal 

Occupational Injuries during 2005–2009 and might differ from estimates published by the BLS; the number of employed workers from the BLS Current Population Survey, 2005–2009. 

Per BLS publication requirements, numbers of deaths are reported for workers of all ages whereas rates are for workers aged ≥15 years. 

† For CFOI, persons born in a foreign country include U.S. citizens born abroad (one or both of whose parents were U.S. citizens), naturalized citizens, and noncitizens. For CPS, persons born in 

the U.S. or its territories include U.S. citizens born abroad (one or both of whose parents were U.S. citizens).Persons of Hispanic ethnicity might be of any race or combination of races.

§ Persons of Hispanic ethnicity might be of any race or combination of races.

¶ Totals include workers of other/unknown race and ethnicity.

** Indicates that demographic-specific (race/ethnicity and place of birth) rate is considered elevated because it is >1.5 times the corresponding U.S. rate for that particular category (i.e., 

sex, age group, industry division and occupation) rate. Also indicates that this demographic-specific rate is considered significantly different because its confidence interval does not 

contain the corresponding overall category rate. †† Indicates that the overall rate for the certain categories (sex, age, industry division, and occupation group) is >1.5 times the U.S. injury-fatality homicide rate of 0.4 per 100,000 workers. 

Also indicates that the overall category rate is significantly different from the U.S. rate because the confidence interval for the category rate does not contain 0.4 (the U.S. rate). §§ Industry in which the decedent worked was coded according to the 2002 North American Industry Classification System (NAICS) (available at http://www.census.gov/eos/www/naics/). 

The detailed codes from the 20 NAICS sectors were combined into eight industry sectors according to the similarity of their occupational safety and health risks. ¶¶ Data do not meet confidential BLS publication criteria.

*** Occupation in which the decedent worked was coded according to the 2000 Standard Occupational Classification Manual (SOC) (available at http://www.bls.gov/soc/). The detailed codes 

from the 22 civilian SOC groups were combined into ten occupation groups according to the similarity of their work and their occupational safety and health risks.

work are homicides, which occur most frequently during a 

robbery. Customer service workers who handle money and 

who often work alone (e.g., cashiers and taxi drivers) are at 

highest risk. AI/AN/A/PI workers in transportation and sales 

occupations were at especially high risk. However for every 

type of occupation, black non-Hispanic workers were twice 

as likely as white non-Hispanic workers to be a homicide 

victim. Efforts to prevent robbery-related homicides include 

establishing workplace policies and procedures that engage 

management and employees; providing appropriate worksite 
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 45

analysis and safety and health training; ensuring that minimal 

cash is kept on hand; and enhancing and securing the physical 

environment with alarm systems, surveillance cameras, mirrors, 

and adequate lighting and barriers (9).

Women were more likely to be the victim of a homicide 

perpetrated by a relative. In these instances, the violence not only 

affects the worker but may also affect co-workers and/or customers 

who may be present during the incident. Multidisciplinary 

workplace violence prevention programs that incorporate training 

and perpetrator-specific prevention strategies should be made 

available and implemented widely (10). 

Limitations

The findings in this report are subject to at least four 

limitations. First, inclusion of cases in CFOI is dependent 

upon identifying work-relatedness. This determination can 

be difficult for certain types of incidents for which the work 

relationship might not be clear. Second, work-related deaths 

enumerated in CFOI are limited to fatal injuries and do not 

include work-related deaths attributable to chronic illnesses 

such as cancer or lung disease. It is estimated that approximately 

49,000 deaths each year can be attributed to work-related 

illnesses (11). Third, CFOI includes fatalities to volunteers. 

However, volunteers are not included in the CPS denominator, 

potentially resulting in an overestimation of fatality rates 

presented in this report by CDC. Finally, CFOI and CPS use 

different approaches to defining place of birth, which might 

result in an underestimate of injury rates for some categories.

Conclusion

These findings highlight the importance of preventing 

work-related deaths. All workers, regardless of their race, 

ethnicity, or immigrant status are afforded equal protection 

under the Occupational Safety and Health Act. Furthering a 

culture in which occupational safety and health is recognized 

and valued as a fundamental component of economic growth 

and prosperity can play an important role in promoting health 

equity. The fatality data presented in this report provide 

important information to focus prevention efforts. These 

findings highlight priority industries and occupations of 

workers in the highest risk jobs for all occupational fatalities 

and for homicides specifically. This information can be used 

to improve intervention efforts by developing programs 

that better meet the needs of the increasing diversity of the 

U.S. workforce. NIOSH’s Occupational Health Disparities 

program has prioritized research projects to improve outreach 

to eliminate health disparities and NIOSH’s National 

Occupational Research Agenda addresses high priority needs in 

individual industry sectors through research and partnerships. 

Prevention recommendations and publications that focus 

on the most serious concerns for these workers are available 

in English and Spanish; topics include workplace violence 

prevention, motor vehicle safety, and machine safety (available 

at http://www.cdc.gov/NIOSH/injury). 

References

1. US Bureau of Labor Statistics. Current population survey. Table 1. 

Employment status of the civilian noninstitutional population, 1942 to 

date . Washington, DC: US Bureau of Labor Statistics; 2013. Available 

at http://www.bls.gov/cps/cpsaat01.pdf.

2. US Bureau of Labor Statistics. Census of Fatal Occupational Injuries 

charts, 1992–2011 (revised data), number of fatal work injuries, 

1992–2011. Washington, DC: US Bureau of Labor Statistics; 2013. 

Available at http://www.bls.gov/iif/oshwc/cfoi/cfch0010.pdf. 

3. Leigh JP. Economic burden of occupational injury and illness in the 

United States. Milbank Q 2011;89:728–72.

4. CDC. Nonfatal work-related injuries and illnesses—United States, 2010. 

In: CDC. CDC health disparities and inequalities report—United States, 

2013. MMWR 2013;62(No. Suppl 3).

5. CDC. CDC health disparities and inequalities report—United States, 

2011. MMWR 2011;60(Suppl; January 14, 2011).

6. CDC. Introduction. In: CDC. CDC health disparities and inequalities 

report—United States, 2013. MMWR 2013;62(No. Suppl 3).

7. US Bureau of Labor Statistics. Occupational safety and health statistics. 

In: BLS handbook of methods. Washington, DC: US Bureau of Labor 

Statistics; 2013. 

8. US Bureau of Labor Statistics. Labor force data derived from the Current 

Population Survey. In: BLS handbook of methods. Washington, DC: 

US Bureau of Labor Statistics; 2013. Available at http://www.bls.gov/

opub/hom/pdf/homch1.pdf. 

9. Occupational Safety and Health Administration. Recommendations for 

workplace violence prevention programs in late-night retail establishments. 

DOL (OSHA) Publication No. 3153-12R 2009. Available at http://

www.osha.gov/Publications/osha3153.pdf. 

10. National Institute for Occupational Safety and Health. Workplace 

violence prevention strategies and research needs. DHHS (NIOSH) 

Publication No. 2006-144. Available at http://www.cdc.gov/niosh/

docs/2006-144/pdfs/2006-144.pdf. 

11. CDC. Workers Memorial Day—April 28, 2012. MMWR 2012;61:281.
Supplement

46 MMWR / November 22, 2013 / Vol. 62 / No. 3

Introduction

Traffic-related air pollution is a main contributor to 

unhealthy ambient air quality, particularly in urban areas 

with high traffic volume. Within urban areas, traffic is a 

major source of local variability in air pollution levels, with 

the highest concentrations and risk of exposure occurring near 

roads. Motor vehicle emissions represent a complex mixture 

of criteria air pollutants, including carbon monoxide (CO), 

nitrogen oxides (NOx), and particulate matter (PM), as well 

as hydrocarbons that react with NOx and sunlight to form 

ground-level ozone. Individually, each of these pollutants is 

a known or suspected cause of adverse health effects (1–4). 

Taking into consideration the entire body of evidence on 

primary traffic emissions, a recent review determined that there 

is sufficient evidence of a causal association between exposure 

to traffic-related air pollution and asthma exacerbation 

and suggestive evidence of a causal association for onset of 

childhood asthma, nonasthma respiratory symptoms, impaired 

lung function, all-cause mortality, cardiovascular mortality, and 

cardiovascular morbidity (5).

The mixture of traffic-related air pollutants can be difficult 

to measure and model. For this reason, many epidemiologic 

studies rely on measures of traffic (e.g., proximity to major 

roads, traffic density on nearest road, and cumulative traffic 

density within a buffer) as surrogates of exposure (6–8). These 

traffic measures typically account for both traffic volume (i.e., 

number of vehicles per day), which is a marker of the type 

and concentration of vehicle emissions, and distance, which 

addresses air pollution gradients near roads. Traffic emissions 

are highest at the point of release and typically diminish to near 

background levels within 150 to 300 meters of the roadway 

(7,9,10); however, the potential exposure zone around roads can 

vary considerably depending on the pollutant, traffic volume, 

ambient pollution concentrations, meteorologic conditions, 

topography, and land use (5). Traffic exposure metrics in the 

published literature have used a variety of different density and 

distance cut-points (6). Nevertheless, numerous epidemiologic 

studies have consistently demonstrated that living close to 

major roads or in areas of high traffic density is associated with 

adverse health effects, including asthma, chronic obstructive 

pulmonary disease, and other respiratory symptoms (11–15); 

cardiovascular disease risk and outcomes (16–20); adverse 

reproductive outcomes (21,22); and mortality (23–25). 

Some studies have observed a dose-response gradient such 

that living closer to major roads is associated with increased 

risk (13,14,16–18). In terms of traffic density, several studies 

have reported adverse health effects associated with residential 

proximity to roads with average daily traffic volume as low as 

10,000 vehicles per day (6,11,15–17).

In the United States, it is widely accepted that economically 

disadvantaged and minority populations share a disproportionate 

burden of air pollution exposure and risk (26,27). A growing body 

of evidence demonstrates that minority populations and persons of 

lower socioeconomic status experience higher residential exposure 

to traffic and traffic-related air pollution than nonminorities and 

persons of higher socioeconomic status (5,28–31). Two recent 

studies have confirmed that these racial/ethnic and socioeconomic 

disparities also exist on a national scale (32,33).

This report is part of the second CDC Health Disparities 

and Inequalities Report (CHDIR). The 2011 CHDIR (34) was 

the first CDC report to assess disparities across a wide range of 

diseases, behavior risk factors, environmental exposures, social 

determinants, and health-care access. The topic presented in 

this report is based on criteria that are described in the 2013 

CHDIR Introduction (35). This report provides descriptive 

data on residential proximity to major highways, a topic that 

was not discussed in the 2011 CHDIR. The purposes of this 

report are to discuss and raise awareness of the characteristics of 

persons exposed to traffic-related air pollution and to prompt 

actions to reduce disparities.

Methods

To characterize the U.S. population living close to major 

highways, CDC examined data from several sources using 

Geographical Information Systems (GIS). Three data 

sources were used for this assessment: 1) the 2010 U.S. 

census (available at http://www.census.gov/2010census), 

Residential Proximity to Major Highways — United States, 2010

Tegan K. Boehmer, PhD1

Stephanie L. Foster, MPH2

Jeffrey R. Henry, BA2

Efomo L. Woghiren-Akinnifesi2

Fuyuen Y. Yip, PhD1

1National Center for Environmental Health, CDC

2Agency for Toxic Substances and Disease Registry

Corresponding author: Tegan K. Boehmer, Division of Environmental Hazards and Health Effects, CDC. Telephone: 770-488-3714; E-mail: tboehmer@cdc.gov.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 47

2) 2006–2010 American Community Survey (ACS) 5-year 

estimates (available at http://www.census.gov/acs), and 3) 2010 

(Quarter 3) road network data from NAVTEQ, a commercial 

data source that provides comprehensive road information for 

the United States (available at http://www.navteq.com). Seven 

sociodemographic variables were examined. Data on age, sex, 

and race/ethnicity were obtained from the 2010 census; data 

on nativity, language spoken at home, educational attainment, 

and poverty status were obtained from the ACS.

The U.S. Census Bureau collects data on race and ethnicity 

(i.e., Hispanic origin) as two separate questions. For this 

analysis, persons of non-Hispanic ethnicity were classified as 

white, black, Asian/Pacific Islander, American Indian/Alaska 

Native, other race, and multiple races. Persons of Hispanic 

ethnicity, who might be of any race or combination of races, 

were grouped together as a single category. Educational 

attainment was defined as less than high school, high school 

graduate or equivalent, some college, or college graduate. For 

the variable nativity, “native born” includes U.S. citizens born 

abroad (one or both of whose parents were citizens at the 

time of birth) and anyone born in the United States or a U.S. 

territory; “foreign-born” denotes persons who were not U.S. 

citizens at birth. Poverty status was categorized by using the 

ratio of income to the federal poverty level (FPL), in which 

“poor” is <1.0 times FPL, “near poor” is 1.0–2.9 times FPL, 

and “nonpoor” is ≥3.0 times FPL.

Major highways were defined as interstates (Class 1) or as 

other freeways and expressways (Class 2) based on the Federal 

Highway Administration (FHWA) Functional Classification 

system. These road types represent the most heavily-trafficked, 

controlled-access highways in the United States. Although traffic 

volume is not factored directly into the Functional Classification 

system, FHWA statistics indicate that the majority of major 

highways have average daily traffic volumes exceeding 10,000 

vehicles per day (i.e., 77% of rural interstates have >10,000 

vehicles per day and >72% of urban interstates and other 

freeways and expressways have >30,000 vehicles per day) (36).

The census tract is the smallest geographic unit of analysis 

available for the variables of interest in the ACS data. ESRI 

ArcGIS v10 GIS software was used to create circular buffers of 

150 meters around all major highways, and the proportion of 

each census tract included within the buffer area was calculated. 

This area proportion was then applied to the census tract-level 

data from the 2010 census and ACS to estimate the number 

of persons living within 150 meters of a major highway for 

the total population and by sociodemographic characteristics. 

Census tract count estimates were summed to obtain state and 

national estimates. The proportion of the population living 

within 150 meters of a major highway was calculated for 

each category of the seven sociodemographic variables, using 

category-specific denominators derived from the 2010 census 

and ACS. No sampling error is associated with the 100% 

population counts obtained from the 2010 census. Standard 

errors were not calculated for the estimated population counts 

derived from the ACS because of the complexity of the 

GIS analysis used to generate these data. Therefore, for this 

descriptive analysis, no statistical testing or calculation of 95% 

confidence intervals was conducted, and it was not possible 

to determine if the observed differences across population 

subgroups are statistically significant.

Results

Approximately 11.3 million persons (or 3.7% of the 308.7 

million U.S. population) live within 150 meters of a major 

highway. State-level estimates ranged from 1.8% in Maine 

to 5.6% in New York (Figure). Regional patterns, based on 

U.S. Census Bureau groupings, indicate that the estimated 

proportion of the population living within 150 meters of a 

major highway ranged from 3.1% in the Midwest and 3.3% 

in the South to 4.3% in the Northeast and 4.4% in the West. 

The proportion of the population living near a major highway 

did not differ by sex (Table). By age group, the estimated 

proportion of persons living close to a major highway varied 

from 3.4% among those aged 45–79 years to ≥4.0% among 

those aged 18–34 years.

The greatest disparities were observed for race/ethnicity, 

nativity, and language spoken at home; the populations with 

the highest estimated percentage living within 150 meters of a 

major highway included members of racial and ethnic minority 

communities, foreign-born persons, and persons who speak a 

language other than English at home (Table). The estimated 

percentage of the population living within 150 meters of 

a major highway ranged from a low of 2.6% for American 

Indians/Alaska Natives and 3.1% for non-Hispanic whites 

to a high of 5.0% for Hispanics and 5.4% for Asians/Pacific 

Islanders. Likewise, the estimated proportion of the population 

living near a major highway was 5.1% for foreign-born persons, 

5.1% for persons who speak Spanish at home, and 4.9% for 

persons who speak another non-English language at home.

Disparities by educational attainment and poverty status 

were less pronounced (Table). The estimated percentage of 

the population living near a major highway varied from 3.4% 

for high school graduates to 4.1% for those with less than a 

high school diploma. A more consistent pattern was observed 

for poverty status; the estimated proportion of the population 

living near a major highway was 4.2% for those in the poor 

category, 3.7% for those in the near-poor category, and 3.5% 

for those in the nonpoor category.
Supplement

48 MMWR / November 22, 2013 / Vol. 62 / No. 3

Discussion

Overall, approximately 4% of the total U.S. population lives 

within 150 meters of a major highway, suggesting increased 

exposure to traffic-related air pollution and elevated risk for 

adverse health outcomes. Estimates of residential proximity to 

major roads are influenced by the number and type of roads 

and the distance or buffer size used. In terms of quantifying the 

total U.S. population exposed to traffic-related air pollution, 

the estimate of 11.3 million people derived from this analysis 

should be considered conservative because only interstates, 

freeways, and expressways were included and a relatively small 

buffer distance of 150 meters was used. These conditions 

were selected to capture persons who are at the highest risk 

for exposure to traffic-related air pollution. In addition, this 

estimate is based on distance to a single road and does not 

account for cumulative exposure to traffic from multiple roads.

The percentage of the population exposed to traffic-related 

air pollution is expected to be larger in urban areas because 

of higher population density, more roads, and higher traffic 

volume. A case study of two North American cities (Los Angeles 

County and Toronto, Canada) estimated that 30%–45% of 

the population in these urban areas lives within 500 meters 

of a highway or 50–100 meters of a major road (5). Although 

this report does not address urban/rural differences directly, 

an additional state-level analysis of these data indicated that 

the percentage of the population living within 150 meters of 

a major highway was correlated positively (R = 0.65) with the 

percentage of the population living in urban areas. Additional 

studies are needed to understand potential sociodemographic 

disparities among populations living near major highways 

across levels of urbanization.

This analysis suggests that social and demographic disparities 

exist with respect to residential proximity to major highways. 

Larger disparities were observed for indicators of minority 

TABLE. Number and percentage of population living within 150 

meters of a major highway, by selected characteristics — United 

States, 2010

Characteristic No. (%)*

Total† 11,337,933 (3.7)

Sex†

Male 5,547,223 (3.7)

Female 5,790,844 (3.7)

Age group (yrs)†

0–4 766,603 (3.8)

5–9 727,279 (3.6)

10–17 1,168,995 (3.5)

18–24 1,219,887 (4.0)

25–34 1,714,903 (4.2)

35–44 1,523,607 (3.7)

45–64 2,808,121 (3.4)

65–79 977,948 (3.4)

≥80 412,215 (3.7)

Race/Ethnicity†

Non-Hispanic

White 6,030,811 (3.1)

Black 1,676,225 (4.4)

Asian/Pacific Islander 800,723 (5.4)

American Indian/Alaska Native 59,378 (2.6)

Other 27,239 (4.5)

Multiple race 235,995 (4.0)

Hispanic§ 2,502,616 (5.0)

Nativity¶

Native born** 9,172,481 (3.5)

Foreign born†† 1,966,763 (5.1)

Language spoken at home (≥5 yrs)¶

English only 7,513,304 (3.3)

Spanish 1,805,261 (5.1)

Other 1,059,572 (4.9)

Educational attainment (≥25 years)¶

Less than high school 1,225,735 (4.1)

High school graduate or equivalent 1,988,228 (3.4)

Some college 1,977,261 (3.5)

College graduate 2,092,232 (3.8)

Poverty status¶,§§

Poor (<1.0 times FPL) 1,733,031 (4.2)

Near-poor (1.0–2.9 times FPL) 3,882,694 (3.7)

Nonpoor (≥3.0 times FPL) 5,227,274 (3.5)

Abbreviation: FPL = federal poverty level.

* Denominator for overall population is 308,745,348. Percentages for all other 

rows were calculated by using category-specific denominators.

† Source: U.S. Census Bureau, 2010 census (available at http://www.census.

gov/2010census). 

§ Persons of Hispanic ethnicity might be of any race or combination of races.

¶ Source: U.S. Census Bureau, 2006–2010 American Community Survey 

(available at http://www.census.gov/acs). 

** Includes U.S. citizens born abroad (one or both of whose parents were citizens 

at the time of birth) and anyone born in the United States or a U.S. territory.

†† Persons who were not U.S. citizens at birth.

§§ Additional information is available at http://aspe.hhs.gov/poverty/figuresfed-reg.cfm. 

3.8–5.6

3.1–3.7

2.7–3.1

1.8–2.7

FIGURE. Percentage* of population living within 150 meters of a 

major highway, by state — United States, 2010

* Calculated by dividing the population within 150 meters of a major highway 

by the total population per state and multiplying by 100. The percentages are 

displayed using quartiles.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 49

status (i.e., race/ethnicity, nativity, and language spoken 

at home) than for traditional indicators of socioeconomic 

status (i.e., poverty and educational attainment). Two 

other national studies have reported similar findings using 

alternative approaches. A study that examined the distribution 

of sociodemographic variables across various traffic exposure 

metrics assessed at the residential address found that race, 

ethnicity, poverty status, and education all were associated 

with one or more traffic exposure metrics (32). Another study 

demonstrated that the correlation between traffic exposure 

metrics and sociodemographic variables across all U.S. census 

tracts was stronger for race and ethnicity than it was for 

poverty, income, and education and that the magnitude of the 

correlations varied spatially by region and state (33).

The environmental justice literature suggests that socially 

disadvantaged groups might experience a phenomenon 

known as “triple jeopardy” (37). First, poor and minority 

groups are known to suffer negative health effects from social 

and behavioral determinants of health (e.g., psychosocial 

stress, poor nutrition, and inadequate access to health care). 

Second, as suggested in this analysis, certain populations (e.g., 

members of minority communities, foreign-born persons, and 

persons who speak a non-English language at home) might be 

at higher risk for exposure to traffic-related air pollution as a 

result of residential proximity to major highways. Third, there 

is evidence suggesting a multiplicative interaction between 

the first two factors, such that socially disadvantaged groups 

experience disproportionately larger adverse health effects from 

exposure to air pollution (37–39).

Limitations

The findings in this report are subject to at least three 

limitations. First, the area-proportion technique used 

assumes a homogeneous population density and population 

distribution by sociodemographic characteristics within each 

census tract, which might result in erroneous count estimates. 

The direction of the bias (overestimate or underestimate) 

could differ across population subgroups. For example, if 

socioeconomic disparities associated with residential proximity 

to major highways exist within census tracts, then the calculated 

percentages for minority subgroups might be underestimated 

and those for nonminority subgroups might be overestimated. 

Second, living within 150 meters of a major highway is only a 

surrogate for exposure to traffic-related air pollution. This study 

did not address the following factors that could affect exposure 

to traffic-related air pollution: number and type of vehicles 

traveling on major highways, cumulative effect of living near 

multiple roads, individual time-activity patterns (e.g., time 

spent at home vs. away, time spent inside vs. outside), 

meteorologic conditions, topography, and land-use patterns. 

Finally, it was not possible to perform testing to determine if 

the differences in the estimated percentages across population 

subgroups were statistically significant. However, the findings 

are consistent with other published research (32,33).

Conclusion

Primary prevention strategies to reduce traffic emissions 

include improving access to alternative transportation options 

(e.g., transit, rideshare programs, walking, and cycling), 

financial incentives to reduce vehicle miles traveled and 

congestion, diesel retrofitting, and promoting the use of 

electric and low emission vehicles. In addition, secondary 

prevention strategies to reduce exposure to traffic emissions 

include mitigation techniques for existing homes and 

buildings (e.g., roadside barriers and improved ventilation 

systems) and land-use policies that limit new development 

close to heavily-trafficked roads. For example, a recent study 

of roadside barriers suggests that solid barriers (i.e., noise 

barriers) might be more effective at mitigating traffic-related 

air pollution than vegetative barriers (i.e., tree stands) (41). In 

California, public health law has been used to restrict siting 

of new schools near major highways and busy traffic corridors 

(California Education Code §7213.c.2.C). Implementation 

of these strategies can help reduce exposures to traffic-related 

air pollution and health risks associated with these exposures.

Focusing prevention and mitigation interventions in urban 

areas, where there is a higher concentration of traffic-related 

air pollution and a greater proportion of the population 

residing near major roads, and in areas with the most socially 

disadvantaged populations will likely result in larger health 

benefits (37). Future and ongoing efforts to address disparities 

in residential proximity to major highways and traffic-related 

air pollution exposures will require an interdisciplinary 

collaboration between transportation, urban planning, and 

public health specialists.

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concentrations under a variety of wind conditions. Sci Total Environ 

2012;419:7–15.
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MMWR / November 22, 2013 / Vol. 62 / No. 3 51

Health-Care Access and Preventive Services
Supplement

52 MMWR / November 22, 2013 / Vol. 62 / No. 3
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 53

Introduction

Colorectal cancer (CRC) is the second leading cause of 

cancer-related deaths in the United States among cancers that 

affect both men and women (1). Screening for CRC reduces 

incidence and mortality (2). In 2008, the U.S. Preventive 

Services Task Force (USPSTF) recommended that persons aged 

50–75 years at average risk for CRC be screened for the disease 

by using one or more of the following methods: fecal occult 

blood testing (FOBT) every year, sigmoidoscopy every 5 years 

(with high-sensitivity FOBT every 3 years), or colonoscopy 

every 10 years (2).

This report is part of the second CDC Health Disparities 

and Inequalities Report (CHDIR). The 2011 CHDIR (3) was 

the first CDC report to assess disparities across a wide range of 

diseases, behavior risk factors, environmental exposures, social 

determinants, and health-care access. The topic presented in 

this report is based on criteria that are described in the 2013 

CHDIR Introduction (4). This report updates information 

regarding CRC screening provided in the 2011 CHDIR (5). 

The purposes of this report are to discuss and raise awareness 

of differences in colorectal cancer incidence, mortality, and 

screening and to prompt actions to reduce these disparities.

Methods

To characterize disparities for CRC incidence, CRC death 

rates, and CRC screening test use by test type, CDC analyzed 

data from multiple sources and years. Different analytic 

approaches were used to characterize disparities depending 

on the data source (i.e., deviation from referent group or 

comparison of weighted estimates and confidence intervals 

[CIs]).

To describe CRC incidence and death rates, CDC analyzed 

2008 CRC incidence and mortality data from U.S. Cancer 

Statistics (USCS) (1). Demographic characteristics analyzed 

included sex, age, race and ethnicity. Data on household 

income and educational attainment are not collected by 

cancer registries. Race was classified as non-Hispanic white, 

non-Hispanic black, Asian/Pacific Islander, or American 

Indian/Alaska Native. Ethnicity was classified as Hispanic or 

non-Hispanic; persons of Hispanic ethnicity might be of any 

race or combination of races. Incidence data were drawn from 

CDC’s National Program of Cancer Registries (NPCR) and 

the National Cancer Institute’s Surveillance, Epidemiology, and 

End Results Program (SEER) registries that met U.S. Cancer 

Statistics publication criteria for the diagnosis year 2008, and 

mortality data were derived from the National Vital Statistics 

System. In 2008, all 50 states and the District of Columbia had 

high-quality incidence and mortality data available, and thus 

100% of the U.S. population is represented for both. Incident 

CRCs were coded* according to the International Classification 

of Diseases for Oncology, Third Edition (ICD-O-3). All death 

certificates with CRC identified as the underlying cause of 

death according to the International Classification of Diseases, 

Tenth Revision (ICD-10) during 2008 were included in this 

analysis. Incidence and death rates were calculated for all age 

groups using SEER*Stat software (version 7.04); rates were 

reported per 100,000 population. Data were age-adjusted 

to the 2000 U.S. standard population by the direct method; 

corresponding 95% CIs were calculated as modified gamma 

intervals (6).

Disparities were measured as the deviations from a “referent” 

category rate. Absolute difference was measured as the simple 

difference between a population subgroup estimate and the 

estimate for its respective reference group. The relative difference, 

a percentage, was calculated by dividing the difference by the 

value in the referent category and multiplying by 100.

To assess disparities in CRC screening test use by test 

type, CDC analyzed 2010 survey data from the Behavioral 

Risk Factor Surveillance System (BRFSS). BRFSS is a 

state-based, random digit-dialed telephone survey of the 

noninstitutionalized, U.S. civilian population aged ≥18 years 

(7). Survey data were available for all 50 states and the District 

*Malignant behavior, ICDO3 site codes 18.0–18.9, 19.9, 20.9, and 26.0; 

excludes histology codes for lymphomas, mesothelioma, and Kaposi Sarcoma 

(9050–9055, 9140, and 9590–9989).

Colorectal Cancer Incidence and Screening — 

United States, 2008 and 2010

C. Brooke Steele, DO

Sun Hee Rim, MPH

Djenaba A. Joseph, MD

Jessica B. King, MPH

Laura C. Seeff, MD

National Center for Chronic Disease Prevention and Health Promotion, CDC

Corresponding author: C. Brooke Steele, Division of Cancer Prevention and Control, CDC. Telephone: 770-488-4261; E-mail: BSteele1@cdc.gov.
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54 MMWR / November 22, 2013 / Vol. 62 / No. 3

of Columbia. In 2010, the median response rate was 54.6%, 

and the median cooperation rate was 76.9% (7). Respondents 

who refused to answer, had a missing answer, or did not know 

the answer to a question were excluded from analysis of that 

specific question. Of the 226,205 persons aged 50–75 years 

who responded in 2010, approximately 4.2% were excluded 

from the analyses. 

Demographic characteristics from BRFSS that were analyzed 

included sex, age, race, ethnicity, educational attainment, 

income level, disability status, health insurance status, and 

geographic location. Race was classified as non-Hispanic white, 

non-Hispanic black, Asian/Pacific Islander, American Indian/

Alaska Native, or other non-Hispanic. Ethnicity was classified 

as Hispanic or non-Hispanic; persons of Hispanic ethnicity 

might be of any race or combination of races. Educational 

attainment was classified as less than high school, some high 

school, high school graduate or equivalent, some college/

technical school, or college graduate. Income level was classified 

as <$15,000, $15,000–$34,999, $35,000–$49,999, $50,000–

$74,999, and ≥$75,000.The median response rate† and the 

median cooperation rate§ are based on Council of American 

Survey and Research Organizations guidelines (available at 

http://www.cdc.gov/brfss/annual_data/annual_2010.htm).

BRFSS respondents aged 50–75 years, the age group for which 

USPSTF recommends CRC screening, were asked if they had 

ever used a “special kit at home to determine whether the stool 

contains blood (FOBT),” whether they had ever had “a tube 

inserted into the rectum to view the colon for signs of cancer 

or other health problems (sigmoidoscopy or colonoscopy),” and 

when these tests were last performed. To allow assessment of 

up-to-date screening according to current USPSTF guidelines, 

the measure of overall screening prevalence used in the 2011 

CHDIR (5) was modified. Percentages were estimated for 

persons aged 50–75 years who reported receiving an FOBT 

within 1 year, a sigmoidoscopy within 5 years with FOBT 

within 3 years, or a colonoscopy within 10 years preceding 

the survey. Data for the three recommended test options were 

combined to estimate overall prevalence of up-to-date CRC 

screening. States were categorized into four poverty quartiles by 

using data from the 2010 Current Population Survey (available 

at http://cps.ipums.org/cps), and composite screening rates per 

quartile were calculated. Composite percentages and 95% CIs 

were calculated by selected characteristics. Data were weighted 

according to the sex, racial/ethnic, and age distribution of the 

adult population of each state by using intercensal estimates 

and were age standardized to the 2010 BRFSS population 

aged 50–75 years.

Results

Compared with women, men had higher CRC incidence 

rates (51.6 versus 38.7 per 100,000 population) and death rates 

(19.7 vs. 13.8 per 100,000 population) in 2008 (Table 1). CRC 

incidence and mortality increased with advancing age (Figure). 

Incidence and death rates were highest among persons aged 

≥75 years. Non-Hispanic blacks had higher CRC incidence and 

death rates than non-Hispanic whites, Asians/Pacific Islanders, 

and American Indians/Alaska Natives. Incidence and death 

rates were higher among non-Hispanics than among Hispanics.

In 2010, among respondents aged 50–75 years, 64.5% 

reported being up-to-date with CRC screening (Table 2). 

The proportion of respondents who reported having had any 

of the test options was greater among persons aged 65–75 

years compared with those aged 50–64 years, among nonHispanics compared with Hispanics, among persons with 

a disability compared with those with no disability, and 

among persons with health insurance compared with those 

with no health insurance. This disparity in reported test use 

by health insurance status was evident for all three test types 

(FOBT, sigmoidoscopy with FOBT, and colonoscopy). The 

proportions for colonoscopy use and for overall CRC screening 

were slightly greater among women than among men. Reported 

rates of test use increased with increasing education level 

and household income, with the greatest increases occurring 

among those who reported having had a colonoscopy within 

10 years preceding the survey. The prevalence of respondents 

who were up-to-date with CRC screening was highest 

among non-Hispanic whites (66.4%), followed closely by 

non-Hispanic blacks (64.8%). Non-Hispanic whites had the 

greatest proportion of respondents reporting having had a 

colonoscopy within 10 years preceding the survey compared 

with all other races; non-Hispanic blacks had the greatest 

proportion of respondents reporting having had FOBT within 

the year preceding the survey.

By composite state poverty quartiles, the relationship 

between reported screening rates and poverty varied by 

test type. No consistent relationship was observed between 

poverty and reported use of FOBT; however, the number of 

respondents who reported use of FOBT testing was small 

(Table 3). An inverse relationship was observed for reported 

use of colonoscopy and poverty, with reported colonoscopy use 

generally decreasing with increasing levels of poverty (Table 3). †The percentage of persons who completed interviews among all eligible persons, 

including those who were not contacted successfully. 

§The percentage of persons who completed interviews among all eligible persons 

who were contacted.
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MMWR / November 22, 2013 / Vol. 62 / No. 3 55

Discussion

CRC incidence and death rates were 

higher among older, male, and non-Hispanic 

populations. CRC incidence and death rates 

for many of these groups exceeded Healthy 

People 2020 targets of 38.6 new CRC cases 

per 100,000 population and 14.5 CRC 

deaths per 100,000 population (8). Progress 

in reducing deaths from CRC has been 

achieved through a combination of primary 

prevention, early detection, and treatment (9). 

In 2010, approximately two thirds of the U.S. 

population aged 50–75 years met USPSTF 

criteria for up-to-date CRC screening. The 

proportion screened in a timely manner varied 

by race and other demographic characteristics. 

Although estimates of the overall prevalence 

of up-to-date CRC screening in this report 

and in the 2011 report were computed 

differently, certain patterns were similar. 

The 2011 report analyzed BRFSS data 

for 2002–2008. During that time period, 

non-Hispanic whites had the highest overall 

prevalence of CRC screening, followed closely 

by non-Hispanic blacks (3). The same finding 

TABLE 1. Colorectal cancer incidence and death rates,* by selected demographic characteristics — United States, 2008†

Characteristic

Incidence Absolute 

difference 

(Rate)

Relative 

difference 

(%)

Death Absolute 

difference 

(Rate)

Relative 

difference 

Rate (95% CI) Rate (95% CI) (%)

Sex

Male 51.6 (51.2–52.0) 12.9 33.3 19.7 (19.4–19.9) 5.9 42.8

Female 38.7 (38.4–39.0) Ref. Ref. 13.8 (13.6–14.0) Ref. Ref.

Age group (yrs)

<50 6.7 (6.6–6.8) Ref. Ref. 1.7 (1.7–1.8) Ref. Ref.

50–54 55.7 (54.7–56.7) 49.0 731.3 13.4 (12.9–13.9) 11.7 688.2

55–59 72.7 (71.5–73.9) 66.0 985.1 21.6 (20.9–22.3) 19.9 1,170.6

60–64 101.2 (99.6–102.9) 94.5 1,410.4 32.7 (31.8–33.6) 31.0 1,823.5

65–69 152.2 (149.9–154.5) 145.5 2,171.6 48.7 (47.4–50.0) 47.0 2,764.7

70–74 199.0 (196.0–201.9) 192.3 2,870.1 70.1 (68.3–71.8) 68.4 4,023.5

≥75 283.5 (281.1–285.9) 276.8 4,131.3 134.0 (132.4–135.7) 132.3 7,782.4

Race

White, non-Hispanic 43.8 (43.6–44.1) Ref. Ref. 16.1 (15.9–16.2) Ref. Ref.

Black, non-Hispanic 53.9 (53.0–54.7) 10.1 23.1 23.5 (22.9–24.1) 7.4 46.0

Asian/Pacific Islander 34.5 (31.8–37.3) -9.3 -21.2 15.9 (14.1–17.9) -0.2 -1.2

American Indian/Alaska Native 35.1 (34.1–36.2) -8.7 -19.9 11.5 (10.9–12.1) -4.6 -28.6

Ethnicity

Hispanic§ 37.8 (37.0–38.6) Ref. Ref. 12.1 (11.7–12.6) Ref. Ref.

Non-Hispanic 45.0 (44.8–45.2) 7.2 19.0 16.7 (16.5–16.8) 4.6 38.0

Abbreviations: 95% CI = 95% confidence interval; Ref. = referent.

* Per 100,000 population

† Rates are age-adjusted to the U.S. Census Bureau’s 2000 US Standard Population for 19 age groups (available at http//seer.cancer.gov/stdpopulations/stdpop.19ages.

html). Incidence data come from CDC’s National Program of Cancer Registries (NPCR) and the National Cancer Institute’s Surveillance, Epidemiology, and End Results 

Program (SEER) registries that met U.S. Cancer Statistics publication criteria for diagnosis year 2008 and cover 100% of the U.S. population. Underlying mortality 

data are provided by the National Vital Statistics System and cover 100% of the U.S. population.

§ Persons of Hispanic ethnicity might be of any race or combination of races.

FIGURE. Colorectal cancer incidence and mortality rates per 100,000 population, by age 

group — United States, 2008*

* Rates are age-adjusted to the 2000 U.S.Census Bureau Standard Population for 19 age groups (available 

at http://seer.cancer.gov/stdpopulations/stdpop.19ages.html). Incidence data come from from CDC’s 

National Program of Cancer Registries (NPCR) and the National Cancer Institute’s Surveillance, 

Epidemiology, and End Results Program (SEER) registries that met U.S. Cancer Statistics publication 

criteria for diagnosis year 2008 and cover 100% of the U.S. population. Underlying mortality data are 

provided by the National Vital Statistics System and cover 100% of the U.S. population.

Incidence

0

50

100

150

200

250

300

<50 50–54 55–59 60–64 65–69 70–74 ≥75

Rate

Age group (yrs)

Mortality
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56 MMWR / November 22, 2013 / Vol. 62 / No. 3

was observed in 2010. American Indians/Alaska Natives and 

Hispanics had lower CRC screening rates in 2002–2008 

than non-Hispanic blacks. This disparity persisted in 2010. 

The pattern was less consistent for the Asian/Pacific Islander 

population; in 2002 and 2004, their overall prevalence of 

up-to-date CRC screening was substantially lower than the 

prevalence for non-Hispanic whites. The gap narrowed in 

2006 and 2008 but widened in 2010.

In 2010, CRC screening test use increased with age, 

educational level, and household income level. The demographic 

disparities were greater for colonoscopy than for sigmoidoscopy 

and FOBT. Similar findings were reported in the 2011 report 

(3) and in other previous studies (10–12). Having health 

insurance is also a strong predictor of screening for colorectal 

cancer (11,13,14). Disparities in the overall prevalence of 

up-to-date CRC screening by health insurance status were 

observed in 2008 (5) and 2010. Screening rates among insured 

respondents were 66.6 in 2008 and 67.5 in 2010. Rates 

among uninsured respondents were 37.5 in 2008 and 35.4 in 

2010. Medicare has covered CRC screening for enrollees since 

2001. Although this expansion of cancer screening coverage 

has increased CRC screening among older persons, persistent 

racial/ethnic, socioeconomic, and geographic disparities in test 

use have been reported among persons aged ≥65 years (15–17). 

Among younger adults, those with lower incomes and less 

than a high school education are less likely to have health-care 

insurance than those with higher incomes and at least some 

college education (18). For many patients, implementation of 

TABLE 2. Percentage* of respondents aged 50–75 years who reported being up-to-date with colorectal cancer screening, by selected 

characteristics and test type — Behavioral Risk Factor Surveillance System, United States, 2010 

Characteristic

FOBT within 1 yr

Flexible sigmoidoscopy 

within 5 yrs with FOBT 

within 3 years

Colonoscopy 

within 10 yrs Total CRC screening†

% (95% CI) % (95% CI) % (95% CI) % (95% CI)

Sex

Male 12.4 (12.0–12.8) 1.4 (1.3–1.5) 59.6 (59.0–60.2) 64.0 (63.4–64.6)

Female 11.1 (10.9–11.4) 1.2 (1.1–1.3) 60.9 (60.4–61.3) 64.9 (64.5–65.4)

Age group (yrs)

50–64 10.3 (10.1–10.6) 1.0 (0.9–1.1) 55.4 (55.0–55.9) 59.7 (59.2–60.1)

65–75 15.1 (14.7–15.6) 1.9 (1.8–2.1) 71.9 (71.3–72.4) 76.1 (75.6–76.7)

Race

White, non-Hispanic 11.3 (11.1–11.6) 1.2 (1.2–1.4) 62.5 (62.1–62.9) 66.4 (66.0–66.8)

Black, non-Hispanic 15.1 (14.2–16.1) 1.4 (1.1–1.7) 59.8 (58.5–61.1) 64.8 (63.6–66.1)

Asian/Pacific Islander 12.5 (10.5–14.7) 1.6 (0.9–2.7) 49.3 (45.9–52.6) 54.4 (51.0–57.8)

American Indian/Alaska Native 14.6 (12.1–17.6) 0.9 (0.5–1.9) 48.9 (45.0–52.8) 55.2 (51.3–59.1)

Other, non-Hispanic 13.5 (11.9–15.4) 2.1 (1.5–3.0) 55.1 (52.4–57.7) 61.3 (58.7–63.8)

Ethnicity

Non-Hispanic 11.8 (11.6–12.1) 1.3 (1.2–1.4) 61.6 (61.2–61.9) 65.7 (65.3–66.1)

Hispanic§ 10.7 (9.6–11.8) 1.2 (0.9–1.7) 45.4 (43.6–47.3) 51.0 (49.1–52.9)

Educational attainment

Less than high school 8.3 (7.1–9.7) 0.7 (0.4–1.2) 34.6 (32.2–37.0) 39.2 (36.7–41.7)

Some high school 10.4 (9.5–11.5) 0.9 (0.6–1.3) 44.3 (42.7–46.0) 49.4 (47.7–51.1)

High school graduate or equivalent 11.0 (10.6–11.5) 0.9 (0.8–1.1) 54.9 (54.2–55.6) 59.3 (58.6–60.0)

Some college/technical school 12.3 (11.9–12.8) 1.4 (1.3–1.6) 61.2 (60.5–61.9) 65.7 (65.0–66.3)

College graduate 12.5 (12.1–12.9) 1.7 (1.5–1.8) 68.3 (67.7–68.9) 72.0 (71.4–72.6)

Income level

<$15,000 11.2 (10.4–12.0) 0.9 (0.7–1.2) 42.3 (41.0–43.6) 47.7 (46.4–49.0)

$15,000–$34,999 11.6 (11.1–12.1) 1.1 (1.0–1.3) 50.9 (50.2–51.7) 56.0 (55.2–56.8)

$35,000–$49,999 12.0 (11.4–12.7) 1.5 (1.3–1.8) 60.5 (59.5–61.5) 65.0 (64.0–65.9)

$50,000–$74,999 12.0 (11.4–12.6) 1.3 (1.1–1.6) 65.1 (64.2–66.0) 68.9 (68.0–69.7)

≥$75,000 12.1 (11.7–12.6) 1.7 (1.5–1.9) 69.9 (69.2–70.7) 73.4 (72.7–74.1)

Disability status

Has a disability 12.5 (12.1–12.9) 1.4 (1.2–1.6) 61.7 (61.1–62.4) 66.3 (65.7–67.0)

Does not have a disability 11.5 (11.2–11.7) 1.2 (1.2–1.4) 59.7 (59.2–60.2) 63.8 (63.3–64.3)

Health insurance status

Has health insurance 12.2 (11.9–12.4) 1.4 (1.3–1.5) 63.3 (62.9–63.7) 67.5 (67.2–67.9)

Does not have health insurance 7.9 (6.8–9.1) 0.4 (0.3–0.6) 31.6 (29.7–33.5) 35.4 (33.5–37.5)

Total 11.7 (11.5–12.0) 1.3 (1.2–1.4) 60.2 (59.9–60.6) 64.5 (64.1–64.8)

Abbreviations: 95% CI = 95% confidence interval; CRC = colorectal cancer; FOBT = fecal occult blood testing.

* Percentages standardized to age distribution in the 2010 Behavioral Risk Factor Surveillance System.

† Home FOBT within the past year, flexible sigmoidoscopy within the past 5 years with FOBT within the past 3 years, or colonoscopy within the past 10 years. 

§ Persons of Hispanic ethnicity might be of any race or combination of races.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 57

the Affordable Care Act has removed financial barriers to CRC 

screening by mandating that nongovernmental health plans 

cover certain preventive health services without cost-sharing 

requirements (19).

FOBT and sigmoidoscopy screening rates were low in 

2010. Previous studies have noted a continued decline in use 

of these tests (12,20–22), despite the fact that screening with 

each has been reported to be associated with reduced mortality 

from CRC (23,24). Some primary care physicians perceive 

FOBT and sigmoidoscopy to be less effective in reducing 

CRC mortality than colonoscopy, which might influence 

which tests they recommend to their patients (25–27). Studies 

indicate that some patients prefer FOBT, and discordance 

between physician and patient preferences might affect uptake 

of CRC screening (28–30). Discussing multiple options 

for CRC screening with patients and acknowledging their 

preferences when recommendations are made could contribute 

to improved completion of testing.

State-level poverty percentages were less clearly associated 

with use of FOBT compared with colonoscopy. During 

2002–2008, screening with annual FOBT or lower endoscopy 

within 10 years was related to both income and state poverty 

levels (3). In previous studies of trends in CRC screening, 

changes in FOBT and colonoscopy use varied substantially by 

income level, health insurance status, race/ethnicity, and other 

demographic characteristics (31,32). Additional studies are 

needed to investigate the effects of poverty and sociocultural 

indicators on test use, independent of insurance status.

CDC has funded activities to improve CRC screening 

rates, including efforts to increase access to screening for 

underserved populations. The Colorectal Cancer Control 

Program (CRCCP) was established in 2009 following the 

successful implementation of a CRC screening demonstration 

program in five sites across the country (33). CRCCP funds 

25 states and four tribes, with the goal of increasing screening 

rates among those aged 50–75 years to 80% in funded states 

(http://www.cdc.gov/cancer/colorectal). Approximately one 

third of funds are used to provide direct screening services and 

follow-up care to low-income men and women aged 50–64 

years who are underinsured or uninsured. The majority of 

funds are used to promote and implement evidence-based 

strategies recommended by the Task Force on Community 

See table footnotes on the next page.

TABLE 3. Percentage of persons reporting having had colorectal cancer screening, by state poverty-level quartiles* — Behavioral Risk Factor 

Surveillance System, United States, 2010

State/Area

Population 

in poverty 

(%)

FOBT within past 1 yr Colonoscopy within past 10 yrs Overall CRC screening†

% (95% CI) % (95% CI) % (95% CI)

Quartile 1

New Hampshire 8.2 10.2 (9.1–11.5) 72.2 (70.3–74.1) 75.1 (73.2–76.9)

Connecticut 9.1 11.5 (10.1–13.0) 72.5 (70.3–74.5) 74.8 (72.8–76.8)

Maryland 9.2 14.7 (13.3–16.1) 68.5 (66.6–70.4) 72.2 (70.3–73.9)

Wyoming 9.4 8.3 (7.3–9.4) 52.9 (50.9–54.9) 56.4 (54.4–58.4)

Vermont 9.8 8.3 (7.4–9.3) 68.8 (67.1–70.5) 71.6 (69.9–73.2)

New Jersey 10.1 11.6 (10.5–12.9) 60.4 (58.6–62.2) 64.6 (62.8–66.3)

Nebraska 10.4 8.8 (7.9–9.8) 56.4 (54.6–58.2) 60.0 (58.2–61.7)

Virginia 11.0 12.7 (10.9–14.6) 63.2 (60.5–65.8) 67.1 (64.4–69.6)

Utah 11.2 4.6 (3.9–5.4) 65.6 (63.9–67.3) 67.0 (65.3–68.7)

Pennsylvania 11.2 8.9 (8.0–9.8) 63.2 (61.4–64.9) 66.3 (64.6–68.0)

Iowa 11.3 10.8 (9.6–12.2) 60.3 (58.3–62.3) 63.3 (61.3–65.3)

Wisconsin 11.4 8.7 (7.4–10.1) 64.3 (61.8–66.7) 68.1 (65.6–70.5)

Massachusetts 11.6 11.9 (10.9–12.9) 72.3 (70.7–73.8) 75.1 (73.6–76.6)

Composite§ 10.3 10.7 (10.3–11.1) 64.8 (64.1–65.5) 68.2 (67.5–68.8)

Quartile 2

North Dakota 11.7 11.1 (9.9–12.6) 51.8 (49.6–54.1) 56.7 (54.4–58.9)

Minnesota 11.9 6.3 (5.4–7.4) 67.5 (65.4–69.5) 69.3 (67.3–71.3)

Maine 12.2 11.6 (10.6–12.7) 70.7 (69.0–72.2) 73.7 (72.1–75.2)

Hawaii 12.4 16.5 (14.8–18.2) 51.1 (48.8–53.4) 60.4 (58.1–62.6)

Washington 12.4 13.9 (13.1–14.7) 66.7 (65.5–67.9) 70.9 (69.7–72.0)

Alaska 12.5 8.1 (5.6–11.5) 57.4 (52.6–62.1) 58.8 (54.0–63.4)

Delaware 13.2 8.7 (7.4–10.2) 68.7 (66.2–71.2) 70.6 (68.1–73.0)

Colorado 13.3 12.0 (11.0–13.0) 59.4 (57.8–61.0) 65.1 (63.6–66.7)

Oklahoma 13.7 9.3 (8.3–10.4) 50.8 (49.0–52.7) 54.4 (52.6–56.3)

Ohio 13.7 11.7 (10.7–12.9) 58.0 (56.2–59.7) 62.6 (60.9–64.4)

Kansas 13.8 11.3 (10.4–12.4) 58.9 (57.2–60.5) 63.0 (61.4–64.6)

Louisiana 14.0 12.6 (11.4–14.0) 55.0 (53.1–56.9) 59.9 (58.0–61.8)

Illinois 14.0 7.3 (6.1–8.6) 55.9 (53.3–58.4) 58.3 (55.7–60.8)

Composite 13.0 10.4 (10.0–10.8) 59.1 (58.3–59.9) 63.0 (62.2–63.8)
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58 MMWR / November 22, 2013 / Vol. 62 / No. 3

Preventive Services (http://www.thecommunityguide.org/

index.html) to increase population-level CRC screening. 

Funded states and tribes are encouraged to partner with healthcare systems, insurers, worksites, and others to maximize the 

impact of implemented interventions. To date, all funded 

states and tribes have implemented at least one evidencebased intervention, with the majority implementing two or 

more. Grantees have partnered with federally qualified health 

centers (i.e., organizations that receive grants under Section 

330 of the Public Health Service act) and private and nonprofit 

health-care systems to implement patient navigation programs 

and interventions to reduce structural barriers to screening, 

with private health insurers and state Medicaid offices to 

implement provider and patient reminder systems, and with 

comprehensive cancer control coalitions and local health 

departments to implement small media campaigns.

CDC also funds the National Comprehensive Cancer 

Control Program (NCCCP), which provides support to all 

50 states and the District of Columbia, seven tribes/tribal 

organizations, and seven U.S.-associated Pacific Islands/

Territories to establish partnerships, determine priorities, and 

create and implement cancer plans to reduce the burden of 

cancer in their communities (34). Activities that have been 

implemented successfully by selected NCCCP programs 

to reduce the burden of CRC have included initiation of 

professional education and practice improvement initiatives 

for primary care providers and collaboration with communitybased organizations to promote CRC prevention (35). Many 

grantees also have made the elimination of health disparities a 

priority. Some include goals and objectives to improve cancer 

prevention, early detection, treatment, and survivorship care 

among disparate populations in their cancer plans (http://

cancercontrolplanet.cancer.gov).

TABLE 3. (Continued) Percentage of persons reporting having had colorectal cancer screening, by state poverty-level quartiles* — Behavioral 

Risk Factor Surveillance System, United States, 2010

State/Area

Population 

in poverty 

(%)

FOBT within past 1 yr Colonoscopy within past 10 yrs Overall CRC screening†

% (95% CI) % (95% CI) % (95% CI)

Quartile 3

Nevada 14.6 10.0 (8.1–12.2) 53.3 (49.8–56.7) 57.5 (54.1–60.9)

Oregon 14.6 11.5 (10.2–12.9) 57.9 (55.7–60.1) 63.8 (61.6–65.9)

Michigan 14.6 11.5 (10.5–12.7) 65.8 (64.0–67.4) 69.3 (67.6–70.9)

Florida 14.7 13.5 (12.5–14.7) 61.3 (59.6–63.1) 65.7 (63.9–67.4)

South Carolina 14.8 9.3 (8.2–10.6) 61.7 (59.5–63.8) 64.9 (62.7–67.0)

Rhode Island 15.1 9.6 (8.4–10.9) 71.7 (69.7–73.6) 74.1 (72.2–76.0)

Idaho 15.2 8.1 (7.2–9.2) 52.5 (50.4–54.5) 55.9 (53.9–57.9)

Montana 15.3 8.8 (7.8–10.0) 53.8 (51.8–55.8) 57.9 (55.8–59.9)

South Dakota 15.5 10.1 (8.9–11.4) 60.9 (58.8–63.0) 63.9 (61.8–66.0)

California 15.9 19.3 (18.3–20.4) 52.6 (51.1–54.1) 62.1 (60.7–63.6)

Missouri 16.3 8.5 (7.0–10.2) 60.1 (57.4–62.7) 63.4 (60.7–65.9)

Tennessee 16.8 12.8 (11.3–14.4) 56.7 (54.2–59.1) 61.0 (58.6–63.4)

New York 17.0 9.8 (8.8–10.9) 66.7 (64.9–68.4) 69.2 (67.4–70.8)

Composite 15.6 13.5 (13.1–14.0) 59.6 (58.9–60.3) 64.8 (64.1–65.5)

Quartile 4

Indiana 17.3 10.0 (8.9–11.1) 57.6 (55.8–59.4) 61.2 (59.4–63.0)

West Virginia 17.4 12.9 (11.4–14.5) 49.9 (47.6–52.2) 54.7 (52.4–57.0)

District of Columbia 17.5 16.5 (14.6–18.6) 65.9 (63.2–68.5) 71.1 (68.5–73.5)

North Carolina 17.6 14.1 (12.9–15.3) 65.0 (63.2–66.7) 68.7 (66.9–70.4)

Kentucky 17.7 8.6 (7.5–9.9) 59.2 (57.0–61.4) 61.7 (59.5–63.9)

Alabama 17.8 10.2 (9.0–11.5) 58.2 (56.0–60.3) 62.4 (60.2–64.5)

Texas 18.4 8.7 (7.8–9.8) 55.1 (53.1–57.2) 58.7 (56.7–60.8)

Georgia 19.0 14.1 (12.6–15.7) 62.5 (60.2–64.7) 66.3 (64.1–68.5)

Arkansas 19.2 10.2 (8.8–11.9) 54.6 (51.9–57.2) 58.9 (56.2–61.5)

New Mexico 19.7 9.9 (8.7–11.2) 55.2 (53.1–57.2) 59.2 (57.2–61.2)

Arizona 22.5 11.7 (10.1–13.6) 60.3 (57.3–63.3) 64.1 (61.1–67.0)

Mississippi 23.4 11.0 (9.9–12.2) 52.8 (50.8–54.7) 57.1 (55.1–59.0)

Composite 18.7 11.0 (10.6–11.5) 58.4 (57.6–59.2) 62.6 (61.3–63.0)

Abbreviations: 95% CI = 95% confidence interval; CRC = colorectal cancer; FOBT = fecal occult blood testing.

* Quartiles were determined by calculating the state-level percent of residents living at or below the poverty level, and the range (low to high); the range was divided 

evenly into three groups by ranking the states from lowest to highest for the percentage living in poverty. Source: Current Population Survey 2010 file (available at 

http://www.census.gov/hhes/www/poverty/data).

† Home FOBT within the past year, flexible sigmoidoscopy within the past 5 years with FOBT in the last 3 years, or colonoscopy within the last 10 years. 

§ The weighted number of persons who received a test divided by the estimated population total of all states within the quartile.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 59

Limitations

The findings in this report are subject to at least six limitations. 

First, cancer registries have an interval of approximately 24 

months after the close of the diagnosis year to submit cases to 

NPCR and SEER, which affects the timely calculation of cancer 

incidence rates. The mose recent year for which incidence data 

were available for this report was 2008. Second, variation in the 

quality of race and ethnicity information in medical records 

and death certificates (36,37) could influence the accuracy of 

surveillance data. Third, BRFSS results might underestimate 

or overestimate actual CRC screening test rates because BRFSS 

does not determine the indication for the test (screening versus 

diagnostic use) or whether the tests are conducted according to 

timelines recommended in CRC screening guidelines. Fourth, 

because BRFSS does not collect information from persons in 

institutions, nursing homes, long-term–care facilities, military 

installations, and correctional institutions, the results cannot 

be generalized to these populations. Fifth, BRFSS responses 

are self-reports and not validated by medical record or claims 

data review. Finally, participation rates for random-digit-dialed 

health surveys have been decreasing. However, although 

BRFSS has a low median response rate, the BRFSS weighting 

procedure partially corrects for nonresponse.

Conclusion

Disparities in CRC incidence, mortality, and screening 

persist. CRC incidence and death rates have decreased among 

adults in the United States since 1999 (38). However, men have 

higher rates of both incidence and mortality than women, and 

non-Hispanic blacks have higher rates than other racial and 

ethnic groups (1). Although increased screening could reduce 

mortality from CRC by an estimated 50% (9), the prevalence 

of up-to-date screening according to USPSTF guidelines 

among Asians/Pacific Islanders and American Indians/Alaska 

Natives remains lower than the prevalence for other racial 

and ethnic groups. Coordinated and systems-focused efforts 

by CDC and other federal agencies, state and local health 

departments, and the medical community to address barriers 

to end disparities in CRC screening should continue so that 

the incidence and mortality associated with this disease can 

be reduced among all populations.

Acknowledgment

Kristine Gerdes, EdS, MPH, Division of Public Affairs, Office of 

the Associate Director of Communication, CDC, provided assistance 

with this report.

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future rates. Cancer 2010;116:544–73.

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13. Doubeni CA, Laiyemo AO, Young AC, et al. Primary care, economic 

barriers to health care, and use of colorectal cancer screening tests among 

Medicare enrollees over time. Ann Fam Med 2010;8:299–307.

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behaviors among average-risk older adults in the United States. Cancer 

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15. White A, Vernon SW, Franzini L, Du XL. Racial and ethnic disparities 

in colorectal cancer screening persisted despite expansion of Medicare’s 

screening reimbursement. Cancer Epidemiol Biomarkers Prev 

2011;20:811–7.

16. Semrad JJ, Tancredi DJ, Baldwin LM, Green P, Fenton JJ. Geographic 

variation of racial/ethnic disparities in colorectal cancer testing among 

Medicare enrollees. Cancer 2011;117:1755–63.

17. Doubeni CA, Laivemo AO, Reed G, Field TS, Fletcher RH. 

Socioeconomic and racial patterns of colorectal cancer screening among 

Medicare enrollees in 2000 to 2005. Cancer Epidemiol Biomarkers Prev 

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18. Agency for Healthcare Research and Quality. National healthcare 

disparities report: 2011. Washington, DC: US Department of Health 

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Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 61

Introduction

One out of four adults aged 19–64 years reported not 

having health insurance at some time during 2011, with a 

majority remaining uninsured for ≥1 year (1). In the first 

quarter of 2010, an estimated 59.1 million persons had no 

health insurance for at least part of the year, an increase from 

58.7 million in 2009 and 56.4 million in 2008 (2). The 

unemployment rate increased from 5.8% to 9.3% from 2008 

to 2009, the largest 1-year increase on record (3). Losing or 

changing jobs was the primary reason persons experienced a 

gap in health insurance (1). Employment-based coverage for 

persons aged <65 years continued to erode for the ninth year 

in a row, falling 3.0 percentage points from 61.9% in 2008 to 

58.9% in 2009 (3). Persons aged 18–64 years with no health 

insurance during the preceding year were seven times as likely 

as those continuously insured to forgo needed health care 

because of cost (2).

This report is part of the second CDC Health Disparities 

and Inequalities Report (CHDIR). The 2011 CHDIR (4) was 

the first CDC report to assess disparities across a wide range 

of diseases, behavioral risk factors, environmental exposures, 

social determinants, and health-care access. The topic presented 

in this report is based on criteria that are described in the 

2013 CHDIR Introduction (5). This report provides updated 

information that complements the health insurance coverage 

data published in the 2011 CHDIR (6). This report on health 

insurance coverage discusses and raises awareness of differences 

in the characteristics of persons who lack health insurance 

coverage, and prompts actions to reduce these disparities.

Methods

To identify disparities in the lack of health insurance 

coverage for adults aged 18–64 years for different demographic 

and socioeconomic groups over time, CDC analyzed data 

from the 2008 and 2010 National Health Interview Survey 

(NHIS). NHIS is a cross-sectional survey of a representative 

sample of the civilian, noninstitutionalized U.S. household 

population. NHIS includes various questions on family health 

insurance (ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/

Survey_Questionnaires/NHIS/2010/English/qfamily.pdf). 

The 2010 NHIS included 27,157 respondents, of whom a 

total of 80 were excluded because of unknown insurance status. 

The overall response rate was 60.8%. The questionnaire begins 

with the question “are you/is anyone in the family covered 

by any kind of health insurance or some kind of health-care 

plan?” Respondents were considered uninsured if they did 

not have any private health insurance, Medicare, Medicaid, 

State Children’s Health Insurance Program coverage, statesponsored or other government-sponsored health plan, or a 

military health-care plan at the time of the interview. Persons 

also were considered uninsured if they reported having only 

Indian Health Service coverage or a private plan that paid for 

only one type of service (e.g., unintentional injuries or dental 

care). Rate of uninsured is the percentage of adults aged 18–64 

who did not have health insurance.

Disparities were examined by characteristics that included 

race and ethnicity, sex, age (adults aged 18–64 years), household 

income, disability status, and educational attainment. Poverty 

status was defined by using the ratio of income to the federal 

poverty level (FPL), in which “poor” is <1.0 times FPL, “near 

poor” is 1.0–2.9 times FPL, and “nonpoor” is ≥3.0 times FPL. 

Educational attainment was defined as less than high school, 

high school graduate or equivalent, some college, and college 

graduate or higher. Disability was defined as limitations in a 

person’s activity because of a health condition or impairment. 

Race was defined as white, black, American Indian/ Alaska 

Native, and other and multiple race. Ethnicity was defined as 

Hispanic or non-Hispanic.

Disparities were measured as deviations from a “referent” 

category for an uninsured rate; defined as the lowest percentage 

for a population group-specific without health insurance. 

Absolute difference was measured as the simple difference 

between an estimate for a population subgroup and the 

estimate for the referent category rate. The relative difference, 

a percentage, was calculated by dividing the absolute difference 

by the value in the referent category and multiplying by 100. 

The 95% confidence intervals for uninsured rates were estimated 

using statistical software (7). Pair-wise differences by sex, age 

Health Insurance Coverage — United States, 2008 and 2010

Ramal Moonesinghe, PhD1

Man-huei Chang, MPH2

Benedict I. Truman, MD3

1Office of Minority Health and Health Equity, Office of the Director, CDC

2Center for Surveillance, Epidemiology, and Laboratory Services, CDC

3National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, CDC

Corresponding author: Ramal Moonesinghe, PhD, Office of Minority Health and Health Equity, Office of the Director, CDC. Telephone: 770-488-8203; 

E-mail: zor7@cdc.gov.
Supplement

62 MMWR / November 22, 2013 / Vol. 62 / No. 3

group, race/ethnicity, disability status, educational achievement, 

and differences between 2008 and 2010 were tested by the 

z-statistic (one-tailed) at the 0.05 level of significance. A 

covariance of zero between estimates in conducting these tests 

was assumed. When testing differences within demographic 

groups, the Bonferroni method was used to account for multiple 

comparisons. If κ comparisons existed within a group, the level of 

significance was set to 0.05/κ. Estimates with a relative standard 

error of >20% were considered unreliable.

Results

During 2010, substantial disparities persisted in uninsured 

rates for all demographic and socioeconomic groups. Statistically 

significant disparities by sex (p<0.001) also continued during 

2010, with a higher percentage of males (24.1%) than females 

(18.8%) being uninsured (Table 1). The uninsured rate for 

young adults aged 18–34 years was approximately double the 

uninsured rate for adults aged 45–64 years (aged 18–34 years, 

28.5%; aged 45–64 years, 15.4%). Uninsured rates for all the 

age groups analyzed were significantly higher (p<0.001) than 

with adults aged 45–64 years.

During 2010, among adults aged 18–64 years, approximately 

two out of five persons of Hispanic ethnicity and one out of 

four non-Hispanic blacks were classified as uninsured. Both 

these groups had significantly higher (p<0.001) uninsured 

rates (41.0% and 26.2%, respectively), compared with 

Asians/Pacific Islanders and non-Hispanic whites (17.3% and 

16.1%, respectively). No significant difference in uninsured 

rates existed between non-Hispanic whites and Asians/Pacific 

Islanders. During 2010, approximately half of uninsured adults 

were non-Hispanic whites (Table 2). Hispanics accounted for 

29.3% of the uninsured population. The estimate of uninsured 

rate for non-Hispanic American Indian and Alaska Native 

persons was not reliable enough to make comparisons with 

estimates from other subpopulations.

TABLE 1. Percentage* of adults aged 18–64 years without health insurance, by selected demographic characteristics — National Health Interview 

Survey, United States, 2008 and 2010

Characteristic

2008 2010

% (95% CI)

Absolute 

difference 

(percentage points)

Relative 

difference 

(%) % (95% CI)

Absolute

 difference 

(percentage points)

Relative 

difference 

(%)

Sex

Male 22.2 (21.0–23.5) 4.9 28.3 24.1 (23.0–25.2) 5.3 28.5

Female 17.3 (16.2–18.3.) Ref. — 18.8 (17.8–19.7) Ref. Ref.

Age group (yrs)

18–24 27.9 (25.4–30.4) 14.3 105.1 29.8 (27.6–31.9) 14.4 93.5

25–34 26.6 (24.7–28.5) 13.0 95.6 27.2 (25.6–28.9) 11.8 76.6

35–44 18.7 (17.3–20.2) 5.1 37.5 21.4 (20.1–22.7) 6.0 39.0

45–64 13.6 (12.6–14.6) Ref. — 15.4 (14.5.–16.2) Ref. Ref.

Poverty status†

Poor 37.0 (34.0–40.0) 28.1 315.1 41.2 (38.9–43.5) 33.1 410.5

Near poor 30.5 (28.8–32.2) 21.6 242.2 34.2 (32.8–35.6) 26.1 323.6

Nonpoor 8.9 (8.1–9.8) Ref. — 8.1 (7.4–8.7) Ref. Ref.

Race/Ethnicity

Hispanic§ 41.6 (38.8–44.4) 27.6 197.1 41.0 (39.0–43.0) 24.9 154.2

White, non-Hispanic 14.6 (13.7–15.5) 0.6 4.3 16.1 (15.3–17.0) Ref. —

Black, non-Hispanic 22.1 (20.3–23.9) 8.1 57.9 26.2 (24.2–28.3) 10.1 62.6

American Indian/Alaska Native 33.7¶ — — 33.5¶ — — —

Asian/Pacific Islander 14.0 (11.2–16.9) Ref. — 17.3 (14.7–19.8) 1.2 7.1

Other, non-Hispanic other, and 

multiple race

20.1¶ — — — 21.5¶ — — —

Disability status

Persons with a disability 17.7 (16.4–19.0) Ref. — 19.6 (18.4–20.7) Ref. —

Persons without a disability 20.5 (19.4–21.5) 2.8 15.8 22.3 (21.4–23.1) 2.7 13.7

Educational attainment

Less than high school 40.5 (37.6–43.3) 32.4 400.0 42.8 (40.6–45.0) 34.8 432.2

High school graduate or equivalent 24.4 (22.8–26.1) 16.3 201.2 27.5 (26.1–28.9) 19.5 242.5

Some college 16.6 (15.4–17.7) 8.5 104.9 20.0 (18.8–21.2) 12.0 148.8

College graduate or higher 8.1 (7.1–9.0) Ref. Ref. 8.0 (7.2–8.8) Ref. —

Abbreviations: 95% CI = 95% confidence interval; Ref. = referent.

* Rate of uninsured is the percentage of adults aged 18–64 who did not have health insurance.

† Poor = ≤1.0 times the federal poverty level (FPL), near poor = 1.0–2.9 times FPL, and nonpoor = ≥3.0 times FPL. FPL was based on U.S. Census Bureau poverty 

thresholds, available at http://www.census.gov/hhes/www/poverty/html. § Persons of Hispanic ethnicity might be of any race or combination of races.

¶ Estimates are considered unreliable because the relative standard errors are >20%.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 63

From 2008 to 2010, uninsured rates increased significantly 

(p<0.05) for all groups considered in this report with the 

exception of persons with less than a high school diploma, 

college graduates, highest income group considered, Hispanics, 

and persons in age group 18–34 years. However, those with less 

than a high school diploma and Hispanics were groups with 

the highest uninsured rates. Chronically ill patients without 

insurance are more likely than those with coverage 1) not to 

have visited a health-care professional, and 2) either not to have 

a standard site for care or to identify their standard site of care 

as an emergency department (8). Because minority populations 

and the poor have high uninsured rates, these populations tend 

to visit the emergency department for nonurgent health care. 

Costly emergency department care could be saved if primary 

care were available to these populations (9).

Limitations

The findings in this report are subject to at least two 

limitations. First, health insurance coverage information 

in NHIS is self-reported and subject to recall bias. Second, 

because NHIS does not include institutionalized persons, the 

results are not generalizable to segments of the population 

that include prison inmates, military personnel, and adults in 

nursing homes and other long-term care facilities.

Conclusion

Disparities in health insurance coverage continue among all 

demographic and socioeconomic groups. Coverage expansion 

resulting from current or future reform of health insurance 

policies is likely to reduce disparities in uninsured rates. For 

example, after implementation of the 2010 Affordable Care Act, 

an estimated 6.6 million adults aged 19–25 years who might 

have been uninsured stayed on or joined their parents’ health 

plans between November 2010 and November 2011 (10).

References

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Affordable Care Act will help. Issue Brief (Commonwealth Fund) 2012 

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2011. MMWR 2011, 60;(Suppl; January 14, 2011).

5. CDC. Introduction. In: CDC health disparities and inequalities report—

United States, 2013. MMWR 2013; 62 (No. Suppl 3).

During 2010, among persons aged 18–64 years, uninsured 

rates for poor (those living at the federal poverty level [FPL]) 

and near poor persons (those at <3.0 times FPL) ranged from 

34.2% to 41.2%, and these rates were significantly higher 

(p<0.001) than the uninsured rate among the nonpoor 

(those at ≥3.0 FPL) (Table 1). Approximately half (50.7%) 

of uninsured adults were near poor (Table 2). During 2010, 

income for the near poor ranged from $22,314 to $66,942 

per year for a family of four. Uninsured rates for persons in the 

poor and near poor categories increased significantly (p<0.014) 

from 2008 (37.0% and 30.5%, respectively) to 2010 (41.2% 

and 34.2%, respectively). The uninsured rate for non-Hispanic 

blacks also increased significantly (p<0.002) from 22.1% in 

2008 to 26.2% in 2010. No significant difference existed in 

the uninsured rate between 2008 (41.6%) and 2010 (41.0%) 

for the Hispanic population (Table 1).

Regarding educational attainment, when compared with 

college graduates, all groups continued to have significantly higher 

uninsured rates (p<0.001). From 2008 to 2010, uninsured rates 

for graduates from high school and with some college education 

increased significantly (p<0.003). The uninsured rate for persons 

without a disability (22.3%) also remained significantly higher 

(p<0.001) than persons with a disability (19.6%).

Discussion

During 2010, similar to the disparities observed in 2004 

and 2008 (6), substantial disparities persisted in uninsured 

rates for all demographic and socioeconomic groups. 

TABLE 2. Number and percentage of adults aged 18–64 years without 

health insurance, by poverty status and race/ethnicity — National 

Health Interview Survey, United States, 2010

Characteristic No. (% of total)

% without 

health 

insurance

Poverty status*

Poor 11,078,526 (30.8) 41.2

Near poor 18,246,425 (50.7) 34.2

Nonpoor 6,641,720 (18.5) 8.1

Total 35,966,671§ (100.0) —

Race/Ethnicity

Hispanic† 11,957,253 (29.3) 41.0

White, non-Hispanic 20,130,159 (49.5) 16.1

Black, non-Hispanic 6,097,277 (15.0) 26.2

American Indian/Alaska Native 363,140 (0.9) 33.5

Asian/Pacific Islander 1,575,972 (3.9) 17.3

Other, non-Hispanic and 

multiple race 560,459 (1.4) 21.5

Total 40,684,260§ (100.0) —

* Poor = ≤1.0 time the federal poverty level (FPL), near poor = 1.0 2.9 times FPL, 

and nonpoor = ≥3.0 times FPL. FPL was based on U.S. Census Bureau poverty 

thresholds, available at http://www.census.gov/hhes/www/poverty/html. † Persons of Hispanic ethnicity might be of any race or combination of races.

§ Totals are different because of unknown poverty status.
Supplement

64 MMWR / November 22, 2013 / Vol. 62 / No. 3

6. CDC. Health insurance coverage—United States, 2004 and 2008. In: 

CDC health disparities and inequalities report—United States, 2011. 

MMWR 2011; (Suppl; January 14, 2011).

7. SAS Institute, Inc. SAS version 9.02. Cary, NC: SAS Institute, Inc.; 2.

8. Wilper AP, Woolhandler S, Lasser KE, et al. A national study of chronic 

disease prevalence and access to care in uninsured US adults. Ann Intern 

Med 2008;149:170–6.

9. Gill JM, Arch GM, Musa N. The effect of continuity of care on 

emergency department use. Arch Fam Med 2000;9:333–8.

10. Collins SR, Robertson R, Garber T, Doty MM. Young, uninsured, and in 

debt: why young adults lack health insurance and how the Affordable Care 

Act is helping. Issue Brief (Commonwealth Fund) 2012. Available at http://

www.commonwealthfund.org/Publications/Issue-Briefs/2012/Jun/YoungAdults-2012.aspx. 
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 65

Introduction

Infection with influenza viruses can cause severe morbidity 

and mortality among all age groups. Children, particularly 

those aged <5 years (1–3), have the highest incidence of 

infection during epidemic periods; however, the highest rates of 

influenza-associated hospitalizations and deaths are among the 

elderly (aged ≥65 years), children aged <2 years, and those of 

any age with underlying medical conditions (1,4,5). Each year, 

influenza-related complications are estimated to result in more 

than 226,000 hospitalizations (6). During 1976–2006, estimates 

of influenza-associated deaths in the United States ranged from 

approximately 3,000 to an estimated 49,000 persons (7,8) 

(http://www.cdc.gov/flu/keyfacts.htm#howserious). Annual 

vaccination is the most effective strategy for preventing influenza 

virus infection and its complications (9).

Racial and ethnic disparities in seasonal influenza vaccination 

coverage have been observed in previous influenza seasons among 

children and adults (10). This summary updates the evaluation 

of these disparities among all persons aged ≥6 months, previously 

reported for the 2000–01 through the 2009–10 season (10), 

with findings from the 2010–11 influenza season and compares 

coverage in 2009–10 and 2010–11. For the 2010–11 influenza 

season, the Advisory Committee on Immunization Practices 

(ACIP) expanded its recommendations to include annual 

influenza vaccination of all persons aged ≥6 months (11). For 

the first time, the 2010–11 ACIP flu season recommendations 

included healthy adults aged 18–49 years.

This report is part of the second CDC Health Disparities and 

Inequalities Report (CHDIR) (12). The 2011 CHDIR (13) was 

the first CDC report to assess disparities across a wide range of 

diseases, behavioral risk factors, environmental exposures, social 

determinants, and health-care access. The criteria for inclusion 

of topics that are presented in the 2013 CHDIR are described 

in the 2013 CHDIR Introduction (14). This report provides 

an update on the progress of influenza vaccination coverage in 

the United States, by age, race/ethnicity, and risk status. The 

purposes of this report on influenza vaccination are to discuss and 

raise awareness of differences in the characteristics of populations 

who received influenza vaccination, and to prompt actions to 

reduce disparities.

Methods

To estimate the progress of influenza vaccination coverage 

in the United States, by age, race/ethnicity, and risk status, 

various data sources were used. Age groups were defined as 

aged <45 years, 45–74 years, <75 years, ≥75 years, and ≥85 

years. Race was defined as white, black, Asian/Pacific Islander 

(A/PI), American Indian/Alaska Native (AI/AN), and other 

and multiple race. Ethnicity was defined as Hispanic or nonHispanic. Race/ethnicity categories are mutually exclusive. 

For the 2009–10 season, high risk conditions included 

asthma, other lung problems, diabetes, heart disease, kidney 

problems, anemia, and weakened immune system caused by a 

chronic illness or by medicines taken for a chronic illness. For 

the 2010–11 season, high risk conditions included asthma, 

diabetes, and heart disease. Other medical conditions that place 

persons at increased risk for complications from influenza (11) 

were not under surveillance for this report. For this update, 

vaccination by household income, educational attainment, 

poverty status, disability status, and geographic location were 

not analyzed. Data on place of birth was not available.

To estimate the proportion of persons aged ≥6 months 

who received influenza vaccination during the 2009–10 

influenza season, combined data from the National 2009 

H1N1 Flu Survey (NHFS) and the Behavioral Risk Factor 

Surveillance System (BRFSS) were used. The NHFS included 

children identified from the National Immunization Survey 

(NIS) and from a stand-alone telephone survey. To estimate 

the proportion of children aged 6 months through 17 years 

who received influenza vaccination during the 2010–11 

influenza season, data from the NIS were used. To estimate 

the proportion of adults aged ≥18 years who received influenza 

vaccination during the 2010–11 influenza season, BRFSS data 

were used. Both NIS and BRFSS collected monthly data on 

vaccinations reported during August 2010 through May 2011 

for all 50 states and the District of Columbia.

Seasonal Influenza Vaccination Coverage — 

United States, 2009–10 and 2010–11

Anne F. McIntyre, PhD

Amparo G. Gonzalez-Feliciano, MPH

Leah N. Bryan, MPH,

Tammy A. Santibanez, PhD

Walter W. Williams, MD

James A. Singleton, PhD

National Center for Immunization and Respiratory Diseases, CDC

Corresponding author: Anne F. McIntyre, PhD, National Center for Immunization and Respiratory Diseases, CDC. Telephone: 404-639-8284; E-mail: 

AMcIntyre@cdc.gov.
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66 MMWR / November 22, 2013 / Vol. 62 / No. 3

Comparisons between the 2010–11 and 2009–10 seasons 

used estimates for the recommended trivalent seasonal 

vaccines (11,15), and all 2009–10 estimates in this report are 

for trivalent seasonal vaccination, although for the 2009–10 

seasons, two vaccines were recommended: the trivalent seasonal 

vaccine (15), along with the influenza A(H1N1)pdm09 

monovalent vaccine to provide immunity against the pandemic 

strain that emerged in 2009 (16). Coverage estimates for all 

persons aged ≥6 months were determined using combined 

state-level monthly estimates weighted by the age-specific 

populations of each state. In 2009–10, the unweighted sample 

sizes for children aged 6 months through 17 years and persons 

≥18 years were 149,872 and 361,485, respectively (http://www.

cdc.gov/flu/professionals/vaccination/coverage_0910estimates.

htm). For 2010–11, the unweighted sample size for children 

aged 6 months through 17 years was 116,799 and 377,569 

for persons ≥18 years (http://www.cdc.gov/flu/professionals/

vaccination/coverage_1011estimates.htm). 

Disparities were measured as the deviations from a “referent” 

category cumulative proportion. Absolute difference was 

measured as the simple difference between a population 

subgroup estimate and the estimate for its respective reference 

group. A description of the methods for estimating national 

influenza vaccination coverage and comparing coverage by 

age group and race/ethnicity has been published previously 

(10). The same statistical methods were used for both seasons 

(2009–10 and 2010–11). Estimates were suppressed if the 

sample size was <30 or the relative standard error was >0.3. 

Student t tests were used to determine statistical significance in 

differences between groups and between 2009–10 and 2010–11 

vaccination coverage levels with significance defined as p<0.05. 

Only statistically significant results are highlighted in this report.

Results

Overall, influenza vaccination coverage was two percentage 

points higher for the 2010–11 season versus the 2009–10 

season (43.0% versus 41.2%, respectively), primarily because 

of an increase in vaccine coverage among children aged 6 

months–17 years (51.0% versus 43.7%, respectively) (Table). 

Vaccine coverage increased significantly among four groups 

of children: Hispanic and non-Hispanic whites, blacks, and 

those of other/multiple races. During the 2010–11 seasons, 

compared with non-Hispanic white children, coverage among 

Hispanic, Asian/Pacific Islander, and children of other and 

multiple races was higher (Table).

Overall, influenza vaccination coverage among adults aged ≥18 

years remained relatively stable, at 40.4% during 2009–10 and 

40.5% during the 2010–11 influenza season (Table). Among 

those aged 18–49 years (regardless of risk status) and 50–64 

years, coverage was similar in both seasons. However, among 

adults aged ≥65 years, coverage decreased from 69.6% to 66.6%.

During 2010–11, among all adults, including persons aged 

18–49 overall, 50–64, and ≥65 years, coverage remained lower 

among non-Hispanic blacks (28.1%, 38.4%, and 56.1%, 

respectively) than among non-Hispanic whites (31.6%, 45.7%, 

67.7%, respectively). Coverage also was lower among Hispanic 

adults aged 18–49 and 50–64 years (27.1% and 41.9%, 

respectively) than among non-Hispanic whites (31.6% and 

45.7%, respectively). During 2010–11, coverage was similar 

among Hispanics and non-Hispanic whites aged ≥65 years; 

however, compared with 2009–10, coverage decreased by 4.0 

percentage points among non-Hispanic whites and increased 

by 10.7 percentage points among Hispanics (Table).

Discussion

Overall, influenza vaccination coverage estimates were 

significantly higher during the 2010–11 season than during the 

2009–10 season because of an increase in vaccinations among 

children. Coverage among non-Hispanic black and Hispanic 

children has improved, and is either similar to, or slightly higher 

than, coverage among non-Hispanic white children. Efforts to 

improve coverage are ongoing. The federally funded Vaccines 

for Children program provides vaccines at no cost to children 

who might not otherwise be vaccinated because of inability to 

pay (17). Community demand for influenza vaccination can be 

increased by client reminder and recall systems (18). Provider 

and systems-based interventions (e.g., provider assessment and 

feedback, and use of immunization information systems) also can 

increase vaccination coverage (http://www.thecommunityguide.

org/vaccines/universally/index.html) (17,18).

Among adults aged ≥65 years, influenza vaccination coverage 

was lower among non-Hispanic blacks than all other racial/ethnic 

groups, suggesting that additional efforts to reach this population 

are needed. Interventions (18) to provide all ACIP recommended 

vaccinations throughout the lifespan could be a step toward 

increasing coverage and addressing disparities among adults.

The revised ACIP recommendations to vaccinate all persons 

aged ≥6 months were in place for the entire 2010–11 influenza 

season, but did not appear to have an effect on coverage 

among those aged 18–49 years (regardless of risk status) 

compared with the previous season. Additional promotion 

of or education about the expanded recommendations might 

increase coverage in this age group. Promising strategies might 

include 1) expanding access through nontraditional settings 

(e.g., pharmacy, workplace, and school venues) for vaccination 

to reach persons who might not visit a traditional provider 

during the flu season; 2) improving the use of evidence-based 

practices at medical sites (e.g., standing orders, reminder/recall 
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MMWR / November 22, 2013 / Vol. 62 / No. 3 67

TABLE. Seasonal influenza vaccination coverage,* by race/ethnicity† — Behavorial Risk Factor Surveillance System, National 2009 H1N1 Flu 

Survey, and National Immunization Survey, United States, 2009–2010 and 2010–2011.

Race/Ethnicity by age group

2009–2010 2010–2011

Coverage difference 

from 2009–10 to 

2010–11 

% (95% CI) (percentage points)

Absolute 

difference§

(percentage 

points) % (95% CI)

Absolute 

difference§ 

(percentage 

points)

≥6 mos

Total 41.2 (40.8–41.6) 43.0¶ (42.6–43.4) 1.8††

White, non-Hispanic 43.9 (43.5–44.3) Ref. 44.3 (43.9–44.7) Ref. 0.4

Black, non-Hispanic 33.7 (32.5–34.9) -10.2†† 39.0 (37.5–40.5) -5.3†† 5.3††

Hispanic 33.6 (32.4–34.8) -10.3†† 40.0 (38.6–41.4) -4.3†† 6.4††

Asian/Pacific Islander 44.3 (42.0–46.6) 0.4 43.1 (40.3–45.9) -1.2 -1.2

American Indian/Alaska Native 46.3 (43.7–48.9) 2.4 42.1 (38.1–46.1) -2.2 -4.2

Other and multiple race 38.6 (36.6–40.6) -5.3†† 42.9 (40.4–45.4) -1.4 4.3††

6 mos–17 yrs

Total 43.7 (42.8–44.6) 51.0¶ (50.1–51.9) 7.3††

White, non-Hispanic 43.2 (42.3–44.1) Ref. 48.5¶ (47.5–49.5) Ref. 5.3††

Black, non-Hispanic 37.0 (34.4–39.6) -6.2†† 50.8¶ (47.9–53.7) 2.3 13.8††

Hispanic** 46.9 (44.3–49.5) 3.7†† 55.1¶ (52.5–57.7) 6.6†† 8.2††

Asian/Pacific Islander 56.1 (52.4–59.8) 12.9†† 59.4 (54.7–64.1) 10.9†† 3.3

American Indian/Alaska Native 51.7 (47.0–56.4) 8.5†† 55.7 (47.7–63.7) 7.2 4.0

Other and multiple race 49.7 (45.7–53.7) 6.5†† 55.6¶ (51.5–59.7) 7.1†† 5.9††

≥18 yrs

Total 40.4 (40.0–40.8) 40.5 (40.1–40.9) 0.1

18–49 yrs

All, including high risk 29.9 (29.4–30.4) 30.5 (29.9–31.1) 0.6

White, non-Hispanic 31.9 (31.3–32.5) Ref. 31.6 (30.8–32.4) Ref. -0.3

Black, non-Hispanic 25.3 (23.6–27.0) -6.6†† 28.1 (25.7–30.5) -3.5†† 2.8

Hispanic 24.7 (23.3–26.1) -7.2†† 27.1 (25.1–29.1) -4.5†† 2.4

Asian/Pacific Islander 35.5 (32.2–38.8) 3.6†† 33.4 (29.5–37.3) 1.8 -2.1

American Indian/Alaska Native 39.3 (35.3–43.3) 7.4†† 31.3 (25.2–37.4) -0.3 -8.0††

Other and multiple race 27.9 (25.0–30.8) -4.0†† 32.1 (27.8–36.4) 0.5 4.2

High risk only§§ 38.2 (36.9–39.5) 39.0 (36.8–41.2) 0.8

White, non-Hispanic 39.9 (38.3–41.5) Ref. 39.2 (36.8–41.6) Ref. -0.7

Black, non-Hispanic 34.8 (31.5–38.1) -5.1†† 37.1 (30.2–44.0) -2.1 2.3

Hispanic 35.9 (32.0–39.8) -4.0 37.3 (30.8–43.8) -1.9 1.4

Asian/Pacific Islander 42.9 (32.3–3.5)§§ 3.0 34.0 (21.5–6.5)¶¶ -5.2 -8.9

American Indian/Alaska Native 45.8 (38.1–53.5) 5.9 40.3 (25.8–54.8)¶¶ 1.1 -5.5

Other and multiple race 36.8 (30.7–42.9) -3.1 45.5 (35.7–55.3) 6.3 -8.9

50–64 yrs

Total 45.0 (44.4–45.6) 44.5 (43.9–45.1) -0.5

White, non-Hispanic 46.5 (45.9–47.1) Ref. 45.7 (44.9–46.5) Ref. -0.8

Black, non-Hispanic 40.3 (38.3–42.3) -6.2†† 38.4 (36.0–40.8) -7.3†† -1.9

Hispanic 40.3 (37.5–43.1) -6.2†† 41.9 (38.6–45.2) -3.8†† 1.6

Asian/Pacific Islander 48.8 (42.6–55.0) 2.3 49.3 (43.6–55.0) 3.6 0.5

American Indian/Alaska Native 48.6 (44.2–53.0) 2.1 44.6 (37.9–51.3) -1.1 -4.0

Other and multiple race 39.2 (35.7–42.7) -7.3†† 40.5 (36.2–44.8) -5.2†† 1.3

≥65 yrs

Total 69.6 (69.0–70.2) 66.6¶ (66.0–67.2) -3.0††

White, non-Hispanic 71.7 (71.2–72.2) Ref. 67.7¶ (67.1–68.3) Ref. -4.0††

Black, non-Hispanic 55.1 (52.8–57.4) -16.6†† 56.1 (52.8–59.4) -11.6†† 1.0

Hispanic 56.1 (52.8–59.4) -15.6†† 66.8¶ (63.1–70.5) -0.9 10.7††

Asian/Pacific Islander 70.7 (65.1–76.3) -1.0 67.9 (61.6–74.2) 0.2 -2.8

American Indian/Alaska Native 61.6 (56.1–67.1) -10.1†† 68.7 (60.7–76.7) 1.0 7.1

Other and multiple race 64.2 (60.1–68.3) -7.5†† 60.7 (56.4–65.0) -7.0†† -3.5

Abbreviations: 95% CI = 95% confidence interval; Ref = referent. 

* Coverage estimates for 2010–2011 are for persons with reported vaccination during August 2010–May 2011 who were interviewed during September 2010–June 

2011. Coverage estimates for 2009–2010 are for persons with reported vaccination during August 2009–May 2010 who were interviewed during October 2009–June 

2010; estimates for 2009–2010 included data from NHFS; season estimates for 2010–2011 use NIS only for children and BRFSS only for adults.

† Race/ethnicity categories are mutually exclusive; Native Hawaiians, Pacific Islanders, and persons of other or multiple races were classified in the “Other and multiple race” group. 

§ Absolute difference (percentage points): (percentage racial/ethnic group of interest) - (percentage white only, non-Hispanic).

¶ Estimated vaccination coverage for the 2010–2011 season is significantly different from the 2009–2010 season (referent) at (p<0.05).

** Persons of Hispanic ethnicity might be of any race or combination of races.

†† Estimated vaccination coverage is significantly different from the white only, non-Hispanic population (referent) within age group at (p<0.05).

§§ For the 2010–2011 season, high risk conditions included asthma, diabetes, and heart disease. For the 2009–2010 season, high risk conditions included asthma, other 

lung problems, diabetes, heart disease, kidney problems, anemia, and weakened immune system caused by a chronic illness or by medicines taken for a chronic illness.

¶¶ Estimates might be unreliable because the confidence interval half-width is >10.
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68 MMWR / November 22, 2013 / Vol. 62 / No. 3

notification, and provider recommendation) to ensure that all 

persons who visit a health-care provider during the flu season 

receive a vaccination recommendation and offer; or 3) using 

immunization information systems, also known as registries, 

at the point of clinical care and at the population level to guide 

clinical and public health vaccination decisions (18).

Limitations

The findings in this report are subject to at least five 

limitations. First, children aged 6 months to <9 years are 

recommended for up to 2 doses of vaccine depending on past 

vaccination history (11); however, this report only measured 

receipt of at least 1 dose for children of all ages. Second, the 

estimates are made on the basis of self-report for adults and 

parental-report for children, and were not validated by medical 

record reviews. Racial/ethnic disparities also might differ on the 

basis of parent versus provider report, child’s age, and whether 

receipt of 1 dose or full vaccination status is measured; previous 

studies have shown racial/ethnic disparities in influenza 

vaccination coverage of children aged 6–23 months on the basis 

of provider-reported data for full vaccination; most children 

in this age group would need 2 doses to be considered fully 

vaccinated (19,20). Third, the sample might not be nationally 

representative because of incomplete sample frames (e.g., NIS 

and BRFSS surveys miss households without phones), and 

selection bias from survey nonresponse might remain after 

weighting adjustments (1,17,21,22). Fourth, misclassification 

of 2009 H1N1 vaccine for seasonal influenza vaccine, unique 

to this season, might have contributed to some overreporting. 

Finally, comparisons of estimates during 2009–10 and 2010–

11 might be affected by different data sources used: NHFS and 

BRFSS for both children and adults for 2009–10, and NIS for 

children and BRFSS for adults in 2010–11.

Conclusion

Compared with the 2009–10 season, estimates for 2010–11 

suggest that progress was made in increasing coverage among 

non-Hispanic white, black, Hispanic, and other and multiple 

race children. In contrast with the past, in which non-Hispanic 

white children generally had the highest coverage, estimates 

for both seasons indicated that Hispanic and A/PI children 

and those of other/multiple races had better coverage than 

non-Hispanic white children. Despite these improvements 

in coverage among historically underserved groups, Healthy 

People 2020 targets for influenza vaccination of children and 

adults—to increase the percentage of children aged 6 months 

through 17 years and adults aged ≥18 years vaccinated to 

70%—were not achieved. Efforts are needed to continue 

improving coverage for all persons (18–23).

References

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2. Glezen WP, Couch RB. Interpandemic influenza in the Houston area, 

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infections on persons with chronic underlying conditions. JAMA 

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aged 19–35 months—United States, 2009. MMWR 2010;59:1171–7.

18. Task Force on Community Preventive Services. Recommendations 

regarding interventions to improve vaccination coverage in children, 

adolescents, and adults. Am J Prev Med 2000;18:92–6. 

19. Santibanez TA, Santoli JM, Bridges CB, Euler GL. Influenza vaccination 

coverage of children aged 6 to 23 months: the 2002–2003 and 2004 

influenza seasons. Pediatrics 2006;118:1167–75. 

20. Yoo BK, Berry A, Kasajima M, Szilagyi PG. Association between 

Medicaid reimbursement and child influenza vaccination rates. Pediatrics 

2010;126:e998–1010. 

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22. Lavrakas PJ, Blumberg S, Battaglia M, et al. New considerations for 

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Behavioral Risk Factors
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MMWR / November 22, 2013 / Vol. 62 / No. 3 71

Introduction

Pregnancy and childbirth among females aged <20 years 

have been the subject of long-standing concern among the 

public, the public health community, and policy makers (1–3). 

Teenagers who give birth are much more likely than older 

women to deliver a low birthweight or preterm infant, and 

their babies are at higher risk for dying in infancy (4–6). The 

annual public costs associated with births among teenage girls 

are an estimated $10.9 billion (7). According to the 2006–2010 

National Survey of Family Growth (NSFG), an estimated 77% 

of births to teenagers aged 15–19 years were unintended (8).

The 2010 U.S. birth rate among females aged 15–19 was 

34.2 births per 1,000. This is a 10% decrease from 2009 

(37.9) and an 18% decrease from 2007 (41.5) (9). A longterm decrease that began in 1991 was continuous except for 

a brief increase during 2005–2007; the birth rate among 

females aged 15–19 years decreased by 45% from 1991 (61.8 

per 1,000) to 2010 (9,10). An analysis found that if the 1991 

birth rates for females aged 15–19 years had remained the same 

during 1992–2010, an additional 3.4 million births would 

have occurred among women aged 15–19 years in the United 

States (11). Significant decreases in birth rates for females aged 

15–19 years occurred among all race and Hispanic ethnicity 

groups from 2007 to 2010, including non-Hispanic whites, 

non-Hispanic blacks, American Indian/Alaska Natives (AI/

ANs), Asians or Pacific Islanders (A/PIs), and Hispanics. Rates 

also decreased for certain Hispanic groups, including those 

of Mexican and Puerto Rican origin. Despite the widespread 

decreases, disparities persist (9,11), and the U.S. birth rate for 

females aged 15–19 years remains one of the highest among 

industrialized countries (12).

This report is part of the second CDC Health Disparities 

and Inequalities Report (CHDIR) and updates information 

presented in the first CHDIR (13). The 2011 CHDIR (14) was 

the first CDC report to assess disparities across a wide range of 

diseases, behavioral risk factors, environmental exposures, social 

determinants, and health-care access. The topics presented in 

this report are based on criteria that are described in the 2013 

CHDIR Introduction (15). The purposes of this pregnancy 

and childbirth analysis and discussion are to highlight and raise 

awareness of differences in the characteristics of females aged 

<20 years (including 10–14, 15–19, 15–17, and 18–19 years) 

who become pregnant and give birth and to prompt actions 

to reduce these disparities.

Methods

To analyze recent trends and variations in birth rates and 

pregnancy rates by selected characteristics among females aged 

10–19 years, CDC examined final 2007 and 2010 natality 

data from the National Vital Statistics System (NVSS) and 

comparable data for earlier years. Characteristics analyzed 

varied by rate calculated and included four age groups (10–14, 

15–19, 15–17, and 18–19 years), race, ethnicity, and state, 

including the District of Columbia. Household income and 

educational attainment were not analyzed because income 

information is not collected on the birth certificate, and data 

on educational attainment are collected in different ways across 

the states. Thus, national data on educational attainment are 

not available. 

Data by maternal race and Hispanic ethnicity are based 

on information reported by the mother during the birth 

registration process. Race and ethnicity are reported separately 

on birth certificates, and persons of Hispanic origin might be of 

any race. Race categories are consistent with the 1977 Office of 

Management and Budget standards (4,9). In 2010, a total of 38 

states and the District of Columbia reported multiple-race data 

that were bridged to the single-race categories for comparability 

with other states (9). Population estimates with bridged-race 

categories for the rates in this report were produced under a 

collaborative arrangement with the U.S. Census Bureau. Rates 

for 2010 are based on the 2010 U.S. census, and rates for earlier 

years are based on intercensal estimates (9,16,17). Rates are 

not shown when the number of births in a given group is <20 

or, for specified Hispanic groups, if <50 females are in the 

denominator in the census year 2010 or <75,000 females are 

in the denominator for all other years.

Birth rates were calculated as the number of births to females 

per 1,000 female population in the specified age, race, and 

Pregnancy and Childbirth Among Females Aged 10–19 Years — 

United States, 2007–2010

Stephanie J. Ventura, MA

Brady E. Hamilton, PhD

T.J. Mathews, MS

National Center for Health Statistics, CDC

Corresponding author: Stephanie J. Ventura, Division of Vital Statistics, National Center for Health Statistics, CDC. Telephone: 301-458-4547; 

E-mail: sventura@cdc.gov.
Supplement

72 MMWR / November 22, 2013 / Vol. 62 / No. 3

Hispanic origin group. The rate in 2010 was compared with 

2007 and previously published rates for 1991. The change in 

birth rate over time (i.e., relative difference) was calculated 

by subtracting the rate at the end of the period from the rate 

at the beginning of the period, dividing the difference by the 

rate at the beginning of the period, and then multiplying by 

100. Percentage change for birth rates in 2007 and 2010 was 

assessed for statistical significance by using the z test at the 

95% confidence level (18). Disparities were measured as the 

deviations from a referent category rate. Absolute difference 

was measured as the simple difference between a population 

group estimate and the estimate for its respective reference 

group. The relative difference, a percentage, was calculated by 

dividing the absolute difference by the value in the referent 

category and multiplying by 100.

Pregnancy rates are presented by pregnancy outcome (live 

births, induced abortions, and fetal losses), race, and Hispanic 

ethnicity. Data on live births are based on complete counts 

of births provided by every state to CDC through the Vital 

Statistics Cooperative Program of NVSS (18). Abortion 

estimates are from abortion surveillance information on the 

characteristics of women who have abortions collected from 

most states by CDC; these estimates are adjusted to national 

totals by the Guttmacher Institute (19–22). Fetal loss rates 

are derived from pregnancy history data collected from several 

cycles of the National Survey of Family Growth (NSFG) 

conducted by CDC (22,23). Data regarding pregnancy are 

not as current, complete, or comprehensive as NVSS data 

regarding births. The most recent pregnancy estimates that 

include data on live births, induced abortions, and fetal losses 

are for 2008 (22).

Results

In 2010, birth rates for females aged 15–19 years varied 

considerably by race and Hispanic origin (Table 1). The rates 

for Hispanics (55.7 births per 1,000 females aged 15–19 years) 

and non-Hispanic blacks (51.5 births) were approximately five 

times the rate for A/PIs (10.9 births) and approximately twice 

the rate for non-Hispanic whites (23.5 births). The rate for AI/

ANs aged 15–19 years was intermediate (38.7 births per 1,000 

females aged 15–19 years). Rates varied considerably across 

specified Hispanic groups. The rate in 2010 was highest for 

“other” Hispanics aged 15–19 years (65.4 births per 1,000), 

followed by Mexican (55.5 births), Puerto Rican (43.7 births), 

and Cuban (24.4 births). 

From 2007 to 2010, birth rates for females aged 15–19 

years decreased significantly for all race groups and for nearly 

all specified Hispanic groups (Table 1). Decreases for females 

aged 15–19 years ranged from 14% to 17% for non-Hispanic 

whites and non-Hispanic blacks, respectively, to 32% for 

Mexicans. Among females aged 15–17 years, significant 

decreases from 2007 to 2010 ranged from 16% to 21% among 

non-Hispanic whites and non-Hispanic blacks, respectively, to 

35% for Mexicans. Among females aged 18–19 years, decreases 

ranged from 16% to 19% among non-Hispanic whites and 

non-Hispanic blacks, respectively, to 30% for Mexicans. The 

trends cannot be reliably analyzed for Cubans because the 

numbers of births were too few and for Puerto Ricans aged 

18–19 years because the estimated number of females in this 

age group in 2007 was <75,000.

In 2010, birth rates among females aged 15–19 years by 

state ranged from <20 per 1,000 females aged 15–19 years 

in four states (New Hampshire [15.7 births], Massachusetts 

[17.2 births], Vermont [17.9 births], and Connecticut [18.7 

births]) to 50 per 1,000 or more in five states (Oklahoma 

[50.4 births], Texas [52.2 births], Arkansas [52.5 births], New 

Mexico [53.0 births], and Mississippi [55.0 births]) (Table 2) 

(9,11). From 2007 to 2010, rates decreased significantly in all 

but three states (Montana, North Dakota, and West Virginia). 

Decreases in 16 states ranged from 20% to 30%.

The pregnancy rate for teenagers aged 15–19 years was 69.8 

per 1,000 in 2008. The rates by pregnancy outcome were 

40.2 for live births, 17.8 for induced abortions, and 11.8 for 

fetal losses; substantial demographic differences were found 

in these rates (Table 3) (22). Within each age group from 

10–14 years through 18–19 years, pregnancy rates among 

non-Hispanic black and Hispanic females were two to three 

times higher than rates for non-Hispanic white females (22). 

The rate decreased by 3% from 71.9 pregnancies per 1,000 

females aged 15–19 years in 2007 to 69.8 per 1,000 in 2008 

(Table 3) (22). Pregnancy rates have decreased for females 

aged 10–14, 15–17, and 18–19 years and for non-Hispanic 

white, non-Hispanic black, and Hispanic females aged 15–19, 

15–17, and 18–19 years. 

Discussion

In 2010, the U.S. birth rate for females aged 15–19 years 

had decreased 45% since the 1991 peak (from 61.8 in 1991 

to 34.2 in 2010) (9–11) Trends in birth rates by age and by 

race and Hispanic ethnicity group indictae that the long-term 

reductions since 1991 have been experienced by all population 

groups but were somewhat greater for certain groups (9–11). 

The birth rate for females aged 10–14 years decreased 71%, 

from 1.4 per 1,000 in 1991 to 0.4 in 2010; rates for those 

aged 10–14 years decreased approximately 60% in each racial 

and Hispanic origin group. The birth rate for all females aged 
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 73

TABLE 1. Birth rates for females aged 10–19 years, by age, race/ethnicity, and Hispanic origin of mother — National Vital Statistics System, 

United States, 2007 and 2010

Characteristic

2007 2010

Change in rate 

from 2007 to 

2010

(%)† Birth rate*

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%) Birth rate

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%)

Ages 10–14 yrs

All races/ethnicities§ 0.6 — — 0.4 — — -33

White, non-Hispanic 0.2 Ref. Ref. 0.2 Ref. Ref. 0

Black, non-Hispanic 1.4 1.2 600 1.0 0.8 400 -29

Asian/Pacific Islander 0.2 0.0 0 0.1 -0.1 -50 -50

American Indian/Alaska Native 0.7 0.5 250 0.5 0.3 150 -29

Hispanic 1.2 1.0 500 0.8 0.6 300 -33

Mexican 1.2 1.0 500 0.8 0.6 300 -33

Puerto Rican 0.8 0.6 300 0.6 0.4 200 -25

Cuban NA¶ NA NA NA NA NA NA

Other Hispanic** 1.2 1.0 500 1.0 0.8 400 -17

Ages 15–19 yrs

All races/ethnicities 41.5 — — 34.2 — — -18

White, non-Hispanic 27.2 Ref. Ref. 23.5 Ref. Ref. -14

Black, non-Hispanic 62.0 34.8 128 51.5 28.0 119 -17

Asian/Pacific Islander 14.8 -12.4 -46 10.9 -12.6 -54 -26

American Indian/Alaska Native 49.4 22.2 82 38.7 15.2 65 -22

Hispanic 75.3 48.1 177 55.7 32.2 137 -26

Mexican 86.6 54.5 200 55.5 32.0 136 -32

Puerto Rican 61.8 34.6 127 43.7 20.2 86 -29

Cuban NA NA NA 24.4 0.9 4 NA

Other Hispanic 68.1 40.9 150 65.4 41.9 178 -4

Ages 15–17 yrs

All races/ethnicities 21.7 — — 17.3 — — -20

White, non-Hispanic 11.9 Ref. Ref. 10.0 Ref. Ref. -16

Black, non-Hispanic 34.6 22.7 191 27.4 17.4 174 -21

Asian/Pacific Islander 7.4 -4.5 -38 5.1 -4.9 -49 -31

American Indian/Alaska Native 26.2 14.3 120 20.1 10.1 101 -23

Hispanic 44.4 32.5 273 32.3 22.3 223 -27

Mexican 49.9 38.0 319 32.4 22.4 224 -35

Puerto Rican 32.8 20.9 176 24.2 14.2 142 -26

Cuban NA NA NA 8.7 -1.3 -13 NA

Other Hispanic 38.8 26.9 226 38.6 28.6 286 -1

Ages 18–19 yrs

All races/ethnicities 71.7 — — 58.2 — — -19

White, non-Hispanic 50.4 Ref. Ref. 42.5 Ref. Ref. -16

Black, non-Hispanic 105.2 54.8 109 85.6 43.1 101 -19

Asian/Pacific Islander 24.9 -25.5 -51 18.7 -23.8 -56 -25

American Indian/Alaska Native 86.4 36.0 71 66.1 23.6 56 -23

Hispanic 124.7 74.3 147 90.7 48.2 113 -27

Mexican 130.6 80.2 159 91.5 49.0 115 -30

Puerto Rican NA NA NA 69.7 27.2 64 NA

Cuban NA NA NA 57.8 15.3 36 NA

Other Hispanic 113.4 63.0 125 101.3 58.8 138 -11

Abbreviations: NA = not available; Ref. = referent.

* Per 1,000 females in specified age, race, and ethnicity group. Reliable birth rates cannot be computed for Cuban women in these age groups except in U.S. census years.

† Statistical testing for significance was assessed by using the z test at the 95% confidence level. All changes are significant (p<0.05) except for the “other Hispanics” 

category of girls aged 15–17 years.

§ Data for persons of Hispanic origin are included in the data for each racial group according to the mother’s reported race. Race and Hispanic origin are reported 

separately on birth certificates. Race categories are consistent with the 1977 Office of Management and Budget standards (available at http://www.whitehouse.

gov/omb/fedreg_race-ethnicity). Persons of Hispanic ethnicity might be of any race or combination of races. Thirty-eight states and the District of Columbia 

reported multiple-race data on the birth certificate in 2010. The multiple-race data for these states were bridged to the single-race categories of the 1977 standards 

for comparability with other states. 

¶ Data do not meet standards of reliability or precision because 1) <20 births are in the numerator or 2) for persons of specified Hispanic origin, <75,000 females 

were in the denominator in 2007.

** Includes Central American and South American as well as other and unknown Hispanic women.
Supplement

74 MMWR / November 22, 2013 / Vol. 62 / No. 3

15–17 years decreased more by approximately half, from 38.6 

per 1,000 in 1991 to 17.3 in 2010; decreases ranged from 

53% for Hispanics aged 15–17 years to 68%–69% for nonHispanic blacks and A/PIs (9). The overall decrease in the rate 

for females aged 18–19 years was 38%, from 94.0 per 1,000 in 

1991 to 58.2 in 2010; decreases ranged from 40% and 42% 

for non-Hispanic white and Hispanic females aged 18–19 

years, to 47% to 56% for non-Hispanic black, AI/AN, and 

A/PI females aged 18–19 years (9,11).

The recent decreases in birth rates have coincided with 

decreases in pregnancy, abortion, and fetal loss rates among 

females aged 10–14, 15–19, 15–17, and 18–19 years (22). The 

pregnancy rate in 2008 for females aged 15–19 years was the 

lowest ever in the more than 3 decades for which a national 

series of rates is available (22,24). However, disparities in rates 

by race and ethnicity have changed little since 1990.

The findings in this report and a recent overview of state-level 

birth rates both have documented the persistent large variation 

across states (Table 2) (11). Birth rates for females aged 15–19 

years tend to be highest in the South and Southwest and 

lowest in the Northeast and upper Midwest, a pattern that has 

persisted for many years (25,26). Some of the variation among 

states reflects differences in the racial/ethnic composition of 

the population within states (26). More in-depth analysis of 

trends and variations in state-specific rates by race and Hispanic 

ethnicity is forthcoming with the recent availability of revised 

intercensal population estimates (9).

Limitations

The findings in this report are subject to at least four 

limitations. First, a full assessment of disparities in childbearing 

among females aged 10–19 years depends on having complete 

data on patterns of pregnancies in this age group. The downward 

trend since 1991 in abortions among females aged <20 years 

has been more substantial than the downward trend among 

births. For example, the abortion rate for females aged 15–19 

years decreased 52% from 1991 to 2008, whereas the birth 

rate decreased 35% during this period. A full understanding of 

patterns in pregnancy among females aged <20 years requires 

timely data on abortions and fetal losses as well as live births. 

The birth rate decreased 15% during 2008–2010. The extent 

to which the downward trends in abortions continued from 

2008 to 2010 is not yet known. Second, the components of 

the pregnancy estimates and pregnancy outcome estimates 

vary in quality and completeness. Birth data are complete 

counts, whereas the abortion estimates are based on incomplete 

surveillance and survey data, and the fetal loss estimates are 

based on pregnancy histories collected from survey data 

TABLE 2. Number of births and birth rates for females aged 15–19 

years, by state — National Vital Statistics System, United States, 2007 

and 2010

State

No. of births

Change in 

rate from 

2007 to 2010

(%)†

Birth rate*

2010 2007 2010 2007

United States 367,678 444,899 34.2 41.5 -18

Alabama 7,343 8,696 43.6 52.2 -16

Alaska 956 1,117 38.3 43.0 -11

Arizona 9,389 12,868 41.9 59.5 -30

Arkansas 5,229 5,926 52.5 60.1 -13

California 43,149 53,417 31.5 39.6 -20

Colorado 5,474 6,737 33.4 41.5 -20

Connecticut 2,274 2,837 18.7 23.0 -19

Delaware 974 1,244 30.5 39.2 -22

District of Columbia 951 1,053 45.4 50.4 -10

Florida 19,127 25,693 32.0 42.9 -25

Georgia 14,378 18,085 41.4 53.3 -22

Hawaii 1,347 1,610 32.5 38.9 -16

Idaho 1,863 2,257 33.0 40.0 -18

Illinois 14,798 18,089 33.0 40.1 -18

Indiana 8,665 9,948 37.3 42.9 -13

Iowa 3,017 3,529 28.6 32.8 -13

Kansas 3,865 4,271 39.3 42.4 -7

Kentucky 6,684 7,547 46.2 52.4 -12

Louisiana 7,689 8,974 47.7 55.1 -13

Maine 917 1,172 21.4 26.0 -18

Maryland 5,396 6,892 27.3 34.3 -20

Massachusetts 3,909 4,949 17.2 21.4 -20

Michigan 10,835 12,497 30.1 33.5 -10

Minnesota 4,035 5,193 22.5 27.9 -19

Mississippi 6,077 7,811 55.0 69.9 -21

Missouri 7,669 9,244 37.1 44.0 -16

Montana 1,128 1,200 35.0 35.3 -1

Nebraska 1,958 2,280 31.1 35.4 -12

Nevada 3,421 4,351 38.6 51.6 -25

New Hampshire 722 924 15.7 19.3 -19

New Jersey 5,793 7,255 20.1 24.9 -19

New Mexico 3,872 4,720 53.0 63.9 -17

New York 15,126 17,621 22.7 26.1 -13

North Carolina 12,309 15,079 38.3 47.9 -20

North Dakota 659 696 28.8 29.3 -2

Ohio 13,752 16,362 34.1 39.9 -15

Oklahoma 6,496 7,543 50.4 58.5 -14

Oregon 3,496 4,343 28.2 34.6 -18

Pennsylvania 11,959 13,841 27.0 30.7 -12

Rhode Island 891 1,192 22.3 29.3 -24

South Carolina 6,849 8,329 42.6 52.0 -18

South Dakota 975 1,191 34.9 41.3 -15

Tennessee 9,254 11,260 43.2 53.3 -19

Texas 47,751 54,281 52.2 61.7 -15

Utah 3,049 3,775 27.9 35.5 -21

Vermont 401 492 17.9 21.1 -15

Virginia 7,374 9,200 27.4 34.2 -20

Washington 6,002 7,430 26.7 33.4 -20

West Virginia 2,608 2,714 44.8 46.0 -3

Wisconsin 5,100 6,243 26.2 31.2 -16

Wyoming 723 921 39.0 50.1 -22

* Births per 1,000 females aged 15–19 years living in each state.

† Statistical testing for significance was assessed by using the z test at the 95% 

confidence level. All changes are significant (p<0.05) except for Montana, 

North Dakota, and West Virginia.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 75

(9,19,21,22,24). Third, data on teen pregnancy are available 

only for the largest population groups: non-Hispanic white, 

non-Hispanic black, and Hispanic. The necessary information 

on abortions and fetal losses is not available for other race 

groups (i.e., A/PI or AI/AN) or for specific Hispanic groups 

(19–21,23,24). Finally, evaluating trends and disparities in 

state-specific birth rates depends on having accurate population 

estimates by age, race, and Hispanic ethnicity. Recently released 

revised intercensal population estimates provide detailed data 

at the state and county level by single year of age. In years 

going forward, these newly released population estimates and 

estimates from the American Community Survey for Hispanic 

population groups will be used to improve the precision of the 

estimated rates.

Conclusion

Data from the 2006–2010 NSFG conducted by CDC have 

shown little change in the proportion of males and females 

aged 15–19 years who have ever had sex (27). This finding 

was corroborated in data released from the 2011 Youth Risk 

Behavior Surveillance report (28). For the period from 2002 

to 2006–2010, NSFG found a significant decrease only 

among non-Hispanic black females aged 15–19 years in the 

percentage of those who were sexually experienced; changes 

for other groups were not significant. However, virtually all 

race and Hispanic origin groups have experienced significant 

long-term decreases in the proportion of those who are sexually 

experienced (27). The 2006–2010 NSFG also documents 

increased use, compared with 2002 and earlier rounds of the 

NSFG, of contraception at first intercourse and increased use 

of two methods of contraception (i.e., condoms and hormonal 

methods) among sexually active male and female teenagers (27). 

The recent NSFG data show fewer differences than in previous 

years by race and Hispanic origin in overall contraceptive use 

at first and last sex, largely reflecting increasing condom use 

among all groups (27). Various other factors contribute to the 

observed variations in teenage birth rates, including differences 

in education and income and in attitudes among teenagers 

toward pregnancy and childbearing; these factors in turn 

affect sexual activity and contraceptive use (27). The impact 

of strong and consistent pregnancy prevention messages and 

programs directed toward teenagers aged <20 years has been 

credited with the long-term decline in teenage birth rates. 

These programs were implemented in the aftermath of rapid 

increases in teenage birth rates from 1986 to 1991. Studies 

have shown that to be effective, programs must be designed 

to meet the specific needs of different groups of teenagers, and 

continually evaluating interventions and programs to assess 

their effectiveness is important (1–3,29–32).

References

1. Hoffman SD, Maynard RA, editors. Kids having kids: economic and 

social consequences of teen pregnancy. 2nd ed. Washington, DC: Urban 

Institute Press; 2008.

TABLE 3. Pregnancy rates and rates of pregnancy outcomes (live 

births, induced abortions, and fetal losses) among females aged 

10–19 years, by age, race, and Hispanic origin of female — National 

Vital Statistics System, National Survey of Family Growth, CDC 

Abortion Surveillance System, and Guttmacher Institute surveys,* 

United States, 2008

Characteristic

Pregnancy 

rate†

Pregnancy outcome

Live birth 

rate§

Induced 

abortion 

rate§

Fetal loss 

rate§

Aged 10–14 yrs

All races/ethnicities¶ 1.4 0.6 0.6 0.2

White, non-Hispanic 0.5 0.2 0.2 0.1

Black, non-Hispanic 3.8 1.4 2.0 0.5

Hispanic** 2.2 1.1 0.6 0.4

Aged 15–19 yrs

All races/ethnicities 69.8 40.2 17.8 11.8

White, non-Hispanic 44.8 26.7 10.4 7.7

Black, non-Hispanic 121.6 60.4 43.4 17.8

Hispanic 111.5 70.3 20.1 21.1

Aged 15–17 yrs

All races/ethnicities 39.5 21.1 10.4 7.9

White, non-Hispanic 21.6 11.6 5.7 4.3

Black, non-Hispanic 72.8 33.6 26.7 12.6

Hispanic 69.7 42.2 11.7 15.8

Aged 18–19 yrs

All races/ethnicities 114.2 68.2 28.6 17.5

White, non-Hispanic 78.0 48.6 17.0 12.4

Black, non-Hispanic 193.8 100.0 68.2 25.6

Hispanic 176.4 114.0 33.2 29.2

* Birth data are from the National Vital Statistics System. Abortion estimates 

are from abortion surveillance information collected from most states by 

CDC on the characteristics of females who have abortions; these estimates 

are adjusted to national totals by the Guttmacher Institute (Sources: CDC. 

Abortion surveillance—United States, 2008. MMWR 2011;60[No. SS-15]; 

Henshaw SK. Unpublished tabulations. The Guttmacher Institute. 2000, 2011, 

2012; Jones RK, Kooistra K. Abortion incidence and access to services in the 

United States, 2008. Perspect Sex Reprod Health 2011;43:41–50; Ventura SJ, 

Curtin SC, Abma JC, Henshaw SK. Estimates pregnancy rates and rates of 

pregnancy outcomes for the United States, 1990-2008. Natl Vital Stat Rep 

2012;60[7]. Available at http://www.cdc.gov/nchs/data/nvsr/nvsr60/

nvsr60_07.pdf). Fetal loss rates are derived from pregnancy history data 

collected from several cycles of the National Survey of Family Growth (NSFG) 

conducted by CDC (Sources: Ventura SJ, Curtin SC, Abma JC, Henshaw SK. 

Estimated pregnancy rates and rates of pregnancy outcomes for the United 

States, 1990–2008. Natl Vital Stat Rep 2012;60[7]. Available at http://www.

cdc.gov/nchs/data/nvsr/nvsr60/nvsr60_07.pdf; Lepkowski JM, Mosher WD, 

Davis KE, et al. The 2006–2010 National Survey of Family Growth: sample 

design and analysis of a continuous survey. Vital Health Stat 2010;2[150]. 

Available at http://www.cdc.gov/nchs/data/series/sr_02/sr02_150.pdf). 

† Per 1,000 females in specified age, race, and Hispanic origin group. Rates 

cannot be calculated for other population groups because the necessary 

data for abortions and fetal losses are not available.

§ Per 1,000 females in specified age, race, and Hispanic origin group.

¶ Rates for “all races/ethnicities” include other races not shown separately and 

origin not stated.

** Persons of Hispanic ethnicity might be of any race or combination of races.
Supplement

76 MMWR / November 22, 2013 / Vol. 62 / No. 3

2. National Campaign to Prevent Teen and Unplanned Pregnancy. National 

data. Washington, DC: National Campaign to Prevent Teen Pregnancy; 

2010. Available at http://www.thenationalcampaign.org/national-data/

default.aspx.

3. CDC. Adolescent reproductive health, teen pregnancy. Atlanta, GA: US 

Department of Health and Human Services, CDC; 2012. Available at 

http://www.cdc.gov/TeenPregnancy/index.htm.

4. Martin JA, Hamilton BE, Ventura SJ, et al. Births: final data for 2009. 

Natl Vital Stat Rep 2011;60. Available at http://www.cdc.gov/nchs/data/

nvsr/nvsr60/nvsr60_01.pdf.

5. Martin JA, Osterman MJK, Sutton PD. Are preterm births on the decline 

in the United States? Recent data from the National Vital Statistics 

System. NCHS data brief no 39. Hyattsville, MD: CDC, National 

Center for Health Statistics; 2010. Available at http://www.cdc.gov/

nchs/data/databriefs/db39.pdf.

6. Mathews TJ, MacDorman MF. Infant mortality statistics from the 2008 

period linked birth/infant death data set. Natl Vital Stat Rep 2012;60. 

Available at http://www.cdc.gov/nchs/data/nvsr/nvsr60/nvsr60_05.pdf.

7. National Campaign to Prevent Teen and Unplanned Pregnancy. 

Counting it up: the public costs of teen childbearing. Key data. 

Washington, DC: National Campaign to Prevent Teen and Unplanned 

Pregnancy; 2012. Available at http://www.thenationalcampaign.org/

costs/pdf/counting-it-up/key-data.pdf.

8. Mosher WD, Jones J, Abma JC. Intended and unintended births in the 

United States: 1982–2010. Natl Health Stat Rep 2012;55. Hyattsville, 

MD: National Center for Health Statistics.

9. Martin JA, Hamilton BE, Ventura SJ, et al. Births: Final data for 2010. 

Natl Vital Stat Rep 2012;61. Available at http://www.cdc.gov/nchs/data/

nvsr/nvsr61/nvsr61_01.pdf.

10. Ventura SJ, Hamilton BE. US teenage birth rate resumes decline. NCHS 

data brief no. 58. Hyattsville, MD: National Center for Health Statistics; 

2011. Available at http://www.cdc.gov/nchs/data/databriefs/db58.pdf.

11. Hamilton BE, Ventura SJ. Birth rates for U.S. teenagers reach historic 

lows for all and ethnic groups. NCHS data brief no. 89. Hyattsville, 

MD: National Center for Health Statistics. 2012. Available at http://

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2010, resident population of the United States, by county, single year 

of age, bridged race, Hispanic origin, and sex. Hyattsville, MD: CDC, 

National Center for Health Statistics; 2011. Available at http://www.

cdc.gov/nchs/nvss/bridged_race/data_documentation.htm#april2010.

17. Hamilton BE, Martin JA, Ventura SJ. Births: Preliminary data for 2010. 

Natl Vital Stat Rep 2011;60. Available at http://www.cdc.gov/nchs/data/

nvsr/nvsr60/nvsr60_02.pdf.

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public use file. Hyattsville, MD: CDC, National Center for Health 

Statistics; 2010. Available at ftp://ftp.cdc.gov/pub/Health_Statistics/

NCHS/Dataset_Documentation/DVS/natality/UserGuide2010.pdf.

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20. Henshaw SK. Unpublished tabulations. The Guttmacher Institute. 2000, 

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21. Jones RK, Kooistra K. Abortion incidence and access to services in the 

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data/series/sr_21/sr21_056.pdf.

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IS-youthdev-community.html.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 77

Binge Drinking — United States, 2011

Dafna Kanny, PhD

Yong Liu, MS

Robert D. Brewer, MD

Hua Lu, MS

National Center for Chronic Disease Prevention and Health Promotion, CDC

Corresponding author: Dafna Kanny, Division of Population Health, National Center for Chronic Disease Prevention and Health Promotion, CDC. 

Telephone: 770-488-5411; E-mail: dkk3@cdc.gov.

Introduction

During 2001–2005, excessive alcohol use accounted for an 

estimated average of 80,000 deaths and 2.3 million years of 

potential life lost (YPLL) in the United States each year, and an 

estimated $223.5 billion in economic costs in 2006 (1). Binge 

drinking, defined as consuming four or more alcoholic drinks 

on one or more occasions for women and five or more drinks 

on one or more occasions for men, was responsible for more 

than half of these deaths, two-thirds of the YPLL (2), and three 

quarters of the economic costs (1). Reducing the prevalence of 

binge drinking among adults is also a leading health indicator 

in Healthy People 2020 (objective SA-14.3) (3).

The binge drinking prevalence, frequency, and intensity 

analysis, and discussion that follows is part of the second 

CDC Health Disparities and Inequalities Report (CHDIR) 

(4). The 2011 CHDIR (5) was the first CDC report to 

assess disparities across a wide range of diseases, behavioral 

risk factors, environmental exposures, social determinants, 

and health-care access. The topic presented in this report is 

described in the criteria for the 2013 CHDIR Introduction 

(6). This report provides more current information on binge 

drinking measures, and updates information on the status 

of evidence-based strategies recommended to prevent binge 

drinking presented in the 2011 CHDIR. The purposes of 

this report are to discuss and raise awareness of differences in 

the characteristics of people who binge drink, and to prompt 

actions to reduce these disparities.

Methods

To examine sociodemographic disparities in binge drinking 

nationwide and by state, CDC analyzed 2011 data from the 

Behavioral Risk Factor Surveillance System (BRFSS). BRFSS 

is a state-based, random-digit–dialed landline and cellular 

telephone survey of the noninstitutionalized civilian U.S. 

adults that is conducted monthly in all states, the District of 

Columbia (DC), and three U.S. territories. BRFSS collects 

data on leading health conditions and health risk behaviors, 

including binge drinking. For this report, responses to questions 

regarding the prevalence, frequency, and largest number of 

drinks consumed by binge drinkers (a measure of the intensity 

of binge drinking) were analyzed, beginning with the question, 

“Considering all types of alcoholic beverages, how many times 

during the past 30 days did you have X [X = 5 for men; X = 4 

for women] or more drinks on an occasion?” Respondents 

then were asked, “During the past 30 days, what is the largest 

number of drinks you had on any occasion?” Responses to this 

question were assessed for binge drinkers only. A more detailed 

description of BRFSS methods has been published (4,7). In 

2011, the median survey response rate* was 49.7%, ranging 

from 33.8% to 64.1%. After excluding 48,912 persons who 

reported ‘don’t know/not sure’ or ‘refused,’ those with missing 

information, and respondents from the U.S. territories, data 

from 457,555 respondents in the 50 states and DC were used 

for analysis.

This report describes binge drinking prevalence, frequency 

(i.e., the average number of binge drinking episodes), and 

intensity (i.e., the average largest number of drinks consumed 

by binge drinkers). Sociodemographic characteristics analyzed 

included sex, age group, race/ethnicity, education level, 

income level, and disability status. Race was defined as white, 

black, Asian/Pacific Islander, and American Indian/Alaska 

Native. Ethnicity was defined as Hispanic or non-Hispanic. 

Annual household income was defined as follows: <$25,000, 

$25,000–$49,999, $50,000–$74,999, and ≥$75,000. 

Educational attainment was defined as follows: less than high 

school, high school or equivalent, some college, and college 

graduate. Disability status was defined as respondents reporting 

limited activities in any way because of physical, mental, or 

emotional problems.

Binge drinking prevalence was calculated by dividing 

the total number of respondents who reported at least one 

binge drinking episode during the preceding 30 days by 

the total number of BRFSS respondents in all 50 states and 

*Response rates for BRFSS are calculated using standards set by the American 

Association of Public Opinion Research (AAPOR) response rate formula no. 4, 

available at http://www.aapor.org/standard_definitions2.htm. The response 

rate is the number of respondents who completed the survey as a proportion 

of all eligible and likely eligible persons. Additional information is available at 

http://cdc.gov/brfss/pdf/2011_Summary_Data_Quality_Report.pdf. 
Supplement

78 MMWR / November 22, 2013 / Vol. 62 / No. 3

DC. Frequency of binge drinking (i.e., the number of binge 

drinking episodes) was calculated by averaging the number of 

episodes reported by all binge drinkers during the preceding 30 

days. Intensity of binge drinking was calculated by averaging 

the largest number of drinks consumed by binge drinkers 

during the past 30 days. BRFSS data were weighted to adjust 

for several demographic variables (e.g., educational attainment, 

marital status, home ownership, and telephone source) (7). 

Data were age- and sex-adjusted to the 2000 U.S. Census 

standard population to provide estimates for race/ethnicity, 

educational attainment, annual household income level, and 

disability status. We calculated 95% confidence intervals for 

binge drinking prevalence. Two-tailed t-tests were used to 

determine differences between subgroups. 

Results

In 2011, the overall prevalence of binge drinking among 

adults in the 50 states and DC was 18.4% (Table). On average, 

binge drinkers reported a frequency of 4.1 binge drinking 

episodes during the preceding 30 days and an intensity of 7.7 

drinks per occasion during the past 30 days. Binge drinking 

prevalence was significantly higher among persons aged 18–24 

years (30.0%) and 25–34 years (29.7%) than among those in 

older age groups. Similarly, the intensity of binge drinking 

was highest among binge drinkers aged 18–24 and 25–34 

(8.9 and 8.2 drinks, respectively); however, the frequency of 

binge drinking was highest among binge drinkers aged ≥65 

years (4.9 episodes). The prevalence of binge drinking was also 

significantly higher among non-Hispanic whites (21.1%) than 

among all other race/ethnicity categories, but the intensity of 

binge drinking was highest among American Indians/Alaska 

Natives (8.4 drinks). Those with household incomes ≥$75,000 

had significantly higher binge drinking prevalence (22.2%) 

than those with lower household incomes. In contrast, binge 

drinkers with household incomes <$25,000 reported the 

highest frequency (4.3 episodes) and intensity (7.1 drinks) of 

binge drinking.

Respondents who did not graduate from high school 

reported significantly lower binge drinking prevalence (16.8%) 

than those with high school or higher education. However, 

binge drinkers with less than high school education had the 

highest frequency (4.7 episodes) and intensity (7.4 drinks) 

of binge drinking. Respondents with disabilities also had a 

significantly lower prevalence of binge drinking (16.9%), but 

those who binge drank had a higher frequency (4.5 episodes) 

and intensity (7.2 drinks) of binge drinking, compared with 

those without disabilities. 

Overall, areas with the highest age- and sex-adjusted 

prevalence of binge drinking were states in the Midwest, as well 

as DC and Hawaii (Figure 1). States with the highest intensity 

of binge drinking were generally located in the Midwest, and 

included some states (e.g. Oklahoma, Arkansas, Kentucky, 

West Virginia, and Utah) that had a lower prevalence of binge 

drinking (Figure 2).

Discussion

Binge drinking is a risk factor for many adverse health 

and social outcomes, including unintentional injuries (e.g., 

motor vehicle crashes); violence; suicide; hypertension; 

acute myocardial infarction; sexually transmitted diseases; 

unintended pregnancy; fetal alcohol syndrome; and sudden 

infant death syndrome (8). This report indicates that in 

2011 binge drinking was common among U.S. adults, and 

persons who binge drank tended to do so frequently (average 

of four times per month) and with high intensity (average of 

eight drinks on occasion), placing themselves and others at a 

significantly greater risk for alcohol-attributable harms (8). In 

a number of states with a lower prevalence of binge drinking, 

those who binge drank did so with high intensity.

The groups at highest risk for binge drinking (i.e., persons 

aged 18–34 years, males, whites, non-Hispanics, and persons 

with higher household incomes), and those who reported 

the highest binge drinking frequency (i.e., binge drinkers 

aged ≥65 years) and intensity (i.e., persons aged 18–24 

years) are consistent with previous reports (4,9), and might 

reflect differences in state and local laws on the marketing of 

alcoholic beverages (e.g., price and availability) (10), as well 

as other cultural and religious factors (11). These differences 

are reflected in state measures of the prevalence and intensity 

of binge drinking, and highlight that states with a lower 

prevalence of binge drinking might still include subgroups 

that binge drink with high intensity. Furthermore, unlike other 

leading health risks (e.g., smoking and obesity) binge drinking 

has not been widely recognized as a health risk or subjected to 

intense prevention efforts (12). 

Limitations

The findings in this analysis are subject to at least three 

limitations. First, BRFSS data are self-reported; alcohol 

consumption, generally, and excessive drinking, in particular, 

is underreported in surveys because of recall bias, social 

desirability response bias, and nonresponse bias (13). A recent 

study reported that BRFSS identifies 22%–32% of presumed 

alcohol consumption in states when compared with alcohol 
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 79

sales data (14). However, a strong correlation existed between 

BRFSS estimates of alcohol consumption and per capita 

alcohol sales in states, suggesting that BRFSS data are still a 

useful measure of alcohol consumption even after taking into 

account known underreporting (14). Second, response rates 

for BRFSS were low, which can increase response bias. Third, 

BRFSS does not collect information from persons living in 

institutional settings (e.g., on college campuses), so findings 

might not be representative of those populations.

Conclusion

Binge drinking is common among U.S. adults, and persons 

who binge drink tend to do so frequently and with high 

intensity. The Community Preventive Services Task Force 

has recommended several population-level, evidence-based 

strategies to reduce binge drinking and related harms (15). 

These include 1) limiting alcohol outlet density (http://

www.thecommunityguide.org/alcohol/outletdensity.html) 

(i.e., the concentration of retail alcohol establishments, 

including bars and restaurants and liquor or package stores, 

in a given geographic area), 2) holding alcohol retailers liable 

for harms related to the sale of alcoholic beverages to minors 

and intoxicated patrons (dram shop liability), 3) measures 

increasing the price of alcohol, 4) maintaining existing limits 

on the days and hours when alcohol is sold, 5) avoiding further 

privatization of alcohol sales in states with government-operated 

or contracted liquor stores, and 6) electronic screening and brief 

interventions (eSBI), including interventions delivered using 

computers, telephones, or mobile devices in the clinical setting. 

TABLE. Prevalence, frequency, and intensity of binge-drinking, by sex, age group, race/ethnicity, education, and disability — Behavioral Risk 

Factor Surveillance System, United States,* 2011 

Characteristic

Prevalence† Frequency§ Intensity¶

No.

Weighted 

% (95% CI) No.

No. of 

episodes (95% CI) No.

No. of 

drinks (95% CI)

Total 457,555 18.4 (18.1–18.6) 59,553 4.1 ( 4.0– 4.2) 55,929 7.7 ( 7.6– 7.7)

Sex**

Men 179,224 24.6 (24.2–25.0) 34,859 4.6 ( 4.0 – 4.7) 32,564 8.7 ( 8.6– 8.8)

Women 278,331 12.5 (12. 2–12.8) 24,694 3.2 ( 3.1– 3.3) 23,365 5.7 ( 5.6– 5.8)

Age group (yrs)**

18–24 20,016 30.0 (28.9-31.1) 6,210 4.4 ( 4.1- 4.6) 5,792 8.9 ( 8.7- 9.1)

25–34 44,441 29.7 (28.9-30.5) 12,167 3.8 ( 3.7- 4.0) 11,493 8.2 ( 8.0- 8.4)

35–44 58,980 21.1 (20.5-21.8) 11,781 3.9 ( 3.8- 4.1) 11,158 7.4 ( 7.2- 7.5)

45–64 187,811 14.1 (13.8-14.5) 23,710 4.2 ( 4.1- 4.3) 22,293 6.6 ( 6.5- 6.7)

≥65 146,307 4.3 ( 4.1- 4.5) 5,685 4.9 ( 4.5- 5.3) 5,193 5.6 ( 5.5- 5.7)

Race/Ethnicity††

White, non-Hispanic 363,127 21.1 (20.7-21.4) 47,879 4.1 ( 4.0- 4.2) 45,255 6.8 ( 6.8- 6.9)

Black, non-Hispanic 35,919 14.2 (13.4-15.0) 3,446 3.8 ( 3.5- 4.1) 3,111 6.1 ( 5.9- 6.3)

Hispanic§§ 28,275 17.7 (16.9-18.4) 4,338 3.3 ( 3.0- 3.6) 3,978 6.8 ( 6.6- 7.0)

Asian/Pacific Islander 8,746 10.3 ( 9.1-11.4) 885 3.4 ( 2.5- 4.3) 839 6.1 ( 5.7- 6.5)

American Indian/Alaska Native 6,248 18.2 (16.1-20.4) 992 4.5 ( 3.7- 5.3) 906 8.4 ( 7.8- 9.1)

Educational attainment††

Less than high school 39,348 16.8 (15.9–17.6) 3,888 4.7 ( 4.3– 5.1) 3,482 7.4 ( 7.2– 7.7)

High school or equivalent 133,510 18.7 (18.2–19.1) 16,670 4.2 ( 4.1– 4.4) 15,455 7.2 ( 7.0– 7.3)

Some college 124,124 20.1 (19.6–20.6) 17,353 4.0 ( 3.8 –4.1) 16,344 6.6 ( 6.5– 6.7)

College graduate 159,762 20.4 (20.0–20.9) 21,593 3.3 ( 3.2– 3.4) 20,611 6.2 ( 6.1– 6.3)

Annual household income ($)††

<25,000 118,636 17.6 (17.0–18.1) 12,656 4.3 ( 4.1 –4.5) 11,733 7.1 ( 7.0– 7.2)

25,000–49,999 107,486 19.4 (18.8–19.9) 13,748 4.2 ( 4.0– 4.4) 12,945 6.9 ( 6.8– 7.0)

50,000–74,999 63,510 19.8 (19.0–20.5) 9,370 3.7 ( 3.5– 3.9) 8,916 6.7 ( 6.6– 6.9)

≥75,000 107,907 22.2 (21.6–22.9) 18,820 3.6 ( 3.4 –3.7) 17,998 6.5 ( 6.4– 6.6)

Disability status††

Yes 131,816 16.9 (16.2–17.5) 11,592 4.5 ( 4.3– 4.7) 10,833 7.2 ( 7.0– 7.4)

No 323,525 19.6 (19.3–19.9) 47,763 3.8 ( 3.7– 3.9) 44,933 6.7 ( 6.6 6.7)

Abbreviation: 95% CI = 95% confidence interval.

* Respondents were from all 50 states and the District of Columbia.

† Prevalence = total number of respondents who reported at least one binge drinking episode during the past 30 days divided by the total number of respondents. 

§ Frequency = average number of binge-drinking episodes reported by all binge drinkers during the past 30 days. 

¶ Intensity = average largest number of drinks consumed by binge drinkers on any occasion during the past 30 days. ** Unadjusted estimates.

†† Age- and sex-adjusted to the 2000 U.S. Census standard population. 

§§ Persons of Hispanic ethnicity might be of any race or combination of races.
Supplement

80 MMWR / November 22, 2013 / Vol. 62 / No. 3

FIGURE 1. Prevalence* of binge drinking† — Behavioral Risk Factor 

Surveillance System, United States,§ 2011

* Total number of respondents who reported at least one binge drinking episode 

during the preceding 30 days divided by the total number of respondents.

† Consuming ≥4 alcoholic drinks on ≥1 occasion for women and ≥5 drinks on 

≥1 occasion for men.

§ States are divided into tertiles.

20.5–25.1

17.9–20.4

10.9–17.8

DC

Prevalence (%)

Despite the array of strategies that have been recommended, 

efforts are needed to implement them to a point of measurable 

success toward reducing binge drinking. The frequency and 

intensity of binge drinking also should be monitored routinely 

to support the implementation and evaluation of Community 

Guide recommendations for reducing binge drinking and to 

monitor changes in this behavior among groups at greater risk.

References

1. Bouchery EE, Harwood HJ, Sacks JJ, Simon CJ, Brewer RD. Economic 

costs of excessive alcohol consumption in the United States, 2006. Am 

J Prev Med 2011;41:516–24.

2. CDC. Alcohol attributable deaths and years of potential life lost—United 

States, 2001. MMWR 2004;53:866–70.

3. US Department of Health and Human Services. SA–14.3 Reduce the 

proportion of persons engaging in binge drinking during the past 30 

days—adults aged 18 years and older. Healthy People 2020. Washington, 

DC: US Department of Health and Human Services; 2011. Available 

at http://healthypeople.gov/2020/lhi/substanceabuse.aspx.

4. CDC. Binge drinking—United States, 2009. MMWR 2011;60(Suppl; 

January 14, 2011).

5. CDC. CDC health disparities and inequalities report—United States, 

2011. MMWR 2011; 60(Suppl; January 14, 2011).

6. CDC. Introduction: CDC health disparities and inequalities report—

United States, 2013. MMWR 2013; 62(Suppl; No. 3).

7. CDC. Methodologic changes in the behavioral risk factor surveillance 

system in 2011 and potential effects on prevalence estimates. MMWR 

2012;61;410–3.

8. National Institute of Alcohol Abuse and Alcoholism. Tenth special report 

to the US Congress on alcohol and health. Bethesda, MD: National 

Institutes of Health; 2000.

9. CDC. Vital signs: binge drinking prevalence, frequency, and intensity 

among adults—United States, 2010. MMWR 2012;61:14–9.

10. National Institute on Alcohol Abuse and Alcoholism. Alcohol policy 

information system. Rockville, MD: US Department of Health and 

Human Services, National Institutes of Health. Available at http://www.

alcoholpolicy.niaaa.nih.gov. 

11. Holt JB, Miller JW, Naimi TS, Sui DZ. Religious affiliation and alcohol 

consumption in the United States. Geog Rev 2006;96:523–42.

12. Brewer RD, Swan MH. Binge drinking and violence. JAMA 2005;

294:616–8.

13. Stockwell T, Donath S, Cooper-Stanbury M, et al. Under-reporting of alcohol 

consumption in household surveys: a comparison of quantity-frequency, 

graduated-frequency and recent recall. Addiction 2004;99:1024–33.

14. Nelson DE, Naimi TS, Brewer RD, Roeber J. US state alcohol sales 

compared to survey data, 1993–2006. Addiction 2010;105:1589–96.

15. Task Force on Community Preventive Services. Preventing excessive 

alcohol use. Atlanta, GA: Task Force on Community Preventive Services; 

Available at http://www.thecommunityguide.org/alcohol/index.html. 

FIGURE 2. Intensity* of binge drinking† — Behavioral Risk Factor 

Surveillance System, United States,§ 2011

* Average largest number of drinks consumed by binge drinkers on any occasion 

during the past 30 days.

† Consuming ≥4 alcoholic drinks on ≥1 occasion for women and ≥5 drinks on 

≥1 occasion for men.

§ States are divided into tertiles.

7.1–7.8

6.8–7.0

6.0–6.7

DC

Most drinks

consumed on

any occasion
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 81

Introduction

Cigarette smoking is the leading cause of preventable disease 

and death in the United States, resulting in approximately 

443,000 deaths and $193 billion in direct health-care 

expenditures and productivity losses each year (1). Declines 

in smoking prevalence would significantly impact the healthcare and economic costs of smoking. Efforts to accelerate the 

decline in cigarette smoking include reducing cigarette smoking 

disparities among specific population groups. Findings from 

the previous report on cigarette use in the first CDC Health 

Disparities and Inequalities Report (CHDIR) indicated that 

progress has been achieved in reducing disparities in cigarette 

smoking among certain racial/ethnic groups (2). However, 

little progress has been made in reducing disparities in cigarette 

smoking among persons of low socioeconomic status (SES) 

and low educational attainment. 

This report on cigarette smoking and the analysis and 

discussion that follows is part of the second CHDIR. The 

2011 CHDIR (3) was the first CDC report to take a broad 

view of disparities across a wide range of diseases, behavioral 

risk factors, environmental exposures, social determinants, and 

health-care access. The topic presented in this report is based 

on criteria that are described in the 2013 CHDIR Introduction 

(4). The report that follows provides more current information 

to what was presented in the 2011 CHDIR (2). The purposes 

of this report are to discuss and raise awareness of differences 

in the smoking prevalence of current smokers and to prompt 

actions to reduce disparities.

Methods

To assess the changes in disparities in smoking prevalence 

by selected sociodemographic characterisitcs during 2006-

2008 and 2009-2010, CDC analyzed aggregated data from 

the National Survey on Drug Use and Health (NSDUH), 

which is sponsored by the Substance Abuse and Mental 

Health Services Administration (SAMHSA) and provides 

annual data on alcohol, tobacco, and illegal drug use among 

the noninstitutionalized U.S. household population aged ≥12 

years (http://www.sahmhsa.gov/data/NSDUH.aspx). Smoking 

prevalence was determined for youths and adults (aged ≥12 

years). Current smokers include persons who reported smoking 

at least one cigarette during the 30 days before the survey.

Aggregated data were analyzed for two survey cycles. The 

2006–2008 survey cycle included 42,693 respondents with 

response rates of 74.0%, 73.9%, and 74.2%, respectively. The 

2009–2010 survey cycle included 27,636 respondents with 

response rates of 75.7% and 74.4%, respectively. Demographic 

characteristics analyzed included race and ethnicity, sex, age, 

household income, employment status, and educational 

attainment. Geographic location was not analyzed because of 

limited data for this variable. Race and ethnicity were defined as 

non-Hispanic white, non-Hispanic black, Hispanic, American 

Indian/Alaska Native, Native Hawaiian/Other Pacific Islander, 

Asian, and multirace. Household income was reported by 

poverty status, which is based on U.S. Census Bureau thresholds 

for federal poverty levels (FPL) (http://www.census.gov/hhes/

www/poverty/html). Employment status was defined as fulltime, 

parttime, unemployed, and other. Educational attainment 

was defined as less than high school, high school diploma or 

equivalent, some college, and college graduate. For adults, 

low-SES was defined as those persons with less than a high 

school diploma unemployed or living at, near, or below the 

U.S. FPL. Disparities were measured as the absolute difference 

between rates. Population-weighted prevalence estimates and 

95% confidence intervals (CIs) were calculated using statistical 

software to account for the multistage probability designs of 

NSDUH. No statistical testing was done for this analysis. In 

this approach, CIs were used as measure of variability and 

nonoverlapping CIs were considered statistically different. Using 

CIs in this way is a conservative evaluation of significance 

differences; infrequently, this might lead to a conclusion that 

estimates are similar when the point estimates do differ. 

Results

Some progress in reducing smoking prevalence among 

certain racial/ethnic groups was observed; however, disparities 

among persons with low-SES persisted. For both youth and 

adults, little to no changes in smoking prevalence for those 

Cigarette Smoking — United States, 2006-2008 and 2009-2010

Bridgette E. Garrett, PhD

Shanta R. Dube, PhD

Cherie Winder, MSPH

Ralph S. Caraballo, PhD

National Center for Chronic Disease Prevention and Health Promotion, CDC

Corresponding author: Bridgette E. Garrett, PhD, Office on Smoking and Health, National Center for Chronic Disease Prevention and Health Promotion, CDC. 

Telephone: 770-488-5715; E-mail: bgarrett@cdc.gov. 
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82 MMWR / November 22, 2013 / Vol. 62 / No. 3

below FPL was observed from 2006–2008 to 2009–2010; 

however, decreases were observed for youth and adults who 

were above FPL (Tables 1 and 2). During 2009–2010, the 

prevalence of smoking was 46.4% among 12th-grade–aged 

youth who had dropped out of school compared with 21.9% 

among youth who were still in the 12th grade (Table 1). Among 

adults, smoking prevalence was 34.6% for those who did not 

graduate from high school compared with 13.2% among 

those with a college degree (Table 2). From 2006–2008 to 

2009–2010, smoking declined from 44.7% to 40.9% among 

adults who were unemployed (Table 2). Among racial/ethnic 

groups, smoking prevalence was lowest among black and 

Asian youth aged 12–17 years during both survey cycles 

(Table 1). Although smoking prevalence remained highest 

among American Indian/Alaska Native youth and adults, 

smoking declined from 17.2% to 13.6% in youth and from 

42.2% to 34.4% in adults (Table 1 and 2).

Discussion

Prevalence of smoking is highest for persons aged ≥18 years 

who do not have high school diploma. Assessing and reporting 

the prevalence of smoking among youth aged <18 years who 

drop out of school is critical because this is the period when 

problems with academic achievement occur. The findings in 

this report indicate that during 2009–2010, approximately 

half of youth who dropped out of school were smokers. These 

findings underscore the need to address tobacco use early in the 

life span, particularly among school-aged youth, who might 

be more vulnerable, to eliminate tobacco-related disparities. 

Implementing the key effective strategies known to prevent 

and reduce tobacco use among youth are needed, including 

reducing tobacco industry influences towards minors, 

particularly those in low SES communities (5). 

To make progress toward reducing the persistent higher 

prevalence of smoking among low-SES populations, current 

tobacco-control interventions should be targeted toward these 

TABLE 1. Prevalence of current smoking* among persons aged 12–17 years, by selected characteristics — National Survey on Drug Use and 

Health, United States, 2006–2010†

Characteristic

2006–2008 2009–2010 Absolute difference from 

2006–2008 to 2009–2010 

% (95% CI) % (95% CI) (percentage points)

Sex

Male 9.7 (9.2–0.2) 8.9 (8.3–9.5) -0.8

Female 9.9 (9.4–0.4) 8.4 (7.8–8.9) -1.5

Race/Ethnicity 

White, non-Hispanic 11.8 (11.4–2.3) 10.2 (9.6–10.8) -1.6

Black, non-Hispanic 5.9 (5.2–6.5) 5.0 (4.2–5.7) -0.9

Hispanic§ 7.4 (6.7–8.2) 7.7 (6.9–8.4) 0.3

American Indian/Alaska Native 17.2 (13.2–1.2) 13.6 (9.6–17.7) -3.6

Native Hawaiian/Other Pacific Islander 5.2 (1.7–8.8) 7.9 (0.515.2) 2.7

Asian 4.1 (3.0–5.3) 3.0 (1.4–4.5) -1.1

Multirace 12.1 (9.5–14.7) 11.2 (8.5–13.9) -0.9

Grade

≤5 1.2 (0.8–1.6) 1.2 (0.6–1.7) 0

6 1.8 (1.4–2.2) 1.2 (0.8–1.6) -0.6

7 4.6 (3.85.4) 3.5 (2.7–4.2) -1.1

8 8.0 (7.3–8.7) 7.3 (6.6–8.0) -0.7

9 12.1 (11.1–13.0) 10.8 (9.8–11.8) -1.3

10 16.3 (15.3–17.2) 14.0 (12.715.3) -2.3

11 18.8 (17.1–20.4) 16.5 (14.7–18.3) -2.3

12 19.0 (14.024.0) 21.9 (15.9–27.9) 2.9

High school dropout 45.7 (40.7–50.7) 46.4 (39.4–53.5) 0.7

Poverty status¶

<100% (below threshold) 10.4 (9.4–11.3) 9.6 (8.6–10.6) -0.8

100%–199% (at or near threshold) 10.7 (10.0–11.5) 9.6 (8.8–10.3) -1.1

≥200% (above threshold) 9.3 (8.9–9.7) 7.9 (7.4–8.5) -1.4

Abbreviation: 95% CI = 95% confidence interval.

* Current smokers include all persons who smoked at least one cigarette during the 30 days before the survey.

† N = 42,693 for 2006–2009; N = 27,636 for 2009–2010.

§ Persons of Hispanic ethnicity might be of any race or combination of races.

¶ Based on self-reported family income or imputed family income and poverty thresholds published by the U.S. Census Bureau, 2005-2009. Available at http://www.

census.gov/hhes/www/poverty.
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MMWR / November 22, 2013 / Vol. 62 / No. 3 83

more vulnerable smokers. Educating the public about the 

harms of tobacco use through mass media campaigns is an 

effective strategy for raising awareness and decreasing smoking 

prevalence in the general population (6). Advertisements that 

are emotionally provocative and contain personal testimonies are 

especially effective in reaching low-SES populations (7). CDC 

recently implemented its first paid national media campaign 

to encourage smokers to quit (www.cdc.gov/quitting/tips). 

Mass media campaigns can be most effective in reaching all 

populations when they are part of a comprehensive tobaccocontrol program that includes comprehensive smoke-free policies 

that make all indoor public places 100% smoke-free, increase 

tobacco price, counter tobacco industry marketing activities, 

and increase the availability and accessibility of evidence-based 

cessation services (6,8).

Limitations

The findings in this report are subject to at least five 

limitations. First, data were based on self-reports and were 

not validated biochemically. However, studies have indicated 

that self-reported smoking status validated by measured 

serum cotinine levels yield similar prevalence estimates (9). 

Second, the NSDUH questionnaire is administered only in 

English and Spanish; therefore, estimates for certain racial/

ethnic populations might be underestimated if neither English 

nor Spanish is the primary language spoken. Moreover, 

race/ethnicity was not adjusted by socioeconomic status. 

Third, because NSDUH does not include institutionalized 

populations and persons in the military, these results might not 

be generalizable to these groups. Fourth, although smoking 

TABLE 2. Prevalence of current smoking* among persons aged ≥18 years, by selected characteristics — National Survey on Drug Use and 

Health, United States, 2006–2010†

Characteristic

2006–2008 2009–2010 Absolute difference from 

2006–2008 to 2009–2010 

% (95% CI) % (95% CI) (percentage points)

Age group (yrs)

18–25 36.8 (36.3–37.4) 35.0 (34.2-35.8) -1.8

26–34 33.7 (32.8–34.7) 33.6 (32.4-34.9) -0.1

35–49 28.1 (27.5–28.8) 26.1 (25.1-27.1) -2.0

50–64 22.9 (21.8–23.9) 22.4 (21.1-23.7) -0.5

≥65 9.4 (8.5–10.4) 9.2 (8.1–10.3) -0.2

Sex

Male 29.2 (28.6–29.8) 27.5 (26.8–28.3) -1.7

Female 23.0 (22.5–23.5) 22.4 (21.7–23.1) -0.6

Race/Ethnicity

White, non-Hispanic 26.9 (26.4–27.3) 25.8 (25.1–26.6) -1.1

Black, non-Hispanic 26.9 (25.6–28.1) 25.4 (23.9–27.0) -1.5

Hispanic§ 22.9 (21.7–24.1) 22.9 (21.3–24.5) 0

American Indian/Alaska Native 42.2 (35.5–48.8) 34.4 (27.9–40.9) -8.0

Native Hawaiian/Other Pacific Islander 28.5 (20.9–36.1) 18.6 (11.5–25.8) -9.9

Asian 14.7 (13.0–16.4) 11.8 (9.9–13.6) -2.9

Multirace 35.2 (31.4–39.0) 33.2 (29.1–37.2) -2.0

Educational attainment

Less than high school 34.3 (33.0–35.6) 34.6 (33.3–35.9) 0.3

High school graduate or equivalent 31.1 (30.3–32.0) 30.4 (29.4–31.4) -0.7

Some college 27.1 (26.3–28.0) 25.6 (24.6–26.5) -1.5

College graduate 14.1 (13.4–14.8) 13.2 (12.4–13.9) -0.9

Employment status

Full-time 27.8 (27.2–28.4) 25.4 (24.7–26.1) -2.4

Part-time 24.5 (23.5–25.4) 24.2 (23.1–25.4) -0.3

Unemployed 44.7 (42.3–47.2) 40.9 (39.2–42.7) -3.8

Other (including not in work force) 20.9 (20.2–21.7) 20.7 (19.6–21.8) -0.2

Poverty status¶

<100% (below threshold) 36.5 (35.1–37.8) 37.9 (36.4–39.4) 1.4

100%–199% (at or near threshold) 32.8 (31.8–33.8) 31.5 (30.3–32.7) -1.3

≥200% (above threshold) 22.5 (21.9–23.0 20.5 19.9–21.0 -2.0

Abbreviation: 95% CI = 95% confidence interval.

* Current smokers include all persons who smoked at least one cigarette during the 30 days before the survey.

† N = 42,693 for 2006–2008; N = 27,636 for 2009–2010.

§ Persons of Hispanic ethnicity might be of any race or combination of races.

¶ Based on self-reported family income or imputed family income and poverty thresholds published by the U.S. Census Bureau, 2005-2009. Available at http://www.

census.gov/hhes/www/poverty.
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84 MMWR / November 22, 2013 / Vol. 62 / No. 3

prevalence was determined to be lowest among Asian and 

Hispanic women, variations in smoking prevalence have been 

observed with specific Asian and Hispanic groups (e.g., Korean 

and Vietnamese men and Puerto Rican men and women) (10). 

Finally, because of limited sample sizes for certain population 

groups (e.g., AI/AN), single-year estimates might have resulted 

in imprecise estimates. 

Conclusion

Comprehensive tobacco-control strategies should be 

implemented in an equitable manner to be effective in 

addressing tobacco-related disparities. These strategies should 

ensure that all populations are covered by comprehensive 

smoke-free policies, including workplaces, restaurants, and 

bars; prices are increased on all tobacco products and coupled 

with access to evidence-based cessation services; exposure to 

industry advertising, promotions, and sponsorship are reduced 

among all populations; and the availability, accessibility, and 

effectiveness of tailored cessation services are increased for all 

populations (11). 

The findings in this report underscore conclusions from the 

2011 CHDIR that efforts to reduce future tobacco-related 

disparities associated with low SES should take a lifespan 

approach (2). Specifically, continuing population-based 

strategies that target youth, particularly among those with 

low academic achievement and drop-outs, will be critical in 

preventing future tobacco-related disparities. Coordinated, 

multicomponent interventions that combine mass media 

campaigns, price increases including those that result from tax 

increases, school-based policies and programs, and statewide 

or community-wide changes in smoke-free policies and 

norms are effective in reducing the initiation, prevalence, and 

intensity of smoking among youth and young adults (5,6). 

Finally, addressing the social determinants of health (e.g., 

socioeconomic status, cultural characteristics, acculturation, 

stress, targeted advertising, price of tobacco products, and 

varying capacities of communities to mount effective tobaccocontrol initiatives) will be necessary to disrupt the cycle of 

smoking among low-SES populations (2,12,13).

References

1. CDC. Smoking-attributable mortality, years of potential life lost, and 

productivity losses—United States, 2000–2004. MMWR 2008;

57:1226-8. 

2. CDC. Cigarette smoking—United States, 1965-2008. In: CDC health 

disparities and inequalities report—United States, 2011. MMWR 

2011;60(Suppl; January 14, 2011).

3. CDC. CDC health disparities and inequalities report—United States, 

2011. MMWR 2011;60(Suppl; January 14, 2011).

4. CDC. Introduction: CDC Health Disparities and Inequalities Report—

United States, 2013. In: CDC Health Disparities and Inequalities 

Report—United States, 2013. MMWR 2013;62(No. Suppl 3)

5. US Department of Health and Human Services, Office of the Surgeon 

General. Preventing tobacco use among youth and young adults; a report 

of the Surgeon General. Atlanta, GA: US Department of Health and 

Human Services, CDC; 2012.

6. Institute of Medicine. Ending the tobacco problem: a blueprint for the 

nation. Washington, DC: Institute of Medicine, 2007.

7. Niederdeppe J, Kuang X, Crock B, Skelton A. Media campaigns to 

promote smoking cessation among socioeconomically disadvantaged 

populations; what do we know, what do we need to learn, and what 

should we do now? Social Science & Medicine 2008;67:1343-55.

8. National Cancer Institute. The role of media in promoting and reducing 

tobacco use. Tobacco Control Monograph No. 19. Bethesda, MD: US 

Department of Health and Human Services, National Institutes of 

Health, National Cancer Institute, NIH Pub. No. 07-6242, June 2008.

9. Caraballo RS, Giovino GA, Pechacek TF, Mowery PD. Factors associated 

with discrepancies between self-reports on cigarette smoking and 

measured serum cotinine levels among persons aged 17 years or older: 

Third National Health and Nutrition Examination Survey, 1988-1994. 

Am J Epidemiol 2001;53:807-14.

10. Caraballo RS, Yee SL, Gfroerer J, Mirza SA. Adult tobacco use among 

racial and ethnic groups living in the United States, 2002–2005. Prev 

Chronic Dis 5. Available at http://www.cdc.gov/pcd/issues/2008/

jul/07_0116.htm. 

11. CDC. Best practices for comprehensive tobacco-control programs. 

Atlanta, GA: U.S. Department of Health and Human Services, CDC; 

2007. Available at http://www.cdc.gov/tobacco/tobacco_control_

program/stateandcommunity/best_practices. 

12. Koh HK, Piotrowski JJ, Kumanyika S, Fielding JE. Healthy People a 

2020 vision for the social determinants approach. Health Education and 

Behavior 2011;38:1207-12.

13. U.S. Department of Health and Human Services, Office of the Surgeon 

General. Tobacco use among U.S. racial/ethnic minority groups—

African Americans, American Indians and Alaska Natives, Asian 

Americans and Pacific Islanders, and Hispanics: a report of the Surgeon 

General. Atlanta, GA: U.S. Department of Health and Human Services, 

CDC; 1998.
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MMWR / November 22, 2013 / Vol. 62 / No. 3 85

Health Outcomes: Morbidity
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MMWR / November 22, 2013 / Vol. 62 / No. 3 87

Introduction

Over the 20th century, the U.S. population has witnessed 

major changes in fatal and nonfatal health outcomes. Mortality 

has declined, and life expectancy has increased continuously; 

chronic conditions have replaced acute diseases as leading causes 

of both illness and death (1). During 1900–2008, average life 

expectancy at birth for the total U.S. population increased from 

47.3 years in 1900 to 78.1 years in 2008 (2), a gain of 30.8 years. 

In addition, an increasing proportion of the U.S. population is 

aged >65 years. According to the U.S. Census Bureau estimates, 

at the beginning of the 20th century, the U.S. population aged 

>65 years constituted only 4.1 percent of the total population; 

by 2008, the percentage of the total U.S. population aged 

>65 years was 12.8% (3,4). However, declines in mortality 

are not necessarily associated with declines in morbidity or 

the consequences of chronic conditions on life activities. The 

possibility that longer life might be accompanied by poor 

health makes it essential to develop measures that account for 

both mortality and morbidity at the same time. Hence, over 

the past 40 years, a new set of health measures (e.g., “healthy 

life expectancies”) have been developed that account for both 

mortality and life spent free of the consequences of ill health. 

One of these newly developed set of measures (called “active 

life expectancy”) is the average number of years expected to be 

lived without activity limitations.

In general, being “active” entails the continuing participation 

of a person in social, economic, cultural, spiritual, and civic 

affairs (5). In health studies, the context in which “being 

active” has been used has varied depending on the population 

group under study (6–9). In this report, “active” is used to 

differentiate between a person with limitations in social roles 

and one without such limitations. This analysis focuses on 

activity limitations caused by chronic conditions. Active life 

expectancy or active life at any age is defined as the remaining 

years of life free of activity limitations (YFAL) caused by 

chronic conditions.

This report is part of the second CDC Health Disparities 

and Inequalities Report (CHDIR). The 2011 CHDIR (10) was 

the first CDC report to assess disparities across a wide range of 

diseases, behavior risk factors, environmental exposures, social 

determinants, and health-care access. The topic presented in 

this report is based on criteria that are described in the 2013 

CHDIR Introduction (11). This report provides information 

on disparities in YFAL as a result of chronic conditions, a topic 

that was not discussed in the 2011 CHDIR. The purposes of 

this report are to discuss and raise awareness of differences 

in the characteristics of persons who experience chronic 

condition–induced physical activity limitations and to prompt 

actions to reduce these disparities.

Methods

To assess disparities in YFAL as a result of chronic conditions, 

CDC analyzed data from the National Vital Statistics System 

(NVSS) and the National Health Interview Survey (NHIS). 

Demographic variables analyzed included sex and race. Period 

life tables for males and females and for the white and black 

populations of the United States for each year from 1999 

through 2008 come from CDC’s National Center for Health 

Statistics (NCHS). Expected years free of chronic condition–

induced activity limitations by ethnicity, environmental, or 

behavior risk factors and socioeconomic determinants of 

access to health care were not included in this analysis because 

officially released NCHS life expectancy estimates by these and 

other similar factors for the years 1999 through 2008 were 

not available. Hence, because officially released annual life 

tables by ethnicity were not available for all the 10 years of the 

study period, the expected YFAL for Hispanics, non-Hispanic 

whites, and non-Hispanic blacks were not analyzed separately. 

The racial category “white” includes person of Hispanic origin 

who identified themselves as white, and the racial category 

“black” includes persons of Hispanic origin who identified 

themselves as black.

Data on activity status come from NHIS, which defines an 

activity limitation as a limit on a person’s ability to perform 

activities normally expected of someone of his or her age. 

Depending on how they answered questions on activity status, 

survey respondents were classified into four categories: 1) not 

limited, 2) unable to perform major activity, 3) limited in kind 

or amount of major activity, and 4) limited in other activities. 

Whenever any form of activity limitation was identified, 

NHIS survey participants also were asked the health condition 

causing the limitation, and the cause was classified as chronic 

Expected Years of Life Free of Chronic Condition–Induced Activity 

Limitations — United States, 1999–2008

Michael T. Molla, PhD

National Center for Health Statistics, CDC

Corresponding author: Michael T. Molla, National Center for Health Statistics, CDC. Telephone: 301-458-4379; E-mail: MMolla@cdc.gov.
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88 MMWR / November 22, 2013 / Vol. 62 / No. 3

or nonchronic. Conditions were considered chronic if they 

cannot be cured once acquired or had existed continuously 

for >3 months after onset (12).

Expected years of life with and without chronic condition–

induced activity limitations were estimated by using a 

demographic-epidemiologic model (13–16) that combined 

average life expectancy and the prevalence of being with and 

without chronic condition–induced activity limitation. The 

expected years without any activity limitations then were used 

to calculate the percentage of remaining life expected to be 

lived without such limitations. Expected years of life with and 

without chronic condition–induced activity limitations were 

estimated separately for four population subgroups: males, 

females, whites, and blacks. The definition of standard error of 

expected YFAL and the statistical test and level of significance 

used have been summarized (Appendix).

Disparities were measured as the deviations from a “referent” 

category rate or prevalence. Absolute difference was measured 

as the simple difference between the rate for a population 

subgroup and the rate for its respective reference group. The 

relative difference, a percentage, was calculated by dividing the 

absolute difference by the value in the referent category and 

multiplying by 100. Whether a disparity in expected YFAL 

between a male and a female or between a white person and 

a black person of the same age was statistically significant was 

tested by using a 2-tailed test at the 95% level of significance. 

A hypothesis of equality was rejected if the value of the absolute 

value of the z-score exceeded 1.96. 

Results

During 1999–2008, total life expectancy improved (Table 1). 

During this 10-year period, total life expectancy at birth for 

males increased by 1.7 years, from 73.9 years in 1999 to 75.6 

years in 2008, and female life expectancy at birth increased by 1.2 

years, from 79.4 years in 1999 to 80.6 years in 2008. Expectation 

of life at birth for the white population increased by 1.2 years, 

from 77.3 years in 1999 to 78.5 years in 2008. Life expectancy 

for the black population increased by 2.6 years, from 71.4 years 

in 1999 to 74.0 years in 2008.

The percentage of total life expectancy that was estimated to 

be spent free of chronic condition–induced activity limitations 

fluctuated over the 10-year period, with the percentage of life 

expectancy spent free of activity limitations slightly lower in 2008 

than in 1999. For males, the percentage of life expected to be 

free of activity limitations was 86.5% in 1999 and declined to 

86.1% in 2008. In 1999, blacks expected to spend about 82.9% 

of their total expected life free of activity limitations compared 

with 82.6% in 2008. For whites, the percentage of life expected 

to be spent free of activity limitations declined from 85.6% in 

1999 to 85.4% in 2008, while it remained almost the same for 

females (84.5% in 1999 and 84.4% in 2008).

In 1999, males would expect to spend 63.9 of their 73.9 years 

of life expectancy free of chronic condition–induced activity 

limitations compared with 67.1 years out of 79.4 years of 

total expected years of life free of such limitations for females. 

The white and black populations would expect to spend 66.2 

years of 77.3 years of total life expectancy and 59.2 years of 

71.4 years of total life expectancy respectively free of chronic 

condition–induced activity limitations. 

By 2008, males would expect to live 65.1 years out of total 

expected years of life of 75.6 years free of activity limitations. 

Females would expect to live 68.0 years of the total life 

expectancy of 80.6 years limitation-free. The white and black 

populations would expect to live 67 activity limitation–free 

years (out of a total life expectancy of 78.5 years) and 61.1 

activity limitation–free years (out of a total life expectancy of 

74.0 years) respectively.

Over the 10 years, improvements in the expected YFAL were 

observed (Table 2). The increase in expected YFAL caused by 

chronic conditions during the 10-year period was 1.2 years for 

males (from 63.9 years in 1999 to 65.1 years in 2008), 0.9 years 

for females (from 67.1 years in 1999 to 68.0 years in 2008), 

0.8 years for whites (from 66.2 years in 1999 to 67.0 years in 

2008), and 1.9 years for blacks (from 59.2 in 1999 to 61.1 

years in 2008). In the 10-year period, the black population 

had the largest increase both in life expectancy at birth as well 

as in expected YFAL caused by chronic conditions.

The changes in the differences in YFAL caused by chronic 

conditions between males and females and between whites 

and blacks from 1999 to 2008 have been calculated (Figure). 

In 1999, the difference in expected YFAL at birth between 

the white and the black populations was 7 years (Figure), 

and the difference between males and females was 3.2 years. 

After 10 years, the difference between the white and the black 

populations had decreased to 5.9 years, and the difference 

between males and females had dropped to 2.9 years.

During the 10-year period, the disparity between the white 

and the black populations declined by 1.1 years, and the 

disparity between males and females dropped by slightly more 

than 0.3 years. However, the observed disparities in expected 

YFAL caused by chronic conditions between men and women 

as well as that between whites and blacks remained statistically 

significant at the 5% level throughout the 10-year period 

(Table 2).
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MMWR / November 22, 2013 / Vol. 62 / No. 3 89

Discussion

Between 1999 and 2008, life expectancy and expected YFAL 

at birth increased for all the population subgroups indicating 

the expected improvements both in mortality and quality of 

life. For males, life expectancy at birth increased from 73.9 

in 1999 to 75.6 years in 2008. For females, life expectancy 

increased from 79.4 years in 1999 to 80.4 years in 2008. For 

the white population, life expectancy increased from 77.3 years 

in 1999 to 78.5 years in 2008, and for the black population, 

it increased from 71.4 years in 1999 to 74.0 years in 2008. 

During the same 10-year period, expected male YFAL at birth 

increased from 63.9 years in 1999 to 65.1 years in 2008. For 

females, expected YAFL at birth increased from 67.1 years in 

1999 to 68.0 years in 2008. For the white population, expected 

YFAL at birth increased from 66.2 years in 1999 to 67.0 years 

in 2008, and for the black population, it increased from 59.2 

years in 1999 to 61.1 years in 2008.

Significant disparities in expected YFAL existed between 

males and females as well as between the white and the black 

populations throughout the 10-year period. However, the 

disparities in expected YFAL have been declining throughout 

the course of the 10-year period. Disparities in YFAL between 

males and females decreased by 0.3 year, from 3.2 years in 1999 

to 2.9 years in 2008. In the same period, disparities between 

the white and the black populations decreased by more than 

one year from 7.0 years in 1999 to 5.9 years in 2008. These 

results are consistent with results of other similar studies and 

federal government health reports (17–18).

The 10-year health initiative Healthy People 2010 had as its 

two overarching goals when it was launched in 2000 increasing 

the quality and years of healthy life of the U.S. population 

and eliminating health disparities. The final assessment of 

this initiative has concluded that during the 10-year period 

2000–2010, life expectancy improved for the populations that 

could be assessed; women had longer life expectancy than men, 

and the white population had a longer life expectancy than 

the black population; and differences were observed both by 

race and sex in life expectancy measure (at birth) and expected 

YFAL. On the basis of data from 2006–2007, on average, the 

TABLE 1. Life expectancy at birth and expected years free of activity limitations caused by chronic conditions, by sex and race — United States, 

1999–2008 

Year

Male Female White Black

LE YFAL

YFAL as 

% of LE LE YAFL

YFAL as 

% of LE LE YAFL

YFAL as 

% of LE LE YAFL

YFAL as 

% of LE

1999 73.9 63.9 86.5 79.4 67.1 84.5 77.3 66.2 85.6 71.4 59.2 82.9

2000 74.1 64.3 86.8 79.3 67.7 85.4 77.3 66.6 86.2 71.8 60.2 83.8

2001 74.2 64.0 86.3 79.4 67.5 85.0 77.4 66.4 85.8 72.0 59.5 82.6

2002 74.3 64.1 86.3 79.5 67.2 84.5 77.4 65.3 84.4 72.1 59.4 82.4

2003 74.5 64.5 86.6 79.5 67.2 84.5 77.6 66.5 85.7 72.3 59.6 82.4

2004 74.9 64.8 86.5 79.9 67.8 84.9 77.9 66.9 85.9 72.8 60.0 82.4

2005 74.9 64.9 86.6 79.9 68.1 85.2 77.9 67.0 86.0 72.8 61.1 83.9

2006 75.1 65.1 86.7 80.2 68.4 85.3 78.2 67.3 86.1 73.2 61.2 83.6

2007 75.4 65.1 86.3 80.4 68.0 84.6 78.4 67.1 85.6 73.6 60.2 81.8

2008 75.6 65.1 86.1 80.6 68.0 84.4 78.5 67.0 85.4 74.0 61.1 82.6

Abbreviations: LE = life expectancy at birth; YFAL = years free of activity limitations.

Source: National Vital Statistics System and National Health Interview Survey, 1999–2008.

TABLE 2. Difference in expected years free of activity limitations caused by chronic conditions, by sex and race — United States, 1999–2008

Year

Expected YFAL

Difference (yrs)* 

Expected YFAL

Male Female White Black Difference (yrs)* 

1999 63.9 67.1 3.2 66.2 59.2 7.0

2000 64.3 67.7 3.4 66.6 60.2 6.4

2001 64.0 67.5 3.5 66.4 59.5 6.9

2002 64.1 67.2 3.1 65.3 59.4 5.9

2003 64.5 67.2 2.7 66.5 59.6 6.9

2004 64.8 67.8 3.0 66.9 60.0 6.9

2005 64.9 68.1 3.2 67.0 61.1 5.9

2006 65.1 68.4 3.3 67.3 61.2 6.1

2007 65.1 68.0 2.9 67.1 60.2 6.9

2008 65.1 68.0 2.9 67.0 61.1 5.9

Abbreviation: YFAL = years free of activity limitations.

Source: National Vital Statistics System and National Health Interview Survey, 1999–2008.

* Implies statistically significant difference in expected years without activity limitations at p<0.05.
Supplement

90 MMWR / November 22, 2013 / Vol. 62 / No. 3

U.S. population was expected to spend 66.2 years of their 

entire lives free of activity limitations (19). 

During 1999–2008, expected YFAL caused by chronic 

conditions increased for both males and females and for 

both blacks and whites. During the entire 10 years, although 

disparities in expected YFAL existed between males and females 

as well as between whites and blacks, the extent of these 

disparities declined during the 10-year period.

Limitations

The findings provided in this report are subject to at least two 

limitations. First, estimates of expected YFAL caused by chronic 

conditions are based on current life expectancy estimates and 

the prevalence of activity limitations. Annual life expectancy 

estimates are based on the total U.S. population whereas 

prevalence rates on activity limitations come from NHIS, 

which does not include the institutionalized population of the 

United States. However, because the size of this population 

is very small compared with the total household population, 

the effect of the exclusion of the group on the comparison of 

estimates over time is assumed to be minimal. Second, estimates 

in this analysis might have been sensitive to the operational 

definition of expected YFAL caused by chronic conditions. 

Activity limitation is part of a larger continuum process known 

as the “disablement process.” Hence, whenever measures such 

as activity limitations induced by chronic conditions (which are 

discrete in nature) are used, cut-off points on the continuum 

have to be determined to differentiate those with and without 

limitations. These cut-off points are functions of the operational 

definitions and might vary from one study to another. Although 

the estimates could be sensitive to these operational definitions, 

the effect of such definitions on the comparison of estimates 

over time is assumed to be minimal.

Conclusion

The findings provided in this report indicate that during 

the 10-year period 1999–2008, while disparities in expected 

years free of chronic condition caused activity limitations still 

existed between males and females as well as between the white 

and the black populations, expected YFAL increased for all 

four population subgroups studied, and disparities decreased. 

Increasing the length of life, improving the quality of life, and 

eliminating health disparities among population groups have 

been the major health goals of all the Healthy People initiatives 

since the decade-long health programs were initiated with 

the publication of the Surgeon General ‘s Report on Health 

Promotion and Disease Prevention in 1979 (20). 

FIGURE. Disparities in expected (at birth) years free of activity limitations caused by chronic conditions, by race and sex — United States, 1999 

and 2008

Source: National Vital Statistics System and National Health Interview Survey, 1999 and 2008.

2.9 years

5.9 years

3.2 years

7.0 years

0 1 2 3 4 5 6 7 8

Female vs. male

White vs. black

1999

2008

Year
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 91

The overarching goals of the first Healthy People initiative 

were to decrease mortality and increase independence among 

older adults by 1990. The first two of the three major goals of 

the second 10-year health initiative (Healthy People 2000) were 

to increase the span of healthy life and to reduce disparities 

in health status (21). Healthy People 2010 focused on two 

major goals: to increase quality of years of healthy life and to 

eliminate health disparities. Healthy People 2020, the fourth 

10-year national health initiative, has four major goals, one 

of which focuses on achieving health equity by eliminating 

health disparities.

The findings of this report as well as reports of the first 

three Healthy People programs demonstrate that expected 

years of life are getting longer, health-related quality of life is 

improving, and health disparities between population groups 

are decreasing. However, group comparisons also demonstrate 

that disparities in mortality (as measured by expectation of 

life at birth) and health-related quality of life (as measured by 

expected YFAL caused by chronic conditions) still exist. 

Reference

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J Med 1980;303:130–5.

2. Miniño AM, Murphy SL, Xu J, Kochanek KD. Death: final data for 

2008. Natl Vital Stat Rep 2011;59(10).

3. US Census Bureau. Resident population estimates of the United States 

by age and sex: April 1, 1990 to July 1, 1999, with short-term projection 

to November 1, 2000. Washington, DC: US Census Bureau; 2001.

4. US Census Bureau. Resident population—estimates by age, sex, and 

race: July 1, 1900 (excluding Alaska and Hawaii). Washington, DC: US 

Census Bureau; 2004.

5. World Health Organization. Active ageing: a policy framework: a 

contribution of the second United Nations World Assembly on Ageing. 

Geneva, Switzerland: World Health Organization; 2002.

6. Katz S, Branch LG, Branson MH, Papsidero JA, Beck JC, Greer DS. 

Active life expectancy. N Engl J Med 1983;309:1218–24.

7. Manton KG, Stallard E. Cross-sectional estimates of active life expectancy 

for the U.S. elderly and oldest-old population. J Gerontol 1991;

46(Suppl):S170–82.

8. Crimmins EM, Hayward MD, Saito Y. Differentials in active life 

expectancy in the older population of the United States. J Gerontol 

1996;51(Suppl):S111–20.

9. Wolf DA, Laditka SB, Laditka JN. Patterns of active life among older 

women: differences within and between groups. J Women Aging 

2002;14:9–26.

10. CDC. CDC health disparities and inequalities report—United States, 

2011. MMWR 2011;60(Suppl; January 14, 2011).

11. CDC. Introduction: In: CDC health disparities and inequalities report—

United States, 2013. MMWR 2013;62(No. Suppl 3).

12. CDC. 2008 National Health Interview Survey (NHIS): public use 

data release. NHIS survey description. Hyattsville, MD: US 

Department of Health and Human Services, CDC, National Center 

for Health Statistics; 2009.

13. Sanders BS. Measuring community health levels. Am J Public Health 

1964;54:1063–70.

14. Sullivan DF. A single index of mortality and morbidity. Health Services 

and Mental Health Administration. Health Reports 1971;86:347–54.

15. Rogers A, Rogers RG, Belanger A. Longer life but worse health? 

Measurement and dynamics. Gerontologist 1990;30:640–9.

16. Molla MT, Wagener DK, Madans JH. Summary measures of population 

health: methods for calculating healthy life expectancy. Healthy People 

2010 Statistical Notes No 21. Hyattsville, MD: US Department of Health 

and Human Services, CDC, National Center for Health Statistics; 2001.

17. Manton KG, Gu X, Lamb VL. Long-term trends in life expectancy and active 

life expectancy in the United States. Popul Dev Rev 2006;32:81–105.

18. Molla MT, Madans JH. Life Expectancy free of chronic condition 

induced activity limitations among white and black Americans, 2000–

2006. Vital Health Stat 2010;3(34).

19. CDC. Healthy people 2010: final review. Atlanta, GA: US Department 

of Health and Human Services, US Public Health Service, CDC; 2011. 

Available at http://www.cdc.gov/nchs/data/hpdata2010/hp2010_final_

review.pdf. 

20. Department of Health, Education, and Welfare, Public Health Service. 

Healthy people: the Surgeon General’s report on health promotion and 

disease prevention. Washington, DC: Government Printing Office; 1979.

21. US Department of Health and Human Services. Healthy people 2010: 

understanding and improving health. Washington, DC: US Department 

of Health and Human Services; 2000.
Supplement

92 MMWR / November 22, 2013 / Vol. 62 / No. 3

Standard Errors for Expected Years 

Free of Activity Limitations

Expected years free of activity limitations (YFAL) ( ) at 

age x is defined as the remaining years of life that is free of 

limitations caused by chronic conditions and is given by:

 [1]

where

 is the remaining years free of activity limitation due to 

chronic conditions for persons who have reached age x; 

lx is the number of survivors at age x;

(1 - nπx) represents the age-specific health state free of 

activity limitations due to chronic conditions;

nLx is the total number of years lived by a cohort in the 

age interval (x, x+n); and 

ω is the oldest age category.

The variance and standard errors of the estimated YFAL 

can be calculated based on the variances of the prevalence 

rates of the different health states. Within each age group, 

the prevalence of each health state is a proportion with an 

associated standard error. Since there are only two health states, 

the variance of the health state with activity limitations equals 

to the variance of its complement. The variance S2 of (nπx ) 

or (1- nπx) is given by the variance of a binomial distribution 

as follows:

Nxxxnxnxn S /)1([)( 2 −= πππ ]. [2]

where nNx is the number of persons in the interval (x, x+n) 

of the sample from which the prevalence rates were computed.

Equation 2 can then be used to calculate the variance of 

expected YFAL, '

x e using the following formula:

)].1([ 1 )( 22

2

'

in

xi

in

x

x SL

l

eVAR π

π

= ∑ − =

 [3]

The standard error of the expected YFAL caused by chronic 

conditions at age x is simply the square root of its variance.

Test of Significance

Disparities between the expected years of life free of activity 

limitations of two population subgroups of the same age group 

can be tested by using a statistical method commonly used 

for testing the significance of a difference between two means 

using the following formula:

)( '

2,

'

1,

2

'

2,

'

1,

xx

xx

eeS

ee

z

−

− =

, [4]

where, '

x 1, e and '

x 2, e are the expected YFAL of two different 

population subgroups of the same age x.

The critical value of a z-score for a 2-tailed test at the 95% 

level of significance is 1.96, i.e., the hypothesis of equality is 

rejected if the absolute value of z exceeds 1.96.

Appendix

Definition of Terms Used in This Report

'

x e

,)1( ' 1

inin

x

x L

l

e ∑ −= π

'

x e
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 93

Introduction

Asthma is a chronic inflammatory disorder of the airways that 

is characterized by episodic and reversible airflow obstruction, 

airway hyper-responsiveness, and underlying inflammation. 

Common asthma symptoms include wheezing, coughing, and 

shortness of breath (1). With correct treatment and avoidance 

of exposure to environmental allergens and irritants that are 

known to exacerbate asthma, the majority of persons who have 

asthma can expect to achieve optimal symptom control (2).

Multiple reports published previously provide detailed 

surveillance information on asthma (1,3–8). A 1987 report 

that included asthma surveillance data for 1965–1984 

identified differences among certain demographic groups by 

age, sex, and race/ethnicity (5). Subsequent asthma surveillance 

reports confirmed these differences and documented that the 

differences have persisted over time (1,3,4,6). These reports 

indicate that population-based asthma prevalence rates, 

emergency department visit rates, and hospitalization rates 

were higher among blacks than among whites, higher among 

females than among males, higher among children (aged 0–17 

years) than among adults (aged ≥18 years), and higher among 

males aged 0–17 years than among females in the same age 

group. In addition, more detailed analysis of ethnicity data 

demonstrated that asthma health outcomes differed among 

Hispanic groups. Hispanics of Puerto Rican descent (origin or 

ancestry) had higher asthma prevalence and death rates than 

other Hispanics (e.g., Hispanics of Mexican descent), nonHispanic blacks, and non-Hispanic whites (7,8). 

Current asthma prevalence rates among the demographic 

groups for the years covered in this report were similar to those 

in previous CDC reports (1,3,4,6). During 2006–2010, an 

estimated 8.0% of the U.S. population had current asthma. 

Asthma prevalence varied by demographic group: 6.9% among 

males, 9.0% among females, 9.4% among children, 7.6% 

among adults, 7.9% among whites, 10.5% among blacks, 

10.8% among American Indians/Alaska Natives, 5.0% among 

Asians, 14.4% among multi-race/other-race persons, 15.9% 

among Puerto Ricans, and 5.4% among Mexicans.

This report is part of the second CDC Health Disparities 

and Inequalities Report (CHDIR). The 2011 CHDIR (9) was 

the first CDC report to assess disparities across a wide range of 

diseases, behavior risk factors, environmental exposures, social 

determinants, and health-care access. The topic presented in 

this report is based on criteria that are described in the 2013 

CHDIR Introduction (10). This report provides information 

regarding asthma attacks among persons with current asthma 

that supplements information about current asthma prevalence 

provided in the 2011 CHDIR (4). The purposes of this report 

are to discuss and raise awareness of differences in asthma 

attacks among persons with current asthma and to prompt 

actions to reduce these disparities. 

Methods

To examine whether disparities in asthma attacks exist 

among persons with current asthma by selected demographic 

characteristics, CDC analyzed data from the 2001–2010 

National Health Interview Survey (NHIS). NHIS is an annual, 

in-person survey of the civilian, noninstitutionalized U.S. 

population based on a multistage sampling of households 

(11). An adult family member is selected to act as a proxy 

respondent for children aged 0–17 years. NHIS includes several 

questions about asthma. The first question, “Have you ever 

been told by a doctor or other health professional that you 

had asthma?” has been used as a lifetime prevalence measure 

for asthma since 1997. A second question, “Do you still have 

asthma?” was added in 2001. Respondents are considered 

to have current asthma if they answer “yes” to both of these 

questions. A response of “yes” to a third question, “During 

the past 12 months, have you had an episode of asthma or an 

asthma attack?” indicates an attack in the past year and was used 

in this analysis as an indicator of symptom control (1,3,4,6). 

The percentage of persons with current asthma who reported 

an asthma attack in the past year, crude prevalence ratios, 

and adjusted prevalence ratios were estimated for selected 

demographic characteristics: race/ethnicity, sex, age (children 

aged 0–17 years, adults aged ≥18 years, and eight age groups), 

Asthma Attacks Among Persons with Current Asthma — 

United States, 2001–2010

Jeanne E. Moorman, MS

Cara J. Person, MPH

Hatice S. Zahran, MD

Division of Environmental Hazards and Health Effects, National Center for Environmental Health, CDC

Corresponding author: Jeanne Moorman, Division of Environmental Hazards and Health Effects, National Center for Environmental Health, CDC. 

Telephone: 770-488-3726; E-mail: zva9@cdc.gov.
Supplement

94 MMWR / November 22, 2013 / Vol. 62 / No. 3

educational attainment for adults, place of birth, geographic 

region, and federal poverty level (FPL). Estimated percentages 

with standard errors and prevalence ratios with 95% confidence 

intervals (CIs) are presented for 2006–2010. Estimated 

percentages with standard errors for more limited demographic 

groups are presented for both 2001–2004 and 2006–2010 for 

a historical comparison. Race/ethnicity is categorized on the 

basis of the respondents’ self-reported race and ethnicity. NonHispanic race groups include white, black, American Indian/

Alaska Native, Asian, and other or multiple races. Persons of 

Hispanic ethnicity might be of any race or combination of 

races. Hispanic subgroups include Puerto Rican, Mexican, and 

other Hispanic. FPL is based on U.S. Census Bureau poverty 

thresholds. The poverty threshold is based on the size of the 

family and the ages of family members. Income divided by the 

poverty threshold is called the “income-to-poverty ratio” (12). 

Multiple years of survey data were combined to provide 

stable estimates for relatively small respondent groups. If 

the relative standard error was >30%, or if the sample size 

(denominator) was <50, estimates were considered unreliable 

and were suppressed. Analysis software was used to account for 

the complex survey design, and sample weights were used to 

produce national estimates. A multivariate (binary response) 

logistic regression model was used to determine the association 

(adjusted prevalence ratio [APR]) between reporting an asthma 

attack in the past year and demographic variables including 

age, sex, race/ethnicity, educational attainment, FPL, place of 

birth, and geographic region. A univariate logistic regression 

model was used to determine the association (crude prevalence 

ratio) between an asthma attack and each variable separately. 

The Wald chi-square test statistic was used for all logistic 

regression models to test for an association between the 

dependent variable (asthma attack status) and independent 

variables of interest. Chi-square tests and z-tests were used to 

test for demographic group and time period differences. All 

statistical tests were 2-sided, with p<0.05 denoting statistical 

significance. Comparative terms used to describe findings in 

this report (e.g., “higher” and “similar”) indicate the results of 

statistical testing at p<0.05. 

Results

During 2006–2010, reported attacks among those with 

current asthma were higher for females (53.5%) than for males 

(48.8%) (Table 1). The difference in reporting an asthma attack 

by sex was not statistically significant among children (adjusted 

prevalence ratio [APR]: 1.1) but was significant among adults 

(APR: 1.4) after adjusting for age, race/ethnicity, educational 

attainment, federal poverty level, place of birth, and geographic 

region (Table 2). Overall, asthma attacks were reported more 

frequently for children (56.1%) than for adults (49.6%) 

(Table 1). Asthma attacks were reported more frequently for 

children aged 0–4 years (APR: 1.9) and 5–11 years (APR: 1.3) 

than for children aged 12–17 years (Table 2). Among adults, 

persons aged 18–34 (APR: 1.4), 35–44 (APR: 2.0), 45–54 

(APR: 1.9), and 55–64 years (APR: 1.6) were more likely to 

report asthma attacks than persons aged ≥65 years. Regardless 

of age, persons with asthma living in the West (54.5%) and 

the South (53.1%) were more likely to report asthma attacks 

than persons living in the Midwest (49.4%) and the Northeast 

(47.8%) (Table 1). The differences in reporting an asthma 

attack between the South and West regions compared with the 

reference region (Northeast) remained statistically significant 

after adjusting for covariates (Table 2). No significant 

interaction terms or multicollinearity effects were identified 

among any of the variables in the final model.

For children, reporting an asthma attack did not differ 

significantly by poverty level (range: 56.3% [FPL <100%]–

57.8% [FPL ≥450%]) (Table 1). However, for adults, reporting 

an asthma attack did differ significantly by poverty level. Adults 

with incomes <100% of FPL (53.9%; APR: 1.4) and adults 

with incomes of 100%–249% of FPL (50.1%; APR: 1.2) were 

more likely to report asthma attacks than adults with incomes 

≥450% of FPL (48.9%) (Tables 1 and 2). Among persons 

with incomes <100% of FPL, asthma attacks did not differ 

significantly by race/ethnicity, sex, age, level of education, place 

of birth, or geographic region (Table 1). However, subgroup 

differences in reported asthma attacks were observed among 

persons with higher income levels (100%–249% of FPL, 

250%–449% of FPL, and ≥450% of FPL). In the three higher 

income groups, asthma attacks were reported more frequently 

among females, children, and persons living in the West than 

among males, adults, and persons living in the Northeast, 

respectively (Table 1). Notable changes in reporting an asthma 

attack in the past year were observed between 2001–2004 and 

2006–2010 (Table 3). For many demographic groups (whites, 

blacks, Puerto Ricans, males, females, children, adults, male 

children, persons with incomes <450% of FPL, and those living 

in the Northeast and Midwest), reporting an asthma attack 

decreased significantly. Between 2001–2004 and 2006–2010, 

the disparity between the various Hispanic subgroups and that 

between male children and female children were eliminated, and 

the disparity between adults and children and that among the 

FPL groups decreased. However, more women than men now 

report having had an asthma attack in the past year, and persons 

in the West and the South now report having had an attack 

more often than persons in the Midwest and the Northeast.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 95

TABLE 1. Percentage of persons with current asthma* who reported an asthma attack in the past year,† by selected characteristics — National 

Health Interview Survey, United States, 2006–2010

Characteristic

Total

FPL§

<100% FPL 100%–249% FPL 250%–449% FPL ≥450% FPL

Weighted 

% SE

Sample 

size¶

Weighted 

% SE

Sample 

size

Weighted 

% SE

Sample 

size

Weighted 

% SE

Sample 

size

Weighted 

% SE

Sample 

size

Race/Ethnicity

Non-Hispanic 51.1 (0.6) 11,586 54.3 (1.3) 2,533 52.0 (1.2) 3,476 48.8 (1.2) 2,843 50.3 (1.2) 2,734

White** 51.1 (0.7) 7,552 56.3 (1.8) 1,229 52.1 (1.5) 2,157 48.6 (1.4) 1,982 50.0 (1.3) 2,184

Black** 49.4 (1.1) 3,015 52.1 (2.1) 1,066 48.1 (1.9) 1,042 48.4 (2.7) 606 45.9 (3.9) 138

American Indian/

Alaska Native**

61.6 (7.8) 92 —†† (—) 36 — (—) 28 — (—) 17 — ( — ) 11

Asian** 53.7 (3.1) 474 50.1 (7.9) 72 46.8 (5.9) 94 55.1 (5.3) 138 57.3 (5.1) 170

Other§§ 56.3 (3.3) 436 50.2 (5.8) 126 68.7 (4.4) 147 42.4 (6.3) 97 60.6 (8.0) 66

Hispanic¶¶ 53.8 (1.5) 2,644 57.0 (2.4) 899 52.1 (2.3) 935 49.3 (3.0) 497 58.1 (3.8) 313

Puerto Rican 55.6 (3.4) 663 61.0 (4.4) 299 54.1 (4.8) 102 47.8 (8.1) 52 53.6 (9.9) 64

Mexican 52.6 (2.1) 1,338 54.0 (3.4) 396 52.3 (3.5) 521 49.6 (4.2) 266 55.8 (5.3) 155

Other*** 54.6 (2.6) 643 57.9 (4.2) 204 50.1 (4.3) 214 49.8 (5.7) 131 66.6 (6.8) 94

Sex

Male 48.8 (0.9) 5,699 52.7 (2.0) 1,242 50.0 (1.5) 1,754 46.1 (1.8) 1,388 47.0 (1.8) 1,316

Female 53.5 (0.7) 8,531 56.3 (1.4) 2,190 53.4 (1.3) 2,657 50.9 (1.4) 1,952 53.9 (1.5) 1,731

Age

Child (aged 0–17 yrs) 56.1 (0.9) 4,739 56.3 (1.9) 1,208 56.1 (1.7) 1,544 54.3 (2.1) 1,111 57.8 (2.2) 876

Adult (aged ≥18 yrs) 49.6 (0.7) 9,491 53.9 (1.4) 2,224 50.1 (1.2) 2,867 46.9 (1.4) 2,228 48.9 (1.4) 2,171

Child

Male 55.7 (1.4) 2,740 54.4 (2.7) 717 57.1 (2.3) 884 54.3 (2.7) 631 56.7 (3.0) 508

Female 56.7 (1.5) 1,999 59.4 (2.7) 491 54.8 (3.0) 660 54.3 (3.1) 480 59.5 (3.5) 368

Adult 

Male 44.1 (1.2) 2,959 50.5 (2.8) 525 44.8 (2.1) 870 41.6 (2.5) 757 42.8 (2.2) 807

Female 52.6 (0.8) 6,532 55.2 (1.5) 1,699 53.0 (1.4) 1,997 50.0 (1.7) 1,472 52.9 (1.1) 1,364

Educational attainment 

(aged ≥18 yrs)

Less than high school 51.2 (1.5) 1,889 55.5 (2.2) 889 49.2 (2.3) 712 47.4 (4.6) 215 46.4 (7.5) 73

High school graduate or 

equivalent

44.6 (1.3) 2,349 50.4 (2.7) 584 44.5 (2.3) 864 40.6 (2.7) 570 44.8 (3.3) 331

Some college 50.3 (1.3) 2,090 53.4 (3.0) 464 53.1 (2.4) 680 46.7 (2.4) 552 48.8 (3.3) 394

College graduate or higher 52.1 (1.2) 3,099 59.1 (3.6) 273 56.7 (2.6) 586 51.0 (2.2) 876 50.2 (1.8) 1,364

Place of birth

U.S. and U.S. territories 51.2 (0.6) 13,277 54.6 (1.2) 3,177 52.0 (1.1) 4,110 48.5 (1.2) 3,148 50.3 (1.2) 2,842

Outside U.S. and 

U.S. territories†††

55.5 (2.1) 950 58.4 (4.4) 255 51.8 (3.6) 300 54.8 (5.4) 192 58.2 (4.8) 203

Geographic region§§§

Northeast 47.8 (1.3) 2,731 52.9 (3.0) 699 47.1 (2.2) 781 45.4 (2.9) 608 47.1 (2.4) 642

Midwest 49.4 (1.1) 3,263 51.9 (2.2) 810 50.2 (2.4) 986 45.4 (2.4) 774 50.5 (2.7) 693

South 53.1 (1.0) 4,957 56.1 (1.9) 1,255 53.8 (1.7) 1,669 51.5 (2.0) 1,113 50.7 (2.1) 920

West 54.5 (1.3) 3,279 58.3 (2.9) 669 55.1 (2.2) 974 51.3 (2.5) 844 54.9 (2.2) 792

Total 51.5 (0.6) 14,230 54.8 (1.2) 3,432 52.0 (1.0) 4,411 48.8 (1.2) 3,340 50.8 (1.2) 3,047

Abbreviations: FPL = federal poverty level; SE = standard error.

* Persons who answered “yes” to the questions, “Have you ever been told by a doctor or other health professional that you had asthma?” or “Has a doctor or other health professional ever 

told you that (sample child) had asthma?” and “yes” to the question, “Do you (does sample child) still have asthma?”

† Persons who answered “yes” to the question, “During the past 12 months, have you (has sample child) had an episode of asthma or an asthma attack?”

§ FPL was based on U.S. Census Bureau poverty thresholds (available at http://www.census.gov/hhes/www/poverty.html). Imputed income values were used when income was not 

reported.

¶ Unweighted pooled sample size, 2006–2010. Because of item nonresponse, individual characteristic categories might not sum to total.

** Includes persons who indicated only a single race. †† If the relative SE is >30%, or if the sample size (denominator) is <50, estimates are considered unreliable and are suppressed. §§ Includes Native Hawaiians and Other Pacific Islanders, persons reporting more than one race, and persons reporting their race as something other than those listed. ¶¶ Persons of Hispanic ethnicity can be of any race or combination of races.

*** Includes persons reporting Cuban, Dominican, Central or South American, Spanish, multiple, and unspecified Hispanic ancestry. ††† Includes U.S. citizens born abroad (one or both of whose parents were U.S. citizens) as well as naturalized citizens and noncitizens. §§§ Northeast: Connecticut, Maine, Massachusetts, New Jersey, New Hampshire, New York, Pennsylvania, Rhode Island, and Vermont; Midwest: Illinois, Indiana, Iowa, Kansas, Michigan, 

Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin; South: Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, 

Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West Virginia; West: Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, 

New Mexico, Oregon, Utah, Washington, and Wyoming.
Supplement

96 MMWR / November 22, 2013 / Vol. 62 / No. 3

TABLE 2. Multivariate association between reporting an asthma attack in the past year* and selected characteristics of persons with current 

asthma† — National Health Interview Survey, United States, 2006–2010

Characteristic

Child (aged 0–17 yrs) Adult (aged ≥18 yrs)

Crude Adjusted Crude Adjusted

PR (95% CI) PR (95% CI) PR (95% CI) PR (95% CI)

Race/Ethnicity

Non-Hispanic, white§ 1.0 Ref. 1.0 Ref. 1.0 Ref. 1.0 Ref.

Non-Hispanic, black§ 0.8 (0.7–1.0) 0.8 (0.7–1.0) 0.9 (0.8–1.0) 0.8 (0.7–0.9)

Non-Hispanic, other¶ 1.0 (0.8–1.4) 0.9 (0.7–1.3) 1.2 (1.0–1.5) 1.1 (0.9–1.4)

Hispanic** 0.9 (0.5–1.1) 0.8 (0.7–1.0) 1.1 (1.0–1.3) 1.0 (0.8–1.2)

Puerto Rican 1.3 (0.9–1.8) 1.6 (1.2–2.3) 1.0 (0.7–1.5) 1.2 (0.8–1.7)

Mexican/Other†† 1.0 Ref. 1.0 Ref. 1.0 Ref. 1.0 Ref.

Sex

Male 1.0 Ref. 1.0 Ref. 1.0 Ref. 1.0 Ref.

Female 1.0 (0.9–1.3) 1.1 (0.9–1.3) 1.4 (1.3–1.6) 1.4 (1.2–1.5)

Age group (yrs)

0–4 1.9 (1.5–2.4) 1.9 (1.5–2.5) NA NA NA NA

5–11 1.3 (1.1–1.5) 1.3 (1.1–1.6) NA NA NA NA

12–17 1.0 Ref. 1.0 Ref. NA NA NA NA

18–34 NA NA NA NA 1.3 (1.1–1.5) 1.4 (1.2–1.6)

35–44 NA NA NA NA 1.9 (1.6–2.3) 2.0 (1.7–2.4)

45–54 NA NA NA NA 1.8 (1.5–2.2) 1.9 (1.6–2.3)

55–64 NA NA NA NA 1.5 (1.3–1.8) 1.6 (1.4–1.9)

≥65 NA NA NA NA 1.0 Ref. 1.0 Ref.

Educational attainment (aged ≥18 years)

Less than high school education NA NA NA NA 1.0 (0.8–1.1) 0.9 (0.8–1.1)

High school graduate or equivalent NA NA NA NA 0.7 (0.7–0.9) 0.7 (0.6–0.9)

Some college NA NA NA NA 0.9 (0.8–1.1) 0.9 (0.8–1.1)

College graduate or higher NA NA NA NA 1.0 Ref. 1.0 Ref.

Federal poverty level (FPL)§§

<100% FPL 0.9 (0.8–1.2) 1.0 (0.8–1.2) 1.2 (1.1–1.4) 1.4 (1.2–1.6)

100%–249% FPL 0.9 (0.4–1.2) 0.9 (0.8–1.2) 1.1 (0.9–1.2) 1.2 (1.1–1.4)

250%–449% FPL 0.9 (0.7–1.1) 0.9 (0.7–1.1) 0.9 (0.8–1.1) 1.0 (0.8–1.2)

≥450% FPL 1.0 Ref. 1.0 Ref. 1.0 Ref. 1.0 Ref.

Place of birth

U.S. and U.S. territories 1.0 Ref. 1.0 Ref. 1.0 Ref. 1.0 Ref.

Outside U.S. and U.S. territories¶¶ 1.1 (0.7–1.8) 1.3 (0.8–2.0) 1.3 (1.1–1.5) 1.2 (1.0–1.4)

Geographic region***

Northeast 1.0 Ref. 1.0 Ref. 1.0 Ref. 1.0 Ref.

Midwest 1.2 (1.0–1.6) 1.2 (1.0–1.6) 1.0 (0.9–1.2) 1.0 (0.9–1.2)

South 1.3 (1.1–1.7) 1.3 (1.1–1.7) 1.2 (1.0–1.4) 1.2 (1.1–1.4)

West 1.5 (1.1–1.9) 1.5 (1.2–2.0) 1.3 (1.1–1.5) 1.3 (1.1–1.5)

Abbreviations: 95% CI = 95% confidence interval; NA = not applicable; PR = prevalence ratio; Ref. = Referent. 

* Persons who answered “yes” to the question, “During the past 12 months, have you (has sample child) had an episode of asthma or an asthma attack?

† Persons who answered “yes” to the questions, “Have you ever been told by a doctor or other health professional that you had asthma?” or “Has a doctor or other 

health professional ever told you that (sample child) had asthma?” and “yes” to the question, “Do you (does sample child) still have asthma?”

§ Includes persons who indicated only a single race group. 

¶ Includes Asians, American Indians/Alaska Natives, Native Hawaiians and Other Pacific Islanders, persons reporting more than one race, and persons reporting 

their race as something other than those listed. 

** Persons of Hispanic ethnicity can be of any race or combination of races.

†† Includes persons reporting Mexican, Cuban, Dominican, Central or South American, Spanish, multiple, and unspecified Hispanic ancestry.

§§ FPL was based on U.S. Census Bureau poverty thresholds (available at http://www.census.gov/hhes/www/poverty.html). Imputed income values were used when 

income was not reported.

¶¶ Includes U.S. citizens born abroad (one or both of whose parents were U.S. citizens), naturalized citizens, and noncitizens.

*** Northeast: Connecticut, Maine, Massachusetts, New Jersey, New Hampshire, New York, Pennsylvania, Rhode Island, and Vermont; Midwest: Illinois, Indiana, Iowa, 

Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin; South: Alabama, Arkansas, Delaware, District of Columbia, 

Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West Virginia; West: Alaska, 

Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 97

Discussion

Findings from this report are comparable to those of previous 

reports (1,3,4,6). Within the U.S. population, the percentage 

of persons with current asthma reporting an asthma attack in 

the past year varies by demographic and economic groups. 

Similar to current asthma prevalence, asthma attacks were 

more prevalent among females, children, the poor, persons 

of multiple races, and Puerto Ricans. However, while current 

asthma prevalence was higher in the Northeast, attacks were 

reported more frequently in the South and in the West than 

in other regions.

Demographic and socioeconomic characteristics associated 

with more frequent reporting of asthma attacks (e.g., 

females, children, persons living in the South and the West, 

Puerto Ricans, and the poor) were identified. However, 

causality cannot be determined from cross-sectional survey 

data. Surveillance data on asthma attacks cannot be used to 

determine the reasons for the observed differences among 

the demographic and economic subgroups examined in this 

report. The differences might be attributable to differing levels 

of exposure to environmental irritants and allergens (e.g., 

environmental irritants such as tobacco smoke or air pollutants 

and environmental allergens such as house dust mites, 

cockroach particles, and cat and dog dander) (7,9,13) or they 

might be attributable to differences in disease self-management 

or medical treatment. The reasons for the differences in the 

frequency of reported asthma attacks can be addressed only by 

research studies designed to determine the effect of a specific 

exposure or a specific disease management protocol.

Limitations

The results of this analysis are subject to at least three 

limitations. First, the asthma attack prevalence estimates in 

this report rely on self-reported data and thus are subject to 

recall bias. The respondent must recall a physician’s diagnosis 

of asthma correctly, which in turn requires that the physician’s 

diagnosis was correct and conveyed successfully to the person. 

Because no definitive test exists for asthma, the diagnosis 

and self-report cannot be validated; however, a 1993 review 

of asthma questionnaires documented a mean sensitivity 

of 68% and a mean specificity of 94% when self-reported 

data on an asthma diagnosis were compared with a clinical 

diagnosis (1). Second, because NHIS includes only the civilian, 

noninstitutionalized population of the United States, results 

might not be representative of other populations. Finally, 

because NHIS is conducted only in English and Spanish, 

results might not be representative of households whose 

residents have other primary languages.

TABLE 3. Percentage of persons with current asthma* who reported 

an asthma attack in the past year,† by selected characteristics — 

National Health Interview Survey, United States, 2001–2004 and 

2006–2010

Characteristic

2001–2004 2006–2010

% (SE) % (SE)

Race/Ethnicity

Non-Hispanic 55.2 (0.6) 51.1 (0.6)

White§ 54.6 (0.7) 51.1 (0.7)

Black§ 56.6 (1.2) 49.4 (1.1)

Other¶ 59.2 (2.3) 55.9 (2.2)

Hispanic** 55.7 (1.3) 53.8 (1.5)

Puerto Rican 64.2 (2.3) 55.6 (3.4)

Mexican 50.3 (1.9) 52.6 (2.1)

Sex

Male 54.3 (0.8) 48.8 (0.9)

Female 56.0 (0.7) 53.5 (0.7)

Child (aged 0–17 yrs) 62.6 (1.0) 56.1 (0.9)

Male 63.9 (1.2) 55.7 (1.4)

Female 60.8 (1.6) 56.7 (1.5)

Adult (aged ≥18 yrs) 52.0 (0.6) 49.6 (0.7)

Male 47.2 (1.2) 44.1 (1.2)

Female 54.6 (0.8) 52.6 (0.8)

Federal poverty level (FPL)††

<100% FPL 58.9 (1.3) 54.8 (1.2)

100%–249% FPL 56.4 (1.0) 52.0 (1.0)

250%–449% FPL 57.3 (1.2) 48.8 (1.2)

≥450% FPL 53.2 (1.1) 50.8 (1.2)

Geographic region§§

Northeast 54.3 (1.1) 47.8 (1.3)

Midwest 55.7 (1.1) 49.4 (1.1)

South 55.5 (0.9) 53.1 (1.0)

West 55.3 (1.2) 54.5 (1.3)

Total 55.3 (0.5) 51.5 (0.6)

Abbreviation: SE = standard error.

* Persons who answered “yes” to the questions, “Have you ever been told by a 

doctor or other health professional that you had asthma?” or “Has a doctor 

or other health professional ever told you that (sample child) had asthma?” 

and “yes” to the question, “Do you (does sample child) still have asthma?”

† Persons who answered “yes” to the question, “During the past 12 months, 

have you (has sample child) had an episode of asthma or an asthma attack?

§ Includes persons who indicated only a single race. 

¶ Includes Asians, American Indians/Alaska Natives, Native Hawaiians and 

Other Pacific Islanders, persons reporting more than one race, and persons 

reporting their race as something other than those listed.

** Persons of Hispanic ethnicity can be of any race or combination of races.

†† FPL was based on U.S. Census Bureau poverty thresholds (available at http://

www.census.gov/hhes/www/poverty.html). Imputed income values were 

used when income was not reported.

§§ Northeast: Connecticut, Maine, Massachusetts, New Jersey, New Hampshire, 

New York, Pennsylvania, Rhode Island, and Vermont; Midwest: Illinois, Indiana, 

Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, 

South Dakota, and Wisconsin; South: Alabama, Arkansas, Delaware, District 

of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, 

North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and 

West Virginia; West: Alaska, Arizona, California, Colorado, Hawaii, Idaho, 

Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming.
Supplement

98 MMWR / November 22, 2013 / Vol. 62 / No. 3

Conclusion

With the exception of a few specific occupational exposures, 

the exact cause of asthma is unknown. Once diagnosed, asthma 

requires ongoing comprehensive management on a long-term 

basis. Appropriate management requires both access to the 

health system and appropriate use of that system. Financial 

resources and social support are instrumental in long-term 

management of all chronic conditions, not just asthma 

(13–15). Acquiring self-management knowledge and skills 

and limiting exposure to environmental allergens and irritants 

are necessary to improve health and quality of life for persons 

with asthma, and especially for those with uncontrolled asthma 

(2,13–15). 

Although surveillance data can identify disproportionately 

affected groups, research is needed into the role of selfmanagement factors, environmental exposures, health-care 

system factors, and financial factors to understand better how 

their interrelations affect individual asthma management 

and control. Identifying the population-specific factors that 

contribute to asthma exacerbations among disproportionately 

affected demographic and socioeconomic groups can lead to 

targeted interventions. Strategies for asthma self-management 

and medical treatment protocols for asthma that are culturally 

appropriate and take into consideration population-specific 

characteristics can reduce the occurrence and severity of 

asthma exacerbations (14). For example, an intervention for 

children with asthma that included the use of multitrigger, 

multicomponent environmental factors resulted in improved 

symptom control and reduced the number of school days 

missed (16). Similar effective interventions are needed to 

address other disproportionately affected demographic and 

economic groups identified in this report.

References 

1. CDC. National surveillance for asthma—United States, 1980–2004. 

MMWR 2007;56(No. SS-8).

2. National Institutes of Health. National Heart, Lung, and Blood Institute. 

Expert panel report 3: guidelines for the diagnosis and management of 

asthma. Bethesda, MD: US Department of Health and Human Services, 

National Institutes of Health, National Heart, Lung, and Blood Institute, 

National Asthma Education Program; 2007.

3. Moorman JE, Akinbami LJ, Bailey CM, et al. National surveillance of 

asthma—United States, 2001–2010. Vital Health Stat 2012;3(35).

4. CDC. Current asthma prevalence—United States, 2006–2008. In: CDC 

health disparities and inequalities report—United States, 2011. MMWR 

2011;60(Suppl; January 14, 2011).

5. Evans R 3rd, Mullally DI, Wilson RW, et al. National trends in the 

morbidity and mortality of asthma in the US. Prevalence, hospitalization 

and death from asthma over two decades: 1965–1984. Chest 

1987;91(Suppl 6):S65–74.

6. Mannino DM, Homa DM, Akinbami LJ, Moorman JE, Gwynn C, 

Redd SC. Surveillance for asthma—United States, 1980–1999. MMWR 

2002;51(No. SS-1):1–13.

7. Homa DM, Mannino DM, Lara M. Asthma mortality in U.S. Hispanics 

of Mexican, Puerto Rican, and Cuban heritage, 1990–1995. Am J Respir 

Crit Care Med 2000;161:504–9.

8. Rose D, Mannino DM, Leaderer BP. Asthma prevalence among US 

adults, 1998–2000: role of Puerto Rican ethnicity and behavioral and 

geographic factors. Am J Public Health 2006:96:880–8.

9. CDC. CDC health disparities and inequalities report—United States, 

2011. MMWR 2011;60(Suppl; January 14, 2011).

10. CDC. Introduction: In: CDC health disparities and inequalities report—

United States, 2013. MMWR 2013;62(No. Suppl 3).

11. CDC. National Health Interview Survey (NHIS), 2007 data release. 

Hyattsville, MD: US Department of Health and Human Services, CDC, 

National Center for Health Statistics; 2010. Available at http://www.

cdc.gov/NCHS/nhis/nhis_2007_data_release.htm.

12. US Census Bureau. How the Census Bureau measures poverty. Available at 

http://www.census.gov/hhes/www/poverty/about/overview/measure.html. 

13. Institute of Medicine. Clearing the air: asthma and indoor air exposures. 

Washington, DC: National Academies Press; 2000.

14. US Department of Health and Human Services. Healthy people 2010: 

understanding and improving health. 2nd ed. Washington, DC: US 

Government Printing Office; 2000. 

15. Wade S, Weil C, Holden G, et al. Psychosocial characteristics of innercity children with asthma: a description of the NCICAS psychosocial 

protocol. National Cooperative Inner-City Asthma Study. Pediatr 

Pulmonol 1997;24:263–76. 

16. US Task Force on Community Prevention Services. Asthma control: 

home-based multi-trigger, multicomponent environmental interventions. 

Available at http://www.thecommunityguide.org/asthma/

multicomponent.html.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 99

Introduction

In 2011, an estimated 26 million persons aged ≥20 years 

(11.3% of the U.S. population) had diabetes (1). Both the 

prevalence and incidence of diabetes have increased rapidly 

since the mid-1990s, with minority racial/ethnic groups and 

socioeconomically disadvantaged groups experiencing the 

steepest increases and most substantial effects from the disease 

(2–5).

This analysis and discussion of diabetes is part of the second 

CDC Health Disparities and Inequalities Report (CHDIR). 

The 2011 CHDIR (6) was the first CDC report to assess 

disparities across a wide range of diseases, behavioral risk 

factors, environmental exposures, social determinants, and 

health-care access. The 2011 CHDIR report discussed the 

magnitude and patterning of absolute and relative measures 

of disparity in the prevalence and incidence rate of medically 

diagnosed diabetes during 2004 and 2008 and identified 

marked disparities in terms of race/ethnicity, socioeconomic 

status, disability status, and geography (7). The topic presented 

in this report is based on criteria that are described in the 2013 

CHDIR Introduction (8). This report updates information 

on disparities in prevalence and incidence rates of diagnosed 

diabetes presented in the 2011 CHDIR. The purposes of this 

report are to discuss and raise awareness about group differences 

in the level of diagnosed diabetes and to prompt actions to 

reduce these disparities.

Methods

To monitor progress toward eliminating health disparities 

in the prevalence and incidence rate of medically diagnosed 

diabetes, CDC used data from the 2006 and 2010 National 

Health Interview Survey (NHIS). NHIS is an ongoing, crosssectional, in-person household interview survey of a probability 

sample of the civilian, noninstitutionalized U.S. population. 

Household interviews were completed for 75,716 persons 

in 2006 and 89,976 persons in 2010, with response rates of 

87.3% and 79.5%, respectively (9,10).

The methods used to assess prevalence and incidence rates of 

medically diagnosed diabetes have been described previously 

(7). Analyses were repeated to assess disparities in each year and 

changes in disparity over time (11), according to the selected 

characteristics of age, sex, race/ethnicity, socioeconomic status,* 

geographic region as defined by the U.S. Census Bureau,† and 

disability status. Because of the association between place of 

birth and diabetes, the data also were examined by place of 

birth, defined as U.S.-born or not U.S.-born§ (12,13). 

Prevalence (cases of diabetes of any duration per 100 

population) was calculated for adults aged ≥18 years. Incidence 

rate (cases of diabetes ≤1 year’s duration per 1,000 population) 

was calculated for adults aged 18–79 years. Estimates were 

standardized by the direct method to the age distribution of the 

U.S. 2000 Census adult population (14). Age-specific estimates 

were not age-standardized. CDC used software to account for 

the complex sample design of NHIS and to produce point 

estimates, standard errors, and 95% confidence intervals (CIs). 

Disparities were measured as the deviations from a referent 

category incidence rate or prevalence. Absolute difference 

was measured as the simple difference between a population 

subgroup estimate and the estimate for its respective reference 

group. The relative difference, a percentage, was calculated by 

dividing the difference by the value in the referent category and 

multiplying by 100. To assess change in disparities over time, 

CDC calculated change in relative difference by subtracting the 

relative difference in the ending time period from the relative 

difference for the beginning period. To test for the statistical 

significance of the observed absolute and relative differences, 

CDC used the z statistic and a 2-tailed test at p<0.05 with 

*Measured as educational attainment and household income by income-topoverty ratio [IPR]. Following the Office of Management and Budget’s Statistical 

Policy Directive 14, the U.S. Census Bureau uses a set of money income 

thresholds that vary by family size and composition to determine who is in 

poverty. IPR is the total family income expressed as a ratio or percentage of the 

family’s official poverty threshold. An IPR <1.00 or <100% of poverty denotes 

a family in poverty; an IPR ≥1.00 or ≥100% of the poverty threshold denotes 

family income equal to or higher than poverty. Official poverty thresholds are 

corrected for inflation using the Consumer Price Index. 

†Northeast: Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, 

New York, Pennsylvania, Rhode Island, and Vermont; Midwest: Illinois, Indiana, 

Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, 

South Dakota, and Wisconsin; South: Alabama, Arkansas, Delaware, District 

of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, 

North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and 

West Virginia; West: Alaska, Arizona, California, Colorado, Hawaii, Idaho, 

Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming.

§ Includes U.S. citizens born abroad (one or both of whose parents were U.S. 

citizens), naturalized citizens, and noncitizens.

Diabetes — United States, 2006 and 2010

Gloria L. Beckles, MD

Chiu-Fang Chou, DrPH

Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion

Corresponding author: Gloria L. Beckles, Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, CDC. 

Telephone: 770-488-1272; E-mail: glb4@cdc.gov.
Supplement

100 MMWR / November 22, 2013 / Vol. 62 / No. 3

Bonferroni correction for multiple comparisons; 95% CIs 

were calculated. Statistically significant increases and decreases 

in relative differences from 2009 to 2011 were interpreted as 

increases and decreases in disparity, respectively. Estimates with 

relative standard error ≥30% were not reported.

Results

Racial/ethnic and socioeconomic disparities were identified 

in the age-standardized prevalence and incidence rate of 

medically diagnosed diabetes in 2006 and 2010 (Tables 1 

and 2). In both years, overall, and for both males and females, 

significant absolute differences for race and ethnicity were 

present between non-Hispanic whites and non-Hispanic blacks 

or Hispanics (p<0.05 for all comparisons). The only significant 

temporal decline in disparity was found for age-standardized 

prevalence of diagnosed diabetes among non-Hispanic black 

females (change: -23.3 percentage points; p<0.05). Temporal 

increases in disparities from 2006 to 2010 were identified 

for prevalence of diagnosed diabetes among Hispanics, with 

increases greater among Hispanic females (change: 34.6 

percentage points; p<0.05) than among Hispanic males 

(change: 11.6 percentage points; p<0.05). Temporal increases 

in disparities in incidence rates were greater among Hispanics 

(change: 79.0 percentage points; p<0.05) than among nonHispanic blacks (change: 12.6 percentage points; p<0.05). 

In 2006 and 2010, the groups with the lowest levels of 

education and income continued to experience the greatest 

socioeconomic disparity in age-standardized prevalence and 

incidence rate of diagnosed diabetes (Tables 1 and 2). Among 

these disadvantaged groups, no significant change in the 

relative difference in prevalence occurred from 2006 to 2010, 

but the disparity in the incidence rate worsened over time. In 

addition, a significant decline in disparity in the prevalence 

and incidence of diagnosed diabetes occurred among persons 

with a high school education (p<0.05 for both comparisons). 

From 2006 to 2010, age disparities in the age-standardized 

prevalence and age-standardized incidence rate of diagnosed 

diabetes worsened, and no significant change occurred in 

the geographic and disability disparities in age-standardized 

prevalence. However, for the age-standardized incidence rate, 

disparities between the Northeast and each of the other U.S. 

Census Bureau regions worsened significantly while disability 

disparities improved (Table 2). No significant disparities 

between U.S.-born and not U.S.-born persons were identified 

in the total population or in any racial/ethnic population.

Discussion

From 2006 to 2010, a decline occurred in the disparity 

between the prevalence of diagnosed diabetes among nonHispanic black women and that among white women; among 

men, no evidence of a decline in racial/ethnic disparities in 

diagnosed diabetes was identified. In addition, during the 

survey years, socioeconomic disparities in the incidence of 

diagnosed diabetes worsened among the groups with the lowest 

level of education and income.

Although racial/ethnic and socioeconomic disparities in the 

prevalence and incidence rate of diagnosed diabetes persist 

in the U.S. adult population, some improvements occurred 

from 2006 to 2010. Significant improvements were noted for 

prevalence of diagnosed diabetes among non-Hispanic black 

women compared with non-Hispanic white women, among 

those with a high school diploma or some college compared 

with those with a college degree or higher, and among the poor 

(IPR <1.0 federal poverty level [FPL]) and middle income (IPR 

2.0–2.9 FPL) groups compared with persons whose incomes 

were high (IPR ≥4.0 FPL). A significant improvement also 

occurred in the disparity in the diabetes incidence rate by 

disability status.

Although improvements are noted for disparities in 

prevalence of diagnosed diabetes, the annual incidence of 

diagnosed diabetes among the U.S. population is increasing 

(2,4), and mortality is declining among age, racial/ethnic, 

socioeconomic, and disabled subgroups in the adult diabetic 

population (15,16). If these circumstances continue, then 

the prevalence of diabetes among the U.S. population is 

projected to increase to as high as 33% by 2050 (15), posing 

major challenges for U.S. public health. Diabetes is the 

principal cause of kidney failure, nontraumatic lower extremity 

amputation, and new cases of blindness, and it is a major cause 

of cardiovascular disease among U.S. adults (1). The economic 

costs of diagnosed diabetes reflect the substantial burden 

imposed on the U.S. society (18). Between 2007 and 2012, the 

total estimated annual cost increased by 41% (in 2007 dollars) 

to $245 billion, including $69 billion in reduced productivity 

(18). Medical expenditure among persons with diabetes is two 

to three times that of persons without diabetes, and the largest 

component (43%) of total medical expenditures attributed to 

diagnosed diabetes is hospital inpatient care.

Limitations

The findings presented in this report are subject to at least 

two limitations. First, all data are self-reported and therefore 

subject to recall and social desirability bias. However, selfreported diabetes data have been reported to have high 
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 101

reliability (18,19). Second, differences were not assessed for 

total prevalence of diabetes (i.e., diagnosed and undiagnosed); 

therefore, the findings might underestimate the extent of 

the disparities in prevalence and incidence among the U.S. 

population. The percentage of persons with undiagnosed 

diabetes is estimated to range from 24% to 40% of the total 

prevalence of diabetes (1,20). However, the racial/ethnic, 

socioeconomic, geographic, disability, and change over time 

of the disparities in prevalence and incidence of medically 

diagnosed diabetes provided in this report are consistent with 

data provided in previously published reports on diabetes risk 

among U.S. adults (2–5,12,20,21).

Conclusion

Obesity and lack of physical activity are major risk factors for 

diabetes (22,23). The Community Preventive Task Force has 

recommended several effective evidence-based interventions 

that communities, policy makers, and public health authorities 

can use to delay or prevent onset of diabetes by reducing 

obesity and increasing physical activity. Strategies to increase 

physical activity and physical fitness include communitywide 

campaigns, school-based physical education, and creation 

of or enhanced access to places for physical activity (22). 

Interventions to prevent or control obesity include behavior 

interventions to reduce screen time, multicomponent 

TABLE 1. Age-standardized prevalence* of medically diagnosed diabetes among adults aged ≥18 years, by selected characteristics — National 

Health Interview Survey, United States, 2006 and 2010

Characteristic

2006 2010 Change in 

relative 

difference from 

2006 to 2010 

(percentage 

points)

Agestandardized 

prevalence

(%) (95% CI)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%)

Agestandardized 

prevalence

(%) (95% CI)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%) 

Sex

Male 7.0 (6.4–7.6) 0.3 4.5 8.6 (8.1–9.2) 1.5† 21.1 16.6

Female 6.7 (6.2–7.3) Ref. Ref. 7.1 (6.7–7.6) Ref. Ref.

Age group (yrs)§

18–44 2.7 (2.3–3.0) Ref. Ref. 2.7 (2.4–3.1) Ref. Ref.

45–64 11.4 (10.3–12.6) 8.8† 330.2 12.3 (11.5–13.1) 9.6† 350.5 20.3¶

65–74 18.9 (16.9–20.9) 16.3† 613.2 21.8 (19.9–23.8) 19.1† 698.5 85.3¶

≥75 18.2 (16.1–20.2) 15.6† 586.8 21.7 (19.8–23.5) 19.0† 694.9 108.1¶

Race/Ethnicity

Both sexes

White, non-Hispanic 6.0 (5.6–6.5) Ref. Ref. 6.8 (6.4–7.2) Ref. Ref.

Black, non-Hispanic 10.9 (9.8–12.1) 4.9† 81.7 11.3 (10.4–12.2) 4.5† 66.2 -15.5

Asian 7.4 (5.7–9.5) 1.4 23.3 7.9 (6.6–9.5) 1.1 16.2 -7.1

Mixed race/Other 10.6 (8.3–10.9) 4.6† 60.0 14.0 (9.2–20.8) 7.2† 105.9 45.9¶

Hispanic** 9.0 (7.9–10.2) 3.0† 50.0 11.5 (10.8–13.0) 4.7† 69.1 19.1¶

Male

White, non-Hispanic 6.3 (5.7–7.0) Ref. Ref. 7.8 (7.1–8.5) Ref. Ref.

Black, non-Hispanic 9.9 (8.4–11.7) 3.6† 57.1 12.4 (10.9–14.0) 4.6† 59.0 1.9

Asian 8.8 (6.3–12.2) 2.5 39.7 10.2 (8.1–12.7) 2.4 30.8 -8.9

Mixed race/Other —†† — NA NA 16.3 (11.5–22.7) 8.5† 109.0 NA

Hispanic 8.4 (6.9–10.2) 2.1† 33.3 11.3 (9.9–12.9) 3.5† 44.9 11.6¶

Female

White, non-Hispanic 5.8 (5.3–6.4) Ref. Ref. 6.0 (5.4–6.5) Ref. Ref.

Black, non-Hispanic 11.6 (10.0–13.4) 5.8† 100.0 10.6 (9.4–11.9) 4.6† 76.7 -23.3¶

Asian 6.3 (4.4–8.9) 0.5 8.6 6.1 (4.6–8.0) 0.1 1.7 -6.9

Mixed race/Other 8.3 (4.2–15.6) 2.5 43.1 13.5 (7.9–22.0) 7.5† 125.0 81.9

Hispanic 9.4 (8.0–10.9) 3.6† 62.1 11.8 (10.6–13.2) 5.8† 96.7 34.6¶

Educational attainment (aged ≥25 years)

Less than high school 9.1 (8.3–10.1) 4.5† 97.8 11.6 (10.6–12.8) 5.8† 100.0 2.2

High school or 

equivalent

7.7 (6.8–8.7) 3.1† 67.4 8.5 (7.7–9.3) 2.7† 46.6 -20.8¶

Some college 8.0 (7.3–8.8) 3.4† 74.1 8.8 (8.1–9.6) 3.0† 51.7 -22.4¶

College degree or 

higher

4.6 (3.9–5.3) Ref. Ref. 5.8 (5.1–6.5) Ref. Ref.

Income-to-poverty ratio§§

Poor 10.1 (8.9–11.4) 4.6† 83.1 10.6 (9.6–11.6) 4.6† 71.5 -11.6¶

Near poor 8.1 (7.2–9.0) 2.6† 46.3 9.6 (8.8–10.5) 3.4† 53.9 7.7¶

Middle income 6.8 (6.1–7.4) 1.2† 22.5 7.6 (7.0–8.2) 1.2† 18.6 -4.0¶

High income 5.5 (4.9–6.2) Ref. Ref. 6.4 (5.7–7.1) Ref. Ref.

See table footnotes on the next page.
Supplement

102 MMWR / November 22, 2013 / Vol. 62 / No. 3

counseling intended to reduce weight and maintain weight 

loss, and worksite programs intended to reduce weight among 

employees by improving diet and physical activity (23). 

The CDC-led National Diabetes Prevention Program (24) 

is designed to bring to communities strategies for adopting 

evidence-based lifestyle changes known to prevent or delay the 

onset of type 2 diabetes among adults at high risk for diabetes, 

including modest weight loss, increased physical activity, and 

reduced fat and calorie intake. Widespread implementation of 

these and similar interventions to prevent obesity and promote 

physical activity might reduce future incidence and prevalence 

of diabetes and reduce disparities in diabetes risk.

TABLE 1. (Continued) Age-standardized prevalence* of medically diagnosed diabetes among adults aged ≥18 years, by selected characteristics 

— National Health Interview Survey, United States, 2006 and 2010

Characteristic

2006 2010 Change in 

relative 

difference from 

2006 to 2010 

(percentage 

points)

Agestandardized 

prevalence

(%) (95% CI)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%)

Agestandardized 

prevalence

(%) (95% CI)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%) 

Place of birth 

All racial/ethnic groups

U.S.-born 6.9 (6.5–7.3) Ref. Ref. 7.7 (7.4–8.1) Ref. Ref.

Not U.S.-born¶¶ 7.2 (6.3–8.2) 0.3 4.3 8.6 (7.8–9.5) 0.9 11.7 7.4

White, non-Hispanic

U.S.-born 6.1 (5.6–6.6) Ref. Ref. 6.8 (6.4–7.2) Ref. Ref.

Not U.S.-born 4.7 (3.5–6.3) -1.4 -23.0 6.8 (5.2–8.9) 0.0 0.0 -23.0

Black, non-Hispanic

U.S.-born 11.3 (10.1–12.6) Ref. Ref. 11.6 (10.6–12.7) Ref. Ref.

Not U.S.-born 7.3 (4.6–11.4) -4.0 -35.4 8.9 (6.6–12.0) -2.7 -23.3 12.1

Asian/Pacific Islander

U.S.-born 5.9 (2.9–11.6) Ref. Ref. 8.7 (5.7–13.0) Ref. Ref.

Not U.S.-born 8.4 (6.6–10.7) 2.5 42.4 7.8 (7.5–8.1) -0.9 -10.3 -27.2

Hispanic

U.S.-born 9.7 (8.0–11.6) Ref. Ref. 13.1 (11.5–15.0) Ref. Ref.

Not U.S.-born 8.3 (6.9–10.0) -1.4 -14.4 10.3 (9.1–11.8) -2.8† -21.4 -7.0

Geographic region***

Northeast 6.2 (5.3–7.3) Ref. Ref. 6.3 (5.4–7.4) Ref. Ref.

Midwest 7.1 (6.3–8.1) 0.9 14.5 7.9 (7.3–8.6) 1.6† 25.4 10.9

South 7.1 (6.5–7.8) 0.9 14.5 8.8 (8.3–9.4) 2.5† 39.7 25.2

West 6.6 (5.9–7.4) 0.4 6.5 7.3 (6.7–8.0) 1.0† 15.9 9.4

Disability status

Has a disability 10.8 (9.9–11.8) 6.4† 145.5 12.1 (11.2–13.0) 7.2† 160.0 14.5

Does not have a 

disability

4.4 (4.0–4.8) Ref. Ref. 4.9 (4.6–5.3) Ref. Ref.

Abbreviations: 95% CI = 95% confidence interval; NA = not available; Ref. = Referent.

* Cases of diabetes of any duration per 100 population. Estimate standardized by the direct method to the U.S. Census 2000 population.

† Simple difference between group estimate and Referent category significant at p<0.05 by z statistic and a 2-tailed test with Bonferroni correction for multiple 

comparisons.

§ Age-specific estimates are not age-standardized.

¶ Difference between the relative differences in 2010 and 2006 significant at p<0.05 by z statistic and 2-tailed test with Bonferroni correction for multiple comparisons. 

** Persons of Hispanic ethnicity might be of any race or combination of races.

†† Unstable estimate; relative standard error ≥30%.

§§ Following the Office of Management and Budget’s Statistical Policy Directive 14, the U.S. Census Bureau uses a set of money income thresholds that vary by family size 

and composition to determine who is in poverty. The Income-to-Poverty Ratio (IPR) is the total family income expressed as a percentage of the family’s official poverty 

threshold. An IPR <100% of poverty denotes a family in poverty; an IPR ≥100% of the poverty threshold denotes a family income equal to or higher than poverty. Official 

poverty thresholds are corrected for inflation using the Consumer Price Index. Additional information is available at http://www.census.gov/hhes/www/poverty/methods/

definitions.html. Poor = <1.0 times the federal poverty level (FPL), near-poor = 1.0–1.9 times FPL, middle income = 2.0–3.9 times FPL, and high income = ≥4.0 times FPL. 

FPL was calculated on the basis of U.S. Census Bureau poverty thresholds (available at http://www.census.gov/hhes/www/poverty.html). ¶¶ Includes U.S. citizens born abroad (one or both of whose parents were U.S. citizens), naturalized citizens, and noncitizens.

*** Northeast: Connecticut, Maine, Massachusetts, New Jersey, New Hampshire, New York, Pennsylvania, Rhode Island, and Vermont; Midwest: Illinois, Indiana, Iowa, 

Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin; South: Alabama, Arkansas, Delaware, District of Columbia, 

Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West Virginia; West: Alaska, 

Arizona, California, Colorado, Hawaii, Idaho, Montana, New Mexico, Nevada, Oregon, Utah, Washington, and Wyoming.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 103

See table footnotes on the next page.

TABLE 2. Age-standardized incidence rate* of medically diagnosed diabetes among adults aged 18–79 years, by selected characteristics — 

National Health Interview Survey, United States, 2006 and 2010. 

Characteristic

2006 2010 Change in 

relative 

difference 

from 2006 to 

2010 

(percentage 

points)

Agestandardized 

incidence rate (95% CI)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%)

Agestandardized 

incidence rate (95% CI)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%)

Sex

Male 7.6 (5.8–9.9) 0.1 0.8 8.9 (7.0–11.3) 2.8† 47.0 46.2§

Female 7.5 (6.1–9.3) Ref. Ref. 6.0 (4.8–8.0) Ref. Ref.

Age group (yrs)¶

18–44 5.1 (3.9–6.7) Ref. Ref. 3.8 (2.8–5.2) Ref. Ref.

45–64 10.8 (8.7–13.5) 5.7† 112.0 11.5 (9.4–14.0) 7.7† 200.0 88.0§

65–74 10.7 (7.0–16.3) 5.6† 109.4 14.7 (10.1–21.3) 10.9† 283.9 174.5§

≥75 14.4 (8.1–25.4) 9.3† 182.3 16.4 (9.4–28.4) 12.6† 328.3 146.0§

Race/Ethnicity

Both sexes

White, non-Hispanic 6.8 (5.5–8.5) Ref. Ref. 6.0 (4.7–7.5) Ref. Ref.

Black, non-Hispanic 9.6 (6.7–13.8) 2.8† 41.1 9.2 (6.6–12.6) 3.2† 53.7 12.6§

Hispanic** 8.6 (5.7–13.0) 1.8† 25.6 12.2 (8.8–17.0) 6.2† 104.6 79.0§

Male

White, non-Hispanic 6.7 (4.7–9.6) Ref. Ref. 7.2 (5.1–10.0) Ref. Ref.

Black, non-Hispanic 10.0 (6.1–16.5) 3.3† 49.6 11.8 (7.3–19.1) 4.6† 64.7 15.1§

Hispanic 6.6 (3.6–11.9) -0.1 -1.8 15.4 (9.4–25.1) 8.2† 114.6 116.4§

Female

White, non-Hispanic 7.0 (5.4–9.2) Ref. Ref. 4.9 (3.5–6.7) Ref. Ref.

Black, non-Hispanic 9.3 (5.6–15.5) 2.3† 32.0 7.1 (4.9–10.3) 2.2† 45.7 13.7§

Hispanic 10.5 (6.0–18.4) 3.5† 49.8 9.5 (6.1–14.7) 4.6† 94.0 44.2§

Educational attainment (aged ≥25 yrs)

Less than high school 10.2 (7.1–14.8) 5.9† 136.4 13.7 (9.5–19.6) 9.4 219.9 83.6§

High school or 

equivalent

10.7 (7.6–15.1) 6.4† 147.1 8.3 (5.8–11.8) 4.0 93.9 -53.2§

Some college 9.9 (7.0–13.8) 5.5† 127.9 10.0 (7.5–13.3) 5.7 133.3 5.4§

College degree or 

higher

4.3 (2.8–6.8) Ref. Ref. 4.3 (2.7–6.8) Ref. Ref.

Income-to-poverty ratio††

Poor 10.9 (6.9–17.1) 4.9† 82.5 11.5 (7.5–16.3) 7.1† 113.7 31.2§

Near poor 8.4 (6.1–11.1) 2.5† 41.4 8.2 (5.9–11.4) 2.2 35.5 -5.9

Middle income 8.4 (6.1–11.3) 2.4† 40.2 8.0 (6.0–10.7) 1.5 24.7 -15.6

High income 6.0 (3.9–9.2) Ref. Ref. 6.2 4.2–9.2) Ref. Ref.

Place of birth 

All races/ethnicities

U.S.-born 7.6 (6.3–9.2) Ref. Ref. 7.3 (6.1–8.8) Ref. Ref.

Not U.S.-born§§ 6.6 (4.5–9.8) -1.0 11.7 7.6 (5.1–11.5) 0.3 11.7 0.0

White, non-Hispanic 

U.S.-born 6.9 (5.5–8.6) Ref. Ref. 6.1 (4.8–7.8) Ref. Ref.

Not U.S.-born —¶¶ — NA NA — — NA NA NA

Black, non-Hispanic

U.S.-born 9.9 (6.8–14.4) Ref. Ref. 10.3 (7.4–14.2) Ref. Ref.

Not U.S-born — — NA NA —†† —†† NA NA NA

Hispanic

U.S.-born — — Ref. Ref. 14.8 (8.6–25.4) Ref. Ref.

Not U.S.-born 6.8 (4.1–11.2) NA NA 10.8 (7.2–16.3) -4.0† -26.9 8.0

Geographic region***

Northeast 7.4 (5.2–10.6) Ref. Ref. 6.3 (3.9–10.1) Ref. Ref.

Midwest 6.7 (4.8–9.6) -0.7 -9.0 7.2 (4.9–10.4) 0.8† 13.4 22.5§

South 8.4 (6.3–11.2) 1.0 13.8 8.3 (6.5–10.4) 2.0† 31.1 17.3§

West 6.6 (4.5–9.9) -0.8 -10.5 7.2 (4.9–10.5) 0.9 13.7 24.2§

Disability status

Has a disability 14.1 (10.8–18.4) 9.2† 187.2 12.0 (9.4–15.2) 6.7† 125.5 -61.6§

Does not have a 

disability

4.9 (3.8–6.3) Ref. Ref. 5.3 (3.2–7.3) Ref. Ref.
Supplement

104 MMWR / November 22, 2013 / Vol. 62 / No. 3

References

1. CDC. National Diabetes Surveillance System: national diabetes fact 

sheet, 2011. Atlanta, GA: US Department of Health and Human 

Services, CDC; 2011. Available at http://www.cdc.gov/diabetes/pubs/

pdf/ndfs_2011.pdf.

2. CDC. National Diabetes Surveillance System. Atlanta, GA: US 

Department of Health and Human Services, CDC; 2010. Available at 

http://www.cdc.gov/diabetes/statistics/index.htm.

3. Kanjilal S, Gregg EW, Cheng YJ, et al. Socioeconomic status and trends 

in disparities in 4 major risk factors for cardiovascular disease among 

US adults, 1971–2002. Arch Intern Med 2006;166:2348–55.

4. Narayan KM, Boyle JP, Geiss LS, Saaddine JB, Thompson TJ. Impact 

of recent increase in incidence on future diabetes burden: U.S., 2005–

2050. Diabetes Care 2006;29:2114–6.

5. Geiss LS, Pan L, Cadwell B, Gregg EW, Benjamin SM, Engelgau MM. 

Changes in incidence of diabetes in U.S. adults, 1997–2003. Am J Prev 

Med 2006;30:371–7.

6. CDC. CDC health disparities and inequalities report—United States, 

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self-rated health, and comorbidity status of Asian and Hispanic 

immigrants. J Immig Minority Health 2008;10:103–18.

14. Anderson RN, Rosenberg HM. Age standardization of death rates: 

implementation of the year 2000 standard. Natl Vital Stat Rep 1998;

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of the year 2050 burden of diabetes in the US adult population: dynamic 

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Metrics 2010;8:29. DOI:10.1186/1478-7954-8-29.

16. Gregg EW, Cheng YJ, Saydah S, et al. Trends in death rates among U.S. 

adults with and without diabetes between 1997 and 2006. Diabetes 

Care 2012;35:1252–7.

17. American Diabetes Association. Economic costs of diabetes in the U.S. in 

2012. Diabetes Care 2013;36. March 6, 2013. DOI: 10.2337/dc12-2625. 

18. Tisando DM, Adams JL, Liu H, et al. What is the concordance between 

the medical record and patient self-report as data sources for ambulatory 

care? Med Care 2006;44:132–40.

19. Newell SA, Girgis A, Sanson-Fisher RW, Savolainen NJ. The accuracy 

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20. Cowie CC, Rust KF, Ford ES, et al. Full accounting of diabetes and 

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21. Volpato S, Maraldi C, Fellin R. Type 2 diabetes and risk for functional decline 

and disability in older persons. Curr Diabetes Rev 2010;6:134–43.

22. Community Preventive Services Task Force. Increasing physical activity. 

Atlanta, GA: Community Preventive Services Task Force; 2012. Available 

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TABLE 2. (Continued) Age-standardized incidence rate* of medically diagnosed diabetes among adults aged 18–79 years, by selected 

characteristics — United States, National Health Interview Survey, 2006 and 2010. 

Abbreviations: 95% CI = 95% confidence interval; NA = not available; Ref. = Referent.

* Cases of diabetes of ≤1 year duration per 1,000 population. Estimates standardized by the direct method to the US Census Bureau 2000 population.

† Difference between group estimate and referent group estimate statistically significant at p<0.05 by z statistic and a 2-tailed test with Bonferroni correction for 

multiple comparisons.

§ Difference between the group relative differences in 2010 and 2006 statistically significant at p<0.05 by z statistic and a 2-tailed test with Bonferroni correction 

for multiple comparisons.

¶ Age-specific estimates are not age-standardized.

** Persons of Hispanic ethnicity might be of any race or combination of races.

†† Following the Office of Management and Budget’s Statistical Policy Directive 14, the U.S. Census Bureau uses a set of money income thresholds that vary by family size 

and composition to determine who is in poverty. The Income-to-Poverty Ratio (IPR) is the total family income expressed as a percentage of the family’s official poverty 

threshold. An IPR <100% of poverty denotes a family in poverty; an IPR ≥100% of the poverty threshold denotes a family income equal to or higher than poverty. Official 

poverty thresholds are corrected for inflation using the Consumer Price Index. Additional information is available at http://www.census.gov/hhes/www/poverty/methods/

definitions.html. Poor = <1.0 times the federal poverty level (FPL), near-poor = 1.0–1.9 times FPL, middle income = 2.0–3.9 times FPL, and high income = ≥4.0 times FPL. 

FPL was calculated on the basis of U.S. Census Bureau poverty thresholds (available at http://www.census.gov/hhes/www/poverty.html). §§ Includes U.S. citizens born abroad (one or both of whose parents were U.S. citizens), naturalized citizens, and noncitizens.

¶¶ Unstable estimate; relative standard error ≥30%.

*** Northeast: Connecticut, Maine, Massachusetts, New Jersey, New Hampshire, New York, Pennsylvania, Rhode Island, and Vermont; Midwest: Illinois, Indiana, Iowa, 

Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin; South: Alabama, Arkansas, Delaware, District of Columbia, 

Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West Virginia; West: Alaska, 

Arizona, California, Colorado, Hawaii, Idaho, Montana, New Mexico, Nevada, Oregon, Utah, Washington, and Wyoming.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 105

Introduction

Health-related quality of life is physical and mental health, 

as perceived by a person or group of people, during a period 

of time (1,2). This measure complements traditional public 

health measures of mortality and morbidity. Fair or poor selfrated health, physically unhealthy days, and mentally unhealthy 

days are reported by higher percentages of women, older 

persons, minority racial/ethnic groups (except Asian/Pacific 

Islanders), and persons with less education, with lower annual 

household incomes, who are unemployed, with a disability 

or a chronic disease, and who are widowed, separated, or 

divorced than, respectively, men, younger persons, and nonHispanic whites, and those with more education, with higher 

annual household incomes, who are employed by others or 

self-employed, without a disability or a chronic disease, and 

who are married (1).

This report is part of the second CDC Health Disparities 

and Inequalities Report (CHDIR). The 2011 CHDIR (3) was 

the first CDC report to assess disparities across a wide range 

of diseases, behavioral risk factors, environmental exposures, 

social determinants, and health-care access. The topic presented 

in this report is based on criteria that are described in the 2013 

CHDIR Introduction (4). This report provides information 

concerning disparities in health-related quality of life, a topic 

that was not discussed in the 2011 CHDIR. The purposes 

of this health-related quality of life report are to describe and 

raise awareness of how different kinds of disparities affect 

health-related quality of life among adults in the United States, 

whether and how these effects changed from 2006 to 2010 

and to prompt actions to reduce disparities.

Methods

To examine health-related quality of life disparities by 

selected characteristics among adults (aged ≥18 years) in the 

United States, CDC analyzed 2006 and 2010 data from the 

Behavioral Risk Factor Surveillance System (BRFSS). BRFSS 

is a continuous, random-digit–dialed telephone survey of 

noninstitutionalized adults aged ≥18 years in the 50 states, the 

District of Columbia (DC), Puerto Rico, the U.S. Virgin Islands, 

Guam (5,6) (available at http://www.cdc.gov/brfss/index.htm). 

This analysis compares health-related quality of life measures 

stratified by specific characteristics in respondents from the 50 

states and DC in 2006 (N = 347,790) and 2010 (N = 444,927).

Two indicators of BRFSS survey quality are its cooperation 

rate and its overall response rate (7,8). The cooperation 

rate is the proportion of all respondents interviewed of all 

eligible units in which a respondent was selected and actually 

contacted. In 2006, the cooperation rate ranged from 56.9% 

in California to 83.5% in Minnesota; in 2010, the cooperation 

rate ranged from 56.8% in California to 86.1% in Minnesota. 

The overall response rate is an outcome rate with the number 

of complete and partial interviews in the numerator and an 

estimate of the number of eligible units in the sample in the 

denominator that assumes that more unknown records are 

eligible, specifically, that all likely households are households 

and that 98% of known or probable households contain an 

adult who uses the telephone number. In 2006, the overall 

response rate ranged from 20.5% in Georgia to 58.4% in 

Utah, and in 2010, from 19.2% in Oregon to 57.4% in Utah.

The three health-related quality of life measures represented 

in BRFSS are 1) self-rated health status, 2) number of physically 

unhealthy days, and 3) number of mentally unhealthy days. 

The related BRFSS questions were as follows: 1) “Would you 

say that in general your health is excellent, very good, good, 

fair, or poor?” 2) “Now thinking about your physical health, 

which includes physical illness and injury, for about how many 

days during the past 30 days was your physical health not 

good?” and 3) “Now thinking about your mental health, which 

includes stress, depression, and problems with emotions, for 

about how many days during the past 30 days was your mental 

health not good?” CDC calculated the percentage reporting 

fair or poor self-rated health, mean number of physically 

unhealthy days, and mean number mentally unhealthy days 

as the primary health-related quality of life outcome measures. 

Respondents with the responses “do not know/not sure” or 

“refused to respond” were excluded from the analysis on a 

question-by-question basis.

Health-related quality of life disparities were assessed by 

stratifying results by sex, age group (18–24, 25–34, 35–44, 

45–64, 65–79, and ≥80 years), race/ethnicity (non-Hispanic 

white, non-Hispanic black, Hispanic [might be of any race 

or any combination of races], non-Hispanic Asian/Pacific 

Islander [A/PI], non-Hispanic American Indian/Alaska Native 

[AI/AN], and other), educational attainment at the time of 

Health-Related Quality of Life — United States, 2006 and 2010

Matthew M. Zack, MD

Division of Population Health, National Center for Chronic Disease Prevention and Health Promotion, CDC

Corresponding author: Matthew M. Zack, Division of Population Health, National Center for Chronic Disease Prevention and Health Promotion, CDC. 

Telephone: 770-488-5460; E-mail: mmz1@cdc.gov.
Supplement

106 MMWR / November 22, 2013 / Vol. 62 / No. 3

the survey (less than high school, high school graduate or 

equivalent, some college, and college graduate), the primary 

language spoken at home (English, Spanish, or other), and 

disability status, which was defined as an affirmative answer 

to either or both of the following questions (9): “Are you 

limited in any way in any activities because of physical, mental, 

or emotional problems?” and “Do you now have any health 

problem that requires you to use special equipment, such as 

a cane, a wheelchair, a special bed, or a special telephone?”) 

Each outcome measure was analyzed separately. Household 

income was not examined because educational attainment 

was considered a sufficient indicator of socioeconomic status 

for examination of disparities and because approximately 

14% of BRFSS respondents did not know or refused to report 

household income, but <2% did not know or refused to report 

their educational attainment.

CDC used statistical software for the analyses to account 

for the stratified, complex sampling design of BRFSS (10). 

Data were weighted using the respondents’ sampling weights 

based on the population of noninstitutionalized adults aged 

≥18 years in their states of residence and aggregated across the 

50 states and DC. Because age is associated with the healthrelated quality of life measures and because the age composition 

differs among the various categories analyzed, CDC adjusted 

the health-related quality of life measures by using age 

group categories in the specific survey year as covariates 

in logistic regression (for fair or poor self-rated health) and 

linear regression (for number of physically and mentally 

unhealthy days). No formal statistical testing was conducted 

for this analysis. Differences were assessed by calculating and 

comparing the 95% confidence intervals (CIs) around the 

age-adjusted percentages and means. In this approach, CIs 

were used as measure of variability, and nonoverlapping CIs 

were considered statistically different. Using CIs in this way 

is a conservative evaluation of significance differences; this 

might lead to a conclusion that estimates are similar when the 

point estimates differ at a significance level of 0.05. CIs were 

assessed before rounding for the tables.

Disparities were measured as the deviations from a referent 

group, which was the group that had the most favorable 

estimate for the variables used to assess disparities during 

the time reported. Absolute difference was measured as the 

simple difference between a population subgroup estimate 

and the estimate for its respective reference group. The 

relative difference, a percentage, was calculated by dividing the 

difference by the value in the referent category and multiplying 

by 100. Change in percentage and mean from 2006 to 2010 

was calculated by subtracting the estimate for 2010 from the 

estimate for 2006. The significance of changes over time was 

assessed by comparing CIs as described in this section.

Results

Overall, the age-adjusted percentage of respondents rating 

their health as fair or poor did not change significantly from 

2006 (16.3%) to 2010 (16.1%) (Table 1). A higher percentage 

of women than men reported fair or poor health in both years. 

However, neither of the groups experienced a significant 

change from 2006 to 2010. A higher percentage of persons 

in older age groups than younger groups rated their health as 

fair or poor in both years. The percentage of persons aged 

≥65 years reporting fair or poor health significantly decreased 

approximately 2 percentage points from 2006 to 2010. Both 

in 2006 and 2010, a significantly lower percentage of nonHispanic whites rated their health as fair or poor than all 

other racial/ethnic groups except A/PIs. However, only two 

of these racial/ethnic groups experienced a significant change 

in self-rated health from 2006 to 2010: the percentage of 

non-Hispanic blacks reporting fair or poor health increased 

by 2 percentage points, and that of Hispanics decreased 

approximately 3 percentage points. In both 2006 and 2010, 

a higher percentage of those who had not graduated from 

high school reported fair or poor health than did high school 

graduates, and a lower percentage of college graduates reported 

fair or poor health than did high school graduates. From 2006 

to 2010, the percentage of high school graduates who reported 

fair or poor self-rated health increased by 1.2 percentage points, 

and the percentage of persons with some college education 

who reported fair or poor self-rated health decreased by 1.6 

percentage points. A higher percentage of persons who spoke 

a language other than English at home reported fair or poor 

health than those who spoke English at home. However, the 

percentage of those who spoke Spanish at home and reported 

fair or poor health decreased by 7 percentage points from 2006 

to 2010. A higher percentage of persons with a disability rated 

their health as fair or poor than did those without a disability 

both in 2006 and 2010. Nonetheless, the percentage of 

persons without a disability who rated their health as fair or 

poor decreased by 0.8 percentage points from 2006 to 2010.

From 2006 to 2010, the overall age-adjusted mean number 

of physically unhealthy days in the last 30 days increased 

by approximately 0.1 days (2006: 3.6 days; 2010: 3.7 days 

(Table 2). A higher mean number of physically unhealthy 

days were reported by women than men in 2006 and 2010. 

However, only men experienced a statistically significant 

increase in mean number of days (0.2 days) over time. A higher 

mean number of physically unhealthy days was reported by 

older respondents than younger respondents. From 2006 to 

2010, only persons aged 25–34 years reported a statistically 

significant increase in mean number of physically unhealthy 

days (0.3 days). In both 2006 and 2010, the fewest physically 
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 107

unhealthy days were reported by A/PIs (2006: 2.4 days; 2010: 

2.5 days) and the most were reported by AI/ANs, (2006: 

6.2 days; 2010: 6.3 days). Hispanics showed an increase in 

mean number of physically unhealthy days from 2006 to 

2010 (0.6 days). Compared with high school graduates, more 

physically unhealthy days were reported by those who had 

not graduated from high school than by those with at least 

some college. However, all but college graduates experienced 

an increase in physically unhealthy days from 2006 to 2010, 

with the least educated showing the largest increase (0.8 days). 

More physically unhealthy days were reported by those who 

spoke a language other than English at home than by those 

who spoke English at home. From 2006 to 2010, those who 

spoke Spanish at home had an 0.8-day increase in physically 

unhealthy days, compared with an almost 10-day decrease 

among those who spoke languages other than English and 

Spanish. Approximately 8 more physically unhealthy days were 

reported by persons with a disability (10 days) than by those 

without a disability (1.8 days). Neither group had a significant 

change in number of days from 2006 to 2010.

From 2006 to 2010, the overall age-adjusted mean number 

of mentally unhealthy days in the last 30 days increased by 

approximately 0.1 days (2006: 3.4 days; 2010: 3.5 days) 

(Table 3). The mean number of mentally unhealthy days for 

women exceeded those for men by approximately 1 day in both 

years. However, only men showed a significant increase from 

2006 to 2010 (by 0.2 days). A higher percentage of younger 

respondents reported a mean number of mentally unhealthy 

days than older respondents. Only those aged 35–79 years 

experienced an increase in mean number of days from 2006 

TABLE 1. Estimated percentage of adults aged ≥18 years who rated their health as fair or poor, by selected characteristics — Behavioral Risk 

Factor Surveillance System, United States, 2006 and 2010

Characteristic

2006 2010

Change from 

2006 to 2010 

(percentage 

points)

Ageadjusted 

percentage (95% CI)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%)

Ageadjusted 

percentage (95% CI)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%)

Total 16.3 (15.9–16.6) — — 16.1 (15.9–16.4) — — -0.2

Sex

Male 15.8 (15.3–16.3) -0.8* -5.3* 15.4 (15.0–15.8) -1.4* -8* -0.4

Female 16.7 (16.3–17.0) Ref. Ref. 16.8 (16.5–17.1) Ref. Ref. 0.1

Age group (yrs)

18–24 9.3 (8.3–10.2) -2.4* -21* 7.6 (6.8–8.4) -4.0* -34* -1.7

25–34 9.9 (9.2–10.7) -1.7* -15* 9.9 (9.3–10.5) -1.6* -14* 0.0

35–44 11.7 (11.0–12.3) Ref. Ref. 11.5 (11.0–12.1) Ref. Ref. -0.2

45–64 18.8 (18.3–19.3) 7.2*. 61* 19.0 (18.6–19.4) 7.5* 65* 0.2

65–79 27.4 (26.6–28.2) 15.8* 135* 25.1 (24.6–25.6) 13.6* 118* -2.3*

≥80 33.2 (31.9–34.5) 21.6* 185* 31.1 (30.3–31.9) 19.6* 170* -2.1*

Race/Ethnicity

White, non-Hispanic 13.1 (12.8–13.4) Ref. Ref. 13.3 (13.0–13.5) Ref. Ref. 0.1

Black, non-Hispanic 21.3 (20.3–22.2) 8.1* 62* 23.3 (22.5–24.1) 10.0* 76* 2.0*

Hispanic† 31.0 (29.5–32.5) 17.9* 137* 28.1 (27.1–29.1) 14.8* 112* -2.9*

Asian/Pacific Islander 11.8 (9.8–13.7) -1.3 -10 11.9 (10.7–13.1) -1.4 -10 0.1

American Indian/

Alaska Native

26.7 (23.9–29.6) 13.6* 104* 30.8 (28.0–33.6) 17.6* 133* 4.1

Other 22.0 (18.5–25.5) 8.9* 68* 18.6 (15.9–21.3) 5.3* 40* -3.4

Educational attainment

Less than high school 39.1 (37.8–40.5) 20.0* 104* 38.4 (37.4–39.5) 18.1* 89* -0.7

High school graduate 

or equivalent

19.2 (18.6–19.7) Ref. Ref. 20.3 (19.8–20.8) Ref. Ref. 1.2*

Some college 13.8 (13.3–14.3) -5.3* -28* 15.5 (15.0–15.9) -4.9* -24* 1.6*

College graduate 7.1 (6.8–7.4) -12.0* -63* 7.3 (7.0–7.6) -13.0* -64* 0.2

Language spoken at home

English 14.8 (14.5–15.1) Ref. Ref. 15.0 (14.8–15.3) Ref. Ref. 0.2

Spanish 44.8 (42.3–47.3) 30.0* 203* 37.6 (35.9–39.3) 22.6* 151* -7.2*

Other language 41.9 (18.2–65.6) 27.1* 183* 40.7 (31.3–50.1) 25.7* 171* -1.2

Disability status

With disability 38.7 (37.9–39.6) 29.3* 312* 39.4 (38.7–40.0) 30.7* 356* 0.6

Without disability 9.4 (9.1–9.7) Ref. Ref. 8.6 (8.4–8.9) Ref. Ref. -0.8*

Abbreviations: 95% CI = 95% confidence interval; Ref. = referent.

* Difference considered statistically significantly different by comparison of nonoverlapping 95% CIs. Unrounded CIs do not overlap.

† Persons of Hispanic ethnicity might be of any race or combination of races.
Supplement

108 MMWR / November 22, 2013 / Vol. 62 / No. 3

to 2010 (0.2–0.3 days). A/PIs reported the fewest mentally 

unhealthy days, and AI/ANs reported the most. However, from 

2006 to 2010, only Hispanics showed a significant increase 

(0.6 days). The number of mentally unhealthy days in 2006 

and 2010 was higher for persons with less education than for 

those with more education. However, all groups without a 

college degree experienced a significant increase in the number 

of days from 2006 to 2010. Similar to the change among 

Hispanic respondents, who experienced an increase of 0.6 

mentally unhealthy days from 2006 to 2010, the mean number 

of mentally unhealthy days increased among those who spoke 

Spanish at home by 0.9 days. The mean number of mentally 

unhealthy days among persons with a disability (7 days) was 

approximately five more than among persons without a disability 

(2 days). Nonetheless, only persons with a disability showed a 

statistically significant increase from 2006 to 2010 (0.3 days).

Discussion

The patterns of the health-related quality of life measures 

among the various groups in this report are similar to previous 

findings (1,11). Groups with higher percentages of fair or 

poor health and who report more physically unhealthy days 

and more mentally unhealthy days are usually women, older 

persons (with respect to physical health), younger persons (with 

respect to mental health), minority racial/ethnic groups (except 

for A/PIs), those with less education, those who speak another 

language besides English at home, and those with a disability.

Groups that had statistically significant changes in healthrelated quality of life from 2006 to 2010 differ from groups 

with statistically significant differences from the reference 

groups during the individual years. Although minimal change 

occurred overall, statistically significant changes occurred in 

TABLE 2. Mean number of physically unhealthy days in the past 30 days among adults aged ≥18 years, by selected characteristics — Behavioral 

Risk Factor Surveillance System, United States, 2006 and 2010

Characteristic

2006 2010

Change from 

2006 to 2010 

(percentage 

points)

Ageadjusted 

mean no. of 

days (95% CI)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%)

Ageadjusted 

mean no. of 

days (95% CI)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%)

Total 3.6 (3.5–3.6) — — 3.7 (3.6–3.7) — — 0.1*

Sex

Male 3.2 (3.1–3.3) -0.7* -19* 3.4 (3.3–3.5) -0.6* -14* 0.2*

Female 3.9 (3.8–4.0) Ref. Ref. 4.0 (3.9–4.0) Ref. Ref. 0.1

Age group (yrs)

18–24 2.1 (1.9–2.3) -0.7* -24* 2.0 (1.9–2.2) -0.8* -29* -0.1

25–34 2.2 (2.1–2.3) -0.6* -20* 2.5 (2.4–2.6) -0.4* -13* 0.3*

35–44 2.8 (2.7–2.9) Ref. Ref. 2.9 (2.8–3.0) Ref. Ref. 0.1

45–64 4.3 (4.2–4.4) 1.6* 55* 4.3 (4.3–4.4) 1.5* 51* 0.1

65–79 5.3 (5.2–5.5) 2.6* 92* 5.1 (5.0–5.2) 2.3* 79* -0.2

≥80 6.6 (6.3–6.9) 3.8* 127* 6.2 (6.0–6.4) 3.3* 116* -0.4

Race/Ethnicity

White, non-Hispanic 3.4 (3.4–3.5) Ref. Ref. 3.5 (3.4–3.6) Ref. Ref. 0.1

Black, non-Hispanic 4.0 (3.9–4.2) 0.6* 18* 4.3 (4.1–4.4) 0.8* 22* 0.2

Hispanic† 3.8 (3.6–4.0) 0.4* 10* 4.4 (4.2–4.5) 0.9* 25* 0.6*

Asian/Pacific Islander 2.4 (2.2–2.7) -1.0* -30* 2.5 (2.3–2.7) -1.0* -29* 0.1

American Indian/

Alaska Native

6.2 (5.5–6.9) 2.8* 82* 6.3 (5.7–6.9) 2.8* 79* 0.0

Other 5.2 (4.3–6.1) 1.8* 52* 4.3 (3.9–4.8) 0.9* 24* -0.9

Educational attainment

Less than high school 5.7 (5.4–5.9) 1.6* 40* 6.5 (6.3–6.7) 2.2* 51* 0.8*

High school graduate 

or equivalent

4.0 (3.9–4.2) Ref. Ref. 4.3 (4.2–4.4) Ref. Ref. 0.3*

Some college 3.7 (3.6–3.8) -0.4* -9* 3.9 (3.8–4.0) -0.4* -9* 0.2*

College graduate 2.2 (2.2–2.3) -1.8* -44* 2.3 (2.2–2.3) -2.1* -48* 0.0

Language spoken at home

English 3.5 (3.5–3.6) Ref. Ref. 3.6 (3.6–3.7) Ref. Ref. 0.1

Spanish 4.0 (3.6–4.4) 0.5* 13* 4.8 (4.5–5.1) 1.2* 33* 0.8*

Other language 12.7 (4.4–21.0) 9.2* 260* 2.9 (1.8–4.0) -0.7 20 -9.8*

Disability status

With disability 10.0 (9.8–10.2) 8.3* 464* 10.2 (10.0–10.3) 8.4* 471* 0.1

Without disability 1.8 (1.7–1.8) Ref. Ref. 1.8 (1.7–1.8) Ref. Ref. 0.0

Abbreviations: 95% CI = 95% confidence interval; Ref. = referent.

* Difference considered statistically significantly different by comparison of nonoverlapping 95% CIs. Unrounded CIs do not overlap.

† Persons of Hispanic ethnicity might be of any race or combination of races.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 109

specific groups. Men (but not women) reported an increase 

in the number of physically and mentally unhealthy days over 

time. Persons aged ≥65 years rated their overall health better in 

2010 than in 2006. Hispanics and those who spoke Spanish 

at home also rated their overall health better in 2010 than in 

2006, despite reporting increases in numbers both of physically 

and mentally unhealthy days. Numbers of physically and 

mentally unhealthy days increased from 2006 to 2010 among 

persons without a college degree. The number of mentally 

unhealthy days but not of physically unhealthy days increased 

among persons with a disability, although persons without a 

disability rated their overall health better.

Reasons for particular changes in health-related quality are 

unclear. Differences in risky and protective health behaviors, 

in socioeconomic circumstances such as employment status 

and household income, and in disease status have been 

associated with differences in the measures used in this analysis 

to assess health-related quality of life (1,11). Hispanics and 

those without a college degree reported more physically and 

mentally unhealthy days in 2010 than in 2006; however, 

others in similar socioeconomic circumstances (e.g., nonHispanic blacks and AI/ANs) did not. What accounted for 

these differences is unclear. Additional analyses that adjust for 

changes in employment status, the effects of housing loss, and 

the recent increase in enforcement against illegal immigrants 

might clarify these differences.

TABLE 3. Mean number of mentally unhealthy days in the past 30 days among adults aged ≥18 years, by selected characteristics — Behavioral 

Risk Factor Surveillance System, United States, 2006 and 2010

Characteristic

2006 2010

Change from 

2006 to 2010 

(percentage 

points)

Ageadjusted 

mean no. of 

days (95% CI)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%)

Ageadjusted 

mean no. of 

days (95% CI)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%)

Total 3.4 (3.3–3.5) — — 3.5 (3.5–3.6) — — 0.1*

Sex

Male 2.7 (2.7–2.8) -1.3* -32 3.0 (2.9–3.0) -1.2* -28* 0.2*

Female 4.0 (4.0–4.1) Ref. Ref. 4.1 (4.0–4.2) Ref. Ref. 0.1

Age group (yrs)

18–24 4.3 (4.0–4.5) 0.9* 26* 4.0 (3.8–4.2) 0.4* 10* -0.3

25–34 3.7 (3.5–3.8) 0.3* 9* 3.8 (3.7–4.0) 0.2 6 0.2

35–44 3.4 (3.3–3.5) Ref. Ref. 3.6 (3.5–3.8) Ref. Ref. 0.3*

45–64 3.6 (3.5–3.7) 0.2 6 3.8 (3.8–3.9) 0.2* 6* 0.3*

65–79 2.1 (2.0–2.3) -1.2* -37* 2.3 (2.3–2.4) -1.3* -36* 0.2*

≥80 2.0 (1.9–2.2) -1.3* -40* 2.0 (1.9–2.1) -1.7* -46* -0.1

Race/Ethnicity

White, non-Hispanic 3.4 (3.3–3.4) Ref. Ref. 3.5 (3.4–3.5) Ref. Ref. 0.1

Black, non-Hispanic 3.8 (3.6–4.0) 0.5* 13* 4.0 (3.8–4.2) 0.5* 15* 0.2

Hispanic† 3.2 (3.0–3.5) -0.1 4 3.8 (3.6–4.0) 0.3* 10* 0.6*

Asian/Pacific Islander 2.1 (1.9–2.4) -1.2* -37* 2.0 (1.7–2.3) -1.5* -42* -0.1

American Indian/

Alaska Native

5.7 (5.0–6.3) 2.3* 68* 5.7 (5.1–6.4) 2.3* 65* 0.1

Other 5.1 (4.3–6.0) 1.7* 52* 3.9 (3.4–4.5) 0.5 14 -1.2

Educational attainment

Less than high school 4.9 (4.6–5.1) 1.1* 28* 5.6 (5.4–5.8) 1.6* 40* 0.7*

High school graduate 

or equivalent

3.8 (3.7–3.9) Ref. Ref. 4.0 (3.9–4.1) Ref. Ref. 0.2*

Some college 3.6 (3.5–3.8) -0.2 -5 3.9 (3.8–4.0) -0.1 -3 0.3*

College graduate 2.3 (2.2–2.4) -1.5* -39* 2.3 (2.3–2.4) -1.7* -42* 0.0

Language spoken at home

English 3.5 (3.4–3.5) Ref. Ref. 3.5 (3.5–3.6) Ref. Ref. 0.1

Spanish 2.9 (2.6–3.3) -0.5* -15* 3.8 (3.6–4.1) 0.3 9 0.9*

Other language 8.9 (0.0–17.9) 5.5 159 3.9 (2.3–5.5) 0.4 11 -5.0

Disability status

With disability 7.2 (7.1–7.4) 4.9* 207* 7.5 (7.4–7.7) 5.2* 220* 0.3*

Without disability 2.4 (2.3–2.4) Ref. Ref. 2.4 (2.3–2.4) Ref. Ref. 0.0

Abbreviations: 95% CI = 95% confidence interval; Ref. = referent.

* Difference considered statistically significantly different by comparison of nonoverlapping 95% CIs. Unrounded CIs do not overlap.

† Persons of Hispanic ethnicity might be of any race or combination of races.
Supplement

110 MMWR / November 22, 2013 / Vol. 62 / No. 3

Limitations

The findings in this report are subject to at least four 

limitations. First, although the BRFSS health-related quality 

of life questions have been shown to be reliable in predicting 

30-day and 1-year hospitalization and mortality (12,13), 

because the health-related quality of life data are self-reported, 

they might be misclassified because they are not objectively 

verifiable and are subject to recall bias and measurement 

error. Second, although BRFSS uses poststratification 

to adjust respondent sampling weights for non-response 

(7,8), this adjustment assumes that nonrespondents would 

have answered in similar ways to respondents with similar 

demographic characteristics; such poststratification might not 

have fully adjusted for differences between nonrespondents 

and respondents, given the low, state-specific overall response 

rates. Third, BRFSS data are cross-sectional; therefore, 

changes in the composition of the BRFSS sample from 2006 

to 2010 that affect responses to the health-related quality of 

life questions might affect measured differences from 2006 to 

2010. Finally, the results were adjusted for age only; therefore, 

other confounding variables also might have affected measured 

differences from 2006 to 2010.

Conclusion

Although direct interventions to improve health-related 

quality of life are not possible, indirect interventions to change 

characteristics associated with health-related quality of life 

might result in improvements. For example, risky health 

behaviors can decrease health-related quality of life. Persons 

who smoke cigarettes have worse health-related quality of life 

than former smokers or never smokers (14), and smoking is 

more prevalent among those with certain health conditions 

such as epilepsy (15).

Cigarette smoking is a well-known cause of multiple types 

of cancer (16). Persons with epilepsy (17) and cancer (18) 

have worse health-related quality of life than those without 

these conditions. Moreover, protective health behaviors can 

increase health-related quality of life. For example, persons 

who engage in physical activity have better health-related 

quality of life than those who are sedentary (19). Physical 

activity also reduces obesity (20) and its complications and has 

been associated both with reduced colon cancer rates (20) and 

reduced complications from different kinds of arthritis (21). 

Persons who are obese (22), have cancer (18), or have arthritis 

(23) have worse health-related quality of life than those without 

these conditions. Therefore, interventions to eliminate risky 

behaviors, promote protective behaviors, and delay or prevent 

complications from diseases and other conditions would 

probably improve health-related quality of life.

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Arthritis Care Res (Hoboken) 2011;63:788–99.
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112 MMWR / November 22, 2013 / Vol. 62 / No. 3

Introduction

At the end of 2009, approximately 1.1 million persons in the 

United States were living with human immunodeficiency virus 

(HIV) infection (1), with approximately 50,000 new infections 

annually (2). The prevalence of HIV continues to be greatest 

among gay, bisexual, and other men who have sex with men 

(MSM), who comprised approximately half of all persons with 

new infections in 2009 (2). Disparities also exist among racial/

ethnic minority populations, with blacks/African Americans 

and Hispanics/Latinos accounting for approximately half of 

all new infections and deaths among persons who received an 

HIV diagnosis in 2009 (2,3). Improving survival of persons 

with HIV and reducing transmission involve a continuum of 

services that includes diagnosis, linkage to and retention in HIV 

medical care, and ongoing HIV prevention interventions (4).

The HIV analysis and discussion that follows is part of 

the second CDC Health Disparities and Inequalities Report 

(CHDIR) and updates information presented in the first 

CHDIR (5). The 2011 CHDIR (6) was the first CDC report 

to assess disparities across a wide range of diseases, behavioral 

risk factors, environmental exposures, social determinants, and 

health-care access. The topic presented in this report is based 

on criteria that are described in the 2013 CHDIR Introduction 

(7). The purposes of this HIV infection report are to discuss 

and raise awareness of differences in the characteristics of 

people with HIV infection and to prompt actions to reduce 

these disparities

Methods

To estimate the number of adults aged ≥18 years who received 

a diagnosis of HIV infection during 2008 and 2010, CDC 

analyzed data reported through June 2011 to the National 

HIV Surveillance System (NHSS). CDC funds and assists 

state and local health departments to collect case information 

on persons with an HIV diagnosis. Health departments 

report deindentified data to CDC, which are compiled for 

national analyses. Analysis of HIV case surveillance data was 

limited to the 46 states that had reported HIV cases since at 

least January 2007 to allow for estimation of diagnoses rates: 

Alabama, Alaska, Arizona, Arkansas, California, Colorado, 

Connecticut, Delaware, Florida, Georgia, Idaho, Illinois, 

Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Michigan, 

Minnesota, Mississippi, Missouri, Montana, Nebraska, 

Nevada, New Hampshire, New Jersey, New Mexico, New York, 

North Carolina, North Dakota, Ohio, Oklahoma, Oregon, 

Pennsylvania, Rhode Island, South Carolina, South Dakota, 

Tennessee, Texas, Utah, Virginia, Washington, West Virginia, 

Wisconsin, and Wyoming. Rates per 100,000 population were 

calculated for 2008 and 2010 by age, sex, and race/ethnicity, 

with population denominators based on postcensal estimates 

for 2009 from the U.S. Census Bureau (8). Household income 

and educational attainment were not calculated because these 

data are not collected by NHSS. Geographic location was 

not calculated because estimates of HIV diagnoses among 

persons in all 50 states and the District of Columbia were 

unable to be calculated at the time of this analysis. Analysis 

of transmission categories was limited to all men and MSM 

because denominator data for transmission categories other 

than MSM were unavailable (9); the category of all men 

was used as the referent group. To compute estimated MSM 

population denominators used for calculating HIV diagnosis 

rates, CDC applied the estimated proportion of men in 

the United States who reported ever having male-to-male 

sex (6.9%; 95% confidence interval [CI]: 5.1%–8.6%) to 

postcensal estimated populations for men (9). Analyses were 

adjusted for reporting delays (i.e., the time between diagnosis 

and report) and for missing risk factor information but not 

for underreporting (3). 

Data from the Medical Monitoring Project (MMP) were 

used to estimate percentages of adults aged ≥18 years receiving 

outpatient medical care whose medical record documented that 

they 1) were prescribed antiretroviral therapy (ART) during 

HIV Infection — United States, 2008 and 2010

Anna Satcher Johnson, MPH

Linda Beer, PhD

Catlainn Sionean, PhD

Xiaohong Hu, MS

Carolyn Furlow-Parmley, PhD

Binh Le, MD

Jacek Skarbinski, MD

H. Irene Hall, PhD

Hazel D. Dean, ScD

National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, CDC

Corresponding author: Anna Satcher Johnson, Division of HIV/AIDS Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, 

CDC. Telephone: 404-639-2050; E-mail: ats5@cdc.gov.
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MMWR / November 22, 2013 / Vol. 62 / No. 3 113

the past 12 months, 2) had a suppressed viral load (defined as 

undetectable or ≤200 copies/mL at their most recent test), and 

3) reported receiving prevention counseling in a clinical setting 

during the 12 months preceding the interview. Nationally 

representative percentages and associated standard errors were 

estimated for patients in care in 2009 and interviewed during 

2009–2010. MMP collects behavioral and clinical information 

from a nationally representative sample of adults receiving 

medical care for HIV infection in outpatient facilities in the 

United States and Puerto Rico (10–12). A total of 23 project 

areas were funded to conduct data collection activities for the 

2009 MMP data collection cycle: California; Chicago, Illinois; 

Delaware; Florida; Georgia; Houston, Texas; Illinois; Indiana; 

Los Angeles County, California; Michigan; Mississippi; New 

Jersey; the state of New York; New York City, New York; North 

Carolina; Oregon; Pennsylvania; Philadelphia, Pennsylvania; 

Puerto Rico; San Francisco, California; Texas; Virginia; and 

Washington. Patients who received medical care during 

January–April 2009 at an MMP participating facility were 

interviewed once during June 2009–April 2010 regarding 

the 12 months preceding the interview. In addition, patients’ 

medical records were abstracted for documentation of medical 

care (including prescription of ART and HIV viral load) for 

the 12 months preceding the interview. All percentages were 

weighted for the probability of selection and adjusted for 

nonresponse bias. Standard errors were calculated and account 

for weighting and complex sample survey design. Associations 

between variables were assessed using Rao-Scott chi-square 

tests, with significance set at p<0.05. Detailed methods for 

MMP have been described previously (10–12). 

Data from the 2008 MSM cycle of the National HIV 

Behavioral Surveillance System (NHBS)§ were used to estimate 

percentages of MSM aged 18–64 years who 1) engaged 

in unprotected anal sex with a casual partner, 2) reported 

testing for HIV during the previous 12 months, and 3) who 

participated in a behavioral intervention. Men who reported 

being infected with HIV or who had no male sex partners 

during the 12 months before interview were excluded from 

this analysis. NHBS monitors HIV-associated behaviors and 

HIV positivity within selected metropolitan statistical areas 

(MSAs) with a high prevalence of acquired immunodeficiency 

syndrome (AIDS) among three populations at high risk for HIV 

infection: MSM, injection-drug users, and heterosexual adults at 

increased risk for HIV infection. Data for NHBS are collected 

in annual rotating cycles. All NHBS participants must be aged 

≥18 years, live in a participating MSA, and be able to complete 

a behavioral survey in English or Spanish. MSM participants 

were recruited using venue-based sampling. Detailed methods 

for NHBS have been described previously (13).

Disparities were measured as the deviations from a referent 

category rate or prevalence. Absolute difference was measured as 

the simple difference between a population subgroup estimate 

and the estimate for its respective reference group. The percentage 

relative difference was calculated by dividing the difference by 

the value in the referent category and multiplying by 100 (14).

Results

In the 46 states for which HIV case surveillance data from the 

NHSS were analyzed, a total of 46,379 adults aged ≥18 years 

received a diagnosis of HIV in 2008, and 46,381 received an 

HIV diagnosis in 2010. During 2010, the relative difference 

in the HIV diagnosis rate among blacks/African Americans 

compared with whites was eightfold and for Hispanics/Latinos, 

persons of multiple races, and Native Hawaiians/other Pacific 

Islanders (NH/OPI), the relative difference was twofold 

compared with whites (Table 1).

Although the racial/ethnic disparities in rates of HIV diagnoses 

among men were similar to the disparities observed for the 

racial/ethnic groups overall, larger differences occurred among 

women. In 2010, among women, the relative difference in HIV 

diagnosis rates among black/African American women was 

twentyfold compared with whites, among women of multiple 

races was fourfold compared with whites, among Hispanic/

Latino women was threefold compared with whites, and 

among AI/ANs was twofold compared with whites (Table 1). 

From 2008 to 2010, the relative differences increased for all 

racial/ethnic groups of women except NH/OPIs and women 

of multiple races compared with whites. The largest relative 

difference was observed for MSM compared with all other men 

(an approximate 46-fold difference) in 2010, as well as the largest 

change from 2008 to 2010 (763 percentage points).

Among adults aged ≥18 years in MMP, representing persons 

receiving medical care in 2009, assessment of the data by age 

group indicated that the percentages of persons who were 

prescribed ART increased as age increased. Compared with 

adults aged ≥55 years, a lower percentage of young persons (aged 

18–24 years and 25–34 years) were prescribed ART (relative 

difference: -18% and -16%, respectively). By race/ethnicity, 

lower percentages of blacks/African Americans were prescribed 

ART than were whites (relative difference: -7%) (Table 2). 

A higher percentage of men were prescribed ART than were 

women, with a relative difference of 5%. Among men, lower 

percentages of blacks/African Americans were prescribed ART 

than were whites, with a relative difference of -6%. A similar 

pattern was observed in the percentage of women prescribed 

ART, with a lower percentage of blacks/African Americans 

prescribed ART than whites, (relative difference: -7%).
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114 MMWR / November 22, 2013 / Vol. 62 / No. 3

Among persons prescribed ART in 2009, persons aged 25–34 

years and 35–44 years accounted for the lowest percentages of 

persons with a suppressed viral load. Compared with persons 

aged ≥55 years, relative differences in viral suppression were 

-18% for persons aged 25–34 years and -15% for persons aged 

35–44 years. By race/ethnicity, lower percentages of blacks/

African Americans and Hispanics/Latinos had a suppressed 

viral load than whites, with relative differences of -15% and 

−5%, respectively (Table 2). A higher percentage of men had 

a suppressed viral load at their most recent test than women 

(relative difference: 10%). Among men, lower percentages 

of blacks/African Americans and Hispanics/Latinos had a 

suppressed viral load than whites, with relative differences 

of -16% and -6%, respectively. Examination of other 

demographic characteristics indicated that a higher percentage 

of persons who spoke Spanish with friends and family had a 

suppressed viral load at their most recent test than Englishspeaking persons, with a relative difference of 6%. A higher 

percentage of persons who self-identified as homosexual had 

a suppressed viral load than persons who self-identified as 

heterosexual, with a relative difference of 11%. The percentage 

TABLE 1. Estimated rate* of HIV infection diagnoses among adults aged ≥18 years† — National HIV Surveillance System, 46 states,§ 2008 and 2010

Characteristic 2008 rate

Absolute 

difference 

(percentage 

points)

Relative difference 

(%) 2010 rate

Absolute 

difference 

(percentage 

points)

Relative difference 

(%)

Age group (yrs)

18–24 27.7 21.9 377.6 32.0 26.2 451.7

25–34 32.2 26.4 455.2 32.3 26.5 456.9

35–44 31.7 25.9 446.6 28.5 22.7 391.4

45–54 21.9 16.1 277.6 21.2 15.4 265.5

≥55 5.8 Ref. Ref. 5.8 Ref. —

Race/Ethnicity

American Indian/Alaska Native 13.3 4.1 44.6 13.5 4.4 48.4

Asian 8.1 -1.1 -12.0 8.4 -0.7 -7.7

Black/African American 86.0 76.8 834.8 84.0 74.9 823.1

Hispanic/Latino¶ 31.1 21.9 238.0 30.9 21.8 239.6

Native Hawaiian/Other Pacific Islander 26.9 17.7 192.4 27.0 17.9 196.7

White 9.2 Ref. Ref. 9.1 Ref. Ref.

Multiple races 34.7 25.5 277.2 28.4 19.3 212.1

Sex 

Male 33.3 23.6 243.3 34.0 25.4 295.3

Female 9.7 Ref. Ref. 8.6 Ref. Ref.

Male

American Indian/Alaska Native 21.3 5.1 31.5 20.2 3.7 22.4

Asian 14.6 -1.6 -9.9 14.8 -1.7 -10.3

Black/African American 125.4 109.2 674.1 128.4 111.9 678.2

Hispanic/Latino 49.7 33.5 206.8 49.9 33.4 202.4

Native Hawaiian/Other Pacific Islander 46.8 30.6 188.9 49.2 32.7 198.2

White 16.2 Ref. Ref. 16.5 Ref. Ref.

Multiple races 52.1 35.9 221.6 46.9 30.4 184.2

Female

American Indian/Alaska Native 5.7 3.1 119.2 7.1 4.9 222.7

Asian 2.2 -0.4 -15.4 2.6 0.4 18.2

Black/African American 51.8 49.2 1,892.3 45.3 43.1 1,959.1

Hispanic/Latino 11.0 8.4 323.1 10.2 8.0 363.6

Native Hawaiian/Other Pacific Islander 7.2 4.6 176.9 5.0 2.8 127.3

White 2.6 Ref. Ref. 2.2 Ref. Ref.

Multiple races 18.7 16.1 619.2 11.4 9.2 418.2

Transmission category

Men who have sex with men** 359.1 349.9 3,803.3 382.6 374.4 4,565.9

All other men 9.2 Ref. Ref. 8.2 Ref. Ref.

Abbreviations: HIV = human immunodeficiency virus; Ref. = referent.

* Per 100,000 population.

† A total of 46,379 adults aged ≥18 years received a diagnosis of HIV in 2008; 46,381 received a diagnosis in 2010.

§ Alabama, Alaska, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, 

Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, Ohio, 

Oklahoma, Oregon, Pennsylvania, Rhode Island, South Carolina, South Dakota, Tennessee, Texas, Utah, Virginia, Washington, West Virginia, Wisconsin, and Wyoming.

¶ Persons of Hispanic/Latino ethnicity might be of any race or combination of races.

** Denominator calculated by applying the estimated proportion of men in the United States who reported ever having male-to-male sex (6.9%; 95% confidence 

interval: 5.1%–8.6%) to postcensal estimated populations for men.
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MMWR / November 22, 2013 / Vol. 62 / No. 3 115

TABLE 2. Percentage of adults aged ≥18 years receiving care for HIV infection who were prescribed antiretroviral therapy, had viral load 

suppression at their most recent HIV viral load test, and received prevention counseling during the past 12 months, by selected characteristics 

— Medical Monitoring Project, United States, 2009*

Characteristic

Prescribed ART

Prescribed ART and most recent HIV 

viral load test was undetectable or 

≤200 copies/mL

Received prevention counseling 

from a health-care provider†

% (SE)§

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%) % (SE)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%) % (SE)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%)

Age group (yrs)

18–24 75.8 (5.8)¶ -16.4 -17.8 77.8 (4.6) -7.5 -8.8 73.3 (5.4)¶ 37.5 104.7

25–34 77.6 (2.8)¶ -14.6 -15.8 70.1 (2.5)¶ -15.2 -17.8 59.0 (3.4)¶ 23.2 64.8

35–44 88.3 (1.4)¶ -3.9 -4.2 72.8 (2.3)¶ -12.5 -14.7 46.7 (2.6)¶ 10.9 30.4

45–54 91.4 (0.7) -0.8 -0.9 79.2 (1.6)¶ -6.1 -7.2 41.6 (2.9)¶ 5.8 16.2

≥55 92.2 (1.1) Ref. Ref. 85.3 (1.4) Ref. Ref. 35.8 (2.6) Ref. Ref.

Race/Ethnicity

Black/African American 86.0 (1.3)¶ -6.2 -6.7 71.4 (1.8)¶ -12.7 -15.1 54.2 (2.7)¶ 25.5 88.9

Hispanic/Latino** 89.2 (1.4) -3.0 -3.3 79.8 (1.8)¶ -4.3 -5.1 51.9 (2.2)¶ 23.2 80.8

White 92.2 (0.8) Ref. Ref. 84.1 (1.7) Ref. Ref. 28.7 (1.8) Ref. Ref.

Other 85.7 (3.5)¶ -6.5 -7.0 76.7 (2.9)¶ -7.4 -8.8 47.9 (4.0)¶ 19.2 66.9

Sex 

Male 89.9 (0.9)¶ 4.1 4.8 79.7 (1.6)¶ 6.9 9.5 42.6 (2.7)¶ -7.3 -14.6

Female 85.8 (1.5) Ref. Ref. 72.8 (1.8) Ref. Ref. 49.9 (2.2) Ref. Ref.

Male

Black/African American 87.1 (1.5)¶ -5.4 -5.8 71.7 (2.4)¶ -13.8 -16.1 55.5 (3.1)¶ 27.9 101.1

Hispanic/Latino 90.7 (1.4) -1.8 -1.9 80.4 (1.9)¶ -5.1 -6.0 50.8 (2.6)¶ 23.2 84.1

White 92.5 (0.9) Ref. Ref. 85.5 (1.7) Ref. Ref. 27.6 (1.9) Ref. Ref.

Other 85.3 (4.1)¶ -7.2 -7.8 79.4 (2.8) -6.1 -7.1 45.3 (4.5)¶ 17.7 64.1

Female

Black/African American 84.4 (1.7)¶ -6.3 -6.9 71.0 (2.1) -4.1 -5.5 52.2 (2.5)¶ 17.1 48.7

Hispanic/Latino 85.2 (3.3) -5.5 -6.1 77.9 (3.6) 2.8 3.7 55.3 (4.0)¶ 20.2 57.5

White 90.7 (2.0) Ref. Ref. 75.1 (3.2) Ref. Ref. 35.1 (3.8) Ref. Ref.

Other 87.2 (5.2) -3.5 -3.9 66.1 (11.8) -9.0 -12.0 58.5 (6.6)¶ 23.4 66.7

Place of birth

United States or U.S. territory 88.9 (0.9) Ref. Ref. 77.3 (1.5) Ref. Ref. 43.6 (2.8) Ref. Ref.

Other 87.5 (2.1) -1.4 -1.6 81.7 (2.4) 4.4 5.7 51.1 (3.1)¶ 7.5 17.2

Language most comfortable 

speaking with family and friends

English 88.6 (1.0) Ref. Ref. 77.3 (1.5) Ref. Ref. 43.1 (2.8) Ref. Ref.

Spanish 90.1 (1.7) 1.5 1.7 81.7 (1.9)¶ 4.4 5.7 54.9 (2.9)¶ 11.8 27.4

Other 88.9 (4.1) 0.3 0.3 79.9 (4.3) 2.6 3.4 54.3 (7.4) 11.2 26.0

Sexual identity 

Heterosexual (straight) 88.6 (1.1) Ref. Ref. 74.4 (1.5) Ref. Ref. 49.0 (2.5) Ref. Ref.

Homosexual (gay or lesbian) 89.7 (0.9) 1.1 1.2 82.2 (1.6)¶ 7.8 10.5 38.5 (2.5)¶ -10.5 -21.4

Bisexual 85.1 (2.3) -3.5 -4.0 76.2 (2.9) 1.8 2.4 49.3 (4.1) 0.3 0.6

Educational attainment

Less than high school 90.1 (1.2) 0.7 0.8 70.9 (2.0)¶ -15.6 -18.0 53.4 (2.5)¶ 18.7 53.9

High school graduate or equivalent 89.1 (1.0) -0.3 -0.3 75.0 (1.9)¶ -11.5 -13.3 47.8 (3.0)¶ 13.1 37.8

Some college 87.2 (1.4) -2.2 -2.5 80.3 (1.8)¶ -6.2 -7.2 41.3 (2.5)¶ 6.6 19.0

College graduate 89.4 (1.4) Ref. Ref. 86.5 (1.8) Ref. Ref. 34.7 (2.6) Ref. Ref.

Abbreviations: ART = antiretroviral therapy; HIV = human immunodeficiency virus; MMP = Medical Monitoring Project; Ref. = referent; SE = standard error.

* A total of 23 project areas were funded to conduct data collection activities for the 2009 MMP data collection cycle: California; Chicago, Illinois; Delaware; Florida; 

Georgia; Houston, Texas; Illinois; Indiana; Los Angeles County, California; Michigan; Mississippi; New Jersey; the state of New York; New York City, New York; North 

Carolina; Oregon; Pennsylvania; Philadelphia, Pennsylvania; Puerto Rico; San Francisco, California; Texas; Virginia; and Washington. Information regarding prescription 

of ART and HIV viral load was abstracted from the patient’s medical record. Patients who received medical care during January–April 2009 at an MMP participating 

facility were interviewed once during June 2009–April 2010 regarding all medical visits during the 12 months before the interview. In addition, patients’ medical 

records were abstracted for documentation of medical care for the 12 months before the interview.

† Based on self-reported information from the patient interview about discussions with a physician, nurse, or other health-care provider. Topics might have included 

condom negotiation, how to practice safer sexual behavior or injection use, or how to talk with partners about safe sex. Discussion occurring during sessions that 

were part of HIV testing and counseling encounters were not included.

§ All percentages are weighted for probability of selection and nonresponse bias adjustment. 

¶ Significant difference between group estimate and referent category, with significance set at p<0.05 by Rao-Scott chi-square test. 

** Persons of Hispanic/Latino ethnicity might be of any race or combination of races.
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116 MMWR / November 22, 2013 / Vol. 62 / No. 3

of persons with a suppressed viral load increased as educational 

attainment increased.

Of persons receiving HIV care in the United States in 2009, 

persons in younger age groups reported higher percentages of 

receipt of HIV prevention counseling than those aged ≥55 

years. Higher percentages of blacks/African Americans and 

Hispanics/Latinos had received HIV prevention counseling 

from a health-care provider during the 12 months before 

their interview than whites, with relative differences of 89% 

and 81%, respectively (Table 2). A lower percentage of men 

received HIV prevention counseling from a health-care 

provider than women (relative difference: -15%). Among men, 

higher percentages of blacks/African Americans and Hispanics/

Latinos received HIV prevention counseling than whites, with 

relative differences of 101% and 84%, respectively. Findings 

were similar among women; higher percentages of blacks/

African Americans and Hispanics/Latinas had received HIV 

prevention counseling than whites, with relative differences of 

49% and 58%, respectively. A higher percentage of persons 

born outside the United States received HIV prevention 

counseling than persons born in the United States, with a 

relative difference of 17%. A higher percentage of persons 

who spoke Spanish with friends and family had received HIV 

prevention counseling than English-speaking persons, with a 

relative difference of 27%. A lower percentage of persons who 

self-identified as homosexual had received HIV prevention 

counseling than persons who self-identified as heterosexual, 

with a relative difference of -21%. The percentage of persons 

receiving prevention counseling increased as educational 

attainment decreased. Compared with college graduates, 

relative differences in the percentage of persons who received 

HIV prevention counseling were 54% for persons with less 

than a high school education, 38% for high school graduates, 

and 19% for persons with some college or the equivalent.

Among MSM in NHBS in 2008, unprotected anal sex with a 

casual male partner was most common in younger age groups, 

with relative differences of 38% among MSM aged 25–34 

years and 26% among those aged 35–44 years, compared with 

MSM aged ≥55 years (Table 3). By race/ethnicity, Hispanic/

Latino MSM and MSM of multiple races accounted for the 

largest percentages of MSM who engaged in unprotected anal 

sex with a casual partner, with relative differences of 14% and 

17%, respectively, compared with whites.

The percentages of MSM who had been tested for HIV 

infection in the 12 months before the interview were higher 

among younger than older MSM and those who identified 

as homosexual than those who did not, similar among racial 

and ethnic groups, and increased with educational attainment 

(Table 3). Specifically, HIV testing in the 12 months before 

interview was highest among MSM aged 18–24 and 25–34 

years, with relative differences of 37% and 36%, respectively, 

compared with men aged ≥55 years. The percentage of MSM 

who reported HIV testing in the 12 months before interview 

was lowest among MSM with less than a high school education, 

with a relative difference of -27% compared with MSM who 

were college graduates.

The percentages of MSM who reported participation in a 

behavioral HIV intervention in the 12 months before interview 

were higher among younger than older MSM and among 

MSM of minority racial/ethnic groups than whites (Table 3). 

MSM aged 18–24 years accounted for the highest percentage 

of MSM who participated in a behavioral intervention, with 

a relative difference of 148% compared with men aged ≥55 

years. The percentage of MSM who participated in a behavioral 

intervention varied by level of educational attainment. 

Compared with MSM who had graduated from college, the 

percentage of MSM who had participated in a behavioral 

intervention was higher among MSM with lower levels of 

educational attainment, with relative differences of 17%, 29%, 

and 46% for less than high school, high school graduate, and 

some college or technical school, respectively.

Discussion

Although the relative difference in HIV infection diagnoses 

between whites and blacks/African Americans decreased from 

2008 to 2010, all racial/ethnic minorities, except Asians, 

continue to experience higher rates of HIV diagnoses than 

whites. These differences might reflect HIV incidence, testing 

patterns, or both. Compared with whites, lower percentages 

of blacks/African Americans were prescribed ART and lower 

percentages of both blacks/African Americans and Hispanics/

Latinos had suppressed viral loads. Differences in rates of ART 

prescription and viral suppression might reflect differences 

in insurance coverage, prescription drug costs, health-care 

providers’ perceptions of patients, or other factors associated 

with adherence (4). Rates of HIV infection are increasing among 

MSM, particularly young black/African American MSM (2). 

However, among MSM, similar percentages of blacks/African 

American and Hispanic/Latino MSM reported HIV testing 

compared with white MSM, and higher percentages reported 

receipt of behavioral interventions than white MSM.

Limitations

The NHSS data presented in this report are subject to at 

least three limitations. First, data were not available from 

all states. According to the cumulative estimated number of 

AIDS diagnoses through 2010, the 46 states with confidential 
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MMWR / November 22, 2013 / Vol. 62 / No. 3 117

name-based reporting since at least 2007 for which data were 

used represent approximately 92% of AIDS diagnoses in the 

50 states and the District of Columbia. Second, adjustments 

made to HIV case surveillance data for reporting delays and 

missing transmission category information are subject to a 

degree of uncertainty that might result in less stable rates for 

the most recent years. Finally, although postcensal estimates 

were used to determine population denominators for women, 

estimated population denominators were calculated for MSM 

and other men by applying the estimated proportion of men in 

the United States who reported ever having male-to-male sex 

(6.9%; 95% CI: 5.1%–8.6%) to the 2009 postcensal estimated 

population for men (9). Population denominators for other 

men were calculated by subtracting the MSM population 

TABLE 3. Percentage of men aged 18–64 years who have sex with men, who are at risk for acquiring HIV infection,* and who engaged in selected 

HIV-related risk behaviors during the 12 months before the interview — National HIV Behavioral Surveillance System, 21 U.S. cities,† 2008

Characteristic

Unprotected anal sex with a 

casual partner§ Received an HIV test

Participated in a behavioral 

intervention¶

No. of 

% participants

Absolute 

difference 

(percentage 

points)

Relative 

difference 

% %

Absolute 

difference 

(percentage 

points)

Relative 

difference 

% %

Absolute 

difference 

(percentage 

points)

Relative 

difference 

%

Age group (yrs)

18–24 24.2 4.3 21.6 67.5 18.4 37.4 26.2 15.6 147.7 1,997

25–34 27.4 7.5 37.7 66.6 17.5 35.7 17.8 7.2 67.6 2,737

35–44 25.1 5.2 26.1 58.4 9.3 18.9 13.2 2.7 25.0 2,076

45–54 24.4 4.5 22.8 52.1 3.1 6.2 11.2 0.7 6.2 978

55–64 19.9 Ref. Ref. 49.1 Ref. Ref. 10.6 Ref. Ref. 387

Race/Ethnicity

American Indian/Alaska Native 20.5 -4.2 -17.2 63.6 1.2 1.9 20.5 7.5 58.1 44

Asian 20.6 -4.1 -16.6 60.3 -2.1 -3.4 13.1 0.1 1.0 199

Black/African American 23.9 -0.8 -3.1 62.0 -0.4 -0.7 22.8 9.9 76.3 1,938

Hispanic/Latino** 28.0 3.3 13.5 61.7 -0.8 -1.3 20.2 7.3 56.2 2,019

Native Hawaiian/Other Pacific Islander 23.7 -1.0 -3.9 64.4 2.0 3.1 30.5 17.6 135.8 59

White 24.7 Ref. Ref. 62.4 Ref. Ref. 12.9 Ref. Ref. 3,579

Multiple races 28.9 4.2 16.9 62.7 0.2 0.4 19.7 6.8 52.4 284

Other single race 19.1 -5.6 -22.5 66.0 3.5 5.6 29.8 16.9 130.3 47

Place of birth

United States or U.S. territory 24.9 Ref. Ref. 62.7 Ref. Ref. 16.9 Ref. Ref. 6,741

Other 27.5 2.6 10.5 59.8 -2.8 -4.5 20.9 4.1 24.1 1,434

Sexual identity 

Heterosexual (straight) 26.3 Ref. Ref. 40.4 Ref. Ref. 17.2 Ref. Ref. 99

Homosexual (gay) 25.4 -0.8 -3.2 64.0 23.6 58.4 17.6 0.4 2.4 6,553

Bisexual 24.9 -1.3 -5.1 55.6 15.2 37.6 17.6 0.4 2.4 1,513

Educational attainment

Less than high school 33.6 10.1 43.2 48.8 -17.7 -26.6 16.8 2.4 16.9 512

High school graduate or equivalent 25.5 2.0 8.6 57.3 -9.2 -13.9 18.5 4.1 28.6 1,868

Some college 25.8 2.4 10.2 63.0 -3.6 -5.4 20.9 6.6 45.7 2,627

College graduate 23.5 Ref. Ref. 66.5 Ref. Ref. 14.4 Ref. Ref. 3,167

Total 25.3 62.2 17.6 8,175

Abbreviations: HIV = human immunodeficiency virus; MSA = metropolitan statistical area; MSM = men who have sex with men; Ref. = referent.

* Participants at risk for acquiring HIV infection were defined as those who reported having never had an HIV test or that their most recent HIV test result was negative, 

indeterminate, or unknown. This group includes those who did not know they were HIV positive before the interview but tested positive during the interview. 

Analyses were limited to men who reported oral or anal sex with another man during the 12 months before interview and did not report a previous positive HIV 

test result or diagnosis.

† Data were collected in the following 21 MSAs; if a metropolitan division is listed, sampling was conducted within that specific division of that MSA: Atlanta-Sandy 

Springs-Marietta, Georgia; Baltimore-Towson, Maryland; Boston-Quincy, Massachusetts; Chicago-Naperville-Joliet, Illinois; Dallas-Plano-Irving, Texas; Denver-AuroraBroomfield, Colorado; Detroit-Livonia-Dearborn, Michigan; Houston-Sugar Land-Baytown, Texas; Los Angeles-Long Beach-Glendale, California; Miami-Miami 

Beach-Kendall, Florida; Nassau-Suffolk, New York; Newark-Union, New Jersey-Pennsylvania; New Orleans-Metairie-Kenner, Louisiana; New York-White Plains-Wayne, 

New York-New Jersey; Philadelphia, Pennsylvania; San Diego-Carlsbad-San Marcos, California; San Francisco-San Mateo-Redwood City, California; San Juan-CaguasGuaynabo, Puerto Rico; Seattle-Bellevue-Everett, Washington; St. Louis, Missouri-Illinois; and Washington-Arlington-Alexandria, DC-Virginia-Maryland-West Virginia.

§ Unprotected sex was defined as insertive or receptive anal sex without a condom. A casual partner was defined as a man with whom the participant did not feel 

committed, whom he did not know very well, or with whom he had sex in exchange for something such as money or drugs.

¶ Includes behavioral interventions received as an individual or as part of a group. An individual intervention was defined as a one-on-one conversation with an 

outreach worker, a counselor, or a prevention program worker about ways to protect against HIV infection or other sexually transmitted diseases. This excludes 

conversations that took place solely as part of obtaining HIV testing (e.g., pretest or posttest counseling). A group behavioral intervention was defined as a smallgroup discussion about ways to protect against HIV or other sexually transmitted diseases.

** Persons of Hispanic ethnicity might be of any race or combination of races.
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118 MMWR / November 22, 2013 / Vol. 62 / No. 3

denominators from the 2009 postcensal estimated population 

for men.

The MMP data presented in this report are subject to at least 

three limitations. First, MMP estimates are not representative 

of all persons with HIV in the United States because only HIVinfected persons in care during the first 4 months of 2009 were 

eligible for selection into the MMP sample. Second, MMP 

data might include persons more likely to be retained in care or 

adhere to ART, leading to overestimation of certain measures. 

For example, measures might be overestimated because persons 

in MMP are more engaged in care and adherent to ART use. 

Finally, documentation of a recent suppressed viral load might 

not indicate persistent viral suppression over time.

The NHBS data presented in this report are subject to at 

least two limitations. First, participants in the MSM cycle of 

NHBS were recruited from venues, primarily bars and clubs, 

within 21 MSAs with a high AIDS prevalence and might not 

be representative of MSM who do not attend such venues or 

of MSM in other areas. Second, NHBS data regarding risk 

behaviors and use of prevention services are self-reported. Social 

desirability might lead to underreporting of risk behaviors and 

overreporting of recent HIV testing and participation in HIV 

behavioral interventions.

Conclusion

The findings in this report highlight a need for continued 

expansion of effective HIV prevention efforts for racial/

ethnic minorities and MSM. In 2007, CDC initiated the 

Expanded HIV Testing Initiative, Expanded and Integrated 

HIV Testing for Populations Disproportionately Affected 

by HIV, which was expanded in 2010 to include MSM. 

(Additional information is available at http://www.cdc.gov/

hiv/topics/funding/ps10-10138/index.htm.) In addition, 

the 2010 national HIV/AIDS strategy has goals that include 

reducing HIV incidence, increasing access to care, improving 

health outcomes for persons living with HIV, and reducing 

HIV-related disparities and health inequities. These goals are 

interdependent (4,15) and also consistent with the Healthy 

People 2020 goal of achieving health equity, eliminating 

disparities, and improving the health of all groups. (Additional 

information available at http://healthypeople.gov/2020/

about/default.aspx.) Reducing HIV incidence and improving 

individual health outcomes require increased access to care 

and elimination of disparities in the quality of care received 

(4). CDC is working with health departments throughout 

the United States to expand efforts in using local data (in 

accordance with privacy and confidentiality policies, laws, and 

regulations) to 1) identify HIV-infected persons who are not 

receiving care and to facilitate efforts to ensure they receive 

appropriate care and 2) identify populations within their 

local areas at greatest risk for HIV and with greatest need for 

prevention services. CDC will continue using its national HIV 

surveillance systems to monitor HIV incidence and diagnosis in 

the population and to monitor receipt of ART, risk behaviors, 

and receipt of prevention services among HIV-infected persons 

in care to identify opportunities for improvement. Information 

will be shared with grantees, partners, health-care providers, 

and other federal agencies (e.g., the Health Resources and 

Services Administration) to improve delivery of care, treatment, 

and prevention services for those with HIV infection (4). 

Behaviors of populations at high risk for HIV infection also 

will be monitored as part of CDC’s comprehensive approach 

to reducing the spread of HIV infection in the United States.

To reduce the number of new HIV infections, CDC 

has devoted HIV resources to High-Impact Prevention, a 

combination of scientifically proven, cost-effective, and scalable 

interventions that have demonstrated the potential to reduce 

new HIV infections in the relevant populations and geographic 

areas to yield a greater reduction in HIV incidence (16). 

Optimally scaled implementation of the most cost-effective 

interventions will have the greatest impact on reducing the 

spread of HIV in the United States.

The progress in HIV prevention since the beginning of the 

U.S. epidemic is a result of a multisectoral approach to HIV 

from governmental, non governmental, and community-based 

organizations, academia, and the business sector. Reducing the 

higher prevalence of HIV infection in racial/ethnic minority 

groups and MSM also will require public health interventions 

and societal actions as a whole that address social, economic, 

health system, and other environmental factors that play a 

role in HIV prevalence in these communities (17). These 

factors might include poverty, which can limit access to health 

care and HIV testing; stigma and discrimination, which can 

discourage individuals from seeking testing, prevention, and 

treatment services; barriers to timely access and use of medical 

and social services; and higher rates of incarceration, which 

can disrupt social and safe sexual networks. The results of this 

report underscore the need for high-priority, carefully targeted 

HIV prevention efforts in these communities to ensure that 

individual, social, health system, and other environmental 

determinants of health are considered in the design and 

implementation of HIV prevention and care programs.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 119

References

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HIV infection in the United States and 6 U.S. dependent areas—2010. 

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Sullivan PS. Surveillance of HIV risk and prevention behaviors of men 

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14. Keppel K, Pamuk E, Lynch J, et al. Methodological issues in measuring 

health disparities. Vital Health Stat 2 2005;141:1–16.

15. White House Office of National AIDS Policy. National HIV/AIDS 

strategy for the United States. Washington, DC: White House Office 

of National AIDS Policy; 2010. Available at http://www.aids.gov/federalresources/policies/national-hiv-aids-strategy/nhas.pdf.

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Health and Human Services; 2011. Available at http://www.cdc.gov/

hiv/strategy/hihp.

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Available at http://www.cdc.gov/socialdeterminants/docs/SDH-WhitePaper-2010.pdf.
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120 MMWR / November 22, 2013 / Vol. 62 / No. 3

Introduction

Obesity is a major public health problem affecting adults 

and children in the United States. Since 1960, the prevalence 

of adult obesity in the United States has nearly tripled, from 

13% in 1960–1962 to 36% during 2009–2010 (1,2). Since 

1970, the prevalence of obesity has more than tripled among 

children, from 5% in 1971–1974 (3) to 17% in 2009–2010 

(4,5). Although the prevalence of obesity is high among all 

U.S. population groups, substantial disparities exist among 

racial/ethnic minorities and vary on the basis of age, sex, and 

socioeconomic status.

This report is part of the second CDC Health Disparities 

and Inequalities Report (CHDIR). The 2011 CHDIR (6) was 

the first CDC report to assess disparities across a wide range of 

diseases, behavior risk factors, environmental exposures, social 

determinants, and health-care access. The topic presented 

in this report is based on criteria that are described in the 

2013 CHDIR Introduction (7). This report provides more 

current information regarding what was presented in the 2011 

CHDIR (8). The purposes of this report are to discuss and raise 

awareness of differences in the characteristics of persons who 

are obese and to prompt actions to reduce these disparities.

Methods

To assess disparities and trends over time in obesity prevalence 

among adults aged ≥18 years and children and adolescents aged 

2–17 years, CDC analyzed data from the National Health and 

Nutrition Examination Survey (NHANES) between 1999 and 

2008 that were included in the 2011 CDC Health Disparities 

and Inequities Report (CHDIR) (8) and data from NHANES 

for 2009–2010. To assess disparities and trends over time in 

obesity prevalence among adults aged ≥18 years and children 

and adolescents aged 2–17 years, CDC analyzed data from 

the National Health and Nutrition Examination Survey 

(NHANES) between 1999 and 2008 that were included in the 

2011 CDC Health Disparities and Inequities Report (CHDIR) 

(8) and data from NHANES for 2009–2010. CDC examined 

obesity prevalence by sex, age, and race/ethnicity and by the 

following variables that were not included in the previous 

report: educational attainment, disability status, country of 

birth, and language spoken at home. Geographic location 

was not examined because this information was not available 

in the publicly available datasets, and educational attainment 

was analyzed rather than family income because a smaller 

number of participants had missing data for educational 

attainment than for income. In addition, for many persons, 

income was categorized into very broad ranges (e.g., <$20,000 

and ≥$20,000). The highest income category was ≥$75,000.

NHANES is a complex, multistage probability sample of 

the noninstitutionalized population of the United States. 

Information regarding the survey’s methodology has been 

published previously (9). Data for NHANES 2-year samples 

were collected from 1999–2000 through 2009–2010 (10) 

using a stratified, multistage cluster design. The sample 

was representative of the U.S. civilian, noninstitutionalized 

population. Weight and height were measured using 

standardized techniques and equipment, and body mass index 

(BMI) (weight [kg]/height [m]2) was calculated (11). Persons 

aged ≥20 years were classified as obese if they had a BMI 

≥30 kg/m2 (12). Persons aged 2–17 years were considered 

obese if they had a BMI ≥95th (sex- and age-specific) percentile 

of the 2000 CDC growth charts (13). Persons aged 18–19 

years were classified as obese if they had a BMI ≥30 kg/m2

or ≥95th percentile of the CDC growth charts. This age 

classification differs from that used in other studies of obesity 

using NHANES data (2,5), which grouped persons aged 

18–19 years with children and adolescents. Information on 

race/ethnicity was self-reported for persons aged ≥16 years. 

For persons aged <16 years, race/ethnicity was reported by a 

family member. Respondents reported race/ethnicity from a 

list provided to them that included an open-ended response. 

Analyses that focused on race/ethnicity were restricted to 

non-Hispanic whites, non-Hispanic blacks, and MexicanAmericans because of insufficient numbers of persons in other 

racial/ethnic groups. However, all race/ethnicity groups were 

included in analyses of other characteristics (e.g., educational 

attainment). Non-Hispanic blacks and Mexican-Americans 

were oversampled to improve the precision of estimates for 

these race/ethnicity groups (14).

Obesity — United States, 1999–2010

Ashleigh L. May, PhD

David Freedman, PhD

Bettylou Sherry, PhD

Heidi M. Blanck, PhD

Division of Nutrition, Physical Activity, and Obesity, National Center for Chronic Disease Prevention and Health Promotion, CDC

Corresponding author: Ashleigh L. May, Division of Nutrition, Physical Activity, and Obesity, National Center for Chronic Disease Prevention and Health 

Promotion, CDC. Telephone: 770-488-8062. E-mail: amay@cdc.gov.
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MMWR / November 22, 2013 / Vol. 62 / No. 3 121

During each household interview, adult respondents were 

asked to report the highest level of school completed or 

the highest degree that they had received. For children and 

adolescents, this information was collected for the adult head 

of household. This information was summarized into four 

categories of education attainment: less than high school, high 

school graduate or equivalent, some college, and college degree 

or higher. Because adults aged 18–22 years were unlikely to 

have completed college, analyses of educational attainment 

among adults were restricted to subjects who were aged ≥23 

years. For approximately 2% of the sample, information on 

education attainment was missing, and these persons were 

excluded from the analyses that focused on this characteristic.

Disability status was determined by self-reports among 

persons aged ≥60 years and was based on responses to 11 

questions concerning problems in memory (confusion) and 

hearing, along with ambulatory difficulties (e.g., difficulties in 

walking, going up steps, and standing) and self-care difficulties 

(e.g., dressing, eating, and getting out of bed). A response 

of “some difficulty” or “much difficulty” was considered 

to indicate that the activity was difficult. The number of 

positive responses was then summed, and this variable was 

categorized into three groups: no difficulties or problems 

(reported by 50% of adults aged ≥60 years), difficulties in 

one to three activities (33%), and difficulties in four or more 

activities (17%). Although this type of classification is based 

on various assumptions, it allows for the assessment of whether 

a dose-response relationship is evident. Standard disability 

classifications also include vision problems (15), but these data 

were not available for NHANES 2009–2010.

Information also was collected on country of birth and, 

on the basis of a question in the acculturation data file, the 

language usually spoken at home. Because few non-Hispanic 

white or non-Hispanic black subjects were born outside the 

United States or spoke a language other than English at home, 

analyses of country of birth and language spoken at home were 

restricted to Mexican-Americans. Of the Mexican-American 

adults for whom information was collected, 57% reported that 

they were born in Mexico, and 56% reported that they usually 

spoke Spanish at home.

Trends in obesity prevalence over the 2-year study cycles were 

examined, with year coded as a six-level interval variable. To 

decrease the variability of the estimates of the prevalence of 

obesity within categories of the examined characteristics, this 

report presents estimates for three 4-year periods: 1999–2002, 

2003–2006, and 2007–2010. Within these three 4-year 

periods, the number of children aged 2–17 years ranged from 

6,081 to 7,293, and the number of adults ranged from 9,630 to 

12,067. All estimates of the prevalence of obesity among adults 

have been age adjusted to the 2000 Standard U.S. Population. 

The estimated prevalence is considered to be unstable if the 

relative SE (SE ÷ prevalence) is ≥30%. Estimates that have a 

relative SE ≥40% are not presented.

The overall (12-year) prevalence of obesity was examined 

across categories of the various characteristics (race/ethnicity, 

educational attainment, number of disabilities, country 

of birth, and language spoken at home). Separate analyses 

were performed by age and sex (i.e., for men, women, boys, 

and girls). Differences in obesity prevalence across these 

characteristics were examined, using an interaction term in 

logistic regression models. Educational attainment (four levels), 

number of disabilities (three levels), and 2-year study cycle 

(six levels) were coded as ordinal variables in these models. All 

analyses accounted for the examination sampling weights and 

for the complex sampling design. All estimate comparisons 

represent absolute differences. Statistical significance (p<0.05) 

was assessed in logistic regression models, with various models 

including age and study period as covariates. All analyses were 

performed with the survey package in R (16,17).

Results

Between 1999–2002 and 2007–2010, the age-adjusted 

prevalence of obesity among adults aged ≥18 years increased 

from 26.5% to 33.0% among men and from 32.4% to 34.9% 

among women (Table 1). Controlling for age and race/

ethnicity in regression models indicated that the increase in 

the prevalence of obesity over the study period was statistically 

significant among men but not among women.

The prevalence of obesity differed substantially across 

categories of various demographic characteristics (Table 1). 

Among men, there was little difference in the prevalence 

of obesity by race/ethnicity, but among women, the overall 

(1999–2010) prevalence among non-Hispanic blacks (51%) 

was 10 percentage points higher than that among MexicanAmericans and 20 percentage points higher than that among 

non-Hispanic white women.

Inverse associations were identified between the prevalence 

of obesity and educational attainment that were statistically 

significant among both men and women; differences were much 

greater among women (Table 1). These associations appeared 

to be nonlinear. For example, among men, the prevalence was 

lowest (25%) among college graduates but highest (35%) among 

those who had completed some college. Among women, the 

overall prevalence of obesity among those who had completed 

college was 13–16 percentage points lower than in other groups, 

but there was little difference in obesity prevalence between those 

who had not finished high school and those who had completed 

some college. The analysis of disability status of adults aged ≥60 
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122 MMWR / November 22, 2013 / Vol. 62 / No. 3

years indicated that the overall prevalence of obesity among those 

who reported having difficulties with four or more activities 

was substantially higher than obesity prevalence among those 

without a disability (men: 16 percentage points higher; women: 

27 percentage points higher).

In contrast to these differences, which were larger among 

women, the association of obesity with country of birth and 

language spoken at home was stronger among men (Table 

1). Mexican-American men who were born in the United 

States had 13 percentage points higher overall prevalence of 

obesity than men born in Mexico (39% versus. 26%), but 

the equivalent difference among Mexican-American women 

was only 3 percentage points. Similarly, Mexican-American 

men who spoke mostly English at home had a 12 percentage 

points higher overall prevalence of obesity compared with those 

who spoke mostly Spanish at home (38% versus 26%), while 

there was no significant difference among Mexican-American 

women. As assessed by an interaction term (each characteristic 

x study period) in sex-specific regression models, there was no 

indication that disparities in obesity prevalence varied across 

the 12-year study period among either men or women.

Between 1999–2002 and 2007–2010, the prevalence of 

obesity among children and adolescents aged 2–17 years 

increased from 15.4% to 18.6% among boys and from 13.8% 

to 15.1% among girls (Table 2). After adjustment for age and 

race/ethnicity in regression models, the increase over the six 

2-year study cycles was statistically significant among boys but 

not among girls.

Differences in the prevalence of obesity among children and 

adolescents over the 12-year study period across categories 

of the various characteristics were somewhat similar to those 

among adults (Tables 1 and 2). Substantial differences existed 

in the prevalence of obesity by race/ethnicity; among boys, 

prevalence was highest among Mexican-Americans (24%), 

whereas among girls, prevalence was highest among nonHispanic blacks (22%). Educational attainment of the adult 

head of household was associated inversely with obesity among 

both boys and girls. Overall, the prevalence of obesity among 

TABLE 1. Prevalence of obesity* among adults aged ≥18 years, by selected characteristics — National Health and Nutrition Examination Survey, 

United States, 1999–2010

Characteristic

Prevalence in males Prevalence in females

Total 1999–2002 2003–2006 2007–2010 Total 1999–2002 2003–2006 2007–2010

% (SE) % (SE) % (SE) % (SE) % (SE) % (SE) % (SE) % (SE)

Total† 30 (1) 26 (1) 31 (1) 33 (1) 34 (1) 32 (1) 33 (1) 35 (1)

Race/Ethnicity

White, non-Hispanic 31 (1) 27 (1) 31 (1) 33 (1) 31§ (1) 30 (1) 31 (1) 32 (1)

Black, non-Hispanic 33 (1) 27 (1) 35 (2) 37 (2) 51§ (1) 47 (2) 53 (2) 53 (2)

Mexican-American 31 (1) 26 (2) 29 (2) 35 (2) 41§ (1) 37 (2) 41 (2) 44 (2)

Educational attainment¶

Less than high school 29** (1) 26 (2) 29 (2) 32 (2) 40§ (1) 39 (2) 40 (2) 41 (2)

High school graduate or 

equivalent

33** (1) 30 (2) 35 (2) 35 (2) 38§ (1) 36 (2) 38 (2) 41 (2)

Some college 35** (1) 28 (2) 35 (2) 41 (2) 37§ (1) 35 (2) 36 (2) 38 (1)

College graduate 25** (1) 23 (2) 26 (2) 26 (2) 24§ (1) 22 (2) 24 (2) 27 (2)

No. of disabilities††

0 31§ (1) 30 (2) 29 (4) 34 (2) 30§ (1) 31 (3) 26 (2) 32 (2)

1–3 41§ (2) 35 (4) 40 (5) 46 (3) 45§ (3) 40 (4) 42 (5) 53 (4)

4–11 47§ (3) 50 (5) 48 (8) 44 (5) 57§ (3) 53 (4) 59 (5) 60 (4)

Country of birth§§

United States 39§ (2) 37 (4) 35 (3) 44 (3) 43** (1) 38 (4) 45 (2) 44 (2)

Mexico 26§ (1) 19 (1) 25 (2) 31 (2) 40** (1) 36 (3) 39 (3) 43 (2)

Language spoken at home§§

English 38§ (2) 36 (4) 34 (3) 41 (3) 41 (2) 35 (3) 42 (2) 44 (2)

Spanish 26§ (1) 20 (1) 25 (2) 32 (2) 41 (1) 37 (3) 41 (3) 43 (2)

Abbreviation: SE = standard error.

* All estimates have been age adjusted to the 2000 Standard U.S. Population.

† The increase in obesity prevalence over the 12-year study period was statistically significant among men (p<0.001) but not among women (p = 0.09).

§ p<0.001. P-values assess whether the overall prevalence of obesity differed across categories of each characteristic (i.e., a main effect). These p-values were calculated 

from sex-specific regression models that included year of study (a six-level ordinal variable) and age as covariates. Educational attainment and the number of 

disabilities were coded as ordinal variables in these models.

¶ Asked of persons aged ≥23 years.

** p<0.05.

†† Asked of persons aged ≥60 years. Disabilities were classified on the basis of responses to 11 questions concerning having memory and hearing problems and some 

or much difficulty in walking, carrying, preparing meals, standing, getting out of bed, eating, dressing, and going out.

§§ Asked of Mexican-Americans.
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MMWR / November 22, 2013 / Vol. 62 / No. 3 123

children and adolescents whose adult head of household had 

completed college was approximately half that of prevalence 

among children whose adult head of household did not 

complete high school. In contrast to the differences among 

adults, the prevalence of obesity among Mexican-American 

children did not differ significantly according to either country 

of birth or language spoken at home.

As assessed by an interaction term (each characteristic x study 

period, which was coded as 1–6) in sex-specific regression 

models, there was little indication that differences in the 

prevalence of obesity across most of the characteristics analyzed 

varied over the 12-year study period among children (Table 2). 

However, the prevalence of obesity among girls whose adult head 

of household had not finished high school increased (17% for 

1999–2002 versus 23% for 2007–2010) while the prevalence 

decreased among girls whose adult head of household had 

completed college (11% for 1999–2002 versus 7% for 2007–

2010). There was not a comparable interaction among boys. 

Because education attainment differs substantially across race/

ethnicity groups, the associations between education attainment 

and obesity prevalence were examined.

The relation of educational attainment to obesity varied 

significantly by sex and race/ethnicity among both adults 

(Figure 1) and children (Figure 2). Among non-Hispanic 

white women (Figure 1), in each 4-year period, the prevalence 

of obesity was approximately 15% lower among those who 

had completed college than it was among those who had not 

completed high school. Although the prevalence of obesity 

among non-Hispanic white men and non-Hispanic black 

women was also lowest among those who had completed college, 

the trend over the four educational attainment categories was 

not consistent in these two groups. Furthermore, there was 

no evidence that educational attainment was associated with 

obesity among non-Hispanic black men or among MexicanAmericans. For example, Mexican-American men who did not 

complete high school had the lowest prevalence of obesity in 

1999–2002 and in 2003–2006.

Associations between obesity and adult head of household 

education attainment among children and adolescents were also 

less consistent after stratifying for race/ethnicity (Figure 2). (The 

prevalence of obesity among Mexican-American children and 

adolescents is not shown because many of the estimates were 

unstable). Although the lowest prevalence of obesity among nonHispanic white children and adolescents was observed among 

those whose adult head of household had completed college, 

this was not the case among non-Hispanic black children. 

TABLE 2. Prevalence of obesity among children and adolescents aged 2–17 years, by selected characteristics — National Health and Nutrition 

Examination Survey, United States, 1999–2010

Characteristic

Prevalence in males Prevalence in females

Total 1999–2002 2003–2006 2007–2010 Total 1999–2002 2003–2006 2007–2010

% (SE) % (SE) % (SE) % (SE) % (SE) % (SE) % (SE) % (SE)

Total* 17 (1) 15 (1) 17 (1) 19 (1) 15 (1) 14 (1) 15 (1) 15 (1)

Race/Ethnicity

White, non-Hispanic 15† (1) 13 (1) 15 (1) 16 (1) 13† (1) 11 (1) 14 (2) 13 (1)

Black, non-Hispanic 18† (1) 16 (1) 17 (1) 21 (2) 22† (1) 20 (1) 23 (1) 23 (2)

Mexican-American 24† (1) 24 (1) 24 (2) 25 (2) 18† (1) 17 (1) 19 (1) 18 (2)

Educational attainment§

Less than high school 21† (1) 21 (2) 19 (2) 24 (2) 19† (1) 17 (1) 18 (2) 23 (2)

High school graduate or 

equivalent

18† (1) 15 (2) 19 (2) 19 (1) 18† (1) 15 (2) 20 (2) 19 (2)

Some college 17† (1) 17 (2) 16 (2) 19 (2) 14† (1) 12 (1) 14 (2) 14 (2)

College graduate 11† (1) 11 (2) 12 (2) 12 (2) 9† (1) 11 (2) 10 (2) 7 (1)

Country of birth¶

United States 24 (1) 24 (1) 24 (2) 24 (2) 18 (1) 18 (2) 19 (2) 18 (2)

Mexico 24 (2) 24 (3) 22 (3) 27 (4) 15 (2) 13 (3) 16 (3) 17 (5)

Language spoken at home¶

English 26 (2) 27 (3) 24 (3) 27 (4) 22 (2) 23 (3) 23 (2) 20 (4)

Spanish 18 (4) 26 (3) 19 (3) 41 (11) 16 (3) 21 (3) 16 (4) —** —

Abbreviation: SE = standard error.

* The increase in obesity prevalence over the 12-year study period was statistically significant among boys (p<0.01) but not girls (p = 0.34).

† p<0.001. P-values assess whether the overall prevalences of obesity differed across categories of each characteristic (i.e., a main effect). These p-values were 

calculated from sex-specific logistic regression models that included year of study (an ordinal variable with six levels) and age as covariates. Adult head of household 

educational attainment was coded as an ordinal variable in these models; other characteristics were considered to be categorical.

§ Asked of adult head of household. As assessed by an interaction term in logistic regression models, disparities in obesity prevalence across educational attainment 

of the head of household increased over the study period among girls (p = 0.01). 

¶ Asked of Mexican-Americans.

** Not shown because SE was ≥40% of the estimated prevalence. During 2007–2010, data were available only for 50 Mexican-American girls whose families usually 

spoke Spanish at home.
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124 MMWR / November 22, 2013 / Vol. 62 / No. 3

Furthermore, there was no evidence of any association between 

obesity and adult head of household educational attainment 

among non-Hispanic black boys, and the trend in the prevalence 

of obesity across the lower three categories of adult head of 

household educational attainment was not consistent in any 

of the sex-race groups. The observed interaction between study 

period and educational attainment among girls (Table 2) was 

largely attributable to the trend among non-Hispanic white girls, 

and the 2007–2010 prevalence of obesity among non-Hispanic 

white girls varied from 6% (± 2) to 28% (± 5) across the four 

groups of educational attainment by the adult head of household 

more than it did in previous years.

FIGURE 1. Prevalence of obesity among adults aged ≥23 years,* by sex, race/ethnicity, and educational attainment — National Health and 

Nutrition Examination Survey, United States, 1999–2010

* Adults aged 18–22 years were unlikely to have completed their education and were excluded from this analysis.

20

30

40

50

60

20

30

40

50

60

1999–2002 2003–2006 2007–2010 1999–2002 2003–2006 2007–2010 1999–2002 2003–2006 2007–2010

Prevalence (%)

Non-Hispanic white Non-Hispanic black Mexican-American

Male Female

Educational attainment

Less than high school

High school graduate 

or equivalent

Some college

College graduate

Survey years
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 125

Discussion

Recent trends suggest that although increases in obesity 

prevalence have slowed or even stopped for some subgroups, 

the prevalence remains high (2,5). This report highlights 

the persistence of substantial disparities among certain 

population groups, all of which further complicate the efforts 

to understand, control, and prevent obesity. Although the 

specific causes of these disparities have not been identified, 

it is likely that they are associated with complex social and 

cultural factors that affect obesity-related behaviors. One 

possible contributing factor is that rates of breastfeeding 

are lower among non-Hispanic black women compared 

with non-Hispanic white women (18). In addition, greater 

FIGURE 2. Prevalence of obesity among children and adolescents aged 2–17 years, by sex, race/ethnicity, and educational attainment of adult 

head of household — National Health and Nutrition Examination Survey, United States 1999–2010*

* Prevalences are not shown for Mexican-American children because many of the estimates were unstable (standard error [SE] >30% of the prevalence) with the 

relative SE reaching a maximum of 49%. An asterisk in the figure indicates that the relative SE is between 30% and 40% of the prevalence; this was seen during 

2003–2006 among white non-Hispanic boys and girls from a household in which the adult head did not complete high school.

*

*

Educational attainment

Less than high school

High school graduate 

or equivalent

Some college

College graduate

5

10

15

20

25

10

15

20

25

1999–2002 2003–2006 2007–2010 1999–2002 2003–2006 2007–2010

Survey years

Prevalence (%)

Non-Hispanic white Non-Hispanic black

Male Female

5
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126 MMWR / November 22, 2013 / Vol. 62 / No. 3

satisfaction in body size among racial/ethnic minority women 

(19), preferences for larger body types (20,21), and previous 

threats of, or experiences with, undernutrition (22) also 

might promote obesogenic behaviors. Further, racial/ethnic 

differences in physical activity levels among adults (23) and 

children (24) and differential preferences for specific types of 

physical activity (25) also might play a role. These and other 

behaviors occur within a broader context of obesity-promoting 

environments that limit opportunities for physical activity, 

encourage excess television viewing and passive screen time, 

and provide easy access to high-calorie, low-nutrient foods 

and beverages, including those high in added sugars and solid 

fats (26,27).

Limitations

The findings presented in this report are subject to at least 

two limitations. First, NHANES does not sample an adequate 

number of persons who are members of racial/ethnic minority 

communities other than non-Hispanic blacks and MexicanAmericans to permit estimating obesity prevalence in these 

communities; however, previous research has reported high 

prevalence levels among American Indians/Alaska Natives 

(28,29). Second, the data presented, although age adjusted, 

do not allow for assessment of covarying issues or stratification 

to further assess independent effects.

Conclusion

The data provided in this report can be used to help identify 

high-priority groups (e.g., those with low levels of educational 

attainment, Mexican-American boys, and non-Hispanic black 

girls and women) for intervention. Because high-priority 

groups frequently are defined by nonmodifiable characteristics 

(e.g., race/ethnicity and sex), designers of effective interventions 

should consider which dietary or physical activity behaviors 

contribute to the differences as well as how those behaviors are 

influenced by social and cultural factors and by the settings in 

which persons spend their time. For example, because studies 

have found that access to healthy foods is more limited in 

low-income communities and communities of color than 

in other communities (30), interventions could focus on 

neighborhood walkability (e.g., sidewalks), Complete Streets 

(31), and community design in these communities. Further, 

opportunities to reduce disparities related to nutrition are also 

present. For example, many low-income household members, 

who also tend to have low educational attainment, consume 

higher amounts of sugary drinks and fewer fruits and vegetables 

than persons in higher income households (32,33). Effective 

interventions that focus on increasing access to healthy food 

outlets, initiatives for local businesses to provide healthier 

foods and beverages such as fruits and vegetables (e.g., Healthy 

Food Financing Initiative [34]), and education combined 

with vouchers for low-income families (e.g., the Special 

Supplemental Nutrition Program for Women, Infants, and 

Children [WIC; 35] participants) that can use to purchase 

healthy foods might help reduce this disparity.

Having a sustainable impact on reducing disparities 

associated with obesity includes making healthy choices easily 

assessable and available to all persons. Environmental strategies 

that support healthy eating and active living opportunities 

within communities can help provide healthy choices for 

persons. In addition, such changes can help provide ongoing 

training and support of public health practitioners with tools 

to implement effective responses to obesity in populations 

that are facing health disparities (36,37). CDC provides 

funding and support to multiple public health programs to 

improve access to healthy foods and beverages in underserved 

communities (38,39), including increased access to markets 

and convenience stores that offer healthier food and beverage 

choices; expanding programs that promote food affordability 

such as WIC farmers’ markets; assisting persons through green 

carts and mobile vans in inner-city neighborhoods (Farmto-Where-You-Are) (40,41); and promotion of food policy 

councils that include diverse stakeholders that often consider 

both food security and improvements of the food environment 

at the state and local levels.

Certain early child care education initiatives promote 

active play and healthier beverage and food offerings such 

as drinking water and fruits and vegetables. These initiatives 

can address disparities by providing age-appropriate health 

curricula, parental outreach, increased healthier foods and 

beverages served, and training and technical support for staff on 

menu planning and food preparation (42) for children of low 

socioeconomic status and children who hold immigrant and/

or refugee status, among other high-priority groups (Adrienne 

Dorf, Child Care Health Program Public Health Seattle and 

King County, personal communication, 2012). Strategies such 

as promoting physical activity early in child care and school, 

increasing low- or no-cost physical activity opportunities, 

building and enhancing trails and parks, developing shareduse agreements with public venues such as schools, improving 

sidewalks, and other initiatives that promote physical activity 

to prevent and reduce obesity have been implemented to help 

all persons and communities to become physically active (43).

Although the rate of obesity has plateaued in recent years 

for some groups, the overall prevalence of the condition 
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 127

remains high for all U.S. residents, and disparities persist in 

the prevalence of obesity. Continued monitoring of obesity 

prevalence and further research are needed to identify and 

understand the factors that influence individual behaviors, 

especially among high-priority groups, and to augment current 

population-based approaches with interventions that are 

tailored to their needs.

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MMWR / November 22, 2013 / Vol. 62 / No. 3 129

Introduction

Periodontal disease, or gum disease, is a chronic infection 

of the hard and soft tissue supporting the teeth (1) and is a 

leading cause of tooth loss in older adults (2). Tooth loss impairs 

dental function and quality of life in older adults (2). The 

chronic infections associated with periodontitis can increase 

the risk for aspiration pneumonia in older adults and has 

been implicated in the pathogenesis of chronic inflammation 

that impairs general health (3,4). The severity of periodontal 

disease can be categorized as mild, moderate, or severe on the 

basis of multiple measurements of periodontal pocket depth, 

attachment loss, and gingival inflammation around teeth (5).

At the national level, monitoring the reduction of moderate 

and severe periodontitis in the adult U.S. population is part 

of the health-promotion and disease-prevention activities of 

Healthy People 2020 (6). Approximately 47% of adults aged 

≥30 years in the United States (approximately 65 million 

adults) have periodontitis: 8.7% with mild periodontitis, 

30.0% with moderate, and 8.5% with severe periodontitis (7). 

Periodontitis increases with age; adults aged ≥65 years have 

periodontitis at rates of 5.9%, 53.0%, and 11.2% for mild, 

moderate, and severe forms, respectively (7). As the U.S. adult 

population ages and is more likely to retain more teeth than 

previous generations, the prevalence of periodontitis is expected 

to increase and consequently could increase the need for 

expenditures for preventive care and periodontal treatment (8).

Periodontitis is directly associated with lower levels of 

education and higher levels of poverty, both of which influence 

the use of dental services by adults (9–12). Educational 

attainment and poverty might mediate significant differences in 

the prevalence of periodontal disease between different racial/

ethnic populations. Smoking and some chronic diseases such as 

diabetes are important modifiable risk factors for periodontitis 

(13). Since the early 1960s, U.S. national surveys have assessed 

the periodontal status of adults (14). However, the validity 

of estimates from these surveys has been limited by the use 

of partial-mouth periodontal examination protocols, which 

significantly underestimate the prevalence of periodontitis 

(15–17). The 2009–2010 National Health and Nutrition 

Examination Survey (NHANES) cycle is the first to include 

a full-mouth periodontal examination for U.S. adults (aged 

≥30 years) and provides the most direct evidence for the true 

prevalence of periodontitis in this population.

This report is part of the second CDC Health Disparities 

and Inequalities Report (CHDIR). The 2011 CHDIR (18) was 

the first CDC report to assess disparities across a wide range 

of diseases, behavioral risk factors, environmental exposures, 

social determinants, and health-care access. The topic presented 

in this report is based on criteria that are described in the 2013 

CHDIR Introduction (19). This report provides information 

concerning disparities in periodontitis, a topic that was not 

discussed in the 2011 CHDIR (18). The purposes of this 

periodontitis in adults report are to discuss and raise awareness 

of differences in the characteristics of people with periodontal 

disease and to prompt actions to reduce these disparities.

Methods

To examine racial/ethnic disparities in the estimated percentage 

of adults aged ≥30 years with periodontitis by age, sex, education, 

poverty levels, and smoking status, CDC analyzed data from the 

2009-2010 NHANES cycle. NHANES is a cross-sectional survey 

designed to monitor the overall health and nutritional status of 

civilian, noninstitutionalized U.S. population. NHANES uses 

a stratified multistage probability sampling design. For 2-year 

Periodontitis Among Adults Aged ≥30 Years — 

United States, 2009–2010

Gina Thornton-Evans, DDS1

Paul Eke, PhD2

Liang Wei, MS3

Astrid Palmer1

Refilwe Moeti, MA4

Sonja Hutchins, MD5

Luisa N. Borrell, DDS6

1Division of Oral Health, National Center for Chronic Disease Prevention and Health Promotion, CDC

2Division of Population Health, National Center for Chronic Disease Prevention and Health Promotion, CDC

3DB Consulting Group, Inc. 4Division of Heart Disease and Stroke Prevention, National Center for Chronic Disease Prevention and Health Promotion, CDC

5Office of Minority Health and Health Equity, CDC 6Department of Health Sciences, Lehman College, City University of New York, Bronx, New York

Corresponding author: Paul Eke, Division of Population Health, National Center for Chronic Disease Prevention and Health Promotion, CDC. Telephone: 

770-488-6092; E-mail: peke@cdc.gov. 
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130 MMWR / November 22, 2013 / Vol. 62 / No. 3

data cycles, NHANES surveys a national representative sample. 

The technical details of the survey, including sampling design, 

periodontal data collection protocols, and data, are available 

online (http://www.cdc.gov/nchs/nhanes.htm). A total of 5,037 

adults aged ≥30 years participated in the survey, and 951 were 

excluded for medical reasons or incomplete oral examinations. In 

this analysis, 343 edentulous participants were excluded, leaving 

a total of 3,743 participants, representing a weighted population 

of approximately 137.1 million civilian noninstitutionalized 

U.S. adults. The findings in this report cannot be compared with 

those of previous studies using NHANES data (9,10) because 

the case definitions and age range used in this analysis differed.

All periodontal examinations were conducted in a mobile 

examination center by dental hygienists registered in at least 

one U.S. state. Gingival recession was defined as the distance 

between the free gingival margin and the cementoenamel 

junction; pocket depth was defined as the distance from free 

gingival margin to the bottom of the sulcus or periodontal 

pocket. These measurements were made at six sites per 

tooth (mesiobuccal, midbuccal, distobuccal, mesiolingual, 

midlingual, and distolingual) for all teeth except third molars. 

For measurements at each tooth site, a periodontal probe 

(Hu-Friedy PCP 2) with graduations of 2 mm, 4 mm, 6 mm, 

8 mm, 10 mm, and 12 mm was positioned parallel to the long 

axis of the tooth at each site. Each measurement was rounded 

to the lowest whole millimeter. Data were recorded directly 

into an NHANES oral health data management program that 

instantly calculated attachment loss as the difference between 

probing depth and gingival recession. Bleeding from probing 

and the presence of dental furcations were not assessed.

Periodontal measurements were used to classify participants 

as having mild, moderate, or severe disease by using standard 

case definitions for surveillance of periodontitis (4); total 

prevalence of periodontitis in the population was calculated 

by combining prevalence of mild, moderate, and severe 

periodontitis. Severe periodontitis was defined as having two or 

more interproximal sites with ≥6 mm attachment (not on the 

same tooth) and one or more interproximal sites with ≥5 mm 

pocket depth. Moderate periodontitis was defined as two or 

more interproximal sites with ≥4 mm clinical attachment 

(not on the same tooth) or two or more interproximal sites 

with pocket depth of ≥5 mm (not on the same tooth). Mild 

periodontitis was defined as two or more interproximal sites 

with ≥3 mm attachment and two or more interproximal sites 

with ≥4 mm pocket depth (not on the same tooth) or one site 

with ≥5 mm.

Race/ethnicity was self-reported; for this analysis, three 

race/ethnicity groups, each with a sample size large enough 

to ensure statistically reliable estimates, were used: nonHispanic white, non-Hispanic black, and Mexican-American. 

Poverty status categories, or percentage of poverty relative to 

the federal poverty level (FPL), was based on family income, 

family size, and number of children in the family, for families 

with two or fewer adults, and on the age of the adults in the 

household. Families or individuals with income below their 

appropriate income thresholds, as determined by family size 

and composition, were classified as living below the FPL. The 

income thresholds are updated annually by the U.S. Census 

Bureau (available at http://aspe.hhs.gov/poverty/11poverty.

shtml). Education was classified as less than high school, high 

school graduate or equivalent, and greater than high school. 

Smoking status was determined by responses to two questions: 

1) “Have you smoked at least 100 cigarettes in your life?” 

and 2) “Do you now smoke cigarettes?” Participants who 

answered yes to both questions were categorized as current 

smokers, participants who answered yes to the first question 

and no to the second were categorized as former smokers, 

and participants who answered no to both questions were 

categorized as never smokers. Geographic regions were not 

analyzed because NHANES is not designed to be representative 

at regional (or lower) levels.

Disparities were assessed by age group, sex, race/ethnicity, 

education, FPL, and smoking status for the total population 

and by race/ethnicity. Referent groups for each category had 

the best overall periodontal health for the category. Disparities 

were measured as deviations from a referent group, which 

was the group that had the most favorable estimate for the 

variables used to assess disparities during the time reported. 

Absolute difference was measured as the simple difference 

between the periodontitis prevalence for the group of interest 

and the referent group. The relative difference, a percentage, 

was calculated by dividing the absolute difference by the value 

in the referent category and multiplying by 100. The z test 

was used to assess significant differences between absolute 

differences from the referent group, with significance set at 

p<0.05. Data (using mobile examination center weights) were 

analyzed using statistical software to adjust for the effects of 

the sampling design, including the unequal probability of 

selection, and to determine standard errors (SEs).

Results

During 2009–2010, an estimated 47.2% of adults aged 

≥30 years in the United States had periodontitis (Table 1). 

The prevalence of total and moderate periodontitis increased 

with increasing age among all adults. However, the prevalence 

of mild and severe periodontitis remained relatively steady at 

<15% across all age groups (Figure).
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 131

The prevalence of periodontitis was significantly higher in 

non-Hispanic blacks (58.6%) and Mexican-Americans (59.7%) 

compared with non-Hispanic whites (42.6%). Among all 

racial/ethnic groups, the prevalence of periodontitis increased 

with age (24.4%–70.1%), with the largest relative difference in 

prevalence within age groups occurring among non-Hispanic 

whites (range: 16.6%–68.0%). The prevalence of periodontitis 

was significantly higher among men (56.4%) than women 

(38.4%) overall, and this finding was consistent among racial/

ethnic groups. By education level, periodontitis was highest 

among persons with less than a high school education (66.9%), 

and the relative difference between those with greater than a 

high school education and those with less education was largest 

in Mexican-Americans (73.8%) and smallest in non-Hispanic 

blacks (28.8%). The prevalence of periodontitis increased as 

FPL percentage decreased, with an estimate of 65.4% of persons 

in the poorest families (<100% FPL), representing an 85% 

relative increase compared with families at ≥400% FPL. The 

relative difference in prevalence between these categories of FPL 

was largest among non-Hispanic whites (82.8%) and smallest 

among non-Hispanic blacks (35.5%). Periodontitis was more 

prevalent among current smokers (64.2%) than nonsmokers 

(39.8%) and significantly higher among non-Hispanic black 

current smokers (79.1%) than non-Hispanic white (60.8%) 

and Mexican-American current smokers (69.1%) (Table 1). 

During 2009–2010, an estimated 8.7% of the U.S. adult 

population had mild periodontitis. The prevalence of moderate 

periodontitis was 30.0% (Table 2). Prevalence of moderate 

periodontitis increased with age and peaked at age ≥65 

years. Overall, prevalence was higher in men (33.8%) than 

women (26.4%) and higher among non-Hispanic black men 

(42.7%) than men in other racial/ethnic groups. Increasing 

prevalence was associated with lower education and poverty 

levels. Specifically, the prevalence of moderate periodontitis 

at the lowest levels of education and poverty were higher 

among non-Hispanic whites and Mexican-Americans than 

non-Hispanic blacks. Prevalence of moderate periodontal 

disease was higher among current smokers (36.5%) and former 

smokers (35.6%) than among nonsmokers (25.6%). However, 

this pattern was not consistent among non-Hispanic blacks 

and Mexican-Americans, among whom the highest prevalence 

of moderate periodontitis was among former smokers. The 

relative difference in prevalence between poverty levels was 

smallest among non-Hispanic blacks, suggesting that income 

TABLE 1. Prevalence of periodontitis among adults aged ≥30 years, by race/ethnicity and selected characteristics — National Health and Nutrition 

Examination Survey, United States, 2009–2010

Characteristics

Total* (N = 3,743)

White, non-Hispanic 

(N = 1,792)

Black, non-Hispanic 

(N = 673) Mexican-American (N = 1,076)

No. of 

adults

Weighted 

no. of 

adults (in 

millions) % (SE)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%) % (SE)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%) % (SE)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%) % (SE)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%)

Total 3,743 137 47.2 (2.1) — — 42.6 (3.0) — — 58.6 (3.1) — — 59.7 (2.2) — —

Age group (yrs)

30–34 435 16.7 24.4 (2.7) Ref. — 16.6 (3.3) Ref. — 37.9 (8.2) Ref. — 43.7 (3.0) Ref. —

35–49 1,352 54.0 36.6 (1.6) 12.2† 50.0 28.5 (2.3) 11.9† 71.7 51.0 (3.7) 13.1 34.6 56.9 (2.9) 13.2† 30.2

50–64 1,128 43.4 57.2 (2.6) 32.8† 134.4 51.2 (3.7) 34.6† 208.4 72.8 (3.2) 34.9† 92.1 72.9 (3.6) 29.2† 66.8

≥65 828 22.9 70.1 (3.0) 45.7† 187.3 68.0 (3.9) 51.4† 309.6 72.7 (4.7) 34.8† 91.8 78.4 (4.1) 34.7† 79.4

Sex

Female 1,871 69.6 38.4 (2.4) Ref. — 34.6 (3.4) Ref. — 47.1 (3.5) Ref. — 47.4 (2.5) Ref. —

Male 1,872 67.5 56.4 (2.1) 18.0† 46.9 50.7 (3.0) 16.1† 46.5 72.9 (3.9) 25.8† 54.8 70.8 (2.3) 23.4† 49.4

Education

Less than high 

school

1,030 23.8 66.9 (2.4) 27.6† 70.2 59.6 (4.6) 21.7† 57.3 64.9 (4.1) 14.5† 28.8 71.8 (2.2) 30.5† 73.8

High school 

graduate or 

equivalent

815 29.6 53.5 (3.2) 14.2† 36.1 49.3 (4.2) 11.4† 30.1 67.3 (4.0) 16.9† 33.5 59.5 (4.6) 18.2† 44.1

Greater than 

high school

1,889 83.3 39.3 (2.3) Ref. — 37.9 (3.0) Ref. — 50.4 (4.1) Ref. — 41.3 (3.2) Ref. —

Poverty level

<100% FPL 625 13.5 65.4 (2.5) 30.0† 84.7 62.7 (6.0) 28.4† 82.8 58.8 (5.7) 15.4 35.5 69.4 (2.9) 28.3† 68.9

100%–199% FPL 901 22.7 57.4 (3.0) 22.0† 62.1 52.6 (6.0) 18.3† 53.4 65.6 (4.1) 22.2† 51.2 59.9 (4.1) 18.8† 45.7

200%–499% FPL 905 37.7 50.2 (2.5) 14.8† 41.8 48.0 (3.0) 13.7† 39.9 62.0 (4.1) 18.6† 42.9 54.8 (3.9) 13.7† 33.3

≥400% FPL 960 52.4 35.4 (3.0) Ref. — 34.3 (3.4) Ref. — 43.4 (6.1) Ref. — 41.1 (4.4) Ref. —

Smoking status

Current smoker 728 23.2 64.2 (2.6) 24.4† 61.3 60.8 (3.0) 26.8† 78.8 79.1 (5.0) 32.6† 70.1 69.1 (3.6) 13.3† 23.8

Former smoker 957 35.7 52.5 (3.1) 12.7† 31.9 48.8 (4.1) 14.8† 43.5 67.1 (5.4) 20.6† 44.3 64.1 (5.7) 8.3 14.9

Nonsmoker 2,058 78.1 39.8 (2.1) Ref. — 34.0 (3.0) Ref. — 46.5 (3.5) Ref. — 55.8 (2.6) Ref. —

Abbreviations: FPL = federal poverty level; Ref. = referent; SE = standard error.

* The 202 respondents in “other” race group are not included.

† Significant at p<0.05 by z test.
Supplement

132 MMWR / November 22, 2013 / Vol. 62 / No. 3

had the least impact on moderate periodontitis in this racial/

ethnic group. Significant absolute differences were found 

in moderate periodontitis among current smokers, former 

smokers, and nonsmokers and was significantly higher among 

non-Hispanic blacks.

Severe periodontitis was estimated to occur in 8.5% of U.S. 

adults aged ≥30 years (Table 3). Severe periodontitis was twice 

as common among non-Hispanic blacks (13.2%) and MexicanAmericans (13.3%) as among non-Hispanic whites (6.3%). 

Severe periodontitis increased with age and peaked at age 50 years 

among all racial/ethnic groups. Overall, severe disease was almost 

three times higher among men (12.5%) than women (4.2%) and 

approximately two times higher among non-Hispanic black men 

(19.3%) and Mexican-American men (18.8%) than among nonHispanic white men (9.4%). Severe periodontitis among persons 

with less than a high school education was an estimated 17.3% 

and decreased with increasing levels of education. Among racial/

ethnic groups, the smallest relative differences by level of education 

occurred among non-Hispanic blacks. Similarly, the prevalence 

of severe periodontitis increased with increasing poverty levels, 

with an estimated 16.3% of adults in families living at <100% 

FPL having severe disease. The relative difference in prevalence 

by poverty level (across all racial/ethnic groups) was smallest 

among non-Hispanic blacks, suggesting that income had the least 

influence on severe periodontitis in this racial/ethnic group. The 

prevalence of severe periodontitis was approximately two times as 

common among smokers at 17.7% than among former smokers 

(9%) and nonsmokers (5.4%) and was significantly higher among 

non-Hispanic blacks (24.4%) and Mexican-Americans (24.5%) 

than among non-Hispanic white smokers (13.9%)

Discussion

Overall, significant disparities exist in the prevalence of 

periodontitis by race/ethnicity, education and poverty level. 

These results suggest that non-Hispanic blacks and MexicanAmericans have similar prevalences of periodontitis but higher 

prevalences than non-Hispanic whites. In addition, the relative 

differences in the prevalence of total periodontitis (i.e., mild, 

moderate, and severe combined) among non-Hispanic blacks 

varied the least by poverty and education levels, possibly 

suggesting that poverty and education have less of an effect 

than other factors on the higher prevalence of periodontitis 

among non-Hispanic Blacks and Mexican-Americans. The 

highest prevalence of periodontitis was found among adults 

aged ≥65 years. By 2030, the number of adults aged ≥65 years 

in the U.S. will double to 71 million adults, or one in every 

five Americans (8), with significant changes in the distribution 

of demographic and socioeconomic groups.

Total periodontitis

Mild periodontitis

Moderate periodontitis

Severe periodontitis

0

10

20

30

40

50

60

70

80

90

30 40 50 60 70 80

Age (yrs)

Percentage

FIGURE. Prevalence of total, mild, moderate, and severe periodontitis among adults aged ≥30 years, by age — National Health and Nutrition 

Examination Survey, United States, 2009–2010
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 133

Limitations

The findings in this report are subject to at least 

four limitations, all of which might have resulted in an 

underestimation of the prevalence of periodontitis cases. First, 

the case definitions for periodontitis used measures from four 

interproximal sites, and not all six of the sites were measured. 

Second, estimates did not include persons with gingivitis. 

Gingivitis is a form of periodontal disease that was not assessed 

in the NHANES 2009–2010 data cycle. Third, NHANES does 

not sample institutionalized persons such as older adults in 

nursing homes, which might have resulted in an underestimate 

for older adults. Fourth, NHANES does not collect data from 

third molars. This exclusion of third molars is consistent with 

previous NHANES data cycles; third molars are difficult to 

assess clinically because of their alignment in the mouth, and 

some are partially impacted. 

Conclusion

Preventive dental care programs should be an integral part 

of preventive health services for all ages and should include 

strategies to make dental care programs accessible to all racial/

ethnic groups to promote health and preserve health-related 

quality of life in older adults. Adults aged ≥65 years do not 

have dental coverage through Medicare, and approximately 

70% of U.S. adults in this age group have no dental coverage 

(20). Management of diabetes and smoking is an important 

component of prevention and treatment of adult periodontitis 

(13). The findings in this report indicate that current smokers 

had a much higher prevalence of severe periodontitis; smoking 

is categorized as a major modifiable risk factor for periodontitis. 

This is consistent with the 2004 Surgeon General’s Report on 

the Health Consequences of Smoking, which infers a causal 

relationship between smoking and periodontitis (21). Because 

the prevalence of severe periodontitis is higher among current 

smokers, tobacco cessation programs are a potential strategy to 

address disparities in periodontitis in the U.S. population (22). 

TABLE 2. Prevalence of moderate periodontitis among adults aged ≥30 years, by race/ethnicity and selected characteristics — National Health 

and Nutrition Examination Survey, United States, 2009–2010

Characterisitcs

Total* (N = 3,743)

White, non-Hispanic 

(N = 1,792) Black, non-Hispanic (N = 673)

Mexican-American 

(N = 1,076)

No. of 

adults

Weighted 

no. of 

adults (in 

millions) % (SE)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%) % (SE)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%) % (SE)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%) % (SE)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%)

Total 3,743 137 30.0 (1.6) —  — 28.5 (2.3) —  — 33.6 (2.1) —  — 32.8 (1.7) —  —

Age groups (yrs)

30–34 435 16.7 13.0 (1.7) Ref. — 9.7 (1.6) Ref. — 18.6 (5.7) Ref. — 21.4 (3.4) Ref. —

35–49 1,352 54.0 19.4 (1.7) 6.4† 49.2 15.5 (2.2) 5.8† 59.8 24.3 (3.6) 5.7 30.6 29.7 (2.4) 8.3† 38.0

50–64 1,128 43.4 37.7 (2.5) 24.7† 190.0 34.2 (3.4) 24.5† 252.6 46.4 (3.0) 27.8† 149.5 42.9 (1.6) 21.5† 100.5

≥65 828 22.9 53.0 (2.3) 40.0† 307.7 52.8 (2.6) 43.1† 444.3 50.2 (4.4) 31.6† 169.9 51.9 (4.3) 30.5† 142.5

Sex

Female 1,871 69.6 26.4 (2.2) Ref. — 24.8 (3.0) Ref. — 26.2 (2.7) Ref. — 29.2 (2.2) Ref. —

Male 1,872 67.5 33.8 (1.4) 7.4† 28.0 32.3 (2.0) 7.5† 30.2 42.7 (3.3) 16.5† 63.0 36.1 (2.1) 6.9† 23.6

Education

Less than 

high school

1,030 23.8 40.6 (2.6) 15.2† 59.8 42.2 (5.4) 17.1† 68.1 33.0 (3.6) 4.0 13.8 40.1 (2.4) 17.5† 77.4

High school 

graduate or 

GED certificate

815 29.6 34.2 (2.2) 8.8† 34.6 32.6 (3.1) 7.5 29.9 41.8 (3.8) 12.8† 44.1 30.6 (4.2) 8.0 35.4

Greater than 

high school

1,889 83.3 25.4 (1.8) Ref. — 25.1 (2.3) Ref. — 29.0 (3.2) Ref. — 22.6 (2.4) Ref. —

Poverty level

<100% FPL 625 13.5 37.8 (3.9) 14.3† 60.9 39.2 (7.9) 16.4† 71.9 30.8 (4.4) 4.1 15.4 38.4 (3.0) 16.2† 73

100%–199% FPL 901 22.7 32.9 (2.2) 9.4† 40.0 31.2 (4.6) 8.4 36.8 40.9 (2.1) 14.2† 53.2 30.5 (3.6) 8.3 37.4

200%–499% FPL 905 37.7 34.4 (2.3) 10.9† 46.4 33.9 (3.0) 11.1† 48.7 35.9 (5.3) 9.2 34.5 32.6 (3.1) 10.4 46.8

≥400% FPL 960 52.4 23.5 (2.0) Ref. — 22.8 (2.4) Ref. — 26.7 (5.4) Ref. — 22.2 (4.6) Ref. —

Smoking status

Current smoker 728 23.2 36.5 (2.1) 10.9† 42.6 38.2 (2.8) 15.1† 65.4 41.5 (3.8) 15.7† 60.9 30.2 (2.6) -0.7 2.3

Former smoker 957 35.7 35.6 (3.1) 10.0† 39.1 33.3 (4.0) 10.2† 44.2 47.0 (3.3) 21.2† 82.2 41.0 (3.8) 10.1† 32.7

Nonsmoker 2,058 78.1 25.6 (1.4) Ref. — 23.1 (2.1) Ref. — 25.8 (1.9) Ref. — 30.9 (2.1) Ref. —

Abbreviations: Ref. = referent; SE = standard error.

* The 202 respondents in “other” race group are not included.

† Significant at p<0.05 by z test.
Supplement

134 MMWR / November 22, 2013 / Vol. 62 / No. 3

Two related Healthy People 2020 objectives are currently being 

monitored. One focuses on dental professionals providing 

tobacco cessation counseling in a dental setting, and another 

monitors consumers’ self-report of tobacco cessation counseling 

in a dental office (9). Overall, this study demonstrates disparities 

in periodontitis by age, race, education, and income, and risk 

factors such as smoking status in the U.S. adult population. 

The capacity of oral health programs within state and local 

health agencies can be broadened to capture this subset of the 

population. The program activities might include efforts to 

1) reduce tobacco use, particularly smoking; 2) educate persons 

on the benefits of regular dental care; and 3) facilitate health 

communication efforts to make key groups aware of effective 

preventive interventions.

References

1. Page RC, Eke PI. Case definitions for use in population-based 

surveillance of periodontitis. J Periodontol 2007;78:1387–99.

2. Martin JA, Page RC, Kaye EK, Hamed MT, Loeb CF. Periodontitis severity 

plus risk as a tooth loss predictor. J Periodontol 2009;80:202–9.

3. Pace CC, McCullough GH. The association between oral microorganisms 

and aspiration pneumonia in the institutionalized elderly: review and 

recommendations. Dysphagia 2010;25:307–22.

4. Lamster IB, DePaola DP, Oppermann RV, Papapanou PN, Wiler RS. 

The relationship of periodontal disease to diseases and disorders at distant 

sites: communication to health care professionals and patients. J Am 

Dent Assoc 2008;139:1389–97.

5. Eke PI, Page RC, Wei L, Thornton-Evans GO, Genco RJ. Update of 

the case definitions for population-based surveillance of periodontitis. 

J Periodontol 2012;83:1449–54.

6. US Department of Health and Human Services. Healthy people 2020. 

Washington, DC: US Department of Health and Human Services; 2011. 

Available at http://www.healthypeople.gov/2020.

7. Eke PI, Dye Ba, Wei L, Thornton-Evans GO, Genco RJ. Prevalence of 

periodontitis in adults in the United States: 2009 and 2010. J Dent Res 

2012;91:914–20.

8. CDC. Trends in aging—United States and worldwide. MMWR 2003;

52:101–6.

9. Borrell LN, Crawford ND. Socioeconomic position indicators and 

periodontitis: examining the evidence. Periodontol 2000 2012;58:69–83.

10. Borrell LN, Burt BA, Taylor GW. Prevalence and trends in periodontitis 

in the USA: from the NHANES III to the NHANES 1988 to 2000. J 

Dent Res 2005;84:924–30.

11. Borrell LN, Crawford ND. Social disparities in periodontitis among 

United States adults 1999–2004. Community Dent Oral Epidemiol 

2008;36:383–91.

12. Gibson RM, Fisher CR. Age differences in health care spending fiscal 

year 1977. Soc Secur Bull 1979;42:3–16.

13. Genco RJ. Current view of risk factors for periodontal diseases. J 

Periodontol 1996;67:1041–9.

TABLE 3. Prevalence of severe periodontitis among adults aged ≥30 years, by race/ethnicity and selected characteristics — National Health 

and Nutrition Examination Survey, United States, 2009–2010

Characteristics

Total* (N = 3,743)

White, non-Hispanic 

(N = 1,792)

Black, non-Hispanic 

(N = 673)

Mexican-American 

(N = 1,076)

No. of 

adults

Weighted 

no. of 

adults (in 

millions) % (SE) Difference

Relative 

difference 

(%) % (SE) Difference

Relative 

difference 

(%) % (SE) Difference

Relative 

difference 

(%) % (SE) Difference

Relative 

difference 

(%)

Total 3,743 137 8.5 (0.9) — — 6.3 (1.1) — — 13.2 (1.8) — — 13.3 (1.8) — —

Age groups (yrs)

30–34 435 16.7 1.9 (0.6) Ref. — 0.8 (0.6) Ref. — 3.1 (2.0) Ref. — 4.6 (2.3) Ref. —

35–49 1,352 54.0 6.7 (0.8) 4.8† 252.6 4.8 (0.9) 4.0† 500.0 10.4 (1.6) 7.3† 235.5 10.2 (1.8) 5.6 121.7

50–64 1,128 43.4 11.7 (1.6) 9.8† 515.8 8.3 (1.4) 7.5† 937.5 19.8 (3.3) 16.7† 538.7 25.2 (3.6) 20.6† 447.8

≥65 828 22.9 11.2 (2.2) 9.3† 489.5 8.8 (2.6) 8.0† 1000.0 17.8 (4.4) 14.7† 474.2 21.3 (3.0) 16.7† 363.0

Sex

Female 1,871 69.6 4.5 (0.7) Ref. — 3.3 (0.8) Ref. — 8.3 (2.0) Ref. — 7.3 (1.7) Ref. —

Male 1,872 67.5 12.6 (1.3) 8.1† 180.0 9.4 (1.7) 6.1† 184.8 19.3 (2.1) 11.0† 132.5 18.8 (2.0) 11.5† 157.5

Education

Less than 

high school

1,030 23.8 17.3 (2.1) 11.8† 214.5 12.0 (2.3) 7.2† 150 21.0 (5.0) 10.7 103.9 18.2 (2.1) 12.5† 219.3

High school 

graduate or 

equivalent

815 29.6 9.8 (1.6) 4.3† 78.2 8.5 (1.9) 3.7 77.1 11.5 (2.6) 1.2 11.7 14.1 (3.3) 8.4† 147.4

Greater than 

high school

1,889 83.3 5.5 (1.0) Ref. — 4.8 (1.1) Ref. — 10.3 (2.3) Ref. — 5.7 (1.2) Ref. —

Poverty level

<100% FPL 625 13.5 16.3 (2.2) 12.2† 297.6 14.2 (3.4) 10.6† 294.4 16.8 (3.5) 7.7 84.6 16.7 (3.5) 10.4† 165.1

100%–199% FPL 901 22.7 14.1 (1.8) 10.0† 243.9 11.9 (2.1) 8.3† 230.6 13.0 (3.5) 3.9 42.9 15.3 (3.1) 9.0† 142.9

200%–499% FPL 905 37.7 7.9 (1.3) 3.8† 92.7 7.0 (1.6) 3.4 94.4 13.0 (1.8) 3.9 42.9 10.0 (1.9) 3.7 58.7

≥400% FPL 960 52.4 4.1 (0.9) Ref. — 3.6 (1.0) Ref. — 9.1 (2.7) Ref. — 6.3 (2.8) Ref. —

Smoking status

Current smoker 728 23.2 17.7 (2.4) 12.3† 227.8 13.9 (3.2) 10.3† 286.1 24.4 (3.3) 17.0† 229.7 24.5 (4.9) 14.2† 137.9

Former smoker 957 35.7 9.0 (1.3) 3.6† 66.7 7.4 (1.6) 3.8† 105.6 14.9 (4.2) 7.5 101.4 13.8 (2.0) 3.5 34.0

Nonsmoker 2,058 78.1 5.4 (0.9) Ref. — 3.6 (0.9) Ref. — 7.4 (1.6) Ref. — 10.3 (1.5) Ref. —

Abbreviations: Ref. = referent; SE = standard error.

* The 202 respondents in “other” race group are not included.

† Significant at p<0.05 by z test.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 135

14. Dye BA, Thornton-Evans GO. A brief history of national surveillance 

efforts for periodontal disease in the United States. J Periodontol 2007;

78:1373–9.

15. Susin C, Kingman A, Albandar JM. Effect of partial recording protocols 

on estimates of prevalence of periodontal disease. J Periodontol 

2005;76:262–7.

16. Hunt RJ, Fann SJ. Effect of examining half the teeth in a partial 

periodontal recording of older adults. J Dent Res 1991;70:1380–5.

17. Eke PI, Thornton-Evans GO, Wei L, Borgnakke WS, Dye BA. Accuracy 

of NHANES periodontal examination protocols. J Dent Res 2010;

89:1208–13.

18. CDC. CDC health disparities and inequalities report—United States, 

2011. MMWR 2011;60(Suppl; January 14, 2011).

19. CDC. Introduction: CDC health disparities and inequalities report—

United States, 2013. MMWR 2013;62(No. Suppl 3)

20. Manski RJ, Brown E. Dental use, expenses, private dental coverage and 

changes, 1996 and 2004. MEPS chartbook No. 17. Rockville MD: 

Agency for Healthcare Research and Quality; 2007.

21. US Department of Health and Human Services, CDC. The health 

consequences of smoking: a report of the Surgeon General. Atlanta, GA: 

US Department of Health and Human Services, CDC; 2004. Available 

at http://www.cdc.gov/tobacco/data_statistics/sgr/2004/complete_

report/index.htm.

22. Carr AB, Ebbert J. Interventions for tobacco cessation in the dental 

setting. Cochrane Database Syst Rev 2012;6:CD005084.
Supplement

136 MMWR / November 22, 2013 / Vol. 62 / No. 3

Introduction

Approximately one third of all infant deaths in the U.S. are 

related to preterm birth (1). Infants who survive a preterm 

birth are at greater risk than those born later in pregnancy 

for early death and lifelong effects such as neurologic and 

cognitive difficulties (1–4). The rate of preterm births (i.e., 

<37 completed weeks’ gestation) increased approximately 

30% during 1981–2006 (5). In 2007, this trend began to 

reverse; the U.S. preterm birth rate decreased for the fourth 

consecutive year in 2010, decreasing from the 2006 high of 

12.8% to 12.0% in 2010 (5). A total of 4,265,555 births were 

reported for 2006, including 542,893 preterm births, and 

3,999,386 births were reported for 2010, including 478,790 

preterm births. Although most of the recent decrease in this 

rate was among infants born at 34 to 36 weeks’ gestation 

(i.e., late preterm), with a decrease from 9.15% to 8.49% 

during 2006–2010, the rate of infants born at <34 weeks’ 

gestation (i.e., early preterm) also decreased from 3.66% 

in 2006 to 3.50% in 2010 (5). Despite improvements in 

the rate of preterm births, the total number of infants born 

preterm remains higher than any year during 1981–2001 (5). 

Substantial differences in preterm birth rates by race/ethnicity 

persist; additional examination of these differences can provide 

insight into potential areas for interventions.

The preterm birth analysis and discussion that follows is 

part of the second CDC Health Disparities and Inequalities 

Report (CHDIR) and updates information presented in the 

2011 CHDIR (6). The 2011 CHDIR (7) was the first CDC 

report to assess disparities across a wide range of diseases, 

behavioral risk factors, environmental exposures, social 

determinants, and health-care access. The topic presented in 

this report is based on criteria that are described in the 2013 

CHDIR Introduction (8). The purposes of this preterm birth 

report are to raise awareness of racial/ethnic differences among 

women giving birth to preterm infants and to motivate actions 

to reduce disparities.

Methods

To assess differences in preterm birth rates by race/ethnicity, 

CDC analyzed final 2006 and 2010 birth certificate data 

from the National Vital Statistics System (9). Birth certificates 

provide demographic and health information on the mother 

and newborn such as sex, race, ethnicity, gestational age, 

and geographic region. Geographic region was not analyzed 

independently because this variable is related to demographic 

characteristics that can influence preterm birth rates. 

Comparable information on educational attainment of the 

mother is not available for the entire national reporting area.

Gestational age measurement is based primarily on the 

interval between the date of the last normal menses, or last 

menstrual period (LMP), and the date of birth. The preterm 

birth rate is defined as births at <37 completed weeks of 

gestation per 100 total births in a given category; early preterm 

birth rate is defined as <34 weeks, and late preterm as 34–36 

weeks. Race/ethnicity of the mother was self-reported in five 

categories; white, black, American Indian/Alaska Native (AI/

AN), Asian/Pacific Islander (A/PI), and Hispanic. In this 

report, references to whites, blacks, AI/ANs, and A/PIs refer 

to non-Hispanic women. Women of Hispanic ethnicity might 

be of any race or combination of races. 

Disparities were measured as the deviations from a referent 

category rate. Births to non-Hispanic white mothers were used 

as the referent group for racial/ethnic comparisons. Absolute 

difference was measured as the simple difference between the 

rate for a population subgroup and the rate for its respective 

reference group. The relative difference, a percentage, was 

calculated by dividing the difference by the value in the referent 

category and multiplying by 100. The statistical significance of 

the differences was determined by using the z test at the 95% 

confidence level (10).

Results

Decreases in preterm births occurred for each of the race/

ethnicity groups; white, black, Hispanic, AI/AN, and A/PI 

from 2006 to 2010 (Table). From 2006 to 2010, the preterm 

birth rate for black infants decreased by 8% to 17.1%, the 

lowest level ever reported (5). Despite the decrease, the 2010 

preterm rate for black infants (17.1%) was approximately 60% 

higher than that for white infants (10.8%). AI/AN (13.6%) 

and Hispanic (11.8%) infants were also at a higher risk for 

preterm birth in 2010 than white and A/PI infants.

Preterm Births — United States, 2006 and 2010

Joyce A. Martin, MPH

Michelle J.K. Osterman, MHS

National Center for Health Statistics, CDC

Corresponding author: Joyce A. Martin, National Center for Health Statistics, CDC. Telephone 301-458-4362; E-mail: JAMartin@cdc.gov.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 137

The largest relative differences among the race/ethnicity 

groups are in early preterm births. Decreases in early preterm 

births occurred from 2006 to 2010 for white, black, and 

Hispanic infants. Despite an 8% decrease in the early preterm 

rate for black infants from 2006 to 2010, the 2010 early 

preterm birth rate among black infants (6.1%) was double the 

rate among white (2.9%) and A/PI (2.9%) infants. 

The rate of late preterm births declined among each of 

the race/ethnicity groups during 2006–2010. In 2010, black 

infants were approximately 40% more likely to be born late 

preterm than white and A/PI infants. AI/AN and Hispanic 

infants also were more likely than white and A/PI infants to 

be born late preterm. 

Discussion

Decreases occurred from 2006 to 2010 in preterm birth 

rates overall and in all racial/ethnic groups examined; however, 

substantial disparities persisted among racial ethnic groups in 

2010. The greatest absolute difference by race/ethnicity in 

total preterm, early preterm, and late preterm birth rates was 

among black infants. Black infants have had the highest risk 

for preterm birth since comparable data on gestational age have 

been available (1981). The causes of preterm births are not well 

understood (2). However, disparities among groups might be 

related to differences in socioeconomic status, prenatal care, 

maternal risk behaviors, infection, nutrition, preconception 

stress, and genetics (2).

Limitations

The findings in this report are subject to at least one limitation. 

The date of the LMP is subject to error from imperfect 

maternal recall, transcription error, or misidentification of 

LMP because of postconception bleeding, delayed ovulation, 

or intervening early miscarriage (5).

Conclusion

Continued reduction in the preterm birth rate is important 

because approximately one out of every eight infants was born 

too early in 2010. If the preterm rate continues to decrease 

at the pace observed from 2006 to 2010, the Healthy People 

2020 objective to reduce the rate to 11.4% (objective no. 

MICH 9-1) (11) will be achieved for the nation overall and 

for some racial/ethnic groups (i.e., white and A/PI). The 2020 

goal for preterm birth rates is further from reach for others; 

the 2010 rate among blacks (17.1%) must decrease by 50% 

percent for 2020 (or 5% per year), and the 2010 rate among 

AI/ANs (13.6%) must decrease by approximately 20% (2% 

TABLE. Total, early, and late preterm birth rates,* by race/ethnicity of mother — National Vital Statistics System, United States, 2006 and 2010

Weeks of gestation at birth and race/ethnicity of mother

2006 2010

Rate 

%

Absolute 

difference 

(percentage 

points)

Relative 

difference

(%)

Rate 

%

Absolute 

difference 

(percentage 

points)

Relative 

difference

(%)†

Total preterm births (<37 weeks’ gestation) 12.8 — — 12.0 — —

White, non-Hispanic 11.7 Ref. Ref. 10.8 Ref. Ref.

Black, non-Hispanic 18.5 6.8 58 17.1 6.3 58

Hispanic§ 12.2 0.5 4 11.8 1.0 9

Asian/Pacific Islander 10.9 -0.8 -7 10.7 -0.1 -1

American Indian/Alaska Native 14.2 2.5 21 13.6 2.8 26

Total early preterm births (<34 weeks’ gestation) 3.7 — — 3.5 — —

White, non-Hispanic 3.1 Ref. Ref. 2.9 Ref. Ref.

Black, non-Hispanic 6.6 3.5 113 6.1 3.2 110

Hispanic 3.4 0.3 10 3.3 0.4 14

Asian/Pacific Islander 2.8 -0.3 -10 2.9 0.0 0

American Indian/Alaska Native 4.0 0.9 29 4.0 1.1 38

Total late preterm births (34–36 weeks’ gestation) 9.1 — — 8.5 — —

White, non-Hispanic 8.6 Ref. Ref. 7.8 Ref. Ref.

Black, non-Hispanic 11.9 3.3 38 11.0 3.2 41

Hispanic 8.8 0.2 2 8.5 0.7 9

Asian/Pacific Islander 8.1 -0.5 -6 7.8 0.0 0

American Indian/Alaska Native 10.2 1.6 19 9.6 1.8 23

Abbreviation: Ref. = referent.

* Per 100 total births in a given category.

† Statistical significance was determined by using the z test at the 95% confidence level. All differences between the reference group and other groups are significant 

(p<0.05) except for Asian/Pacific Islander.

§ Persons of Hispanic ethnicity might be of any race or any combination of races. 
Supplement

138 MMWR / November 22, 2013 / Vol. 62 / No. 3

per year). Additional research is needed to clarify the causes of 

preterm delivery and to develop policies for a future in which 

preterm birth is a rare event for all populations.

References

1. Mathews TJ, MacDorman MF. Infant mortality statistics from the 2008 

period linked birth/infant death data set. Natl Vital Stat Rep 2010;60.

2. Behrman RE, Butler AS, editors. Preterm birth: causes, consequences, 

and prevention. Washington, DC: National Academies Press; 2007.

3. Pitcher JB, Schneider LA, Drysdale JL, et al. Motor system development of 

the preterm and low birthweight infant. Clin Perinatol 2011;38:605–25.

4. Engle WA. Morbidity and mortality in late preterm and early term 

newborns: a continuum. Clin Perinatol 2011;38:493–516.

5. Martin JA, Hamilton BE, Ventura SJ, et al. Births: final data for 2010. 

Natl Vital Stat Rep 2012;61.

6. Martin JA. Preterm births—United States, 2007. In: CDC health 

disparities and inequalities report—United States, 2011. MMWR 

2011;60(Suppl; January 14, 2011).

7. CDC. CDC health disparities and inequalities report—United States, 

2011. MMWR 2011;60(Suppl; January 14, 2011).

8. CDC. Introduction. In: CDC health disparities and inequalities report—

United States, 2013. MMWR 2013;62(No. Suppl 3)

9. CDC. Natality public use files: 2006–2010 and CD-ROM. Hyattsville, 

MD: National Center for Health Statistics. Available at http://www.cdc.

gov/nchs/data_access/VitalStatsOnline.htm.

10. National Center for Health Statistics. User guide to the 2010 natality 

public use file. Hyattsville, MD. Available from: ftp://ftp.cdc.gov/pub/. 

11. US Department of Health and Human Services. Healthy People 2020 

topics and objectives. Washington, DC: US Department of Health and 

Human Services; 2011. Available at http://www.healthypeople.

gov/2020/topicsobjectives2020/default.aspx. 
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 139

Introduction

Potentially preventable hospitalizations are admissions 

to a hospital for certain acute illnesses (e.g., dehydration) 

or worsening chronic conditions (e.g., diabetes) that might 

not have required hospitalization had these conditions been 

managed successfully by primary care providers in outpatient 

settings. Although not all such hospitalizations can be avoided, 

admission rates in populations and communities can vary 

depending on access to primary care, care-seeking behaviors, 

and the quality of care available (1,2). Because hospitalization 

tends to be costlier than outpatient or primary care, potentially 

preventable hospitalizations often are tracked as markers of 

health system efficiency. The number and cost of potentially 

preventable hospitalizations also can be calculated to help 

identify potential cost savings associated with reducing these 

hospitalizations overall and for specific populations.

This report is part of the second CDC Health Disparities 

and Inequalities Report (CHDIR). The 2011 CHDIR (2) was 

the first CDC report to assess disparities across a wide range 

of diseases, behavior risk factors, environmental exposures, 

social determinants, and health-care access. The topic 

presented in this report is based on criteria that are described 

in the 2013 CHDIR Introduction (3). This report updates 

information on potentially preventable hospitalizations that 

was presented in the first CHDIR (4). The purposes of this 

report are to discuss and raise awareness of differences in the 

race/ethnicity and income of persons with excess potential 

preventable hospitalizations and to prompt actions to reduce 

these disparities.

Methods

To examine trends in a composite measure of potentially 

preventable hospitalizations among adults aged ≥18 years in the 

United States, the Agency for Healthcare Research and Quality 

(AHRQ) analyzed data for 2001–2009 from the Healthcare 

Cost and Utilization Project (HCUP) databases (available at 

http://www.ahrq.gov/research/data/hcup/index.html). HCUP 

databases combine the data-collection efforts of state data 

organizations, hospital associations, private data organizations, 

and the federal government to create a national information 

resource of discharge-level health-care data. HCUP includes 

the largest collection of longitudinal hospital care data in the 

United States with all-payer, encounter-level information, 

beginning with 1988. 

Numbers of potentially preventable hospitalizations in 2009 

were estimated by race/ethnicity and income quartile for the 

following eight conditions: diabetes, hypertension, congestive 

heart failure, angina without procedure, asthma, dehydration, 

bacterial pneumonia, and urinary infections. Hospitalizations 

include all inpatient stays with these conditions listed as 

the principal diagnosis regardless of admitting source (e.g., 

admissions through an emergency room, transfers from 

other facilities, and direct admissions by a provider). Because 

coding of race/ethnicity varies across state hospital databases, 

analyses by race/ethnicity used a specially created 40% 

sample of hospitals from states that contribute comparable 

race/ethnicity data to HCUP (concerning approximately 16 

million discharges from 2,000 hospitals in 36 states in 2009) 

(5). Race was classified as non-Hispanic white, non-Hispanic 

black, Asian/Pacific Islander (A/PI), and other. Ethnicity was 

classified as Hispanic and non-Hispanic. Persons of Hispanic 

ethnicity might be of any race or combination of races. Area 

income, based on the income of the neighborhood in which 

a patient lives, was used as a proxy for socioeconomic status. 

Area income was divided into quartiles on the basis of the 

mean household income by the patient’s ZIP Code. Quartile 

1 refers to the lowest income communities, and quartile 4 

refers to the wealthiest communities. Analyses by area income 

used the Nationwide Inpatient Sample, a nationally stratified 

20% sample of hospitals from states that contribute data to 

HCUP (concerning approximately 8 million discharges from 

1,000 hospitals in 44 states in 2009). Data regarding patients’ 

educational attainment or disability status were unavailable 

or insufficient to provide estimates for certain populations 

(i.e., American Indians/Alaska Natives, Native Hawaiians and 

Other Pacific Islanders, and persons of multiple races). Data 

on disparities related to sex and geographic location are not 

presented but are available at http://www.ahrq.gov/research/

findings/nhqrdr/nhqrdr11/index.html#Efficiency. 

Potentially Preventable Hospitalizations — United States, 2001–2009

Ernest Moy, MD1

Eva Chang, MPH2

Marguerite Barrett, MS3

1Agency for Healthcare Research and Quality, Rockville, Maryland

2Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland

3M. L. Barrett, Inc., Del Mar, California

Corresponding author: Ernest Moy, Agency for Healthcare Research and Quality. Telephone: 301-427-1329; E-mail: Ernest.moy@ahrq.hhs.gov.
Supplement

140 MMWR / November 22, 2013 / Vol. 62 / No. 3

Disparities in hospital admission rates per 100,000 

population for 2001–2009 were estimated, and potential cost 

savings related to income and race/ethnicity were examined. 

Hospitalization rates for the potentially preventable conditions 

were calculated by using the AHRQ Prevention Quality 

Indicators (PQIs) modified version 4.2, which were adjusted 

by age and gender on the basis of the 2000 U.S. standard 

population (5). Excess potentially preventable hospitalizations 

by area income were estimated by comparing the 2009 AHRQ 

PQI composite rate of hospitalizations for residents of the 

neighborhoods in the highest income quartile, the group 

with the lowest rate, with the composite rate for residents of 

neighborhoods in lower income quartiles. Similarly, excess 

potentially preventable hospitalizations by race/ethnicity were 

estimated by comparing the 2009 AHRQ PQI composite rate 

of hospitalizations for A/PIs, the group with the lowest rate, 

with the composite rate for other racial/ethnic groups. Total 

charges included on hospital claims were converted to costs by 

using hospital-level cost-to-charge ratios based on the Centers 

for Medicare and Medicaid Services’ (CMS) hospital cost 

report data (5). Costs associated with potentially preventable 

hospitalizations were estimated by multiplying numbers of 

excess hospitalizations for a group by the average cost per 

hospitalization for that group. Costs are for the hospital cost 

of producing the services and do not include physician costs 

associated with hospital stay.

Results

During 2001–2009, the AHRQ PQI composite rate 

decreased from 1,635 to 1,395 per 100,000 adults. Declines 

in potentially preventable hospitalization rates were observed 

across all income quartiles between 2001 and 2009 (Figure 1). 

In all years, rates of hospitalizations were higher among residents 

of neighborhoods in the three lower income quartiles (quartiles 

1–3) compared with residents of neighborhoods in the highest 

income quartile (quartile 4). 

During 2009, if residents of the lowest income neighborhoods 

(quartile 1) had the same rate of hospitalizations as residents 

of the highest income neighborhoods (quartile 4), they would 

have had approximately 500,000 fewer hospitalizations and 

FIGURE 1. Rate* of potentially preventable hospitalizations† among adults aged ≥18 years, by income quartile§ — United States, 2001–2009

Source: Agency for Healthcare Research and Quality, Healthcare Cost and Utilization Project, Nationwide Inpatient Sample, 2001–2009.

* Per 100,000 population.

† For diabetes, hypertension, congestive heart failure, angina without procedure, asthma, dehydration, bacterial pneumonia, and urinary infections.

§ Area income was divided into quartiles based on the mean household income by the patient’s ZIP Code. Quartile 1 refers to the lowest income communities, and 

quartile 4 refers to the wealthiest communities. 

0

500

1,000

1,500

2,000

2,500

2001 2002 2003 2004 2005 2006 2007 2008 2009

Rate

Year

Quartile 1 

Quartile 2

Quartile 3

Quartile 4 
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 141

saved $3.6 billion in hospitalization costs 

(Figure 2). If residents of income quartiles 2 

and 3 had had the same hospitalization rate 

as residents of income quartile 4, they would 

have had approximately 220,000 and 90,000 

fewer hospitalizations and saved $1.7 billion 

and $700 million, respectively, in 2009. 

Significant declines in hospitalization rates 

also were observed across all race/ethnicity 

groups during 2001–2009 (Figure 3). In 

general, non-Hispanic blacks and Hispanics 

had higher rates of hospitalizations than nonHispanic whites, and A/PIs had lower rates 

than non-Hispanic whites.

During 2009, if non-Hispanic whites had had 

the same rate of hospitalizations as A/PIs, they 

would have had 700,000 fewer hospitalizations 

and saved $7.7 billion in hospitalization 

costs (Figure 4). If non-Hispanic blacks and 

Hispanics had the same hospitalization rate 

as A/PIs, they would have had 540,000 and 

240,000 fewer hospitalizations, respectively, 

and saved $3.7 billion and $700 million, 

respectively, in 2009.

Discussion

The findings in this report are consistent with previous 

studies showing decreasing rates of potentially preventable 

hospitalizations for specific conditions such as congestive 

heart failure (6,7) and for these hospitalizations in aggregate 

(8). Because rates of all groups defined by race/ethnicity and 

neighborhood income decreased at a similar pace, disparities 

that were present in 2001 persisted through 2009. These 

findings extend previous work by demonstrating that these 

disparities accounted for a considerable share of costs associated 

with potentially preventable hospitalizations.

Reducing hospitalization rates is a key to controlling 

health-care costs. For many chronic conditions, inpatient 

costs are the dominant expense. For example, approximately 

half of the expenditures of persons with diabetes are spent on 

hospital inpatient care, compared with 12% spent on diabetes 

medications and supplies and 9% spent on physician office 

visits (9). Disease management programs typically incur higher 

outpatient and pharmacy costs that are offset by lower inpatient 

costs (10). Programs to prevent chronic diseases also generate 

savings by lowering rates of hospitalizations. Patient-centered 

medical homes generate most of their savings by reducing 

hospitalizations, and it is anticipated that the success of 

accountable care organizations also will depend on their ability 

to hold down inpatient costs (11).

Populations with the highest rates of potentially preventable 

hospitalizations have the largest potential for lower rates and 

inpatient costs. Communities with high rates of potentially 

preventable hospitalizations might see the benefit of investing in 

primary care, care coordination, and community health worker 

strategies that can lower inpatient costs. The national Million 

Hearts initiative (http://millionhearts.hhs.gov/index.html) 

aims to prevent one million heart attacks and strokes by 

focusing in primary care settings on the “ABCS”: aspirin when 

appropriate, blood pressure control, cholesterol management, 

and smoking cessation. Community-based prevention efforts 

are focused on reducing sodium in the food supply, eliminating 

consumption of transfats, and promoting tobacco cessation. 

Implementation of these interventions can reduce potentially 

preventable hospitalizations and decrease disparities for 

hypertension, congestive heart failure, and angina.

Limitations

The data provided in this report are subject to at least 

three limitations. First, hospital administrative data might be 

incomplete. Data from only two thirds of the 50 states were 

usable in this analysis, and individual states might differ in how 

conditions and race/ethnicity are coded. Coding of conditions 

FIGURE 2. Number of potentially preventable hospitalizations* among adults aged ≥18 

years, by income quartile† — United States, 2009

Source: Agency for Healthcare Research and Quality, Healthcare Cost and Utilization Project, Nationwide 

Inpatient Sample, 2009.

* For diabetes, hypertension, congestive heart failure, angina without procedure, asthma, dehydration, 

bacterial pneumonia, and urinary infections.

† Area income was divided into quartiles based on the mean household income by the patient’s ZIP 

Code. Quartile 1 refers to the lowest income communities, and quartile 4 refers to the wealthiest 

communities.

0

200

400

600

800

1,000

1,200

1 (lowest) 2 3 4 (highest)

No. (in 1,000s)

Income quartile

Excess

Expected at best rate
Supplement

142 MMWR / November 22, 2013 / Vol. 62 / No. 3

FIGURE 3. Rate* of potentially preventable hospitalizations† among adults aged ≥18 years, by race/ethnicity — United States, 2001–2009

Source: Agency for Healthcare Research and Quality, Healthcare Cost and Utilization Project, State Inpatient Databases disparities analytic file, 2001–2009.

* Per 100,000 population.

† For diabetes, hypertension, congestive heart failure, angina without procedure, asthma, dehydration, bacterial pneumonia, and urinary infections.

§ Persons of Hispanic ethnicity can be of any race or combination of races

0

500

1,000

1,500

2,000

2,500

3,000

3,500

2001 2002 2003 2004 2005 2006 2007 2008 2009

Rate

Year

Non-Hispanic white

Non-Hispanic black

Asian/Pacic Islander

Hispanic§

by hospitals also might change over time. 

Second, cost estimates capture only hospital 

facility costs during the inpatient stay and do 

not include costs of inpatient or outpatient 

physician visits, including follow-up outpatient 

care. Although incomplete, because hospital 

facility costs are the largest type of health 

expenditure, analyses over time and across 

populations might be informative. Finally, 

these analyses cannot address causality. Rather 

than residence in low-income neighborhoods 

contributing to poorer health and thus 

requiring hospitalization, for some patients, 

poorer health might lead to residence in lowincome neighborhoods. Moreover, whether 

providing improved primary care to residents 

of low-income neighborhoods can reduce their 

rates of potentially preventable hospitalizations 

to the rates experienced by residents of highincome neighborhoods is unclear.

FIGURE 4. Number of potentially preventable hospitalizations* among adults aged ≥18 

years, by race/ethnicity — United States, 2009

Source: Agency for Healthcare Research and Quality, Healthcare Cost and Utilization Project, State 

Inpatient Databases disparities analytic file, 2009.

* For diabetes, hypertension, congestive heart failure, angina without procedure, asthma, dehydration, 

bacterial pneumonia, and urinary infections.

† Persons of Hispanic ethnicity can be of any race or combination of races.

Excess

Expected at best rate

0

500

1,000

1,500

2,000

2,500

Non-Hispanic white Non-Hispanic black Hispanic† Asian/Pacic Islander

No. (in 1,000s)

Race/Ethnicity
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 143

Conclusion

Potentially preventable hospitalizations are common and 

costly. Identification of disparities in potentially preventable 

hospitalizations rates is necessary to address communities 

and groups that would benefit the most. Because residents 

of low-income neighborhoods have the highest rates or 

preventable hospitalizations, providing interventions among 

low-income neighborhoods might yield the largest reductions 

in hospitalizations.

Acknowledgment 

This report is based in part on data provided by the partner 

organizations that participated in the HCUP Nationwide Inpatient 

Sample and the State Inpatient Databases. A list of these organizations 

is available at http://www.hcup-us.ahrq.gov/db/hcupdatapartners.jsp.

References

1. Bindman AB, Grumbach K, Osmond D, et al. Preventable 

hospitalizations and access to health care. JAMA 1995;274:305–11.

2. CDC. CDC Health disparities and inequalities report—United States, 

2011. MMWR 2011;60 (Suppl; January 14, 2011).

3. CDC. Introduction: In: CDC health disparities and inequalities report—

United States, 2013. MMWR 2013;62(No. Suppl 3).

4. CDC. Potentially preventable hospitalizations—United States, 2004–

2007. In: CDC health disparities and inequalities report—United States, 

2011. MMWR 2011;60(Suppl; January 14, 2011).

5. Coffey R, Barrett M, Houchens R, et al. Methods applying AHRQ 

quality indicators to Healthcare Cost and Utilization Project (HCUP) 

data for the tenth (2012) National Healthcare Quality Report (NHQR) 

and National Healthcare Disparities Report (NHDR). Rockville, MD:

Agency for Healthcare Research and Quality. Available at http://www.

hcup-us.ahrq.gov/reports/methods/methods.jsp.

6. Chen J, Normand SL, Wang Y, Krumholz HM. National and regional 

trends in heart failure hospitalization and mortality rates for Medicare 

beneficiaries, 1998–2008. JAMA 2011;306:1669–78.

7. Will JC, Valderrama AL, Yoon PW. Preventable hospitalizations for 

congestive heart failure: establishing a baseline to monitor trends and 

disparities. Prev Chronic Dis 2012;9:110260. DOI: http://dx.doi.

org/10.5888/pcd9.110260.

8. Agency for Healthcare Research and Quality. 2011 national healthcare 

quality report. Rockville, MD: Agency for Healthcare Research and 

Quality; 2012. Available at http://www.ahrq.gov/research/findings/

nhqrdr/nhqr11/index.html.

9. American Diabetes Association. Economic costs of diabetes in the U.S. 

in 2007. Diabetes Care 2008;31:596–615.

10. Dall TM, Roary M, Yang W, et al. Health care use and costs for 

participants in a diabetes disease management program, United States, 

2007–2008. Prev Chronic Dis 2011;8:A53.

11. Longworth DL. Accountable care organizations, the patient-centered 

medical home, and health care reform: what does it all mean? Cleve Clin 

J Med 2011;78:571–82.
Supplement

144 MMWR / November 22, 2013 / Vol. 62 / No. 3

Introduction

Hypertension is a major risk factor for heart disease and 

stroke. As the first and fourth leading causes of death in the 

United States, heart disease and stroke occur in approximately 

30% of adults aged ≥18 years in the United States (1). 

Disparities in the prevalence of hypertension among racial/

ethnic groups have persisted at least since 1960, with the 

prevalence remaining highest among non-Hispanic black 

adults (1–4). Blood pressure control among those with 

hypertension can reduce the risk of subsequent cardiovascular 

diseases (5). Among adults with hypertension, MexicanAmerican persons born outside the United States, and persons 

without health insurance had lower rates of blood pressure 

control in 2005–2008 (3). Not only do non-Hispanic black 

adults have higher rates of hypertension, but among those 

with hypertension they also have lower rates of blood pressure 

control than non-Hispanic white adults (2,3).

Healthy People 2020 includes objectives to reduce the 

prevalence of hypertension among adults to 26.9% (objective 

HDS-5.1) and to increase the prevalence of blood pressure 

control among adults with hypertension to 61.2% (objective 

HDS-12) (6). Further, in 2011, the U.S. Department of 

Health and Human Services launched the Million Hearts 

initiative, which is intended to bring together communities, 

health systems, nonprofit organizations, federal agencies, and 

private-sector partners from across the country to prevent 

1 million heart attacks and strokes over the course of 5 years 

(available at http://millionhearts.hhs.gov/index.html). Blood 

pressure control is a part of the initiative in the prevention 

of these adverse events. Therefore, hypertension prevalence 

and blood pressure control among those with hypertension 

are important indicators to monitor over time to identify 

improvements or persistent challenges in vulnerable segments 

of the U.S. population.

This analysis of hypertension and the discussion that follows 

are part of the second CDC Health Disparities and Inequalities 

Report (2013 CHDIR) (3).The 2011 CHDIR was the first 

CDC report to assess disparities across a wide range of diseases, 

behavioral risk factors, environmental exposures, social 

determinants, and health-care access. The topic presented in 

this report is based on criteria that are described in the 2013 

CHDIR Introduction (7). This report provides more current 

information on the prevalence of hypertension and blood 

pressure control among adults aged ≥18 years. The purposes of 

this report on hypertension and controlled hypertension are to 

discuss and raise awareness of differences in the characteristics 

of persons with hypertension and controlled hypertension, and 

to prompt actions to reduce disparities.

Methods

To estimate the age-adjusted prevalence of hypertension and 

blood pressure control among adults aged ≥18 years by selected 

demographic and health characteristics, CDC analyzed data 

from the National Health and Nutrition Examination Survey 

(NHANES) aggregated from two survey cycles: 2007–2008 

and 2009–2010. NHANES is a national survey representative 

of the U.S. civilian noninstitutionalized population. Details of 

the NHANES survey methodology are available at http://www.

cdc.gov/nchs/nhanes/about_nhanes.htm. During 2007–2010, 

the response rate among persons screened was 76.3%. Data 

were analyzed for 11,782 participants who had adequate data 

from the interview and examination components of the survey 

necessary to determine hypertension status. Blood pressure 

was determined by an average of up to three measurements 

taken during a single examination. Hypertension was defined 

as an average systolic blood pressure (SBP) ≥140 mmHg, an 

average diastolic blood pressure (DBP) ≥90 mmHg, or if the 

participant reported the current use of blood pressure lowering 

medication. Blood pressure control was defined as an average 

SBP <140 mmHg and an average DBP <90 mmHg among 

persons with hypertension. Pregnant women were excluded. 

Hypertension prevalence and control estimates were analyzed 

by selected demographic and health characteristics: sex, age 

group (18–44, 45–64, and ≥65 years), race and ethnicity, marital 

status, educational attainment, country of birth, family income 

to federal poverty threshold, health insurance status (for persons 

aged 18–64 years), veteran status, diagnosed diabetes status, 

Prevalence of Hypertension and Controlled Hypertension — 

United States, 2007–2010

Cathleen D. Gillespie, MS1

Kimberly A. Hurvitz, MHS2

1National Center for Chronic Disease Prevention and Health Promotion, CDC

2National Center for Health Statistics, CDC

Corresponding author: Cathleen D. Gillespie, MS, Division for Heart Disease and Stroke Prevention, National Center for Health Statistics, CDC. Telephone: 

770-488-5855; E-mail: ckg2@cdc.gov.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 145

obesity status, and disability. Race was defined as white, black, 

and Mexican American. Ethnicity was defined as Hispanic or 

non-Hispanic. Educational attainment among adults aged ≥25 

years was defined as follows: less than high school, high school 

graduate or equivalent, some college or Associate of Arts (AA) 

degree, and college graduate or above. Household income was 

defined as family income to federal poverty threshold, as defined 

by the Department of Health and Human Services poverty 

guidelines (8), specific to family size and appropriate year and 

state. Health insurance status was defined as having either private 

or public insurance, or being uninsured. Obesity among adults 

aged ≥20 years is defined as a body mass index ≥30 kg/m2

based on measured weight and height.

 Veteran status, diagnosed 

diabetes,* and disability† status were self-reported (Table). 

Disparities were measured as the deviations from a “referent” 

category prevalence. The referent group was the group that 

had the most favorable estimate for the variables used to assess 

disparities during the period reported. Absolute difference 

was measured as the simple difference between a population 

subgroup estimate and the estimate for its respective reference 

group. The relative difference, a percentage, was calculated by 

dividing the absolute difference by the value in the referent 

category and multiplying by 100.

Statistical analyses were weighted to account for the complex 

survey design. Prevalence estimates, except those by age group, 

were age adjusted to the 2000 U.S. standard population using 

the direct method. Estimates of hypertension control that are 

age-adjusted to the 2000 U.S. standard population tend to be 

lower than those adjusted to the population with hypertension 

because of the difference between the age distribution of 

the general population and that of the population with 

hypertension (9).

Results

During 2007–2010, the overall age-adjusted prevalence 

of hypertension among persons aged ≥18 years was 29.6% 

(Table). Among persons aged ≥18 years with hypertension, 

the overall age-adjusted prevalence of blood pressure control 

was 48.0%. Substantial differences (relative difference >10%) 

in the prevalence of hypertension were indicated by age group, 

race/ethnicity, educational attainment, country of birth, family 

income, health insurance, diabetes, obesity, and disability status. 

The highest rates of hypertension were observed among those 

aged ≥65 years (71.6%), adults with diabetes (59.4%), and 

non-Hispanic black adults (41.3%). Although the difference 

in hypertension prevalence by sex was statistically significant, 

the difference was not substantial. Hypertension prevalence 

increased with age and decreased with increasing income level, 

but no significant trend was observed by educational attainment. 

Non-Hispanic blacks had a higher rate of hypertension 

(41.3%) than non-Hispanic whites (28.6%) and Hispanics 

(27.7%). Adults born in the United States had a higher rate 

of hypertension (30.6%) than non-U.S.-born adults (25.7%). 

Adults aged <65 years with public insurance had a higher rate 

of hypertension (28.3%) than those with private insurance 

(20.0%) and those with no insurance (20.4%). Persons with 

diabetes had a significantly higher rate of hypertension than 

those without diabetes (59.4% versus 27.7%), as did those who 

were obese compared with those who were not (40.5% versus 

25.0%) and those with a disability compared with those with 

no disability (40.2% versus 29.0%). 

Substantial differences in the prevalence of blood pressure 

control were observed among all population groups 

except veteran status. Among persons aged ≥18 years with 

hypertension, rates of blood pressure control were lowest 

among those without health insurance (27.9%), MexicanAmericans (30.3%), those who were never married (34.9%), 

and those born outside the United States (38.9%). Men, adults 

aged 18–44 years, Hispanics, Non-Hispanic blacks, those who 

were never married, non-U.S.-born, persons without health 

insurance had a lower prevalence of hypertension control 

than their counterparts. Men had a lower rate of hypertension 

control than women (42.7%). Adults aged 18–44 years had a 

lower rate of hypertension control (40.9%) than adults aged 

45–64 years (53.3%) and 64 years and over (51.4%). The 

rate of controlled blood pressure was lower among Hispanics 

(34.4%) and non-Hispanic blacks (42.5%) than non-Hispanic 

whites (52.6%). Non-U.S.-born adults had a lower rate of 

hypertension control (38.9%) than U.S.-born adults (49.3%). 

Adults aged <65 years with no insurance had a lower rate 

of hypertension control (27.9%) than those with public 

insurance (60.2%) or private insurance (50.6%). Controlled 

hypertension was also lower among those classified as not 

obese compared with those who were obese (41.4% versus 

54.0%), persons without diabetes compared with those with 

diabetes (45.4% versus 63.6%), and persons with no disability 

compared with those with a disability (45.0% versus 59.3%). 

Controlled hypertension was not linearly associated with age, 

educational attainment, or income level. 

*Persons with diagnosed diabetes are defined as those who have ever been told 

by a health-care professional that they have diabetes. Persons without diabetes 

are defined as those who have never been told by a health-care professional that 

they have diabetes or have never been told that they have borderline diabetes.

† Persons classified as having a disability provided the answer ‘Yes’ to any of four 

questions: 

	 •	Unableto work at a job or business because of a physical, mental, oremotional

problem

	 •	Limited because of difficulty remembering or because of periods of confusion

	 •	Limited in any activity because of a physical, mental or emotional problem

	 •	Uses special equipment, such as a cane, a wheelchair a special bed, or a special

telephone. 
Supplement

146 MMWR / November 22, 2013 / Vol. 62 / No. 3

Discussion

The prevalence of hypertension has remained consistent 

over the past 10 years, at an overall rate of approximately 30% 

(1,13). During 2007–2010, the prevalence of hypertension 

by the analyzed demographic characteristics was highest 

among those aged ≥65 years (71.6%) and among nonHispanic blacks (41.3%), two population groups known to 

be disproportionately affected (1,3,12). Although the overall 

prevalence of blood pressure control has improved over the 

past 10 years, non-Hispanic blacks and Hispanics continue 

to have lower prevalence of control than their non-Hispanic 

white counterparts (8,12). Also consistent with other research, 

the prevalence of hypertension was higher among those with 

diagnosed diabetes, obese persons, and persons with disabilities 

(3). However, all three of these groups had higher rates of 

blood pressure control than their counterparts in 2007–2010 

(63.6%, 54.0%, and 59.3%, respectively, versus 45.4%, 

41.4%, and 45.0% among those without diagnosed diabetes, 

obesity, and disabilities, respectively). This difference is likely 

because of higher rates of treatment with medication among 

these groups (5,14). In contrast, although the prevalence of 

hypertension was lowest among those aged 18–44 years (9.8%), 

the prevalence of blood pressure control was significantly lower 

among this group than their older counterparts. This is most 

likely because of lower rates of hypertension awareness and 

treatment with medication among younger adults (13,15). 

See table footnotes on the next page.

TABLE. Age-adjusted* prevalence of hypertension and blood pressure control among adults aged ≥18 years, by selected demographic and 

health characteristics — National Health and Nutrition Examination Survey, United States, 2007–2010

Characteristic

Hypertension§ Blood pressure control¶

Sample 

Size† % (95% CI)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%) % (95% CI)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%)

Total 11,782 29.6 (28.6–30.7) 48.0 (44.6–51.4)

Sex

Male 5,854 30.5 (29.0–31.9) 1.9** 6.6 42.7 (38.3–47.2) -12.8** -23.1

Female 5,928 28.6 (27.4–29.7) Ref. Ref. 55.5 (51.8–59.3) Ref. Ref.

Age group (yrs), unadjusted††

18–44 5,051 9.8 (8.9–10.7) Ref. Ref. 40.9 (34.4–47.5) -12.4** -23.3

45–64 3,854 40.4 (37.9–43.0) 30.6** 312.2 53.3 (49.8–56.8) Ref. Ref.

≥65 2,877 71.6 (68.4–74.7) 61.8** 630.6 51.4 (48.2–54.6) -1.9 -3.6

Race/Ethnicity

White, non-Hispanic 5,559 28.6 (27.1–30.2) 0.9 3.2 52.6 (48.8–56.5) Ref. Ref.

Black, non-Hispanic 2,305 41.3 (39.1–43.5) 13.6** 49.1 42.5 (37.6–47.5) -10.1** -19.2

Hispanic§§ 3,372 27.7 (26.4–29.1) Ref. Ref. 34.4 (30.7–38.2) -18.2** -34.6

Mexican American 2,121 27.5 (25.8–29.2) NA NA 30.3 (26.1–34.5) -22.3** -42.4

Marital status (persons aged ≥20 years)

Never married 1,885 31.8 (29.4–34.3) 1.7 5.6 34.9 (29.1–40.6) -15.7** -31.0

Married or living with partner 6,678 30.1 (28.6–31.6) Ref. Ref. 50.6 (46.8–54.5) Ref. Ref.

Divorced/separated or widowed 2,656 31.1 (29.2–33.1) 1.0 3.3 50.4 (42.3–58.4) -0.2 -0.4

Educational attainment (persons aged ≥25 years)††

Less than high school 3,127 36.9 (34.5–39.4) 8.9** 31.8 41.8 (33.9–49.6) -10.8 -20.5

High school graduate or equivalent 2,422 36.3 (34.2–38.4) 8.3** 29.6 51.6 (45.6–57.7) -1.0 -1.9

Some college or AA degree 2,677 34.5 (32.7–36.4) 6.5** 23.2 49.3 (44.3–54.2) -3.3 -6.3

College graduate or higher 2,096 28.0 (25.3–30.6) Ref. Ref. 52.6 (46.0–59.2) Ref. Ref.

Country of birth

United States 8,784 30.6 (29.5–31.7) 4.9** 19.1 49.3 (45.7–52.9) Ref. Ref.

Outside of the United States 2,993 25.7 (24.2–27.1) Ref. Ref. 38.9 (32.4–45.5) -10.4** -21.1

Family income to federal poverty threshold††,¶¶ (%)

<100 2,359 32.8 (30.6–34.9) 5.2 18.8 46.2 (38.0–54.3) -6.9 -13.0

100–199 2,940 32.5 (30.9–34.1) 4.9** 17.8 42.0 (34.7–49.4) -11.1 -20.9

200–399 2,777 30.6 (28.8–32.5) 3.0 10.9 53.1 (47.1–59.2) Ref. Ref.

400–499 840 28.0 (25.0–31.0) 0.4 1.4 45.7 (35.2–56.3) -7.4 -13.9

≥500 1,773 27.6 (25.1, 30.1) Ref. Ref. 51.4 (46.6–56.2) -1.7 -3.2

Health insurance status*** (persons aged 18–64 years)

Private 4,555 20.0 (18.4–21.5) Ref. Ref. 50.6 (46.3–54.9) -9.6** -15.9

Public 1,489 28.3 (25.6–30.9) 8.3 41.5 60.2 (51.8–68.6) Ref. Ref.

Uninsured 2,829 20.4 (18.2–22.6) 0.4 2.0 27.9 (21.7–34.2) -32.3** -53.7
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 147

Limitations

The findings in this report are subject to at least four 

limitations. First, NHANES data are restricted to the civilian 

noninstitutionalized population; thus, results from this study 

are not generalizable to persons who live in nursing homes, 

long-term care facilities, or prisons, or to military personnel. 

Second, reliable data were not available for persons of certain 

racial/ethnic groups or sexual orientation/gender identity. 

Only non-Hispanic blacks and Hispanics were oversampled; 

consequently, estimates cannot be calculated for other racial/

ethnic populations (e.g., American Indians/Alaska Natives, 

Asians/Pacific Islanders). Third, the cross-sectional study 

design provides a one-time only assessment of blood pressure, 

although blood pressure can be measured multiple times 

during one visit. This one-time assessment can overestimate 

or underestimate hypertension prevalence. However, the 

standardized measurement of blood pressure in a mobile 

examination center makes NHANES the best source of 

national data on hypertension. Finally, this report does not 

examine time trends in disparities to assess progress toward 

eliminating disparities. Although other studies included time 

trends, only a limited number of demographic characteristics 

such as race/ethnicity, age, and sex were examined (10).

TABLE. (Continued) Age-adjusted* prevalence of hypertension and blood pressure control among adults aged ≥18 years, by selected demographic 

and health characteristics — National Health and Nutrition Examination Survey, United States, 2007–2010

Characteristic

Hypertension§ Blood pressure control¶

Sample 

Size† (%) (95% CI)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%) (%) (95% CI)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%)

Veteran status

Yes 1,473 30.8 (26.9–34.8) 1.1 3.7 52.5 (44.4–60.5) Ref. Ref.

No (referent) 10,307 29.7 (28.7–30.7) Ref. Ref. 47.4 (43.4–51.5) -5.1 -9.7

Diagnosed diabetes status†††

Yes 1,421 59.4 (54.1–64.7) 31.7 114.4 63.6 (56.2–71.1) Ref. Ref.

No (referent) 10,352 27.7 (26.6–28.8) Ref. Ref. 45.4 (41.7–49.0) -18.2** -28.6

Obesity status§§§ (persons aged ≥20 years)

Yes 4,197 40.5 (39.0–41.9) 15.5 62.0 54.0 (50.2–57.8) Ref. Ref.

No (referent) 6,890 25.0 (23.5–26.4) Ref. Ref. 41.4 (36.5–46.3) -12.6** -23.3

Disability¶¶¶

Yes 2,612 40.2 (37.6–42.9) 11.2 38.6 59.3 (53.2–65.3) Ref. Ref.

No (referent) 8,613 29.0 (27.8–30.1) Ref. Ref. 45.0 (41.2–48.8) -14.3** -24.1

Abbreviations: 95% CI = 95% confidence interval; Ref. = referent; NA = not applicable.

* Age adjusted to the 2000 U.S. standard population. Age specific data are not age adjusted. Hypertension prevalence data (except those by education status, health 

insurance coverage, diabetes status, and age group) are age adjusted to the following seven age groups: 18–29, 30–39, 40–49, 50–59, 60–69, 70–79, and ≥80 years. 

Data by health insurance status are age adjusted using the age groups 18–29, 30–39, 40–49, 50–59, and 60–64 years. Data by diabetes status are age adjusted 

using the age groups 18–49, 50–59, 60–69, 70–79, and ≥80 years. Blood pressure control data (except those by education status, health insurance coverage, and 

age group) are age adjusted to the following five age groups: 18–49, 50–59, 60–69, 70–79, and ≥80 years. Data by education status are age adjusted using the age 

groups 25–49, 50–59, 60–69, 70–79, and ≥80 years. Data by health insurance status are age adjusted using the age groups 18–49, 50–59, and 60–64 years.

† Pregnant women were excluded.

§ Hypertension among adults is defined as an average systolic blood pressure ≥140 mmHg, an average diastolic blood pressure ≥90 mmHg, or self-reported current 

use of blood pressure lowering medication.

¶ Blood pressure control is defined as an average systolic blood pressure <140 mmHg and an average diastolic blood pressure <90 mmHg among adults with 

hypertension.

** p<0.05 for absolute difference compared with referent group, with Bonferroni adjustment for demographic variables with more than two categories.

†† p<0.05, test of trend for hypertension prevalence by income and age using weighted least squares regression on the categorical variable; not significant by 

education or for controlled hypertension.

§§ Persons of Hispanic ethnicity might be of any race or combination of races.

¶¶ Family income: income of all persons within a household who are related to each other by blood, marriage, or adoption. Family income to federal poverty threshold: 

the ratio of family income to the federal poverty threshold as defined by the Department of Health and Human Services’ (HHS) poverty guidelines, specific to 

family size, as well as the appropriate year and state.

*** Private health insurance: private health insurance or Medigap insurance. Public health insurance: Medicare, Medicaid, State Children’s Health Insurance Program, 

military health care, state-sponsored health plan, or other government insurance.

††† Persons with diagnosed diabetes: those who have ever been told by a health-care professional that they have diabetes. Persons without diabetes: those who have 

never been told by a health-care professional that they have diabetes or have never been told that they have borderline diabetes.

§§§ Obesity: body mass index ≥30 kg/m2 based on measured weight and height.

¶¶¶ Disability: inability to work at a job or business because of a physical, mental, or emotional problem; limitation caused by difficulty remembering or periods of 

confusion; limitation in any activity because of a physical, mental, or emotional problem; or use of special equipment (e.g., a cane, wheelchair, special bed, or 

special telephone).
Supplement

148 MMWR / November 22, 2013 / Vol. 62 / No. 3

Conclusion

Consistent with the 2011 CHDIR and other studies, no 

change has occurred in the prevalence of hypertension over the 

last decade, although the rate of hypertension control continues 

to improve (2,3,10). Disparities in hypertension prevalence 

and control persist among most population groups assessed 

similar to what has been published elsewhere. Although rates 

of control have continued to show improvement (2,10), more 

time is needed to determine whether the population will meet 

the Healthy People 2020 target of 61.2%. Certain subgroups 

of persons with hypertension exhibit even lower rates of blood 

pressure control, indicating a need for interventions that span 

the population and focus on vulnerable subgroups. The United 

States Preventive Services Task Force (USPSTF) recommends 

blood pressure screening for all adults aged ≥18 years, and as 

a result of provisions in the Patient Protection and Affordable 

Care Act (ACA), Medicare now covers certain adult clinical 

preventive services recommended by the USPSTF without 

patient cost sharing (§4103) (11,12). The law also requires 

that “nongradfathered” private health plans include these 

same services without cost sharing (§1001). In addition, the 

ACA ensures certain preventive and wellness services without 

cost-sharing for Medicare recipients (§4103), a group most 

in need of hypertension management. The national Million 

Hearts initiative endeavors to increase the number of persons 

in the United States whose hypertension is under control by 10 

million, as part of its goal to prevent 1 million heart attacks and 

strokes by the year 2017. The Guide to Community Preventive 

Services Task Force recommends system interventions to 

improve blood pressure control, including clinical decision 

support systems, reducing out-of-pocket costs for CVD 

preventive services for patients with hypertension, and teambased care. Because the rate of blood pressure control is lowest 

among persons without health insurance, compared to those 

with insurance coverage, it will be important to monitor this 

and other vulnerable population groups in the future.

References

1. CDC. Vital signs: prevalence, treatment, and control of hypertension—

United States, 1999–2002 and 2005–2008. MMWR 2011;60:103–8.

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2012: with special feature on emergency care. Hyattsville, MD: US 

Department of Health and Human Services; 2013.

3. CDC. Prevalence of hypertension and controlled hypertension—United 

States, 2005–2008. MMWR 2011:60;94–97. In: CDC. CDC health 

disparities and inequalities report—United States, 2011. MMWR 2011, 

60(Suppl; January 14, 2011).

4. Burt VL, Cutler JA, Higgins M, et al. Trends in the prevalence, awareness, 

treatment, and control of hypertension in the adult US population. Data 

from the health examination surveys, 1960 to 1991. Hypertension 

1995;26:60–9

5. National Heart, Lung, and Blood Institute. Seventh report of the Joint 

National Committee on Prevention, Detection, Evaluation, and 

Treatment of High Blood Pressure. Hypertension 2003;42:1206–52.

6. US Department of Health and Human Services. Healthy people 2020. 

Available at http://www.healthypeople.gov/2020/default.aspx. 

7. CDC. Introduction: In: CDC Health disparities and inequalities report—

United States, 2011. MMWR 2011;60(Suppl; January 14, 2011).

8. US Department of Health and Human Services. Poverty Guidelines, 

Research, and Measurement. Available at http://aspe.hhs.gov/poverty/

index.cfm. 

9. Crim MT, Yoon SS, Ortiz E, et al. National surveillance definitions for 

hypertension prevalence and control among adults. Circ Cardiovasc 

Qual Outcomes 2012;5:343–51.

10. Yoon S, Burt V, Louis T, Carroll MD. Hypertension among adults in 

the United States, 2009–2010. NCHS data brief, no 107. Hyattsville, 

MD: National Center for Health Statistics; 2012.

11. Agency for Healthcare Research and Quality: Screening for high blood 

pressure. Available at http://www.uspreventiveservicestaskforce.org/

uspstf07/hbp/hbprs.htm. 

12. Patient Protection and Affordable Care Act. Public. Law. 111-148 111th

Congress, March 23, 2010. Government Printing Office, 2010. Available 

at http://www.gpo.gov/fdsys/pkg/PLAW-111publ148/pdf/PLAW111publ148.pdf. 

13. Yoon S, Ostchega Y, Louis T. Recent trends in the prevalence of high blood 

pressure and its treatment and control, 1999-2008. NCHS data brief, no 

48. Hyattsville, MD: National Center for Health Statistics; 2010.

14. Bertoia ML, Waring ME, Gupta PS, et al. Implications of new 

hypertension guidelines in the United States. Hypertension 2012;

60:639–44.

15. Egan BM, Zhoa Y, Axon RN. US trends in prevalence, awareness, treatment, 

and control of hypertension, 1988-2008. JAMA 2010;303:2043–50.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 149

Tuberculosis — United States, 1993–2010

Awal D. Khan, PhD, Elvin Magee, MPH, Gail Grant

National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, CDC

Corresponding author: Awal D. Khan, PhD, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, CDC. Telephone 404-639-6272; 

E-mail: AKhan2@cdc.gov.

Introduction

Tuberculosis (TB) is transmitted via the airborne route by 

person-to-person contact. Although TB is a leading cause 

of death on a global scale (1), most cases can be cured with 

treatment. From 1993 to 2010, the number of TB cases 

reported in the United States decreased from 25,103 to 

11,182. Despite the decrease, TB continues to affect many 

communities in the United States disproportionately and 

unequally, especially racial/ethnic minorities and foreignborn persons (2). TB remains one of many diseases and 

health conditions with large disparities and inequalities by 

income, race/ethnicity, educational attainment, and other 

sociodemographic characteristics (3).

This report is part of the second CDC Health Disparities 

and Inequalities Report (CHDIR). The 2011 CHDIR (4) was 

the first CDC report to assess disparities across a wide range 

of diseases, behavioral risk factors, environmental exposures, 

social determinants, and health-care access. The topic 

presented in this report is based on criteria that are described 

in the 2013 CHDIR Introduction (5). This report provides 

new information on TB, a topic not covered in the 2011 

CHDIR. The purposes of this TB report are to discuss and 

raise awareness of differences in the characteristics of people 

who have TB in the United States and to prompt actions to 

reduce these disparities.

Methods

Tuberculosis (TB) is a disease caused by bacteria that is spread 

from person to person through the air when a TB sufferer 

coughs, sneezes, speaks, sings, or laughs. TB usually affects 

the lungs, but it can also affect other parts of the body, such as 

the brain, the kidneys, or the spine. This analysis included all 

TB cases, and no cases or latent TB infection (LTBI). To assess 

disparities in newly reported cases of TB disease among persons 

of all ages in the United States, CDC analyzed 1993–2010 

data from the National TB Surveillance System (NTSS). TB 

is a nationally notifiable disease (2). Since 1953, state and local 

health departments have submitted information to CDC on 

each newly reported case of TB disease in the United States. 

Currently, all 50 U.S. states and the District of Columbia 

(DC) as well as Puerto Rico, the U.S. Virgin Islands, and six 

other jurisdictions in the Pacific region (American Samoa, the 

Commonwealth of the Northern Mariana Islands, the Federated 

States of Micronesia, Guam, the Republic of the Marshall 

Islands, and the Republic of Palau) report information on newly 

diagnosed TB cases electronically using NTSS. The Report of 

a Verified Case of TB (RVCT) form (http://ftp.cdc.gov/pub/

software/tims/2009%20rvct%20documentation/rvct%20

training%20materials/rvct%20instruction%20manual.pdf), 

which was released in 1993, was expanded to collect additional 

information for each case, including human immunodeficiency 

virus (HIV) status, occupation, and history of substance 

abuse, homelessness, and drug susceptibility test results (2). 

Subsequent revisions of the RVCT form in 2009 include risk 

factors (e.g., diabetes, end-stage renal disease, and contact with 

a drug-resistant person), residential status, immigration status, 

and reasons for longer than usual TB therapy.

This report examines the number of TB cases and rates during 

2006 and 2010 by patient-reported sex at birth, race/ethnicity, 

country of birth, patient primary occupation, employment 

status, number of years patient has been living in the United 

States, and type of health-care provider. Race was defined as 

white, black, Asian/Pacific Islander, and American Indian/

Alaska Native. Ethnicity was defined as Hispanic and nonHispanic. A person was considered U.S.-born or foreign-born 

on the basis of definitions used in the 2010 TB surveillance 

report (2). For employment status, a person was considered 

unemployed if not employed during the 12 months preceding 

TB diagnosis. During 1993–2008, occupation was assessed for 

the previous 2 years and multiple choices were accepted, but 

starting in 2009, occupation was assessed for 1 year before and 

multiple choice answers were no longer accepted. Geographic 

region was not analyzed because, in 2010, approximately half 

(49.2%) of all TB cases were concentrated in a small number 

of states (California, Florida, Texas, and New York), in which 

67.5% of cases occurred in foreign-born persons (2).

Trends in TB rates during 1993–2010 by race/ethnicity 

and cases by country of birth are presented. TB case rates 

per 100,000 population and by sex and race/ethnicity were 

calculated using population estimates from the U.S. Census 

Bureau’s Federated Electronic Research, Review, Extra, and 

Tabulation Tool (DataFerrett version 1.3.3), which were 

available during 2006–2010. The 2010 Current Population 

Survey was used to obtain population estimates stratified by 
Supplement

150 MMWR / November 22, 2013 / Vol. 62 / No. 3

country of birth (6). Disparities were measured as deviations 

from a “referent” category rate or percentage. Referent 

categories were selected because they demonstrated the most 

favorable group estimates for the variables used to assess 

disparities during 2006 and 2010 (7–8). For example, nonHispanic white was selected as the referent category for the 

racial/ethnic variable. Absolute difference was measured as the 

simple difference between a group estimate and the estimate 

for its respective reference category, or referent group. Relative 

difference, a percentage, was obtained by dividing the absolute 

difference by the value in the referent category and multiplying 

by 100. To evaluate changes in disparity over time, relative 

differences for the groups in 2006 were subtracted from relative 

differences in 2010. No statistical testing was performed.

Results

During 2006–2010, a total of 62,642 verified TB cases were 

reported to CDC’s NTSS from the 50 states, DC, Puerto Rico, 

the U.S. Virgin Islands, and six other jurisdictions in the Pacific 

region. Of these, 13,732 were reported in 2006 and 11,182 

were reported in 2010. The national TB case rate was 4.6 cases 

per 100,000 population in 2006 and 3.6 cases per 100,000 

population in 2010, a 20% decline over 5 years. The rate for 

males was 5.8 in 2006 and 4.5 in 2010. 

The relative difference between males and females in reported 

TB rates was 70.6% in 2006 and 66.7% in 2010 (Table). From 

2006 to 2010, the changes in relative differences for the various 

age groups were as follows: persons aged 15–24 years (22.9%), 

persons aged 25–44 years (40.0%), persons aged 45–64 years 

(44.3%), and persons aged ≥65 years (35.7%) (Table). In 2010, 

the relative difference between persons aged ≥65 years and the 

referent group was 450%.

From 2006 to 2010, all racial/ethnic minorities experienced 

decreases in TB case rates (Table). In 2006, Hispanics had case 

rates of 9.2 per 100,000, compared with 6.5 in 2010. Asians/

Pacific Islanders had a rate of 26.1 in 2006 and 22.4 in 2010. 

Compared with whites, TB rates in 2010 were approximately 

seven times higher among Hispanics, eight times higher among 

blacks, and 25 times higher among Asians/Pacific Islanders.

During 2006–2010, 59% of 62,642 reported TB cases 

occurred among foreign-born persons. In 2006, the relative 

difference among foreign-born persons compared with U.S.-

born persons was 857% and in 2010, the relative difference 

in reported TB rates among foreign-born persons compared 

with U.S.-born persons was 1,031%. The change in the relative 

difference from 2006 to 2010 was 175% (1,031% versus 

856.5%, respectively) (Table).

Although racial/ethnic relative differences in TB case rates 

were similar in both U.S.-born and foreign-born persons, the 

magnitude of the relative disparities varied markedly between 

U.S.-born and foreign-born persons and was three-to-four 

times greater among foreign-born persons. In U.S.-born 

persons in 2010, the relative difference in TB rates compared 

with whites was 614% for blacks, 429% for Asians/Pacific 

Islanders, 286% for Hispanics, and 757% for American 

Indians/Alaska Natives (Table). Among foreign-born persons 

in 2010, the relative difference in TB rates compared with 

whites was 2,271% for Asians/Pacific Islanders, 1,771% for 

blacks, and 836% for Hispanics.

Among 6,748 foreign-born persons in the United States 

during 2010 with reported TB, approximately 21% received a 

diagnosis of TB disease within <2 years of arrival in the United 

States, approximately 18% within 2–5 years of arrival, and 50% 

in >5 years after arrival; an additional 11% had no information 

on arrival dates. The relative difference in TB cases diagnosed 

>5 years after arrival in the United States compared with cases 

diagnosed 2–5 years after arrival was 178.8% in 2010. The 

change in the relative difference between 2006 to 2010 for TB 

cases diagnosed >5 years after arrival in the United States was 

41% (136.3% vs. 177.8%, respectively) (Table).

The proportion of TB cases among unemployed persons was 

53% (7,245 of 13,732) in 2006 and 59% (6,217 of 10,520) in 

2010. During 2010, the relative difference in reported TB cases 

among unemployed persons compared with those employed 

in fields other than health care (referent) was 74%. a change 

in the relative difference of 44.2% over time (Table). 

The relative difference in reported TB cases among persons 

whose primary health-care provider for TB disease was a health 

department compared with persons whose primary health-care 

provider for TB disease was private/other providers (referent 

category) was 217% a change in the relative difference of 109% 

over time (326.3% in 2006 and 216.7% in 2010) (Table). The 

proportion of TB cases treated at health departments was 81% 

(10,830 of 13,308) in 2006 and 76% (4,587 of 6,011) in 2010.

From 1993 to 2010, TB case rates declined by approximately 

63% (Figure 1). TB rates for Asians/Pacific Islanders were 

41.2 per 100,000 population in 1993 and 22.4 per 100,000 

in 2010, with differences in rates of 45.6%. From 1993 to 

2010, among blacks, the rates ranged from 28.5 to 7.0 per 

100,000 population, among Hispanics from 19.9 to 6.5, 

among American Indians/Alaska Natives from 14.0 to 6.4, 

and among non-Hispanic whites from 3.6 to 0.9.

From 1993 to 2010, the proportion of TB cases among 

foreign-born persons increased from 29% to 60% (Figure 2). 

From 1993 to 2010, the TB case rate in the United States has 

declined annually in both U.S.-born and foreign-born persons; 
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MMWR / November 22, 2013 / Vol. 62 / No. 3 151

TABLE. Reported tuberculosis rates,* by date and selected characteristics — United States, 2006 and 2010 

Characteristic

2006 2010

TB rate†

Absolute 

difference§ 

(percentage 

points)

Relative 

difference 

(%)¶ TB rate†

Absolute 

difference§ 

(percentage 

points)

Relative 

difference 

(%)¶

Sex at birth

Male 5.8 2.4 70.6 4.5 1.8 66.7 

Female 3.4 Ref Ref 2.7 Ref. Ref.

Age group (yrs)

<15 1.4 Ref. Ref. 1.0 Ref. Ref.

15–24 3.6 2.2 157.1 2.8 1.8 180.0 

25–44 5.6 4.2 300.0 4.4 3.4 340.0

45–64 5.4 4.0 285.7 4.3 3.3 330.0

≥65 7.2 5.8 414.3 5.5 4.5 450.0

Race/Ethnicity

White, non-Hispanic 1.2 Ref. Ref. 0.9 Ref. Ref.

Black, non-Hispanic 10.2 9.0 750.0 7.0 6.1 677.8 

Hispanic** 9.2 8.0 666.7 6.5 5.6 622.2 

Asian/Pacific Islander 26.1 24.9 2,075.0 22.4 21.5 2,388.9 

American Indian/Alaska Native 7.2 6.0 500.0 6.4 5.5 611.1 

Country of birth 

Born in United States 2.3 Ref. Ref. 1.6 Ref. Ref.

Born outside United States 22.0 19.7 856.5 18.1 16.5 1,031.3 

Born in the United States

White, non-Hispanic 0.9 Ref. Ref. 0.7 Ref. Ref.

Black, non-Hispanic 7.7 6.8 755.6 5.0 4.3 614.3 

Hispanic 3.9 3.0 333.3 2.7 2.0 285.7

Asian/Pacific Islander 3.4 2.5 277.8 3.7 3.0 428.6 

American Indian/Alaska Native†† 7.9 7.0 777.8 6.0 5.3 757.1 

Born outside the United States

White, non-Hispanic 1.7 Ref. Ref. 1.4 Ref. Ref.

Black, non-Hispanic 36.3 34.6 2,035.2 26.2 24.8 1,771.4

Hispanic 17.5 15.8 929.4 13.1 11.7 835.7

Asian/Pacific Islander 38.3 36.6 2,152.9 33.2 31.8 2,271.4 

American Indian/Alaska Native†† 1.3 -0.4 -23.5 1.9 0.5 35.7 

Years in the United States (foreign-born)§§

<2 years 27 8 42.1 21 3 16.7 

2–5 years 19 Ref. Ref. 18 Ref. Ref. 

 >5 years 45 26 136.8 50 32 177.8 

Patient’s primary occupation¶¶

Unemployed/no occupation 53 12 29.3 59 25 73.5

Health-care worker 3 -38 -92.7 4 -30 -88.2 

Other employment¶¶,*** 41 Ref. Ref. 34 Ref. Ref. 

Unknown 3 3

Health-care provider type§§

Any health department 81 62 326.3 76 52 216.7 

Private/other providers 19 Ref. Ref. 24 Ref. Ref.

Abbreviations: Ref. = referent group; TB = tuberculosis.

* This analysis included all TB cases, and no cases of latent TB infection (LTBI).

† Per 100,000 U.S. standard population; based on the U.S. Census Bureau’s Federated Electronic Research, Review, Extra, and Tabulation tool (DataFerrett version 

1.3.3) that were available during 2006–2010.

§ Absolute difference = the simple difference between a particular group rate and the rate for its respective referent group.

¶ Obtained by expressing the value for the difference as a percentage of the estimate for its respective referent group.

** Persons of Hispanic ethnicity might be of any race or combination of races.

†† Small sample size (n = 4) and uncertainty of data quality, mostly misclassification.

§§ Includes available information for Patient’s “Month-Year” Arrived in the United States.

¶¶ Used proportions of all reported and available information for Patient’s Primary Occupation and Type of Health Care Provider. During 1993–2008, occupation was 

assessed for the previous 2 years and multiple choices were accepted, but starting in 2009, occupation was assessed for 1 year before and multiple choice answers 

were no longer accepted.

***Includes migrant and seasonal workers.
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152 MMWR / November 22, 2013 / Vol. 62 / No. 3

overall, TB cases have declined 78% among U.S.-born persons 

compared with 47% among foreign-born persons.

In 1993, approximately 69% of reported TB cases occurred 

among U.S.-born persons (7.4 cases per 100,000) and 29% 

occurred among foreign-born persons (34.0 cases per 100,000). 

In comparison, during 2006–2010, on average, approximately 

59% of reported TB cases occurred among foreign-born 

persons and remained relatively stable, and the rates of cases 

reported were 1.9 per 100,000 for U.S.-born and 22.0 for 

foreign-born persons.

Discussion

The number of new TB cases reported in the United 

States in 2010 represented an 87% decrease since reporting 

began in 1953 and a 58% decrease since the peak resurgence 

of TB reported in 1992 (2). Despite the downward trend, 

TB continues to affect many U.S. racial/ethnic minorities 

disproportionately, both U.S.-born and foreign-born. 

Approximately half of new TB cases in the United States occur 

among foreign-born persons and the TB rate in foreign-born 

persons was approximately 10 times that of persons born in 

the United States. This disparity has become more recognizable 

since 1993, when surveillance was enhanced to include routine 

collection of country of birth information (9). The foreignborn population presents a challenge to health-care staff and 

TB programs for providing diagnosis and care, and these 

challenges include the unequal prevalence of TB risk factors 

and barriers to access to TB care.

Several factors contribute to the disproportionate prevalence 

of TB among racial/ethnic and foreign-born minorities. 

Persons who were born in countries where TB morbidity 

is high might have acquired TB before immigrating and 

not have symptoms of active TB disease until after arrival 

in the United States. Different social and environmental 

living conditions create large and predictable differences in 

health outcomes among nations and between population 

groups within nations (10). In the United States, adjusting 

for six socioeconomic indicators (i.e., crowding, income, 

poverty, public assistant, education, and unemployment), 

low socioeconomic status accounted for approximately half 

of the increased risk for TB among blacks, Hispanics, and 

Native Americans (11). Unequal prevalence of TB risk factors 

(e.g., HIV infection, homelessness, incarceration, substance 

use, and TB disease severity) among racial/ethnic groups 

also might contribute to increased exposure to TB or to an 

increased risk for developing TB once infected. Economically 

disadvantaged persons, the uninsured, low-income children, 

the elderly, the homeless, those with HIV, and those with other 

chronic health conditions (e.g., diabetes and severe mental 

illness) encounter barriers to accessing health-care services.

The effects of these barriers on TB prevention and control 

* Cases per 100,000 population, from the U.S. Census Bureau’s Federated Research Review, Extra, and Tabulation too (DataFerrett version 1.3.3, available during 

2006–2010 .

† All races are non-Hispanic. In 2003, the Asian/Pacific Islander category included persons who reported race as Asian only and/or Native Hawaiian or Other Pacific 

Islander only. Updated on July 21, 2011.

FIGURE 1. Tuberculosis rates,* by race/ethnicity† — United States, 1993–2010

0

10

20

30

40

50

1993 1996 1999 2002 2005 2008 2010

White

Black or African-American

Hispanic

American Indian/Alaska Native

Asian/Pacic Islander

Year

Rate
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 153

vary across racial/ethnic groups (12–14). In addition, poverty, 

language barriers, and immigration status also can be additional 

barriers to ameliorating TB disparities and inequality, jointly 

or independently (15–17).

Controlling and preventing TB in the United States 

necessitates addressing disparities among racial/ethnic 

minorities and foreign-born persons. The continuous arrival 

of new immigrants and refugees from countries with a high 

prevalence of TB has impeded elimination efforts. Reduction 

in TB rates among foreign born communities can be 

accomplished by identification of local at-risk populations, 

increased knowledge of issues affecting immigrants and foreignborn persons and modification of existing TB programs to meet 

the needs of these communities.  In particular, training and 

education can aid health-care staff serving the foreign-born 

community at risk for TB disease.

Limitations

The findings in this report are subject to at least four 

limitations. First, certain data (e.g., race/ethnicity and years 

in the United States) were incomplete and did not include 

U.S. territories and the U.S.-affiliated Pacific Islands. Second, 

the analysis does not assess the effects of socioeconomic risk 

factors (e.g., homelessness, substance abuse, and incarceration), 

HIV coinfection, and drug resistance on TB disparities. The 

prevalence of certain risk factors is particularly extensive in 

minority groups (e.g., persons with HIV/AIDS and diabetes). 

Third, educational attainment and family or household 

income, two indicators used commonly to explain health 

disparities and inequalities, were not available. Finally, social 

aspects that include language barriers and cultural differences 

with respect to health-seeking behaviors and the ability to 

access the complex U.S. health-care system were not examined.

Conclusion

Progress toward TB elimination in the United States will 

require ongoing surveillance and improved TB control and 

prevention activities to address persistent disparities between 

U.S.-born and foreign-born persons and between whites and 

racial/ethnic minorities. Disparities and inequalities among 

racial/ethnic minorities are affected by many unmeasured 

factors. CDC recommends improving awareness, testing, and 

treatment of latent infection and TB disease in minorities and 

foreign-born populations to reduce TB (9). 

FIGURE 2. Number and and percentage of tuberculosis cases, by origin of birth — United States, 1993–2010

0

10

20

30

40

50

60

70

80

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

1993 1994 1995 1996 1997 1998 1999 2000

Year

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

% of cases

No. of cases

Foreign-born

U.S.-born

Foreign-born

U.S.-born
Supplement

154 MMWR / November 22, 2013 / Vol. 62 / No. 3

References

1. Mandell G, Bennett J, Dolin R. Mandell, Douglas, and Bennett’s 

principles and practice of infectious diseases. 7th ed. Philadelphia, PA: 

Churchill Livingstone; 2009.

2. CDC. Reported tuberculosis in the United States, 2010. Atlanta, GA: 

US Department of Health and Human Services, CDC; 2011. Available 

at http://www.cdc.gov/tb/statistics/reports/2010/default.htm. 

3. Keppel KG, Pearcy JN, Wagener DK. Trends in racial and ethnic-specific 

rates for the health status indicators: United States, 1990–1998. Healthy 

People 2000 Stat Notes 2002; 23:1–6.

4. CDC. CDC health disparities and inequalities report—United States, 

2011. MMWR 2011;60(Suppl; January 14, 2011).

5. CDC. Introduction: In: CDC health disparities and inequalities report—

United States, 2013. MMWR 2013;62(No. Suppl 3).

6. US Census Bureau. Current population survey, March supplements (2006–

2010). DataFerrett. Available at http://www.thedataweb.org/faq.html. 

7. Keppel K, Pamuk E, Lynch J, et al. Methodological issues in measuring 

health disparities. Hyattsville, MD: US Department of Health and 

Human Services, CDC. Vital Health Stat 2 2005; 141:1–16.

8. US Census Bureau. Current population survey: design and methodology. 

Washington, DC: US Census Bureau; 2006. Technical Paper No. 6.

9. CDC. Report of Verified Case of Tuberculosis (RVCT) instruction 

manual. Atlanta, GA: US Department of Health and Human Services, 

CDC; 2009. Available at http://ftp.cdc.gov/pub/software/tims/2009%20

rvct%20documentation/rvct%20training%20materials/rvct%20

instruction%20manual.pdf.

10. Agency for Health Care Research Quality. National health care disparities 

report. Publ No. 11-0004; 2010. Available at http://www.ahrq.gov/

research/findings/nhqrdr/nhdr10/pdf/nhdr10.pdf. 

11. Cantwell MF, McKenna MT, McCray E, Onorato IM. Tuberculosis and 

race/ethnicity in the United States: impact of socioeconomic status. Am 

J Respir Crit Care Med 1998;157: 1016–20.

12. CDC; Infectious Disease Society of America. Controlling tuberculosis 

in the United States—Recommendations from the American Thoracic 

Society. MMWR 2005;54(No. RR-12).

13. Nahid P, Horne DJ, Jarlsberg LG, et al. Racial differences in tuberculosis 

infection in United States communities: the coronary artery risk 

development in young adult study. CID 2011;53: 291–4.

14. Serpa JA, Teeter LD, Musser JM, Gravis EA. Tuberculosis disparity 

between US-born backs and whites, Houston, Texas, USA. Emerg Infect 

Dis 2009;15: 899–904.

15. Lonnroth K, Castro KG, Chakaya JM, et al. Tuberculosis control and 

elimination 2010–50: cure, care, and social development. Lancet 

2010;375:1814–29.

16. Craig GM, Booth H, Story A, The impact of social factors on tuberculosis 

management. J Adv Nurs 2007;58:418–24.

17. Lonnroth K, Jaramillo E, Williams BG, Dye C, Raviglione MC. Drivers 

of tuberculosis epidemics: the role of risk factors and social determinants. 

Soc Sci Med 2009;68: 2240–6.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 155

Health Outcomes: Mortality
Supplement

156 MMWR / November 22, 2013 / Vol. 62 / No. 3
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 157

Introduction

Heart disease and stroke are the first and fourth leading 

causes of death, respectively in the United States (1,2). In 

2008, heart disease and stroke were responsible for nearly a 

third of all deaths in the United States (30.4%), killing more 

than three-quarters of a million people that year (1). Coronary 

heart disease (CHD) is the cause of more than two-thirds of 

all heart disease-related deaths (1,2). One of the Healthy People 

2020 objectives includes reducing the rate of CHD deaths 

by 20% from the baseline rate of 126 deaths per 100,000 

population per year, to a goal of 100.8 deaths per 100,000 

(objective HDS-2) (3). The objectives also include reducing 

the rate of stroke deaths by 20% over the baseline of 42.2 

deaths per 100,000, to a goal of 33.8 deaths per 100,000 

population. Although the rates of death from both CHD and 

stroke have declined continuously in recent decades and the 

Healthy People 2010 goals for these two objectives were met 

among the overall U.S. population in 2004, the death rates 

remain high, particularly among men and blacks (4–6).

This heart disease and stroke analysis and discussion that 

follows is part of the second CDC Health Disparities and 

Inequalities Report (2013 CHDIR) (6). The 2011 CHDIR 

(7) was the first CDC report to assess disparities across a 

wide range of diseases, behavioral risk factors, environmental 

exposures, social determinants, and health-care access. The 

topic presented in this report is based on criteria described 

in the 2013 CHDIR Introduction (8). This report provides 

more current information on CHD and stroke deaths among 

different age and racial/ethnic groups. The purposes of the 

coronary heart disease and stroke mortality report are to 

discuss and raise awareness of differences in the characteristics 

of persons dying from coronary heart disease and stroke, and 

to prompt actions to reduce disparities.

Methods

To examine the number and age-specific CHD and stroke 

mortality rates of persons of all ages, by sex, age group, and race/

ethnicity, CDC analyzed final 2009 data from the National 

Vital Statistics System (NVSS). NVSS data are described in 

detail elsewhere (http://www.cdc.gov/nchs/nvss.htm). Race 

was defined as white, black, American Indian/Alaska Native 

(AI/AN), and Asian/Pacific Islander (A/PI). Ethnicity was 

defined as Hispanic or non-Hispanic. Sociodemographic 

information beyond age, sex, and race/ethnicity is not available 

in the NVSS.

CDC estimated the number of deaths and the rate of 

death per 100,000 population for which coronary heart 

disease or stroke were the underlying cause of death (ICD10 codes I20–I25 for CHD, I60–I69 for stroke), and 95% 

confidence intervals were calculated based on a Poisson 

distribution, consistent with NCHS methodology (1). Rates 

per 100,000 population were age-adjusted to the 2000 U.S. 

standard population, except where stratified by age group (9). 

Disparities were measured as the deviations from a “referent” 

category rate and by characteristics that included sex, age, 

and race/ethnicity. Absolute difference was measured as the 

simple difference between a population subgroup estimate 

and the estimate for its respective reference group. The 

relative difference, a percentage, was calculated by dividing 

the difference by the value in the referent category and 

multiplying by 100. Significant differences between rates were 

determined by nonoverlapping 95% confidence intervals. All 

tests for differences in age-adjusted death rates were significant 

compared with the indicated referent group after Bonferroni 

adjustment for multiple comparisons.

Results

The age-adjusted rate of death from CHD in 2009 was 116.1 

per 100,000 population (Table), and CHD was listed as the 

underlying cause of death in 386,324 persons in the United 

States. The age-adjusted death rate per 100,000 population 

from CHD was higher among men than women (155.8 versus 

86.2) and higher among non-Hispanic blacks (141.3) than 

among any other racial/ethnic group. The rate of premature 

death (death among persons aged <75 years) was higher 

among non-Hispanic blacks than their white counterparts 

(65.5 versus 43.2).

Similar differences were observed for deaths from stroke, 

which was listed as the underlying cause of death in 128,842 

Coronary Heart Disease and Stroke Deaths — United States, 2009

Cathleen D. Gillespie, MS

Charles Wigington, MPH

Yuling Hong, MD, 

National Center for Chronic Disease Prevention and Health Promotion, CDC

Corresponding author: Cathleen D. Gillespie, MS, Division for Heart Disease and Stroke Prevention, National Center for Chronic Disease Prevention and 

Health Promotion, CDC. Telephone: 770-488-5855; E-mail: ckg2@cdc.gov.
Supplement

158 MMWR / November 22, 2013 / Vol. 62 / No. 3

persons in the United States in 2009, an age-adjusted rate of 

38.9 deaths per 100,000 population. The age-adjusted death 

rate per 100,000 population from stroke was higher among 

non-Hispanic blacks (73.6) than among any other racial/ethnic 

group. The rate of premature death (death among persons aged 

<75 years) from stroke was higher among non-Hispanic blacks 

than their white counterparts (25.0 versus 10.2).

Discussion

Although death rates from CHD and stroke are declining 

overall (4), disparities still remain in the rate of death from 

these events between racial/ethnic groups. The premature death 

rate from CHD and stroke continues to be higher among black 

adults than their white counterparts. The Healthy People 2020

See table footnotes on the next page.

TABLE. Number of deaths and age-adjusted death rates* from persons within coronary heart disease† and stroke§ by sex, age, and race/ethnicity 

— National Vital Statistics System, United States, 2009

Coronary heart disease† No.

Age-adjusted (except where noted)

Rate* (95% CI)

Absolute 

difference 

(rate)

Relative 

difference 

(%)

Total 386,324 116.1 (115.7–116.5)

Male¶ 210,069 155.8 (155.2–156.5) Ref. Ref.

Female 176,255 86.2 (85.8–86.6) -69.6 -44.7

Age in years

<45 6,679 3.9 (3.8–4.0) -129.3 -97.1

0–24 150 0.1 (0.1–0.2)

25–44 6,529 8.4 (8.2–8.6)

45–74¶ 131,632 133.2 (132.5–133.9) Ref. Ref.

45–54 23,285 52.2 (51.5–52.9)

55–64 46,018 132.3 (131.1–133.5)

65–74 62,329 299.8 (297.4–302.2)

<75 138,311 43.5 (43.3–43.8)

≥75 247,990 1,245.80 (1,240.8–1,250.7) 1112.6 835.3

≥85 (crude) 143,204 2543.3 (2,530.1–2,556.5)

Race/Ethnicity

Hispanic** 20,228 86.5 (85.3–87.7) -31.2 -26.5

Non-Hispanic 365,119 118.2 (117.8–118.6)

White, non-Hispanic¶ 315,810 117.7 (117.3–118.1) Ref. Ref.

Black, non-Hispanic 39,956 141.3 (139.9–142.8) 23.6 20.1

American Indian/Alaska Native 1,737 92 (87.5–96.5) -25.7 -21.8

Asian/Pacific Islander 7,616 67.3 (65.8–68.8) -50.4 -42.8

Age in years/race-ethnicity

<45 years

Hispanic** 561 1.9 (1.7–2.0) -2.2 -53.7

Non-Hispanic 6,094 4.3 (4.2–4.4)

White, non-Hispanic¶ 4,459 4.1 (4.0– 4.3) Ref. Ref.

Black, non-Hispanic 1,369 6.2 (5.9–6.5) 2.1 51.2

45–74

Hispanic** 8,176 98 (95.8–100.2) -33.5 -25.5

Non-Hispanic 122,907 136.1 (135.3–136.9)

White, non-Hispanic¶ 99,389 131.5 (130.7–132.3) Ref. Ref.

Black, non-Hispanic 19,820 199.5 (196.7–202.3) 68 51.7

<75

Hispanic** 8,737 31.3 (30.7–32.0) -11.9 -27.5

Non-Hispanic 129,001 44.7 (44.5–45.0)

White, non-Hispanic¶ 103,848 43.2 (42.9–43.5) Ref. Ref.

Black, non-Hispanic 21,189 65.5 (64.6–66.4) 22.3 51.6

≥75

Hispanic** 11,490 945.7 (928.4–963.0) -331.6 -26

Non-Hispanic 236,100 1,262.9 (1,257.8–1,268.1)

White, non-Hispanic¶ 211,949 1,277.3 (1,271.8–1,282.8) Ref. Ref.

Black, non-Hispanic 18,763 1,322.8 (1,303.9–1,341.8) 45.5 3.6

≥85

Hispanic** 5,793 1,787.5 (1,741.5–1,833.5) -841.1 -32

Non-Hispanic 137,234 2,586.1 (2,572.4–2,599.8)

White, non-Hispanic¶ 125,303 2,628.6 (2,614.0–2,643.2) Ref. Ref.

Black, non-Hispanic 9,085 2,555.2 (2,502.7–2,607.7) -73.4 -2.8
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MMWR / November 22, 2013 / Vol. 62 / No. 3 159

objectives and goals for heart disease and stroke are intended 

to reduce premature deaths by promoting prevention of these 

events and reducing their recurrence. In 2011, CDC launched 

the Million Hearts initiative, which is intended to bring together 

communities, health systems, nonprofit organizations, federal 

agencies, and private-sector partners from across the country to 

prevent 1 million heart attacks and strokes over 5 years.

TABLE. (Continued) Number of deaths and age-adjusted death rates* from persons within coronary heart disease† and stroke§ by sex, age, and 

race/ethnicity — National Vital Statistics System, United States, 2009

Stroke§  No.

Age-adjusted (except where noted)

Rate* (95% CI)

Absolute 

difference 

(rate)

Relative 

difference 

(%)

Total 128,842 38.9 (38.7–39.1)

Male¶ 52,073 39.7 (39.3–40.0) Ref. Ref.

Female 76,769 37.8 (37.5–38.1) -1.9 -4.8

Age in years

<45 2,914 1.6 (1.6–1.7) -33.4 -95.4

0–24 461 0.4 (0.4–0.5)

25–44 2,453 3.1 (3.0–3.2)

45–74¶ 34,264 35 (34.6–35.3) Ref. Ref.

45–54 (crude) 6,163 13.8 (13.5–14.1)

55–64 (crude) 10,523 30.2 (29.6–30.8)

65–74 (crude) 17,578 84.5 (83.3–85.7)

<75 37,178 11.9 (11.7–12.0)

≥75 91,660 460.1 (457.1–463.1) 425.1 1,214.6

≥85 (crude) 53,253 945.8 (937.8–953.8)

Race/Ethnicity

Hispanic** 7,065 29.5 (28.8–30.2) -8.3 -22.0

Non-Hispanic 121,540 39.5 (39.3–39.7)

White, non-Hispanic¶ 101,703 37.8 (37.5–38.0) Ref. Ref.

Black, non-Hispanic 15,718 55.7 (54.8–56.6) 17.9 47.4

American Indian/Alaska Native 533 29.8 (27.2–32.4) -8 -21.2

Asian/Pacific Islander 3,586 31.6 (30.6–32.7) -6.2 -16.4

Age in years/race-ethnicity

<45 years

Hispanic 498 1.5 (1.4–1.6) 0.2 1.5

Non-Hispanic 2,406 1.7 (1.6–1.7) 0.4 3.1

White, non-Hispanic¶ 1,439 1.3 (1.3–1.4) Ref. Ref.

Black, non-Hispanic 796 3.5 (3.2–3.7) 2.2 16.9

45–74

Hispanic 2,654 31.5 (30.3–32.7) 1.2 0.4

Non-Hispanic 31,506 35.2 (34.8– 35.6)

White, non-Hispanic¶ 22,699 30.3 (29.9–30.7) Ref. Ref.

Black, non-Hispanic 7,338 73.6 (71.9–75.3) 43.3 142.9

<75

Hispanic 3,152 10.7 (10.3–11.1) 0.5 0.5

Non-Hispanic 33,912 12 (11.8–12.1)

White, non-Hispanic¶ 24,138 10.2 (10.1–10.3) Ref. Ref.

Black, non-Hispanic 8,134 25 (24.4–25.5) 14.8 14.5

≥75

Hispanic 3,913 322.5 (312.4–332.6) -144 -30.9

Non-Hispanic 87,624 468.3 (465.1–471.4)

White, non-Hispanic¶ 77,562 466.5 (463.5–469.8) Ref. Ref.

Black, non-Hispanic 7,584 534.5 (522.5–546.5) 68 14.6

≥85 (crude)

Hispanic 1,901 586.6 (560.2–613.0) -387.3 -39.8

Non-Hispanic 51,292 966.6 (958.2–975.0)

White, non-Hispanic¶ 46,426 973.9 (965.0–982.8) Ref. Ref.

Black, non-Hispanic 3,619 1,017.9 (984.7–1,051.1) 44 4.5

Abbreviations: 95% CI = 95% confidence interval; Ref = referent.

* Per 100,000. Directly standardized to the 2000 U.S. standard population, except where stratified by age.

† ICD-10 codes: I20–I25

§ ICD-10 codes: I60–I69

¶ All tests for differences in age-adjusted death rates were significant compared with the indicated referent group after Bonferroni adjustment for multiple comparisons.

** Persons of Hispanic ethnicity might be of any race or combination of races.
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160 MMWR / November 22, 2013 / Vol. 62 / No. 3

Limitations

The findings in this report are subject to at least three 

limitations. First, misclassification of race and ethnicity of the 

decedent on the death certificate might underestimate rates 

among AI/ANs, A/PIs, and Hispanics (10). Second, results 

from a study in New York City, New York, indicated that 

CHD is overreported as a cause of death on death certificates 

(11). However, these results might be specific to New York 

City. Third, the death rates reflect only the underlying cause 

of death and no other contributing causes of death such as 

diabetes, which vary substantially across racial/ethnic groups.

Conclusion

Risk factors for cardiovascular disease include tobacco use, 

physical inactivity, poor diet, diabetes, obesity, hypertension, 

and dyslipidemia. Preventing or controlling hypertension 

and high low-density lipoprotein (LDL) cholesterol have 

been shown to greatly reduce the risk for stroke and CHD, 

respectively (12,13). In 2011, the U.S. Department of Health 

and Human Services launched the Million Hearts initiative to 

prevent 1 million heart attacks and strokes by the year 2017, 

through focused clinical and policy strategies. The Guide 

to Community Preventive Services Task Force recommends 

system interventions to improve CVD risk factors, including 

clinical decision support systems, reducing out-of-pocket costs 

for CVD preventive services for patients with hypertension 

and high cholesterol, and team-based care to improve blood 

pressure control. The United States Preventive Services Task 

Force (USPSTF) recommends blood pressure screening for all 

adults aged ≥18 years and LDL-cholesterol screening for adults 

in certain sex, age, and heart disease risk groups (14,15). As a 

result of provisions in the Patient Protection and Affordable 

Care Act, USPSTF-recommended clinical preventive services 

covered by Medicare now have no patient cost sharing (§4104) 

(16,17). The law also requires that “nongrandfathered” private 

health plans include these same services without cost sharing 

(§1001) and encourages Medicaid to cover them through an 

increase in the federal matching rate for those services (§4006). 

Because the rates of premature death from CHD and stroke are 

higher among blacks, it will be important to monitor this and 

other vulnerable population groups (i.e., those with limited 

access to regular medical care) to determine if improvements 

are evident in the future.

References

1. Miniño AM, Murphy SL, Xu J, Kochanek KD. Deaths: Final data for 

2008. Hyattsville, MD: US Department of Health and Human Services, 

CDC, National Center for Health Statistics; 2011. National Vital 

Statistics Report, vol 59, no 10. Available at http://www.cdc.gov/nchs/

data/nvsr/nvsr59/nvsr59_10.pdf.

2. Murphy SL, Jiaquan Xu J, Kochanek KD. Deaths: Preliminary Data for 

2010. Hyattsville, MD: US Department of Health and Human Services, 

CDC, National Center for Health Statistics; 2011. National Vital 

Statistics Report, vol 59, no 10. Available at http://www.cdc.gov/nchs/

data/nvsr/nvsr60/nvsr60_04.pdf.

3. US Department of Health and Human Services. Healthy people 2020. 

Available at http://www.healthypeople.gov/2020/default.aspx.

4. Hoyert DL. 75 Years of mortality in the United States, 1935–2010. 

Hyattsville, MD: US Department of Health and Human Services, CDC, 

National Center for Health Statistics; 2012. NCHS Data Brief no 88. 

Available at http://www.cdc.gov/nchs/data/databriefs/db88.pdf.

5. CDC. Healthy people data 2010: the healthy people database. Atlanta, 

GA: US Department of Health and Human Services, CDC: 2010. 

Available at http://wonder.cdc.gov/data2010.

6. CDC. Coronary heart disease and stroke deaths—United States, 2006. 

In: CDC. CDC health disparities and inequalities report. MMWR 

2011;60(Suppl; January 14, 2011).

7. CDC. CDC health disparities and inequalities report—United States, 

2011. MMWR 2011;60(Suppl; January 14, 2011).

8. CDC. Introduction. In: CDC health disparities and inequalities report—

United States, 2013. MMWR 2013;62(No. Suppl 3)

9. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected 

U.S. population. Hyattsville, MD: US Department of Health and 

Human Services, CDC, National Center for Health Statistics; 2001. 

NCHS Statistical Notes no 20. Available at http://www.cdc.gov/nchs/

data/statnt/statnt20.pdf.

10. Arias E, Schauman WS, Eschbach K, Sorlie PD, Backlund E. The validity 

of race and Hispanic origin reporting on death certificates in the United 

States. National Center for Health Statistics. Vital Health Stat 

2(148);2008.

11. Agarwal R, Norton JM, Konty K, et al. Overreporting of deaths from 

coronary heart disease in New York City hospitals, 2003. Prev Chronic 

Dis 2010:A47. Available at http://www.cdc.gov/pcd/issues/2010/

may/09_0086.htm.

12. Chobanian AV, Bakris GL, Black HR, et al. Seventh report of the Joint 

National Committee on Prevention, Detection, Evaluation, and 

Treatment of High Blood Pressure. Hypertension 2003;43:1206–52.

13. National Institutes of Health. Third report of the National Cholesterol 

Education Program (NCEP) Expert Panel on Detection, Evaluation, 

and Treatment of High Blood Cholesterol in Adults (Adult Treatment 

Panel III). Executive Summary. Heart, Lung, and Blood Institute. 

National Institutes of Health. NIH Publication No. 01–3670. May 

2001. Available at http://www.nhlbi.nih.gov/guidelines/cholesterol/

atp3_rpt.htm.

14. Agency for Healthcare Research and Quality. Screening for high blood 

pressure. Available at http://www.uspreventiveservicestaskforce.org/

uspstf07/hbp/hbprs.htm. 

15. Agency for Healthcare Research and Quality. Screening for lipid disorders 

in adults. Available at http://www.uspreventiveservicestaskforce.org/

uspstf/uspschol.htm.

16. US Department of Health and Human Services. Patient Protection and 

Affordable Care Act of 2010. Pub. L. No. 114–48 (March 23, 2010), 

as amended through May 1, 2010. Available at http://www.healthcare.

gov/law/full/index.html.

17. Koh HK, Sebelius KG. Promoting prevention through the Affordable 

Care Act. New Engl J Med 2010;363:1296–9.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 161

Introduction

Drug-induced deaths include all deaths for which drugs are 

the underlying cause (1), including those attributable to acute 

poisoning by drugs (drug overdoses) and deaths from medical 

conditions resulting from chronic drug use (e.g., drug-induced 

Cushing’s syndrome). A drug includes illicit or street drugs 

(e.g., heroin and cocaine), as well as legal prescription and 

over-the-counter drugs; alcohol is not included. Deaths from 

drug overdose have increased sharply in the past decade. This 

increase has been associated with overdoses of prescription 

opioid pain relievers, which have more than tripled in the past 

20 years, escalating to 16,651 deaths in the United States in 

2010 (2). Most drug-induced deaths are unintentional drug 

poisoning deaths, with suicidal drug poisoning and drug 

poisoning of undetermined intent comprising the majority 

of the remainder (3). 

This drug-induced deaths analysis and discussion that 

follows are part of the second CDC Health Disparities and 

Inequalities Report (CHDIR) (3). The 2011 CHDIR (4) was 

the first CDC report to assess disparities across a wide range of 

diseases, behavioral risk factors, environmental exposures, social 

determinants, and health-care access. The topic presented in 

this report is based on criteria that are described in the 2013 

CHDIR Introduction (5). This report provides more current 

information to what was presented in the 2011 CHDIR (3). The 

purpose of this drug-induced deaths analysis is to raise awareness 

of disparities by age, gender, racial/ethnic and/or geographic 

differences, and to prompt actions to reduce disparities.

Methods

To determine differences in the prevalence of drug-induced 

deaths by sex, race/ethnicity, age, and geographic region in the 

United States, CDC analyzed 2010 data from the mortality 

component of the National Vital Statistics System (NVSS). 

To examine patterns of drug-induced death rates by age group 

and race/ethnicity, NVSS data from 1999 through 2010 were 

aggregated because limited sample sizes are available for any 

single year for certain groups. 

Death certificates provide information on the decedent’s age, 

sex, race, ethnicity, and geographic region. They do not provide 

information on decedent income, disability, or language 

spoken at home. Race is categorized as white, black, American 

Indian/Alaska Native, or Asian/Pacific Islander. Ethnicity was 

categorized as Hispanic or non-Hispanic. Geographic location 

is categorized as Northeast, Midwest, South, and West.* 

Adverse effects from drugs taken as directed and infections 

resulting from drug use are not included. 

The number of drug-induced deaths are presented and 

unadjusted (crude) drug-induced death rates per 100,000 

population are calculated for 2010 by age, racial/ethnic group, 

sex, and geographic region (based on the U.S. Census 2010 

population survey) (Table). The 95% confidence intervals (CIs) 

for unadjusted drug-induced death rates are based on ≥100 

deaths and were calculated using a normal approximation; CIs 

based on <100 deaths were calculated using a gamma method. 

(Additional information is available from Vital Statistics of the 

United States: Mortality, 1999 Technical Appendix, available 

at http://wonder.cdc.gov/wonder/sci_data/mort/mcmort/

type_txt/mcmort05/techap99.pdf). 

Results

During 2010 (the year in which the latest national NVSS 

mortality data are available), a total of 40,393 drug-induced 

deaths occurred in the United States. The majority of druginduced deaths were unintentional 74.3%; remainder: 13.1%; 

suicidal drug poisoning; 7.3% drug poisoning of undetermined 

intent; 5.1% mental and behavioral disorders from drug use; 

<1% homicide; <1% medical conditions from chronic drug 

use. Drug-induced mortality was highest among persons aged 

40–49 years (25.1) (Table). Rates for males exceeded those for 

females aged ≥10 years. Rates were lowest in the Northeast 

region of the United States (11.6), and the largest percentage 

of cases was in the South (38.2%). Non-Hispanic whites 

Drug-Induced Deaths — United States, 1999–2010

Karin A. Mack, PhD

National Center for Injury Prevention and Control, CDC

Corresponding author: Karin A. Mack, Division of Analysis, Research, and Practice Integration, National Center for Injury Prevention and Control, CDC. 

Telephone: 770-488-4389; E-mail: kim9@cdc.gov.

*Northeast (Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, 

New York, Pennsylvania, Rhode Island, and Vermont), Midwest (Illinois, 

Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North 

Dakota, Ohio, South Dakota, and Wisconsin), South (Alabama, Arkansas, 

Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, 

Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, 

Texas, Virginia, and West Virginia), and West (Alaska, Arizona, California, 

Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, 

Washington, and Wyoming).
Supplement

162 MMWR / November 22, 2013 / Vol. 62 / No. 3

accounted for 82.1% of all 40,393 drug-induced deaths. The 

highest rates were among American Indians/Alaska Natives 

(17.1) and non-Hispanic whites (16.6).

During 1999–2010, drug-induced death rates by race/

ethnicity and age group demonstrated varying patterns by 

racial/ethnic group, although the highest rate occurred in the 

40–49 year age group for non-Hispanic whites, American 

Indians/Alaska Natives, and non-Hispanic blacks (Figure). 

Rates among American Indians/Alaska Natives were highest 

in the 30–39 and 40–49 year age groups and then decreased 

in the older ages. Rates among non-Hispanic blacks increased 

dramatically in persons aged 40–49, remained high in persons 

aged 50–59, and then decreased. Rates were lowest at all ages 

for Asians/Pacific Islanders.

Discussion

American Indians/Alaska Natives and non-Hispanic whites 

had the highest drug-induced death rates overall. This finding is 

consistent with the previous report for rates during 2003–2007 

(6). However, it does reflect a change from the 1980s and 

1990s, when drug-induced mortality rates were higher among 

blacks than whites (3). Prescribed drugs have replaced illicit 

drugs as a leading cause of drug-related overdose deaths (7). 

Non-Hispanic blacks are less likely than non-Hispanic whites 

to use prescription drugs, and therefore might be less likely to 

misuse such drugs (8).

TABLE. Number and rate* of drug-induced deaths, by age group, race/ethnicity, and sex — National Vital Statistics System, United States, 2010

Characteristic

Female Male Total

Percentage 

of total

No. of 

deaths Rate (95% CI)†

No. of 

deaths Rate (95% CI)†

No. of 

deaths Rate (95% CI)†

Total 16,017 10.2 (10.0–10.4) 24,376 16.1 (15.9–16.3) 40,393 13.1 (13.0–13.2) 100.00

Age group (yrs)

0–9 32 0.2 (0.1–.02) 48 0.2 (0.2–0.3) 80 0.2 (0.2–0.2) 0.0

10–19 258 1.2 (1.1–1.4) 636 2.9 (2.7–3.1) 894 2.1 (2.0–2.2) 2.2

20–29 1,943 9.2 (8.8–9.6) 4,788 22.1 (21.5–22.7) 6,731 15.8 (15.4–16.1) 16.7

30–39 2,978 14.8 (14.3–15.3) 5,115 25.5 (24.8–26.2) 8,093 20.2 (19.7–20.6) 20.0

40–49 4,620 21.0 (20.4–21.6) 6,333 29.3 (28.6–30.0) 10,953 25.1 (24.7–25.6) 27.1

50–59 4,240 19.7 (19.1–20.3) 5,474 26.8 (26.0–27.5) 9,714 23.1 (22.7–23.6) 24.0

60–69 1,258 8.2 (7.8–8.7) 1,447 10.4 (9.9–10.9) 2,705 9.2 (8.9–9.6) 6.7

70–79 373 4.1 (3.7–4.5) 314 4.2 (3.8–4.7) 687 4.1 (3.8–4.4) 1.7

≥80 314 4.4 (3.9–4.9) 218 5.3 (4.6–6.0) 532 4.7 (4.3–5.1) 1.3

Geographic region§

Northeast 2,245 7.9 (7.6–8.2) 4,154 15.5 (15.0–15.9) 6,399 11.6 (11.3–11.9) 15.8

Midwest 3,480 10.2 (9.9–10.6) 5,298 16.1 (15.71–6.5) 8,778 13.1 (12.8–13.4) 21.7

South 6,243 10.7 (10.4–11.0) 9,202 16.4 (16.1–16.7) 15,445 13.5 (13.3–13.7) 38.2

West 4,049 11.2 (10.9–11.6) 5,722 16 (15.5–16.4) 9,771 13.6 (13.3–13.9) 24.2

Race/Ethnicity

White, non-Hispanic 13,456 13.2 (13.0–13.4) 19,689 20.0 (19.7–20.3) 33,145 16.6 (16.4–16.7) 82.1

Black, non-Hispanic 1,332 6.5 (6.1–6.8) 2,170 11.5 (11.0–12.0) 3,502 8.9 (8.6–9.2) 8.7

American Indian/

Alaska Native

200 15.3 (13.2–17.4) 239 19.0 (16.5–21.4) 439 17.1 (15.5–18.7) 1.1

Asian/Pacific Islander 129 1.5 (1.3–1.8) 205 2.7 (2.3–3.0) 334 2.1 (1.8–2.3) 0.8

Hispanic¶ 844 3.4 (3.2–3.6) 1,944 7.6 (7.3–7.9) 2,788 5.5 (5.3–5.7) 6.9

Unknown** 56 — — 129 — — 185 — — 0.5

Abbreviation: 95% CI = 95% confidence interval.

* Unadjusted (crude) death rates per 100,000 population. 

† CIs based on ≥100 deaths were calculated using a normal approximation; CIs based on <100 deaths were calculated using a gamma method. (Additional information 

available from Vital Statistics of The United States: Mortality, 1999 Technical Appendix. Available at http://wonder.cdc.gov/wonder/sci_data/mort/mcmort/type_txt/

mcmort05/techap99.pdf).

§ Northeast: Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont. Midwest: Illinois, Indiana, Iowa, 

Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin. South: Alabama, Arkansas, Delaware, District of Columbia, 

Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West Virginia. West: Alaska, 

Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming.

¶ Persons of Hispanic ethnicity might be of any race or combination of races.

** Rates for persons with unknown race/ethnicity were not included because population data were unavailable.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 163

Limitations

The findings in this report are subject to at least two 

limitations. First, overdose deaths are likely underestimated 

because lengthy investigations are often required. This 

sometimes results in a “pending manner and cause of death” 

category being selected at the close of the mortality file. Second, 

injury mortality data might underestimate the actual number 

of deaths for American Indians/Alaska Natives and certain 

other racial/ethnic populations (e.g., Hispanics) because of 

the misclassification of race/ethnicity of decedents on death 

certificates (9).

Conclusion

Preventing drug-induced deaths will require change at many 

levels (10). Improving prescription drug monitoring programs, 

which are electronic databases that track prescriptions for 

opioid pain relievers and other controlled prescription drugs 

in a state, can assist with identification of improper prescribing 

and use of these drugs. Health insurers and pharmacy benefit 

managers can develop prescription claims review programs 

to identify and address improper prescribing and use of pain 

relievers. Health-care providers can follow guidelines for 

responsible prescribing, including screening and monitoring 

for substance abuse and mental health problems. Patients 

also should be encouraged to use prescription pain relievers 

only as directed by a health-care provider, and store and 

dispose of them properly (http://www.cdc.gov/injury/pdfs/

NCIPC_Overview_FactSheet_PPO-a.pdf).

References

1. Miniño AM, Murphy SL, Xu J, Kochanek KD. Deaths: final data for 

2008. Natl Vital Stat Rep 2011;59:1–126. 

2. Jones CM, Mack KA, Paulozzi LJ. Pharmaceutical overdose deaths, 

United States, 2010. JAMA 2013;309:657–9. 

3. Paulozzi LJ, Annest JL. US data show sharply rising drug-induced death 

rates. Inj Prev 2007;13:130–2. 

4. CDC. CDC health disparities and inequalities report—United States, 

2011. MMWR 2011;60 (Suppl; January 14, 2011). 

5. CDC. Introduction. In: CDC health disparities and inequalities report—

United States, 2013. MMWR 2013;62(Suppl No. 3). 

6. CDC. Drug-induced deaths—United States, 2003–2007. MMWR 2011;

60 (Suppl; January 14, 2011). 

7. Paulozzi LJ, Budnitz DS, Xi Y. Increasing deaths from opioid analgesics 

in the United States. Pharmacoepidemiol Drug Saf 2006;15:618–27. 

8. CDC. QuickStats: Percentage of persons reporting use of at least one 

prescription drug during the preceding month, by sex and race/

ethnicity—United States, 1999–2002. MMWR 2006;55:15. 

9. Arias E, Schauman WS, Eschbach K, Sorlie PD, Backlund E. The validity 

of race and Hispanic origin reporting on death certificates in the United 

States. Vital Health Stat 2 2008:1–23.

10. CDC. Vital signs: overdoses of prescription opioid pain relievers—United 

States, 1999–2008. MMWR 2011;60:1487–92. 

FIGURE. Drug-induced death rates,* by race/ethnicity and age group — National Vital Statistics System, United States, 1999–2010

* Crude death rates per 100,000 population.

0

5

10

15

20

30

0–9 10–19 20–29 30–39 40–49 50–59 60–69 70–79 ≥80

Rate* 

Age group (yrs)

Non-Hispanic white

Non-Hispanic black

American Indian/

 Alaska Native

Asian/Pacic Islander

Hispanic

25
Supplement

164 MMWR / November 22, 2013 / Vol. 62 / No. 3

Introduction

According to 1981–2009 data, homicide accounts for 

16,000–26,000 deaths annually in the United States and 

ranks within the top four leading causes of death among U.S. 

residents aged 1–40 years (1). Homicide can have profound 

long-term emotional consequences on families and friends of 

victims and on witnesses to the violence (2,3), as well as cause 

excessive economic costs to residents of affected communities 

(1,4). For years, homicide rates have been substantially higher 

among certain populations. Previous reports have found that 

homicides are higher among males (5–7), adolescents and 

young adults (6), and certain racial/ethnic groups, such as 

non-Hispanic blacks, non-Hispanic American Indian/Alaska 

Natives (AI/ANs), and Hispanics (6–9). The 2011 CDC 

Health Disparities and Inequalities Report (CHDIR) described 

similar findings for the year 2007 (10). For example, the 2011 

report showed that the 2007 homicide rate was highest among 

non-Hispanic blacks (23.1 deaths per 100,000), followed by 

AI/ANs (7.8 deaths per 100,000), Hispanics (7.6 deaths per 

100,000), non-Hispanic whites (2.7 deaths per 100,000), and 

Asian/Pacific Islanders (A/PIs) (2.4 deaths per 100,000) (10). 

In addition, non-Hispanic black men aged 20–24 years were 

at greatest risk for homicide in 2007, with a rate that exceeded 

100 deaths per 100,000 population (10). Other studies have 

reported that community factors such as poverty and economic 

inequality and individual factors such as unemployment and 

involvement in criminal activities can play a substantial role 

in these persistent disparities in homicide rates (11). Public 

health strategies are needed in communities at high risk for 

homicide to prevent violence and save lives.

The homicide analysis and discussion that follow are part of 

the second CHDIR and update information presented in the 

first CHDIR (10). The 2011 CHDIR (12) was the first CDC 

report to take a broad view of disparities across a wide range 

of diseases, behavioral risk factors, environmental exposures, 

social determinants, and health-care access. The topic presented 

in this report is based on criteria that are described in the 2013 

CHDIR Introduction (13). The purposes of this homicide 

report are to discuss and raise awareness of differences in 

homicide rates by some of these characteristics and to prompt 

actions to reduce these disparities.

Methods

To assess disparities in homicide rates in the United States, 

CDC analyzed data from the CDC National Vital Statistics 

System (NVSS), the same data source used for the 2011 

CHDIR on homicides (10). In this report, NVSS data provided 

as of February 2012 were used. NVSS collects death certificate 

data filed in the 50 states and the District of Columbia (DC) 

(14). Death certificates provide information on the decedent’s 

age, sex, race, ethnicity, and geographic region. They do not 

provide information on decedent income, sexual orientation, 

disability, or language spoken at home.

This report summarizes the homicide data by providing the 

number, proportion, and rates of homicides by age, sex, and 

race/ethnicity for the year 2009; providing the homicide rates 

by U.S. state for the year 2009; and comparing 2009 with 

2007 data. Data in this report are based on homicides caused 

by any mechanism. More details on homicide rates by age, 

sex, and race/ethnicity for each state and census region can be 

accessed through the CDC Web-based Injury Statistics Query 

and Reporting System — Fatal (WISQARS Fatal) Injury Data 

module (available at http://www.cdc.gov/injury/wisqars/index.

html). Data on individual and socioeconomic risk factors for 

homicide were unavailable for analysis. In addition, sufficient 

data were not available to assess disparities by certain racial/

ethnic subgroups, household income, disability status, and 

sexual orientation. NVSS codes racial categories as white, black, 

AI/AN, and A/PI, and ethnicity is coded separately as Hispanic 

or non-Hispanic (14). In this report, references to whites, 

blacks, AI/ANs, and A/PIs refer to non-Hispanic persons. 

Hispanics might be of any race or combination of races. Crude 

homicide rates per 100,000 population were calculated by age, 

sex, and race/ethnicity, as well as by the combination of these 

three variables. Crude rates per 100,000 population by state 

in 2009 also are provided. Confidence intervals (CIs) of rates 

were calculated in two ways: 1) groupings of annual death 

counts of <100 were calculated by using a gamma estimation 

method (14), and 2) groupings of annual death counts of ≥100 

were calculated by using a normal approximation approach. 

Rates calculated from <20 deaths were considered unreliable 

and are not reported.

Homicides — United States, 2007 and 2009

Joseph E. Logan, PhD

Jeffrey Hall, PhD

Dawn McDaniel, PhD

Mark R. Stevens, MSPH, MA

National Center for Injury Prevention and Control, CDC

Corresponding author: Joseph E. Logan, National Center for Injury Prevention and Control, CDC. Telephone: 770-488-1529; E-mail: ffa3@cdc.gov.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 165

Disparities were measured as deviations from a referent 

category rate or prevalence. The group with the largest 

population of the U.S. census data in each demographic 

category was used as the referent (e.g., females, non-Hispanic 

whites, or persons aged 30–49 years). Absolute difference 

was measured as the simple difference between a population 

subgroup estimate and the estimate for its respective reference 

group. The relative difference as a percentage was calculated 

by dividing the absolute difference by the value in the referent 

category and multiplying by 100. Relative differences in rates 

between each race/ethnic category were also stratified by 

sex and age. Rate comparisons were considered significantly 

different if they had nonoverlapping 95% CIs, which provide 

a conservative test for statistical significance.

Results

An estimated 18,361 homicides occurred in 2007 and 

16,799 occurred in 2009 (Table 1). The relative rate difference 

reported for males was at least 250% higher than that of 

females in both data years. In addition, in each data year, the 

relative rate difference for non-Hispanic blacks was at least 

650% higher than the rate reported for non-Hispanic whites. 

Non-Hispanic AI/ANs and Hispanics also had rates that far 

exceeded those of non-Hispanic whites in both years. Rates 

were highest among persons aged 15–29 years both in 2007 

and 2009 and then decreased with each subsequent age group; 

however, the lowest rates reported in both years were among 

children aged 0–14 years.

The homicide rate for the U.S. population in 2009 was 

significantly lower than the U.S. homicide rate reported 

in 2007. Differences in rates also occurred among certain 

populations. Specifically, homicide rates were lower in 2009 

than those reported in 2007 for males, non-Hispanic whites, 

non-Hispanic blacks, Hispanics, persons aged 15–29 years, and 

persons aged 30–49 years. None of the demographic groups 

had significantly higher rates in 2009 compared with 2007.

Among males, the risk for homicide was greatest among 

non-Hispanic blacks aged 15–29 years in both 2007 and 2009 

(Table 2). Furthermore, for both years, the male homicide 

rate was significantly higher among non-Hispanic blacks 

than among those in other racial/ethnic groups in each age 

category assessed, except among men aged 50–64 years, for 

whom the 95% CIs overlapped with the rate for AI/ANs in 

2009. Hispanic males had higher rates than non-Hispanic 

white males in every age group among males aged ≥15 years 

in both years as well. 

TABLE 1. Number, percentage, and crude rate* of homicides, by sex, race/ethnicity, and age group — National Vital Statistics System, United 

States, 2007 and 2009

Characteristic

2007 2009

No. of 

deaths (%) Rate (95% CI)†

Absolute 

difference 

(percentage 

points)

Relative 

difference§

(%)

No. of 

deaths (%) Rate (95% CI)

Absolute 

difference 

(percentage 

points)

Relative 

difference 

(%)

Sex

Male 14,538 (79.2) 9.8 (9.6–10.0) 7.3 291.4 13,126 (78.1) 8.7 (8.5–8.8) 6.3 267.1

Female 3,823 (20.8) 2.5 (2.4–2.6) Ref. Ref. 3,673 (21.9) 2.4 (2.3–2.4) Ref. Ref.

Race/Ethnicity

White, non-Hispanic 5,512 (30.0) 2.7 (2.7–2.8) Ref. Ref. 5,163 (30.7) 2.6 (2.5–2.6) Ref. Ref.

Black, non-Hispanic 8,746 (47.6) 23.1 (22.6–23.6) 20.3 742.5 7,733 (46.0) 19.9 (19.5–20.3) 17.3 679.1

American Indian/Alaska Native 199 (1.1) 7.8 (6.7–8.9) 5.1 185.9 235 (1.4) 9.0 (7.9–10.2) 6.5 252.6

Asian/Pacific Islander 341 (1.9) 2.4 (2.2–2.7) -0.3 -11.9 325 (1.9) 2.2 (1.9–2.4) –0.4 -14.9

Hispanic¶ 3,466 (18.9) 7.6 (7.4–7.9) 4.9 178.3 3,179 (18.9) 6.6 (6.3–6.8) 4.0 157.1

Age group (yrs)

0–14 1,096 (6.0) 1.8 (1.7–1.9) -5.6 -75.7 998 (5.9) 1.6 (1.5–1.7) –5.2 -76.5

15–29 8,268 (45.0) 13.0 (12.8–13.3) 5.6 76.1 7,241 (43.1) 11.2 (10.9–11.4) 4.3 63.1

30–49 6,327 (34.5) 7.4 (7.2–7.6) Ref. Ref. 5,776 (34.4) 6.9 (6.7–7.0) Ref. Ref.

50–64 1,886 (10.3) 3.5 (3.4–3.7) -3.9 -52.6 1,906 (11.3) 3.4 (3.2–3.5) –3.5 -50.8

≥65 759 (4.1) 2.0 (1.9–2.1) -5.4 -73.0 860 (5.1) 2.2 (2.0–2.3) –4.7 -68.3

Total** 18,361 (100.0) 6.1 (6.0–6.2) — — 16,799 (100.0) 5.5 (5.4–5.6) — —

Abbreviations: 95% CI = 95% confidence interval; Ref. = referent.

* Per 100,000 population.

† CIs based on <100 deaths were calculated using a gamma method, and those based ≥100 deaths were calculated using a normal approximation (Source: Xu J, 

Kockanek KD, Murphy SL, Tejada-Vera B. Deaths: final data for 2007. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health 

Statistics. Natl Vital Stat Rep 2010;58).

§ Relative differences were calculated based on rates that were estimated to five decimal places. Therefore, relative differences calculated based on the rates provided 

in the table might differ from those displayed because of rounding.

¶ Persons of Hispanic ethnicity might be of any race or combination of races.

** Total counts for 2007 include 97 deaths of persons of unknown race/ethnicity and 25 deaths of persons unknown age. Total counts for 2009 include 164 deaths of 

persons of unknown race/ethnicity and 18 deaths of persons of unknown age.
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166 MMWR / November 22, 2013 / Vol. 62 / No. 3

Among females, the homicide rates also were generally 

higher among racial/ethnic minorities (Table 2). For example, 

in both years, female homicide rates were markedly higher 

among non-Hispanic blacks than among non-Hispanic whites 

in every age group <65 years. Female homicide rates also were 

higher among Hispanic than among non-Hispanic whites in 

every age group <30 years for both years. In 2009, the female 

homicide rate was higher among AI/ANs than among nonHispanic whites aged 15–29 years as well.

Compared with 2007, homicide rates were significantly 

lower in 2009 in certain demographic populations (Table 2). 

Among non-Hispanic blacks, rates were significantly lower 

among males aged 15–29, 30–49, and 50–64 years and women 

aged 30–49 years. The homicide rates for each age category 

among Hispanic males aged 15–49 years also were lower in 

2009 than in 2007. 

State-specific homicide rates for 2009 ranged from 1.1 to 

12.8 deaths per 100,000 population, and rates were generally 

higher in the southern states (Figure 1). Most states did not 

have any significant changes in homicide rates from 2007 to 

2009; however, 10 states experienced significant decreases: 

Arizona, California, Florida, Georgia, Idaho, Maryland, New 

Jersey, North Carolina, Ohio, and Pennsylvania. Decreases 

in rates ranged from 12.4% in California to 55.8% in Idaho. 

The 2009 crude homicide rate for DC was an estimated 22.8 

per 100,000 population.

Discussion

Homicide rates are still particularly high among nonHispanic black, Hispanic, and non-Hispanic AI/AN 

TABLE 2. Crude homicide rates,* by sex, age group, and race/ethnicity — National Vital Statistics System, United States, 2007 and 2009

Characteristic

2007 2009

Rate (95% CI)†

Relative difference§

(%) Rate (95% CI)

Relative difference 

(%)

Male

0–14 yrs

White, non-Hispanic 1.3 (1.1–1.4) Ref. 1.2 (1.0–1.4) Ref.

Black, non-Hispanic 5.4 (4.7–6.0) 319.6 4.6 (4.0–5.2) 280.5

Hispanic¶ 1.8 (1.5–2.1) 39.8 1.5 (1.2–1.8) 21.9

American Indian/Alaska Native —** — — — — —

Asian/Pacific Islander — — — — — —

15–29 yrs

White, non-Hispanic 5.4 (5.0–5.7) Ref. 4.3 (4.0–4.6) Ref.

Black, non-Hispanic 90.1 (87.4–92.9) 1,584.5 75.3 (72.9–77.8) 1,640.6

Hispanic 27.4 (26.1–28.7) 411.2 22.7 (21.5–23.9) 424.4

American Indian/Alaska Native 20.1 (15.5–25.5) 275.0 24.7 (19.7–30.7) 471.8

Asian/Pacific Islander 6.1 (4.9–7.5) 14.4 4.5 (3.5–5.7) 4.3

30–49 yrs

White, non-Hispanic 5.1 (4.8–5.4) Ref. 4.7 (4.4–4.9) Ref.

Black, non-Hispanic 47.2 (45.3–49.1) 826.0 43.1 (41.3–44.9) 821.7

Hispanic 13.0 (12.1–13.8) 154.0 11.3 (10.5–12.0) 141.0

American Indian/Alaska Native 17.1 (13.0–22.1) 235.8 18.6 (14.3–23.7) 296.6

Asian/Pacific Islander 3.7 (2.9–4.5) -28.4 3.2 (2.5–4.0) -32.3

50–64 yrs

White, non-Hispanic 3.4 (3.2–3.7) Ref. 3.3 (3.1–3.6) Ref.

Black, non-Hispanic 20.7 (18.9–22.4) 504.0 16.5 (15.0–18.0) 392.9

Hispanic 6.7 (5.6–7.7) 95.6 6.2 (5.3–7.2) 85.9

American Indian/Alaska Native — — — 9.9 (6.1–15.3) 196.5

Asian/Pacific Islander 3.4 (2.4–4.7) -0.5 3.5 (2.5–4.8) 5.1

≥65 yrs

White, non-Hispanic 2.0 (1.8–2.3) Ref. 2.3 (2.1–2.6) Ref.

Black, non-Hispanic 11.0 (9.2–12.9) 447.6 9.1 (7.4–10.7) 293.8

Hispanic 3.7 (2.7–5.1) 84.5 4.7 (3.6–6.1) 104.7

American Indian/Alaska Native — — — — — —

Asian/Pacific Islander — — — — — —

Female

0–14 yrs

White, non-Hispanic 1.1 (0.9–1.2) Ref. 1.0 (0.8–1.1) Ref.

Black, non-Hispanic 3.5 (2.9–4.0) 216.9 3.2 (2.7–3.8) 232.3

Hispanic 1.7 (1.4–2.0) 56.1 1.4 (1.1–1.7) 44.8

American Indian/Alaska Native — — — — — —

Asian/Pacific Islander — — — — — —

See table footnotes on the next page.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 167

populations and remain highest among young, non-Hispanic 

black males. The findings in this report estimate that 75 out of 

100,000 non-Hispanic black males aged 15–29 years die from 

homicide in a given year. Moreover, 2009 data from the 16 U.S. 

states that report data on homicides to the National Violent 

Death Reporting System suggest that nearly half of homicides 

in this population were outcomes of escalated arguments and 

conflicts; one third were precipitated by another crime such 

as burglary, robbery, or assault; one fifth involved illicit drug 

activity; and approximately 16% were gang related (1,6).

Homicide remains less common among women; however, 

in 2009, homicide was the sixth leading cause of death 

among females aged 15–49 years (1). Similar to the findings 

from the 2011 CHDIR, data from this report indicate 

that non-Hispanic black and non-Hispanic AI/AN females 

experience death by homicide more frequently than women 

in other racial/ethnic populations (10). Female homicides 

are characteristically different from male homicides in that 

females are more likely to be killed by a family member during 

childhood or adolescence (15) and by an intimate partner 

during adulthood (16). Increased equality between men and 

women in regards to education, wages, and occupational 

status might increase women’s access to services that prevent 

intimate partner homicide, such as protective orders, shelters, 

and advocacy services (17).

Although the findings in this report do not indicate whether 

a long-term decrease in homicide rates is occurring, rates 

were noticeably lower in 2009 than in 2007. The decrease 

in homicide rates between these 2 years was considerable, 

particularly among males aged 15–29 years, which is consistent 

with the long-term decreasing trend in homicide rate that 

has been observed among this demographic population since 

the early 1990s (1) (Figure 2). Possible explanations for this 

decreasing homicide rate among young males are reductions 

in drug trade and sales, increases in police response to youths 

who carry firearms, and increases in incarceration (17). Despite 

TABLE 2. (Continued) Crude homicide rates,* by sex, age group, and race/ethnicity — National Vital Statistics System, United States, 2007 and 2009

Characteristic

2007 2009

Rate (95% CI)†

Relative difference 

(%) Rate (95% CI)

Relative difference 

(%)

Female

15–29 yrs

White, non-Hispanic 2.3 (2.1–2.5) Ref. 2.0 (1.8–2.2) Ref.

Black, non-Hispanic 9.8 (8.9–10.7) 326.2 8.7 (7.8–9.5) 332.6

Hispanic 3.6 (3.1–4.2) 58.7 3.4 (2.9–3.9) 68.3

American Indian/Alaska Native — — — 6.7 (4.2–10.2) 235.3

Asian/Pacific Islander 1.8 (1.2–2.7) -20.0 1.5 (0.9–2.2) -26.5

30–49 yrs

White, non-Hispanic 2.5 (2.3–2.7) Ref. 2.4 (2.2–2.6) Ref.

Black, non-Hispanic 8.8 (8.0–9.5) 252.4 7.2 (6.5–7.9) 199.5

Hispanic 2.9 (2.5–3.3) 15.8 2.9 (2.5–3.3) 19.8

American Indian/Alaska Native 6.8 (4.4–10.2) 174.6 — — —

Asian/Pacific Islander 1.8 (1.3–2.4) -28.3 1.8 (1.3–2.4) -26.6

50–64 yrs

White, non-Hispanic 1.4 (1.3–1.6) Ref. 1.5 (1.3–1.7) Ref.

Black, non-Hispanic 3.5 (2.9–4.2) 149.2 3.4 (2.7–4.0) 125.1

Hispanic 1.6 (1.1–2.2) 12.8 1.8 (1.3–2.3) 18.3

American Indian/Alaska Native — — — — — —

Asian/Pacific Islander 1.6 (1.0–2.5) 15.1 1.9 (1.2–2.8) 27.9

≥65 yrs

White, non-Hispanic 1.3 (1.1–1.4) Ref. 1.5 (1.3–1.7) Ref.

Black, non-Hispanic 2.7 (2.0–3.5) 112.6 2.2 (1.6–3.0) 48.5

Hispanic 1.0 (0.5–1.6) -23.7 1.3 (0.8–2.0) -16.0

American Indian/Alaska Native — — — — — —

Asian/Pacific Islander — — — — — —

Abbreviations: 95% CI = 95% confidence interval; Ref. = referent.

* Per 100,000 population.

† CIs based on <100 deaths were calculated using a gamma method, and those based ≥100 deaths were calculated using a normal approximation (Source: Xu J, 

Kockanek KD, Murphy SL, Tejada-Vera B. Deaths: final data for 2007. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health 

Statistics. Natl Vital Stat Rep 2010;58).

§ Relative differences were calculated based on rates that were estimated to five decimal places. Therefore, relative differences calculated based on the rates provided 

in the table might differ from those displayed because of rounding.

¶ Persons of Hispanic ethnicity might be of any race or combination of races.

** Rates unreliable (calculated from <20 deaths).
Supplement

168 MMWR / November 22, 2013 / Vol. 62 / No. 3

the decreases, the disparity in homicide rates between nonHispanic black males and non-Hispanic white males is still 

pronounced. Although the rate among non-Hispanic black 

males aged 15–29 years in 2009 is half the rate reported 

in 1993 (75 vs. 158 per 100,000, respectively) (1), similar 

decreases have been reported for males of similar age and of 

other races/ethnicities.

Socioeconomic factors play a substantial role in homicide 

disparities by race/ethnicity, sex, age, and geographic area. 

For example, racial/ethnic minorities are more likely to live 

in disadvantaged neighborhoods (11). Residential areas with 

high levels of poverty, unemployment, and jobs with low wages 

can increase risk of income-generating crimes such as burglary 

and robbery, stress and conflict, and substance abuse among 

residents (18,19), all factors that increase risk for homicide and 

violence (11,20). One longitudinal study reported that after 

controlling for similar socioeconomic factors, such as living 

in a disadvantaged community, being on welfare, and having 

a young or single parent, race was not predictive of being a 

homicide offender (21). Similar risk factors might explain 

the differences in homicide rates by age and geographic area. 

Future studies controlling for socioeconomic factors might 

offer additional support for this conclusion.

Prevention strategies that can change the characteristics of 

communities, relationships, and persons that are associated 

with violence perpetration might reduce violence rates not only 

for all persons in the United States but also among groups with 

the highest rates of violence (22). Certain promising strategies 

have been developed that use multicomponent approaches, 

involve coordinated efforts by numerous relevant stakeholders, 

and include an appropriate mix of both universal interventions 

and interventions that address the needs of groups at highest 

risk for violence (22). Communities That Care (23,24), 

Promoting School-community-university Partnerships to 

Enhance Resilience (PROSPER) (25,26), Striving to Reduce 

Youth Violence Everywhere (STRYVE) (available at http://

www.safeyouth.gov), and Urban Networks to Increase Thriving 

Youth (UNITY) (available at http://preventioninstitute.org/

unity.html) are examples of coalition-based operating systems 

and violence prevention initiatives that can assist communities 

in developing the type of tailored, broad strategies described. 

Promising multicomponent programs such as CeaseFire (27) 

and Safe Streets (28), which work to change community norms 

regarding violence, cultivate skills for using alternatives to 

violence, and interrupt escalating tensions, also are promising 

strategies for preventing general violence, shootings, and 

shooting-related homicides.

The high homicide rates among youths in late adolescence 

and young adulthood suggest that the school years are an 

important developmental point for intervention. Creating 

a positive school environment is an example of one way to 

improve youths’ access to a safe, stable, nurturing setting; 

promote norms of nonviolence; facilitate the formation of 

supportive and positive social relationships; and maximize the 

development of social and problem-solving skills (29). The 

influences, experiences, and socialization provided by such 

school environments could help youths become more adept at 

navigating problematic interactions and adapting to challenges 

and difficulties that could lead to serious violence (29). Such 

school environments might particularly be important for 

youths who lack other positive influences (29).

Limitations

These findings in this report are subject to at least three 

limitations. First, small numbers of homicides precluded stable 

rate estimations among some populations. Second, data on 

individual and environmental risk factors for homicide were 

unavailable, which precluded closer examination of possible 

sources of disparities by age, sex, race/ethnicity, and geography. 

Third, racial misclassification might result in overestimated 

homicide rates for non-Hispanic blacks and non-Hispanic 

whites and underestimated rates for AI/ANs, A/PIs, and 

Hispanics (8).

FIGURE 1. Crude homicide rates,* by state — National Vital Statistics 

System, United States, 2009†

* Number of deaths per 100,000 population.

† Ten states experienced a significant decrease from 2007 to 2009: Arizona 

(29.3%), California (12.4%), Florida (15.5%), Georgia (19.5%), Idaho (55.6%), 

Maryland (21.1%), New Jersey (19.3%), North Carolina (19.1%), Ohio (15.5%), 

and Pennsylvania (13.9%).

7.51–22.51

5.51–7.50 

3.01–5.50

0–3.00

Suppressed (≤1.0 death)
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 169

Conclusion

Effective evidence-based strategies to reduce violence are 

available (30); however, additional work is needed to build 

organizational and community capacity to make best use 

of these programs, policies, and strategies. Many healthrelated disparities can be reduced by altering influential 

socially embedded conditions such as 1) neighborhood 

living conditions, 2) opportunities for learning and capacity 

for development, and 3) employment opportunities and 

community development (31,32). Because these outcomes 

mediate the effects of social determinants of health, they 

might be viable mechanisms for changing or eliminating 

social influences that create or increase disparities in homicide 

rates. Promising strategies such as implementing business or 

community improvement districts might help decrease levels 

of violent crimes by increasing employment opportunities for 

local residents and creating physical or cultural environments 

that are more aesthetically and economically attractive 

(33,34). These community-level strategies might reduce or 

offset the effects of poverty, improve the social environments 

of communities, and implement safety measures (33,34). To 

eliminate homicide disparities, more research is needed to 

understand the scope of the problem and the risk and protective 

factors implicated in these violent events, evaluate programs 

that prevent and reduce violence, and better understand how 

to adapt, disseminate, and implement these strategies in the 

communities and populations in greatest need.

Non-Hispanic, white

Non-Hispanic, black

American Indian/Alaska Native

Asian/Pacic Islander

Hispanic§

0

20

40

60

80

100

120

140

160

180

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Crude homicide rate

Year

FIGURE 2. Crude homicide rates* among males aged 15–29 years, by racial/ethnic group† and year — National Vital Statistics System, United 

States, 1990–2009

Abbreviations: ICD-10 = International Classification of Diseases, 10th Revision; ICD-9 = International Classification of Diseases, 9th Revision.

* ICD-10 to ICD-9 comparability ratio for homicides = 0.998.

† Number of deaths per 100,000 population.

§ Persons of Hispanic ethnicity might be of any race or combination of races.
Supplement

170 MMWR / November 22, 2013 / Vol. 62 / No. 3

Acknowledgment

The findings in this report are based, in part, on contributions by 

Nimeshkumar Patel.

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MMWR / November 22, 2013 / Vol. 62 / No. 3 171

Introduction

Infant mortality rates are associated with maternal health, 

quality of and access to medical care, socioeconomic 

conditions, and public health practices, which makes infant 

mortality an increasingly important public health concern 

(1,2). After large declines throughout the twentieth century, the 

U.S. infant mortality rate did not decline significantly during 

2000–2005 (3). Analysis of 2000–2004 infant mortality in the 

United States indicated considerable disparities by race and 

Hispanic origin (4). Race and ethnic disparities in U.S. infant 

mortality have been apparent since vital statistics data began to 

be collected more than 100 years ago. These disparities have 

persisted over time, and research indicates that not all groups 

have benefited equally from social and medical advances (5–7).

The infant mortality analysis and discussion that follows is 

part of the second CDC Health Disparities and Inequalities 

Report (CHDIR) (4). The 2011 CHDIR (8) was the first 

CDC report to assess disparities across a wide range of diseases, 

behavioral risk factors, environmental exposures, social 

determinants, and health-care access. The criteria for inclusion 

of topics that are presented in the 2013 CHDIR are based on 

criteria that are described in the 2013 CHDIR Introduction 

(9). This report provides more current information on infant 

mortality rates on the basis of race/ethnicity, mother’s place 

of birth, and by state and region. The purposes of this infant 

mortality analysis are to raise awareness of differences in infant 

mortality by selected maternal and infant characteristics, and 

to prompt actions to reduce these disparities.

Methods

To estimate disparities in infant mortality rate by selected 

characteristics and specified group, CDC analyzed data from 

the United States linked birth/infant death data sets (linked 

files) for 2005 through 2008 (the latest year for which accurate 

race/ethnicity data are available) (5). In these data sets, 

information from the birth certificate is linked to information 

from the death certificate for each infant (aged <1 year) who 

dies in the United States. Characteristics analyzed included sex, 

maternal race/ethnicity, maternal place of birth, and the state 

of residence of the mother at the time of birth. Household 

income and educational attainment were not analyzed because 

they were either not collected or not collected consistently on 

birth certificates. Maternal race was defined as white, black, 

Asian/Pacific Islander, and American Indian/Alaska native. 

Ethnicity is defined as Hispanic or non-Hispanic. Hispanic 

data were further subdivided into Mexican, Puerto Rican, 

Cuban, and Central and South American. Place of birth was 

defined as born in the 50 states and DC, or born outside of 

the 50 states and DC.

Infant mortality rates were calculated as the number of 

infant deaths per 1,000 live births in the specified group (i.e., 

by maternal race/Hispanic origin, maternal birthplace, state 

of residence, and infant gender). Ratios of non-Hispanic black 

to non-Hispanic white infant mortality rates were computed 

to assess the magnitude of the disparity in non-Hispanic 

black and non-Hispanic white infant mortality rates by state. 

Data from 2006–2008 were aggregated to obtain statistically 

reliable state-specific rates by race and Hispanic origin; rates 

are not shown for cells with <20 infant deaths. Rates based 

on <20 infant deaths are not shown separately as they do not 

meet standards of reliability or precision. Differences between 

infant mortality rates were assessed for statistical significance 

by using the z test (p<0.05).

Disparities were measured as the deviations from a “referent” 

category rate. Absolute difference was measured as the simple 

difference between a population subgroup mortality rate and 

the rate for its respective reference group. The relative difference, 

a percentage, was calculated by dividing the difference by the 

value in the referent category and multiplying by 100.

Results

The U.S. infant mortality rate declined 10% from 2005 to 

2010, from 6.86 infant deaths per 1,000 live births in 2005 

to a preliminary estimate of 6.14 in 2010 (5,13). In 2008, 

the overall U.S. infant mortality rate was 6.61 infant deaths 

per 1,000 live births, with differences by race and Hispanic 

origin (Table 1). The highest infant mortality rate was for nonHispanic black women (12.67), with a rate 2.3 times that for 

non-Hispanic white women (5.52) (Table 1). Compared with 

Infant Deaths — United States, 2005–2008

Marian F. MacDorman, PhD, T. J. Mathews, MS

National Center for Health Statistics, CDC

Corresponding author: Marian F. MacDorman, PhD, Division of Vital Statistics, National Center for Health Statistics, CDC. Telephone: 301-458-4356; 

E-mail: mfm1@cdc.gov.
Supplement

172 MMWR / November 22, 2013 / Vol. 62 / No. 3

non-Hispanic white women, infant mortality rates were 53% 

higher for American Indian/Alaska Native* women (8.42) and 

32% higher for Puerto Rican women (7.29). Infant mortality 

rates for Asian/Pacific Islanders* (4.51) and Central or South 

American women (4.76) were lower than those for nonHispanic white women. From 2005 to 2008, infant mortality 

rates declined approximately 4% for the total population and 

for non-Hispanic white women, approximately 7% for nonHispanic black women, and 12% for Puerto Rican women; 

changes for other racial/ethnic groups were not statistically 

significant. When examined by place of birth of the mother, 

the 2008 infant mortality rate was 38% higher for women 

born in the 50 states and DC than for women born elsewhere 

(Table 1). The infant mortality rate was 21% higher for male 

than for female infants.

Differences also exist in infant mortality rates between 

various states, with a twofold or greater difference in rates 

between the states with the highest and lowest rates for the total 

population and for each race/ethnic group studied. Across the 

United States, infant mortality rates are generally higher in the 

South and Midwest and lower in other parts of the country. 

During 2006–2008, total infant mortality rates ranged from 

a high of 11.97 per 1,000 live births for DC and Mississippi 

10.16 to a low of 4.94 for Massachusetts and Utah. However, 

because DC has high concentrations of high-risk women, its 

rate is more appropriately compared with rates for other large 

U.S. cities. For non-Hispanic white women, Alabama had the 

highest rate (7.67) and New Jersey the lowest rate (3.78). For 

non-Hispanic black women, the rate was highest in Hawaii 

(18.54) and lowest in Washington (7.66). For Hispanic 

women, the rate was highest in Pennsylvania (7.94) and lowest 

in Louisiana (3.92).

Ratios of non-Hispanic black to non-Hispanic white infant 

mortality rates were computed to assess the magnitude of the 

disparity in non-Hispanic black and non-Hispanic white infant 

mortality rates by state (Figure). Although the average rate ratio 

in the United States was 2.35, seven areas (Connecticut, DC, 

Hawaii, Massachusetts, New Jersey, New York, and Wisconsin) 

had rate ratios of 2.60 or greater. In contrast, seven other 

states (Arkansas, Alabama, Kentucky, Mississippi, Oklahoma, 

Oregon, and Washington) had ratios <2.10. Rate ratios are not 

shown for states with <20 non-Hispanic black infant deaths.

*Includes Hispanic and non-Hispanic women.

TABLE 1. Infant mortality rates* by selected characteristics — United States, 2005 and 2008

Characteristic

2005 2008

Infant 

mortality rate

Absolute 

difference 

(rate)

Relative 

difference (%)

Infant 

mortality rate

Absolute 

difference 

(rate)

Relative 

difference (%)

Total 6.86 6.61

Sex

Male 7.56 1.4 23.5 7.22 1.3 20.9

Female 6.12 Ref. Ref. 5.97 Ref. Ref.

Race/Ethnicity†

White, non-Hispanic 5.76 Ref. Ref. 5.52 Ref. Ref.

Black, non-Hispanic 13.63 7.9 136.6 12.67 7.2 129.5

Asian/Pacific Islander§ 4.89 -0.9 -15.1 4.51 -1.0 -18.3

American Indian/Alaska Native 8.06 2.3 39.9 8.42 2.9 52.5

Hispanic¶ 5.62 -0.1 -2.4 5.59 0.1 1.3

Mexican 5.53 -0.2 -4.0 5.58 0.1 1.1

Puerto Rican 8.30 2.5 44.1 7.29 1.8 32.1

Cuban 4.42 -1.3 -23.3 4.90 -0.6 -11.2

Central and South American 4.68 -1.1 -18.8 4.76 -0.8 -13.8

Place of birth

Born in the 50 states and DC 7.26 2.2 42.9 6.99 1.9 38.4

Born outside the 50 states and DC 5.08 Ref. Ref. 5.05 Ref. Ref.

Abbreviation: Ref. = Referent.

Source: CDC. Period linked birth/infant death public-use data files (Downloadable data files). Hyattsville, MD: US Department of Health and Human Services, CDC, 

National Center for Health Statistics. Available at http://www.cdc.gov/nchs/data_access/VitalStatsOnline.htm. 

* Infant mortality rate = number of deaths among infants aged <1 year per 1,000 live births in a specific group. 

† Race and Hispanic origin are reported separately on birth certificates. Race categories are consistent with the 1977 Office of Management and Budget standards.

Thirty states reported multiple-race data on the birth certificate in 2008. For the <2% of events in these states that reported multiple race data, the multiple-race 

data were bridged to the single race categories of the 1977 standards for compatibility with other states. 

§ Includes persons of Hispanic or non-Hispanic origin.

¶ Persons of Hispanic ethnicity might be of any race or combination of races.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 173

Discussion

The U.S. infant mortality rate has declined 10% from 2005 

(6.86) to 2010 (preliminary estimate: 6.14) (5,13); however, 

disparities have persisted. Higher infant mortality rates for 

male than for female infants have persisted for many years and 

occur among most world populations, and have been explained 

in part by differences in genetic susceptibility to disease (14). 

Differences in infant mortality rates by race/ethnicity, maternal 

birthplace, and geographic area might reflect in part different 

population profiles, with regard to sociodemographic and 

behavioral risk factors. For example, infant mortality rates 

are higher than the U.S. average for adolescents, women aged 

≥35 years, unmarried mothers, smokers, those with lower 

educational levels, or inadequate prenatal care (5). Substantial 

differences between groups in income and access to health care 

also might contribute to differences in infant mortality (15). 

Population groups with the lowest infant mortality rates tended 

to have a smaller percentage of births to women with some 

or all of these characteristics, whereas groups with the highest 

infant mortality rates tended to have a higher percentage of 

births in women with some or all of these characteristics. Other 

factors that might contribute to racial/ethnic differences in 

infant mortality include differences in maternal preconception 

health, infection, stress, racism, and social and cultural 

FIGURE. Ratio of non-Hispanic black and non-Hispanic white infant 

mortality rates,* by state — United States, 2006–2008

Source: National Vital Statistics System, NCHS, CDC.

* Infant mortality rate = number of deaths among infants aged <1 year per 1,000 

live births in a specific group. 

≥2.60

2.35–2.59

DC

U.S. average 

 rate ratio = 2.35

Ratio not available; 

 <20 infant deaths 

 for either group

2.10–2.34

<2.10

TABLE 2. Infant mortality rates,* by race and Hispanic origin of mother 

and by state — United States, 2006–2008

Total

White, 

nonHispanic

Black, 

nonHispanic Hispanic

United States 6.68 5.58 13.11 5.50

Alabama 9.47 7.67 13.73 7.50

Alaska 6.54 4.10 † †

Arizona 6.54 6.04 14.85 6.13

Arkansas 7.89 6.70 13.53 5.71

California 5.12 4.51 10.72 4.88

Colorado 6.04 5.13 11.97 6.96

Connecticut 6.27 4.80 13.11 6.35

Delaware 8.03 5.89 13.46 7.10

District of Columbia 11.97 4.46 17.68 †

Florida 7.21 5.71 12.83 5.38

Georgia 8.02 5.87 12.70 5.06

Hawaii 6.04 4.58 18.54 4.98

Idaho 6.46 5.95 † 7.91

Illinois 7.10 5.70 13.45 5.91

Indiana 7.44 6.47 15.36 6.28

Iowa 5.43 5.06 11.10 6.61

Kansas 7.50 6.94 14.62 7.15

Kentucky 7.04 6.62 12.13 5.07

Louisiana 9.38 6.62 13.88 3.92

Maine 6.04 5.90 † †

Maryland 7.98 5.50 12.98 5.33

Massachusetts 4.94 4.04 10.90 6.08

Michigan 7.56 5.87 14.70 7.09

Minnesota 5.55 4.77 11.33 4.64

Mississippi 10.16 7.07 13.82 6.64

Missouri 7.34 6.18 14.49 5.12

Montana 6.47 5.89 † †

Nebraska 5.93 5.33 12.98 5.21

Nevada 6.10 5.29 12.54 5.69

New Hampshire 5.10 5.00 † †

New Jersey 5.35 3.78 12.06 5.12

New Mexico 5.81 6.12 † 5.60

New York 5.57 4.29 11.29 5.01

North Carolina 8.29 6.17 14.62 6.32

North Dakota 6.44 5.63 † †

Ohio 7.74 6.25 15.03 6.88

Oklahoma 7.85 7.52 13.91 5.09

Oregon 5.41 5.22 10.16 5.36

Pennsylvania 7.52 5.78 14.04 7.94

Rhode Island 6.47 4.28 10.56 7.77

South Carolina 8.30 6.04 12.97 5.87

South Dakota 7.15 5.59 † †

Tennessee 8.37 6.54 15.36 6.47

Texas 6.22 5.48 11.69 5.61

Utah 4.94 4.73 † 5.03

Vermont 5.12 4.95 † †

Virginia 7.24 5.48 13.40 5.97

Washington 5.01 4.33 7.66 5.28

West Virginia 7.38 7.11 14.93 †

Wisconsin 6.57 5.37 15.14 6.34

Wyoming 7.05 6.32 † 7.90

* Infant mortality rate = number of deaths among infants aged <1 year per 1,000 

live births in a specific group. 

† Does not meet standards of reliability or precision; based on <20 deaths in 

the numerator.
Supplement

174 MMWR / November 22, 2013 / Vol. 62 / No. 3

differences (7,16–21). However, the influence of an individual 

risk factor can vary considerably between population groups, 

indicating different medical profiles and life experiences for 

women of different backgrounds (7,16,21–22).

The risk factors for infant mortality discussed earlier (e.g., 

maternal age, tobacco use, lower income or educational levels, 

and inadequate prenatal care) are very similar to the risk factors 

for preterm or low birthweight delivery, and these risk factors 

can affect infant mortality either directly or through the 

mechanism of preterm or low birthweight delivery. In 2008, 

the percentage of infants born preterm (<37 completed weeks’ 

gestation) was higher for non-Hispanic black (17.5%), Puerto 

Rican (14.1%), and American Indian/Alaska Native (13.6%) 

mothers, than for non-Hispanic white mothers (11.1%) (5). 

Infant mortality rates are substantially higher for preterm 

and low birthweight infants, and even limited changes in the 

percentages of preterm or low birthweight births can have a 

major impact on infant mortality (5,6). In fact, the recent 

decline in U.S. infant mortality is linked to a recent decline in 

the percentage of preterm births, from a high of 12.8% in 2006 

to 12.0% in 2010 (5,22). Still the U.S. infant mortality rate 

was higher than for the majority of other developed countries, 

in part because of a substantially higher percentage of preterm 

births, a critical risk factor for infant mortality (23–24).

Limitation

The findings in this report are subject to at least one 

limitation. Differences in infant mortality rates for smaller 

states and certain race/ethnic groups (e.g., American Indians/

Alaska Natives, Asians/Pacific Islanders, and Cubans) should 

be interpreted with caution, as small numbers of infant deaths 

(i.e., <20) in specific subcategories might lead to a lack of 

statistical precision.

Conclusion

Infant mortality remains a complex and multifactorial 

problem that will continue to challenge researchers and 

policymakers in the years ahead. Despite recent declines in 

the overall infant mortality rate, the longstanding disparities 

in infant mortality by racial/ethnic group, mother’s birthplace, 

and geographic area persist. One of the Healthy People 2020

objectives is to achieve an infant mortality rate of 6.0 for the 

total population and for each race/ethnic group. Although the 

U.S. infant mortality rate of 6.14 in 2010 approximates the 

Healthy People 2020 objective, rates for several racial/ethnic 

groups are substantially higher than the goal (25). Prevention 

of preterm birth is critical to both lowering the overall infant 

mortality rate and to reducing racial/ethnic disparities (5,6).

References

1. CDC. Achievements in public health, 1900–1999: healthier mothers 

and babies. MMWR 1999;48:849–58.

2. Guyer B, Freedman MA, Strobino DM, Sondik EJ. Annual summary 

of vital statistics: trends in the health of Americans during the 20th 

century. Pediatrics 2000;106:1307–17.

3. MacDorman MF, Mathews TJ. Recent trends in infant mortality in the 

United States. Hyattsville, MD: US Department of Health and Human 

Services, CDC, National Center for Health Statistics; 2008. NCHS Data 

Brief no. 9. Available at http://www.cdc.gov/nchs/data/databriefs/db09.pdf.

4. CDC. Infant deaths—United States, 2000–2007. In: CDC health 

disparities and inequalities report—United States, 2011. MMWR 2011;

60(Suppl; January 14, 2011).

5. Mathews TJ, MacDorman MF. Infant mortality statistics from the 2008 

period linked birth/infant death data set. National Vital Statistics Reports 

vol 60 no 5. Hyattsville, MD: US Department of Health and Human 

Services, CDC, National Center for Health Statistics; 2012.

6. MacDorman MF, Mathews TJ. Understanding racial and ethnic 

disparities in US infant mortality rates. NCHS data brief, No. 74. 

Hyattsville, MD: National Center for Health Statistics. 2011. Available 

at http://www.cdc.gov/nchs/data/databriefs/db74.pdf.

7. Krieger N, Rehkopf DH, Chen JT, et al. The fall and rise of US inequities 

in premature mortality: 1960–2002. PLoS Med 2008;5:e46.

8. CDC. CDC health disparities and inequalities report—United States, 

2011. MMWR 2011; 60(Suppl; January 14, 2011).

9. CDC. Introduction. In: CDC health disparities and inequalities report—

United States, 2013. MMWR 2013;62(No. Suppl 3).

10. CDC. Period linked birth-infant death public-use data files 

[Downloadable data files]. Hyattsville, MD: US Department of Health 

and Human Services; 2012. Available at http://www.cdc.gov/nchs/

data_access/VitalStatsOnline.htm.

11. CDC. User’s guide for the 2008 period linked birth/infant death data 

set. 2012. Available at http://www.cdc.gov/nchs/data_access/

VitalStatsOnline.htm.

12. Rosenberg HM, Maurer JD, Sorlie PD, et al. Quality of death rates by 

race and Hispanic origin: a summary of current research, 1999. Vital 

Health Stat 2 1999:1–13.

13. Murphy SL, Xu J, Kochanek KD. Deaths: preliminary data for 2010. 

In: National Vital Statistics Reports. Hyattsville, MD: US Department 

of Health and Human Services, CDC, National Center for Health 

Statistics. NCHS Data Brief no 4; 2012. Available at http://www.cdc.

gov/nchs/data/nvsr/nvsr60/nvsr60_04.pdf.

14. Pongou R. Why is infant mortality higher in boys than in girls? A new 

hypothesis based on preconception environment and evidence from a 

large sample of twins. Demography [Epub ahead of print].

15. DeNavas-Walt C, Proctor BD, Smith JC. Income, poverty, and health 

insurance coverage in the United States: 2010. Washington, DC: US 

Census Bureau; 2011. Current Population Reports no. P60–239. 

Available at http://www.census.gov/prod/2011pubs/p60-239.pdf.

16. Geronimus AT. Black/white differences in the relationship of maternal 

age to birthweight: a population-based test of the weathering hypothesis. 

Soc Sci Med 1996;42:589–97.

17. Fiscella K. Racial disparity in infant and maternal mortality: confluence 

of infection, and microvascular dysfunction. Matern Child Health J 

2004;8:45–54.

18. Hogan VK, Njoroge T, Durant TM, Ferre CD. Eliminating disparities 

in perinatal outcomes: lessons learned. Matern Child Health J 2001;

5:135–40.

19. Collins JW Jr, David RJ, Handler A, Wall S, Andes S. Very low 

birthweight in African American infants: the role of maternal exposure 

to interpersonal racial discrimination. Am J Public Health 2004;

94:2132–8.
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MMWR / November 22, 2013 / Vol. 62 / No. 3 175

20. Martin JA, Hamilton BE, Sutton PD, et al. Births: final data for 2008. 

Hyattsville, MD: US Department of Health and Human Services, CDC, 

National Center for Health Statistics; 2010. National Vital Statistics 

Reports Vol. 59, no. 1. Available at http://www.cdc.gov/nchs/data/nvsr/

nvsr59/nvsr59_01.pdf.

21. Hummer RA, Powers DA, Pullum SG, Gossman GL, Frisbie WP. Paradox 

found (again): infant mortality among the Mexican-origin population 

in the United States. Demography 2007;44:441–57.

22. Hamilton BE, Martin JA, Ventura SJ. Births: preliminary data for 2010. 

Hyattsville, MD: US Department of Health and Human Services, CDC, 

National Center for Health Statistics; National Vital Statistics Report 

vol 60 no 2. Available at http://www.cdc.gov/nchs/data/nvsr/nvsr60/

nvsr60_02.pdf.

23. MacDorman MF, Mathews TJ. Behind international rankings of infant 

mortality: how the United States compares with Europe. Hyattsville, 

MD: US Department of Health and Human Services, CDC, National 

Center for Health Statistics; 2009. NCHS Data Brief no 23. Available 

at http://www.cdc.gov/nchs/data/databriefs/db23.htm.

24. Organization for Economic Cooperation and Development. OECD 

Health Data 2013. Available at http://www.oecd.org/health/healthsystems/oecdhealthdata.htm.

25. CDC. HealthyPeople.gov; 2020 topics and objectives—maternal, infant 

and child health. Available at http://www.healthypeople.gov/2020/

topicsobjectives2020/objectiveslist.aspx?topicId=26.
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176 MMWR / November 22, 2013 / Vol. 62 / No. 3

Introduction

Motor vehicle crashes are a leading cause of death for 

children, teenagers, and young adults in the United States (1). 

In 2009, approximately 36,000 persons were killed in motor 

vehicle crashes, and racial/ethnic minorities were affected 

disproportionally (1,2). Approximately 4.3% of all American 

Indian/Alaska Native (AI/AN) deaths and 3.3% of all Hispanic 

deaths were attributed to crashes, whereas crashes were the 

cause of death for <1.7% of blacks, whites, and Asian/Pacific 

Islanders (A/PI) (1).

The motor vehicle–related death rate analysis and discussion 

that follows is part of the second CDC Health Disparities and 

Inequalities Report (CHDIR). The 2011 CHDIR (3) was the 

first CDC report to take a broad view of disparities across a 

wide range of diseases, behavioral risk factors, environmental 

exposures, social determinants, and health care access. The topic 

presented in this report is based on criteria that are described in 

the CHDIR Introduction (4). The report that follows provides 

more current information to what was presented in the 2011 

CHDIR (2). The purposes of this motor vehicle–related death 

report are to discuss and raise awareness of differences in the 

characteristics of persons who die from motor vehicle–related 

crashes and to prompt actions to reduce disparities.

Methods

To assess disparities in motor vehicle–related death rates by 

race/ethnicity and sex, CDC analyzed data from the National 

Vital Statistics System (NVSS). NVSS does not collect data 

on other variables such as education and income. Race/

ethnicity was divided into five mutually exclusive categories: 

non-Hispanic whites, non-Hispanic blacks, non-Hispanic AI/

ANs, non-Hispanic A/PIs, and Hispanics of all races.

Bridged-race postcensal population estimates from the U.S. 

Census Bureau were used to calculate death rates. Death rates 

and corresponding 95% confidence intervals were calculated 

and age-adjusted to the 2000 standard U.S. population.

Absolute and relative differences in rates were calculated by sex 

and race/ethnicity. Disparities were measured as the deviations 

from a “referent” category rate. The absolute difference was 

measured as the simple difference between a population 

subgroup estimate and the estimate for its respective reference 

group. The relative difference, a percentage, was calculated by 

dividing the difference by the value in the referent category and 

multiplying by 100. Differences between age-adjusted death 

rates in 2005 and 2009 were compared using the z statistic 

based on a normal approximation, and p values ≤0.05 were 

considered statistically significant.

Results

The overall motor vehicle–related age-adjusted death rate 

was 11.7 deaths per 100,000 population in 2009 (Table 1). 

The death rate for males was 2.5 times that for females (16.8 

vs. 6.8). In 2009, AI/ANs consistently had the highest motor 

vehicle–related death rates among both males and females 

(Table). Among males, the AI/AN death rate (33.6) was 

approximately 2–5 times the rates of other races/ethnicities. 

Black males had the second-highest death rate (18.5), followed 

by whites (17.3), Hispanics (14.7), and A/PIs (6.3). Among 

females, the AI/AN motor vehicle–related death rate (17.3) 

was approximately 2-4 times the rates of other races/ethnicities. 

White females had the second-highest death rate (7.1), 

followed by blacks (6.4), Hispanics (5.7), and A/PIs (4.0).

Between 2005 and 2009, age-adjusted death rates showed 

statistically significant declines by sex among all race/ethnicities 

with the exception of AI/AN women (Table). The greatest 

decrease in rates for males occurred among AI/AN, from a 

death rate of 42.7 per 100,000 population in 2005 to 33.6 in 

2009 (absolute rate change: -9.1). Among females, the greatest 

decrease occurred among whites, from a death rate of 9.4 in 

2005 to 7.1 in 2009 (absolute rate change: -2.3).

Discussion

Evidence-based strategies to reduce overall motor vehicle–

related deaths and injuries include primary seat belt laws (i.e., 

legislation allowing police to stop a vehicle solely for a safety 

belt violation), age- and size-appropriate child safety seat and 

booster seat use laws, focused child restraint distribution plus 

education programs, ignition interlock devices (i.e., devices 

that disable a vehicle’s ignition after detection of alcohol in the 

Motor Vehicle–Related Deaths — United States, 2005 and 2009

Bethany A. West, MPH

Rebecca B. Naumann, MSPH

National Center for Injury Prevention and Control, CDC

Corresponding author: Bethany A. West, MPH, Division of Unintentional Injury Prevention, National Center for Injury Prevention and Control, CDC. 

Telephone: 770-488-0602; E-mail: bwest2@cdc.gov.
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 177

driver’s breath), sobriety checkpoints, minimum drinking age 

laws (21 years), and 0.08 g/dL blood alcohol concentration 

laws (5). Tailoring these strategies to the unique cultures of 

different racial/ethnic groups can help reduce disparities in 

motor vehicle–related mortality (6,7).

To address the disparities in motor vehicle–related death 

and injury among AI/AN, CDC funded four American Indian 

tribes during 2004–2009 to tailor, implement, and evaluate 

evidence-based interventions to reduce motor vehicle–related 

injury and death in their communities. These pilot programs 

were successful at increasing seat belt use, increasing child 

safety seat use, and decreasing motor vehicle crashes (6,7). 

Across the four pilot programs, relative increases in drivers’ 

observed seat belt use ranged from a 38% increase to a 315% 

increase and child safety seat use increases ranged from a 45% 

increase to an 85% increase in use. Declines in motor vehicle 

crashes ranged from a 29% decrease to a 36% decrease in the 

number of motor vehicle crashes and the number of motor 

vehicle crashes in which someone was injured, respectively. As 

a result, CDC has expanded the tribal programs and is funding 

eight new tribes during 2010–2014.

Limitations

The findings in this report are subject to at least one 

limitation. Because NVSS data are extracted from death 

certificates and not self-reported, some racial misclassification 

is likely, particularly for AI/AN (8).

Conclusion

Despite the recent declines in motor vehicle–related death 

rates noted in this report, the need remains for increased use of 

evidence-based strategies to reduce disparities. More translational 

research is warranted on the scalability of interventions that have 

successfully been tailored to communities of different racial/

ethnic and cultural backgrounds.

References

1. CDC. Web-based Injury Statistics Query and Reporting System 

(WISQARS) [Online database]. Atlanta, GA: US Department of Health 

and Human Services, CDC, National Center for Injury Prevention and 

Control; 2010. Available at http://www.cdc.gov/injury/wisqars/index.html.

TABLE. Age-adjusted rates* of motor vehicle–related deaths, by race/ethnicity, sex, and year — National Vital Statistics System, United States, 

2005 and 2009

Characteristic

2005 2009 Absolute 

change in rate 

from 2005 to 

2009

P-value for 

difference 

between 2005 

and 2009

Ageadjusted 

death rate (95% CI)

Absolute 

difference 

(rate)

Relative 

difference 

(%)

Ageadjusted 

death rate (95% CI)

Absolute 

difference 

(rate)

Relative 

difference 

(%)

Total 15.2 (15.1–15.4) — — 11.7 (11.6–11.8) — — -3.55 <0.0001

Sex

Male 21.8 (21.6–22.1) 12.9 143.4 16.8 (16.6–17.0) 10.0 148.2 -5.0 <0.0001

Female 9.0 (8.8–9.1) Ref. Ref. 6.8 (6.6–6.9) Ref. Ref. -2.2 <0.0001

Race/Ethnicity 

White 15.6 (15.41–5.8) Ref. Ref. 12.1 (11.9–12.2) Ref. Ref. -3.5 <0.0001

Black 14.9 (14.5–15.4) -0.7 -4.2 12.0 (11.6–12.3) -0.1 -0.9 -3.0 <0.0001

Hispanic† 14.8 (14.4–15.3) -0.8 -4.9 10.4 (10.1–10.7) -1.7 -14.0 -4.5 <0.0001

American Indian/ 

Alaska Native

30.6 (28.3–32.8) 15.0 96.0 25.2 (23.2–27.2) 13.2 109.0 -5.4 0.0004

Asian/Pacific Islander 7.7 (7.2–8.2) -7.9 -50.7 5.1 (4.85–.5) -6.9 -57.5 -2.6 <0.0001

Race/Ethnicity (Males)

White 22.1 (21.8–22.4) Ref. Ref. 17.3 (17.0–17.5) Ref. Ref. -4.9 <0.0001

Black 23.3 (22.6–24.1) 1.2 5.5 18.5 (17.8–19.1) 1.2 6.9 -4.9 <0.0001

Hispanic 21.4 (20.7–22.2) -0.7 -3.1 14.7 (14.2–15.3) -2.6 -14.8 -6.7 <0.0001

American Indian/ 

Alaska Native

42.7 (38.9–46.5) 20.6 93.1 33.6 (30.3–36.9) 16.3 94.6 -9.1 0.0004

Asian/Pacific Islander 9.7 (8.9–10.5) -12.4 -56.2 6.3 (5.7–7.0) -11.0 -63.4 -3.4 <0.0001

Race/Ethnicity (Females)

White 9.4 (9.2–9.6) Ref. Ref. 7.1 (6.9–7.2) Ref. Ref. -2.3 <0.0001

Black 7.9 (7.5–8.3) -1.5 -16.2 6.4 (6.1–6.8) -0.6 -9.1 -1.5 <0.0001

Hispanic 7.9 (7.5–8.4) -1.5 -15.6 5.7 (5.4–6.1) -1.3 -19.0 -2.2 <0.0001

American Indian/ 

Alaska Native

18.9 (16.4–21.4) 9.5 100.7 17.3 (15.0–19.5) 10.2 144.6 -1.6 0.3469

Asian/Pacific Islander 5.9 (5.3–6.5) -3.5 -37.2 4.0 (3.6–4.5) -3.0 -42.9 -1.9 <0.0001

Abbreviation: 95% CI = 95% confidence interval; Ref. = Referent.

* Age adjusted death rates per 100,000 population. 

† Persons of Hispanic ethnicity might be of any race or combination of races.
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178 MMWR / November 22, 2013 / Vol. 62 / No. 3

2. CDC. Motor vehicle-related deaths—United States, 2003-2007. In: CDC 

health disparities and inequalities report—United States, 2011. MMWR 

2011;60(Suppl; January 14, 2011):52-55.

3. CDC. CDC health disparities and inequalities report—United States, 

2011. MMWR 2011;60 (Suppl; January 14, 2011).

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5. Task Force on Community Preventive Services. Motor vehicle–related 

injury prevention. Atlanta, GA: Task Force on Community Preventive 

Services; 2010. Available at http://www.thecommunityguide.org/mvoi/

index.html.

6. Reede C, Piontkowski S, Tsatoke G. Using evidence-based strategies to 

reduce motor vehicle injuries on the San Carlos Apache Reservation. IHS 

Primary Care Provider 2007;32:209–12.

7. Letourneau RJ, Crump CD, Thunder N, Voss R. Increasing occupant 

restraint use among Ho-Chunk Nation members: tailoring evidence-based 

strategies to local context. IHS Primary Care Provider 2009;34:212–7.

8. US Department of Health and Human Services; Westat. Data on health 

and well-being of American Indians, Alaska Natives, and other Native 

Americans: data catalog. December 2006. Available at http://aspe.hhs.

gov/hsp/06/Catalog-AI-AN-NA/index.htm.
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MMWR / November 22, 2013 / Vol. 62 / No. 3 179

Introduction

Injury from self-directed violence, which includes suicidal 

behavior and its consequences, is a leading cause of death 

and disability. In 2009, suicide was the 10th-leading cause 

of death in the United States and the cause of 36,909 deaths 

(1). In 2005, the estimated cost of self-directed violence (fatal 

and nonfatal treated) was $41.2 billion (including $38.9 

billion in productivity losses and $2.2 billion in medical costs) 

(2). Suicide is a complex human behavior that results from 

an interaction of multiple biological, psychological, social, 

political, and economic factors (3). Although self-directed 

violence affects members of all racial/ethnic groups in the 

United States, it often is misperceived to be a problem affecting 

primarily non-Hispanic white males (4).

This report is part of the second CDC Health Disparities 

and Inequalities Report (CHDIR). The 2011 CHDIR (5) was 

the first CDC report to assess disparities across a wide range of 

diseases, behavior risk factors, environmental exposures, social 

determinants, and health-care access. The topic presented in 

this report is based on criteria that are described in the 2013 

CHDIR Introduction (6). This report updates information 

that was presented in the 2011 CHDIR (7) by providing more 

current data on suicide in the United States. The purposes of 

this report are to discuss and raise awareness of differences in 

the characteristics of suicide decedents and to prompt actions 

to reduce these disparities.

Methods

To determine differences in the prevalence of suicide by 

sex, race/ethnicity, age, and educational attainment in the 

United States, CDC analyzed 2005–2009 data from the Webbased Injury Statistics Query and Reporting System — Fatal 

(WISQARS Fatal) (8) and the National Vital Statistics System 

(NVSS). In this report, NVSS data provided as of February 

2012 were used. The 2009 data were used to describe the overall 

patterns in suicides. The aggregate 2005–2009 reporting period 

was used to describe patterns for the combined age group and 

race/ethnicity because sample sizes for any single year were 

limited. Mortality data were drawn from CDC’s National 

Vital Statistics System (NVSS), which collects death certificate 

data filed in the 50 states and the District of Columbia (1). 

Data in this report include suicides from any cause during 

2005–2009. The WISQARS database contains mortality data 

based on NVSS and population counts for all U.S. counties 

based on U.S. Census data. Counts and rates of death can be 

obtained by underlying cause of death, mechanism of injury, 

state, county, age, race, sex, year, injury cause of death (e.g., 

firearm, poisoning, or suffocation) and by manner of death 

(e.g., suicide, homicide, or unintentional injury) (8).

NVSS codes racial categories as non-Hispanic white, nonHispanic black, American Indian/Alaska Native (AI/AN), and 

Asian/Pacific Islander (A/PI); ethnicity is coded separately as 

Hispanic or non-Hispanic (1). Persons of Hispanic ethnicity 

might be of any race or combination of races. Absolute 

differences in rates between two populations were compared 

using a test statistic, z, based on a normal approximation at a 

critical value of α = 0.05 (9).

Educational attainment is recorded by two methods on 

death certificates. In 28 states* and the District of Columbia 

(DC), the 2003 version of the standard certificate of death is 

used (which collects the highest degree completed), whereas 20 

states† use the 1989 version of the certificate (which collects 

the number of years of education completed). For this reason, 

these two groups of states were analyzed separately. Death rates 

by educational attainment were based on population estimates 

from the U.S. Census Bureau’s 2009 American Community 

Survey (ACS) (10). Data for Georgia and Rhode Island were 

excluded because educational attainment was not recorded on 

their death certificates. Rates are presented only for persons 

aged ≥25 years because persons aged <25 years might not have 

completed their formal education (9).

Suicides — United States, 2005–2009

Alex E. Crosby, MD1

LaVonne Ortega, MD2

Mark R. Stevens, MSPH, MA1

1National Center for Injury Prevention and Control, CDC

2Center for Surveillance, Epidemiology, and Laboratory Services, CDC

Corresponding author: Alex E. Crosby, Division of Violence Prevention, National Center for Injury Prevention and Control, CDC. Telephone: 770-488-4272; 

E-mail: aec1@cdc.gov.

*Arkansas, California, Connecticut, Delaware, Florida, Idaho, Illinois, Indiana, 

Kansas, Michigan, Montana, Nebraska, Nevada, New Hampshire, New Jersey, 

New Mexico, New York, North Dakota, Ohio, Oklahoma, Oregon, South 

Carolina, South Dakota, Texas, Utah, Vermont, Washington, and Wyoming.

†Alabama, Alaska, Arizona, Colorado, Hawaii, Iowa, Kentucky, Louisiana, Maine, 

Maryland, Massachusetts, Minnesota, Mississippi, Missouri, North Carolina, 

Pennsylvania, Tennessee, Virginia, Wisconsin, and West Virginia.
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180 MMWR / November 22, 2013 / Vol. 62 / No. 3

Unadjusted (crude) suicide rates were based on resident 

population data from the U.S. Census Bureau (10). Rates based 

on <20 deaths were considered unreliable and not included 

in the analysis. Confidence intervals were calculated in two 

ways: 1) groupings of <100 deaths were calculated by using 

the gamma method (9), and 2) groupings of ≥100 deaths were 

calculated by using a normal approximation (9).

Results

In 2009, a total of 36,909 suicides occurred in the United 

States, 83.5% of which were among non-Hispanic whites, 

7.0% among Hispanics, 5.5% among non-Hispanic blacks, 

2.5% among A/PIs, and 1.1% among AI/ANs (Table). 

Although AI/ANs represented the smallest proportion of 

suicides of all racial/ethnic groups, they shared the highest rates 

with whites. Overall, the crude suicide rate for males (19.2 per 

100,000 population) was approximately four times higher than 

the rate for females (5.0 per 100,000 population). In each of 

the racial/ethnic groups, suicide rates were higher for males 

than for females, but the male-female ratio for suicide differs 

among these groups. Among non-Hispanic whites, the malefemale ratio was 3.8:1; among Hispanics it was 4.5:1; among 

non-Hispanic blacks it was 4.7:1; among A/PIs it was 2.3:1; 

and among AI/ANs it was 2.8:1. These male-female ratios did 

not change significantly from those reported previously (7).

Overall, suicide rates varied by the level of educational 

attainment. Persons with the highest educational attainment 

had the lowest rates, those with the lowest educational 

attainment had intermediate rates, and those who had 

completed only the equivalent of high school (or 12 years of 

education) had the highest rates. This pattern was consistent 

for males, but the pattern of educational inequalities was 

different among females. Females with a lower educational 

level had the lowest suicide rates followed by those with the 

highest educational level, while those females with a high 

school education (12 years of education) had the highest suicide 

rates. For each version of the death certificate, whether overall 

or by sex, suicide rates differed significantly between levels of 

educational attainment, except that rates for females did not 

differ significantly between the lowest and highest educational 

attainment levels in the states on the basis of data from the 

1989 death certificate version.

Suicide rates by race/ethnicity and age group demonstrated 

different patterns by racial/ethnic group, with the highest rates 

occurring among AI/AN adolescents and young adults aged 

15–34 years (Figure). Rates among AI/ANs and non-Hispanic 

blacks were highest among adolescents and young adults, then 

declined or leveled off with increasing age, respectively. Among 

A/PIs and Hispanics, rates were highest among young adults in 

their early 20s, then leveled off among other adults but increased 

for those aged ≥65 years. In contrast, rates among non-Hispanic 

whites were highest among those aged 40–54 years. Although 

the 2009 overall rates for AI/ANs are similar to those of nonHispanic whites, the 2005–2009 rates among adolescent and 

young adult AI/ANs aged 15–29 years were substantially higher. 

Discussion

The burden of suicide among AI/AN youths is considerably 

higher than that among other racial/ethnic groups. In 2009, 

suicide ranked as the fourth leading cause of years of potential 

life lost (YPLL) for AI/ANs aged <75 years, accounting for 

6.8% of all YPLL among AI/ANs (8). Studies examining 

the historical and cultural context of suicide among AI/AN 

populations have identified multiple contributors to the high 

rates such as individual-level factors (e.g., alcohol and substance 

misuse and mental illness), family- or peer-level factors (e.g., 

family disruption or suicidal behavior of others), and societallevel factors (e.g., poverty, unemployment, discrimination, and 

historic trauma [i.e., cumulative emotional and psychological 

wounding across generations]) (11). Although certain 

protective factors exist within AI/AN communities, including 

spirituality and cultural continuity, these factors often are 

overwhelmed by the magnitude of the risk factors (11). If the 

overall suicide rate among the AI/AN population (highest 

rate) could be decreased to that of non-Hispanic blacks (lowest 

rate), 271 (66.6%) of the total 407 AI/AN deaths during 2009 

might have been prevented. This idea of achieving rates of the 

lowest group is similar to that proposed in the Healthy People 

2010 objectives (12).

Prevention efforts and resources also should be directed 

toward adults aged 40–54 years because this age group has the 

highest (and increasing) suicide rate, but this age group often 

is overlooked as a group at which prevention efforts should 

focus (13). The National Strategy for Suicide Prevention has 

identified males in this age group as one of the populations 

at increased risk for suicide for whom additional surveillance, 

research, and prevention programs need to be focused (14).

The findings regarding the association of suicide rates and 

educational attainment are mixed in this study and in others. 

Certain studies (15) have found an inverse relationship 

between educational status and suicide among males (i.e., 

suicide rates decrease as educational attainment increases), 

whereas other studies (16) have not found this pattern. 

Patterns among females identified in other studies seem more 

consistent (i.e., the lowest rates occur among those with the 

lowest educational attainment) or find no association, but the 
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MMWR / November 22, 2013 / Vol. 62 / No. 3 181

TABLE. Number and rate* of suicides, by selected characteristics — National Vital Statistics System, United States, 2009

Characteristic

Male Female Total

% of total

No. of 

deaths Rate (95% CI)†

No. of 

deaths Rate (95% CI)

No. of 

deaths Rate (95% CI)

Age group (yrs)

0–4 0 —§ — 0 — — 0 — — —

5–9 4 — — 2 — — 6 — — —

10–14 171 1.7 (1.4–1.9) 88 0.9 (0.7–1.1) 259 1.30 (1.1–1.5) 0.7

15–19 1,325 12.0 (11.3–12.6) 344 3.3 (2.9–3.6) 1,669 7.75 (7.4–8.1) 4.5

20–24 2,270 20.5 (19.6–21.3) 432 4.1 (3.8–4.5) 2,702 12.5 (12.1–13.0) 7.3

25–29 2,184 19.7 (18.8–20.5) 515 4.9 (4.5–5.3) 2,699 12.5 (12.0–12.9) 7.3

30–34 2,109 20.9 (20.0–21.8) 512 5.2 (4.8–5.7) 2,621 13.2 (12.7–13.7) 7.1

35–39 2,414 23.3 (22.4–24.3) 681 6.7 (6.2–7.2) 3,095 15.1 (14.5–15.6) 8.4

40–44 2,728 26.0 (25.0–27.0) 854 8.1 (7.6–8.7) 3,582 17.1 (16.5–17.6) 9.7

45–49 3,176 28.1 (27.1–29.1) 1,036 9.0 (8.4–9.5) 4,212 18.5 (17.9–19.0) 11.4

50–54 3,293 30.8 (29.8–31.9) 1,093 9.9 (9.3–10.5) 4,386 20.2 (19.6–20.8) 11.9

55–59 2,661 28.9 (27.8–30.0) 830 8.5 (7.9–9.1) 3,491 18.4 (17.8–19.0) 9.5

60–64 1,796 23.7 (22.6–24.8) 521 6.3 (5.8–6.9) 2,317 14.7 (14.1–15.3) 6.3

65–69 1,357 24.6 (23.3–25.9) 312 5.0 (4.4–5.5) 1,669 14.2 (13.5–14.8) 4.5

70–74 1,037 25.4 (23.9–27.0) 211 4.3 (3.7–4.9) 1,248 13.9 (13.1–14.6) 3.4

75–79 945 30.0 (28.1–31.9) 157 3.8 (3.2–4.4) 1,102 15.0 (14.2–15.9) 3.0

80–84 844 36.7 (34.3–39.2) 117 3.3 (2.7–3.9) 961 16.5 (15.5–17.6) 2.6

≥85 763 42.8 (39.8–45.8) 115 3.0 (2.4–3.5) 878 15.6 (14.6–16.6) 2.4

Unknown 12 — — 0 — — 12 — — —

Race/Ethnicity

White, non-Hispanic 24,248 24.4 (24.1–24.7) 6565 6.4 (6.2–6.5) 30,813 15.2 (15.1–15.4) 83.5

Black, non-Hispanic 1,649 8.9 (8.5–9.3) 385 1.9 (1.7–2.1) 2,034 5.2 (5.0–5.5) 5.5

American Indian/Alaska Native 298 23.2 (20.6–25.8) 109 8.2 (6.7–9.8) 407 15.6 (14.1–17.1) 1.1

Asian/Pacific Islander 639 8.8 (8.1–9.5) 295 3.8 (3.4–4.3) 934 6.2 (5.8–6.6) 2.5

Hispanic¶ 2,135 8.5 (8.2–8.9) 438 1.9 (1.7–2.1) 2,573 5.3 (5.1–5.5) 7.0

Unknown 120 — — 28 — — 148 — — 0.4

Educational attainment (28 states** and DC)

Less than high school graduate 

or equivalent

2,211 22.4 (21.5–23.4) 431 4.5 (4.0–4.9) 2,642 13.6 (13.0–14.1) 14.0††

High school graduate or 

equivalent

6,283 37.1 (36.2–38.0) 1,436 7.9 (7.5–8.4) 7,719 22.0 (21.6–22.5) 40.8††

Some college/college graduate 6,336 18.1 (17.7–18.6) 2,242 5.9 (5.7–6.1) 8,578 11.8 (11.5–12.0) 45.3††

Educational attainment (20 states§§ )

<12 yrs 1,548 33.3 (31.7–35.0) 244 5.4 (4.7–6.1) 1,792 19.5 (18.6–20.5) 16.0††

12 yrs 3,963 39.7 (38.4–40.9) 948 9.0 (8.4–9.6) 4,911 23.9 (23.3–24.6) 43.9††

≥13 yrs 3,273 18.5 (17.8–19.1) 1,203 6.0 (5.7–6.3) 4,476 11.9 (11.5–12.2) 40.0††

Total 29,089 19.2 (19.0–19.4) 7,820 5.0 (4.9–5.1) 36,909 12.0 (11.9–12.1) 100.0

Abbreviations: 95% CI = 95% confidence interval; DC = District of Columbia; GED =general equivalency diploma.

* Unadjusted (crude) suicide rates per 100,000 population.

† CIs based on <100 deaths were calculated by using a gamma method; CIs based on ≥100 deaths were calculated by using a normal approximation. Additional information 

available from Xu J, Kochanek KD, Murphy SL, Tejada-Vera B. Deaths: final data for 2007. Hyattsville, MD: US Department of Health and Human Services, CDC, National 

Center for Health Statistics; 2010. National Vital Statistics Reports Vol. 58, No. 19. Available at http://www.cdc.gov/nchs/data/nvsr/nvsr58/nvsr58_19.pdf. § Rates based on <20 deaths were considered unreliable and not included in the analysis.

¶ Persons of Hispanic ethnicity might be of any race or combination of races.

** Arkansas, California, Connecticut, Delaware, Florida, Idaho, Illinois, Indiana, Kansas, Michigan, Montana, Nebraska, Nevada, New Hampshire, New Jersey, New 

Mexico, New York, North Dakota, Ohio, Oklahoma, Oregon, South Carolina, South Dakota, Texas, Utah, Vermont, Washington, and Wyoming used the 2003 version 

of the Standard Certificate of Death for persons aged ≥25 years.

†† Percentages reflect a percentage of the three levels of educational attainment shown for each set of states with their respective versions of the Standard Certificate 

of Death, not a percentage of the total count of suicides. Suicide counts shown do not include 1) 4,648 suicides for persons aged 0–24 years or whose age was 

unknown; 2) 1,104 suicides from Georgia and Rhode Island, which do not collect data on educational attainment; and 3) 1,039 suicides for whom the state in which 

the suicide occurred records educational attainment in a different version than that used by the deceased’s state of residence.

§§ Alabama, Alaska, Arizona, Colorado, Hawaii, Iowa, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Minnesota, Mississippi, Missouri, North Carolina, Pennsylvania, 

Tennessee, Virginia, Wisconsin, and West Virginia used the 1989 version of the Standard Certificate of Death for persons aged ≥25 years. For rates, the definition of 

educational attainment differs for the numerator and the denominator. Additional information available from Xu J, Kochanek KD, Murphy SL, Tejada-Vera B. Deaths: 

final data for 2007. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2010. National Vital Statistics Reports 

Vol. 58, No. 19. Available at http://www.cdc.gov/nchs/data/nvsr/nvsr58/nvsr58_19.pdf.
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182 MMWR / November 22, 2013 / Vol. 62 / No. 3

underlying explanation is unclear (17,18). It has been suggested 

that studies on the association between education and suicide 

should perform more specific analysis (e.g., by examining 

combinations of age, ethnicity, culture, and sex variables to 

assess the true association) (19).

As a result of multiple challenges (e.g., narrow theoretical 

focus, lack of longitudinal studies to provide a range of 

modifiable risk and protective factors, and insufficient study 

designs), the evidence for the proven effectiveness of suicide 

prevention programs is sparse (20). Suicide prevention efforts 

often focus on counseling, education, and clinical intervention 

strategies for persons at high risk for suicide, neglecting a broader 

population-based approach (20). Although these efforts might 

assist those persons at the highest risk for adverse outcomes, 

they also require high levels of effort and commitment and 

might have a limited population-level impact, a critical goal of 

public health (21). In contrast, strategies that seek to address 

societal-level factors demonstrated to be associated with suicide 

(e.g., economic strain, poverty, and misuse of alcohol and other 

psychoactive substances) and improving the health-care system 

infrastructure in impoverished and underserved communities 

to address this problem might have a greater population impact 

but need additional development and testing (22).

Limitations

The findings presented in this report are subject to at least 

four limitations. First, suicides often are undercounted on 

death certificates, and studies have indicated that they are 

differentially undercounted for females and racial/ethnic 

minorities (23); therefore, the suicide rates in this analysis are 

likely to be underestimated. Second, injury mortality data likely 

underestimate by 25%–35% the actual numbers of deaths 

for AI/ANs and certain other racial/ethnic populations (e.g., 

Hispanics) because of the misclassification of race/ethnicity of 

decedents on death certificates (24). Third, data on educational 

attainment must be interpreted with caution because of 

misclassification of the decedent’s years of education, which has 

been shown with comparisons between educational attainment 

as recorded on the death certificate versus that in census surveys 

(9). Finally, certain variables that have been associated with 

FIGURE. Suicide rates,* by race/ethnicity and age group — National Vital Statistics System, United States, 2005–2009

* Unadjusted (crude) suicide rates per 100,000 population.

† Persons of Hispanic ethnicity might be of any race or combination of races.

0

5

10

15

20

25

30

35

Age group (yrs)

Rate 

Non-Hispanic white 

Non-Hispanic black

American Indian/Alaska Native

Asian/Pacic Islander

Hispanic†

Overall

≤4 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 ≥65
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 183

suicidal behavior (e.g., psychiatric illness, sexual orientation, 

and social isolation) are not collected in U.S. mortality data, 

and therefore patterns of suicide based on these factors cannot 

be described. Other data sources (e.g., the National Violent 

Death Reporting System) that collect a broader array of 

information about the circumstances surrounding suicides 

and other violent deaths can provide additional insight (25).

Conclusion

Comprehensive suicide prevention programs focus on 

risk and protective factors, including coping skills, access to 

mental health treatment, substance misuse, and social support. 

However, only a limited number of programs have been 

developed specifically for higher risk or racial/ethnic minority 

populations (3). An example of a comprehensive prevention 

program that has been reported to reduce suicidal behavior 

within an AI/AN community is the Natural Helpers Program 

(26). This program includes health education and outreach 

activities to the community and at-risk persons, training for 

community members in identification of at-risk persons, and 

support for local behavioral health efforts like alcohol and 

substance abuse programs.

Strategies that address the health and well-being of persons 

at risk and that support the widespread implementation of 

culturally relevant and effective programs are needed to reduce 

the rates of suicide among groups that are disproportionately 

affected. To address some of these issues, CDC has focused 

on studying and promoting individual and organizational 

connectedness as a way to prevent suicide (27).

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The reports in this supplement document persistent 

disparities between some population groups in health 

outcomes, access to health care, adoption of health promoting 

behaviors, and exposure to health-promoting environments. 

Some improvements in overall rates and even reductions in 

some health disparities are noted; however, many gaps persist. 

These finding highlight the importance of monitoring health 

status, outcomes, behaviors, and exposures by population 

groups to assess trends and target interventions. In this report, 

disparities were found between race and ethnic groups across all 

of the health topics examined. Differences also were observed 

by other population characteristics. For example, persons with 

low socioeconomic status were more likely to be affected by 

diabetes, hypertension, and human immunodeficiency virus 

(HIV) infection and were less likely to be screened for colorectal 

cancer and vaccinated against influenza.

CDC plays a key role in addressing disparities by collecting 

and analyzing data and identifying, monitoring, and reporting 

differences and trends. CDC’s national survey data and some 

state level data are used by the Healthy People initiative to 

monitor trends in health outcomes and determinants. The 

Healthy People initiative provides goals and objectives with 

10-year targets designed to guide national health promotion 

and disease prevention efforts to improve the health of all 

persons in the United States. It is grounded in the principle that 

setting national objectives and monitoring progress can prompt 

action and improve health. The Healthy People 2000 initiative 

had goals to reduce health disparities among persons in the 

United States. Healthy People 2010 added elimination, not just 

reduction, of health disparities, and Healthy People 2020 goes 

even further to achieve health equity, eliminate disparities, and 

improve the health of all population groups (1). State and local 

health departments use the Healthy People program as a way to 

track the effectiveness of local health initiatives.

This supplement provides decision makers with information 

that they can use to select interventions for certain populations 

to reduce health disparities. There is a growing awareness that 

limited health-care coverage and lack of access are only part of 

the reason why health disparities exist. Many of the strongest 

predictors of health are social, economic, and environmental 

factors. Reducing disparities requires national leadership to 

engage a diverse array of stakeholders; facilitate coordination 

and alignment among federal departments, agencies, offices, 

and nonfederal partners; champion the implementation of 

effective policies and programs; and ensure accountability (2). 

In addition, a complementary national strategy is required that 

focuses on a comprehensive, community-driven approach to 

reduce health disparities in the United States and achieve health 

equity through collaboration and synergy (3).

Data to Identify Health Disparities

A persistent barrier to documenting health disparities is the 

lack of data on certain population characteristics. All of the 

reports in this supplement (n=29) examined differences by race/

ethnicity. The next most frequent population characteristics 

that were examined were age (n=24), sex (n=24), education 

(n=17), poverty (n=14), place of birth (n=11), and disability 

(n=nine). The least frequently reported characteristics were 

the language spoken at home (n=four) and sexual orientation 

(n=one). Only the paper on HIV infection reported rates 

by sexual orientation. Some data sources used in this report 

have very limited information on social and demographic 

characteristics. For example, death certificates do not have 

information on sexual orientation, poverty level, or disability 

(4). Some national health surveys have begun to collect data 

on sexual orientation, but there are issues that affect the 

usability of this data. In many instances, the sample sizes are 

too small to provide meaningful estimates for categories other 

Conclusion and Future Directions: CDC Health Disparities and 

Inequalities Report — United States, 2013

Pamela A. Meyer, PhD1, 

Ana Penman-Aguilar, PhD2,

Vincent A. Campbell. PhD3,

Corinne Graffunder, DrPH4,

Ann E. O’Connor, MPA5, 

Paula W. Yoon, ScD6

1 Office for State, Tribal, Local and Territorial Support, CDC

2Office of Minority Health and Health Equity, CDC

3National Center on Birth Defects and Developmental Disabilities, CDC 4Office of the Associate Director of Policy, CDC

5Office of the Associate Director for Program, CDC 6Center for Surveillance, Epidemiology and Laboratory Services, CDC
Supplement

MMWR / November 22, 2013 / Vol. 62 / No. 3 185

than heterosexual, and the response rate to the questions on 

sexual identity is often low. In addition, lack of familiarity 

with terminology used to describe sexual orientation might 

have resulted in some misclassification (5).

To promote uniform collection of data on sex, race, ethnicity, 

primary language, and disability status, in October 2011, 

the U.S. Department of Health and Human Services (HHS) 

promulgated standards pursuant to Sec. 4302 of the Patient 

Protection and Affordable Care Act (ACA) (6). The standards 

were developed by the Section 4302 Workgroup organized by 

the HHS Data Council in collaboration with the Office of 

Management and Budget (OMB) and the U.S. Census Bureau. 

These data standards apply to all population health surveys 

conducted or supported by the federal government that use 

self- or proxy-reported data “to the extent practicable.” The 

recommended data standards require that questions be tested 

and demonstrate adequate performance in national surveys 

and comply with any existing mandates by OMB (7). The 

recommended questions are considered to be a minimum set, 

and additional questions can be asked in surveys “provided 

that the additional detail could be aggregated back to the 

minimum standard and the sample design and sample size 

support estimates at that level of granularity” (7). The purpose 

of this provision of ACA is to provide standard approaches 

for collecting, analyzing, and reporting on health disparities 

that might exist between various demographic segments of 

the U.S. population. A standard set of questions to be asked 

in all national population surveys will improve understanding 

of the role of certain conditions as a risk factor for preventable 

poor health. CDC is evaluating the feasibility of incorporating 

the new data standards into many of its surveys and public 

health surveillance systems. Some modes of questionnaire 

administration might not readily support elements of the data 

standards because of the length of time needed to collect the 

data. For example, the 2011 data standard for classifying race 

establishes 14 categories that can be collapsed into the five 

categories established in 1997 by OMB. The 14 categories 

are preferred when sample sizes can support the increased 

detail. More than one race can be specified but there is no 

“multiracial” category.

Although a mandatory minimum set of six questions was 

established for determining disability status, no provision exists 

for modifying data collection modes to accommodate the new 

questions. One of the questions is, “Are you deaf or do you 

have serious difficulty hearing?” Notably, persons with serious 

hearing difficulties might not be able to participate in telephone 

surveys without the assistance of a Telecommunications Relay 

Service or other adaptive telephone equipment or services, 

making it problematic to collect reliable information on the 

prevalence of this disability and health outcomes for which 

they might be at risk. Another option for gathering data on 

persons with serious hearing difficulty is internet panel surveys.

CDC Initiatives to Reduce and 

Prevent Health Disparities

CDC is conducting many activities that support reducing 

health disparities and promoting health equity. For example, 

CDC provides technical support to the independent 

Community Preventive Services Task Force (Task Force). 

The Task Force makes recommendations based on systematic 

reviews of published studies on many important public 

health topics (8). These recommendations identify programs, 

services, and policies proven effective in a variety of real-world 

settings (e.g., communities, worksites, schools, and health 

plans). One topic the Task Force considered was effectiveness 

of interventions to improve colorectal, breast, and cervical 

cancer screening. After reviewing the evidence, the Task Force 

recommended nine strategies for helping bring those who are 

eligible for colorectal, breast, and cervical cancer screening to 

the point of care, including such services and programs as client 

reminders, one-on-one education, reducing structural barriers 

(e.g., providing scheduling assistance and transportation 

and offering extended hours), and provider reminders to 

screen patients. Many of these services have been effective for 

underserved populations and communities that are at greatest 

risks for cancer (9).

CDC’s Office of Minority Health and Health Equity 

(OMHHE) advances policy, scientific, and programmatic efforts 

to eliminate health disparities affecting populations at social, 

economic, or environmental disadvantage and achieve health 

equity in the U.S. population. For example, OMHHE provides 

leadership in the development and promotion of Healthy 

People 2020 Social Determinants of Health objectives and is 

leading an effort to compile promising practices and strategies 

used by CDC-funded programs to address health disparities. 

Through all of its activities, OMHHE focuses attention on 

efforts to achieve health equity, facilitating implementation of 

relevant policies, furthering the science of health equity and 

its application, and building and strengthening national and 

global partnerships for health equity.

CDC provides scientific and technical support to the 

National Prevention Council. Created by ACA, the National 

Prevention Council developed the National Prevention 

Strategy (NPS) to realize the benefits of prevention for all 

persons in the United States. Eliminating health disparities 

is one of four strategic directions identified in NPS (2). NPS 

recommends five approaches to reducing disparities: 1) focus 

on communities at greatest risk, 2) increase access to quality 
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186 MMWR / November 22, 2013 / Vol. 62 / No. 3

health care, 3) increase workforce capacity to address disparities, 

4) support research to identify effective strategies to eliminate 

disparities, and 5) standardize and collect data to better identify 

and address disparities. Recognizing that disparities are closely 

linked with social, economic, and environmental disadvantage 

(e.g., lack of access to quality affordable health care, healthy 

food, safe opportunities for physical activity, and educational 

and employment opportunities), the National Prevention 

Council has representation from 20 federal departments 

including Agriculture, Housing and Urban Development, 

Defense, Education and Transportation, and is chaired by the 

Surgeon General.

CDC supports the implementation of the National 

Prevention Council Action Plan (10). The plan outlines 

the Federal commitment to implementing the vision, goal, 

and recommendations of NPS. CDC is working with the 

U.S. Department of Housing and Urban Development and 

the Environmental Protection Agency on Health Impact 

Assessments (HIAs). HIAs examine ways to create healthy 

communities, provide health protection, and promote health.

CDC also works to reinforce cross-sector collaborations that 

can advance CDC programs, priorities, and initiatives. CDC’s 

Community Transformation Grant (CTG) program seeks 

to improve health and wellness by implementing strategies 

included in NPS. CTG communities are engaging partners 

from multiple sectors, such as education, transportation, 

housing, and business, to create healthier communities where 

persons work, live, learn, and play. CDC’s grant programs strive 

to achieve the greatest possible health impact and eliminate 

health disparities. Since October 1, 2012, all CDC domestic 

nonresearch funding opportunity announcements require 

that grantees describe how health disparities will be addressed, 

where relevant. When addressing health disparities, CDC 

programs might require grantees to identify existing health 

disparities in their communities and develop or implement 

evidence-based strategies to address those disparities.

Future Directions

Despite persistent racial, ethnic, and socioeconomic gaps 

in health care and health status, awareness of such disparities 

remains low among the general public (11). Much can be 

accomplished within the health and public health arena; 

however, the multiple and complex web of causes of health 

disparities can be fully addressed only with the involvement 

of many partners in fields that influence health such as 

housing, transportation, education and business. Identifying 

disparities and monitoring them over time is a necessary first 

step toward the development and evaluation of evidence-based 

interventions that can reduce disparities. CDC will continue 

to document health disparities and promote awareness of 

disparities as part of its contribution to the national goal 

to eliminate health disparities for vulnerable populations as 

defined by race/ethnicity, socioeconomic status, geography, 

sex, age, disability status, sexual orientation, and primary 

language, and among other populations identified to be at-risk 

for health disparities.

References

1. U.S. Department of Health and Human Services. Office of Disease and 

Prevention and Health Promotion. Healthy People 2020. Washington, 

DC. Available at http://www.healthypeople.gov/2020.

2. National Prevention Council. National Prevention Strategy, Washington, 

DC: U.S. Department of Health and Human Services, Office of the 

Surgeon General, 2011. Available at http://www.surgeongeneral.gov/

initiatives/prevention/strategy/report.pdf.

3. U.S. Department of Health and Human Services. HHS Action plan to 

reduce Racial and ethnic health disparities: a nation free of disparities 

in health and health care. Available at http://minorityhealth.hhs.gov/

npa/files/Plans/HHS/HHS_Plan_complete.pdf.

4. CDC. 2003 Revisions of the U.S. standard certificates of live birth and 

death and the fetal death report. Available at http://www.cdc.gov/nchs/

nvss/vital_certificate_revisions.htm.

5. Miller K, Ryan JM. Design, development and testing of the NHIS sexual 

identity question. October 2011. Available at http://wwwn.cdc.gov/

qbank/report/Miller_NCHS_2011_NHIS%20Sexual%20Identity.pdf.

6. Patient Protection and Affordable Care Act (Pub.L. 111-148, 124 Stat. 

119, March 23, 2010). Data Collection to Support Standards Related 

to Essential Health Benefits; Recognition of Entities for the Accreditation 

of Qualified Health Plans. Federal Register, July 2012.

7. U.S Department of Health and Human Services. Implementation 

Guidance on Data Collection Standards for Race, Ethnicity, Sex, Primary 

Language, and Disability Status. Available at http://aspe.hhs.gov/

datacncl/standards/ACA/4302/index.pdf.

8. The Guide to Community Preventive Services. Available at http://www.

thecommunityguide.org/index.html.

9. The Guide to Community Preventive Services. Cancer Prevention and 

Control. National Prevention Council, National Prevention Strategy, 

Washington, DC: U.S. Department of Health and Human Services, 

Office of the Surgeon General, 2011. Available at http://www.healthcare.

gov/prevention/nphpphc/2012-npc-action-plan.pdf.

10. National Prevention Council, National Prevention Strategy, Washington, 

DC: U.S. Department of Health and Human Services, Office of the 

Surgeon General, 2011. Available at http://www.healthcare.gov/

prevention/nphpphc/2012-npc-action-plan.pdf.

11. Benz JK, Espinosa O, Welsh V, Fontes A. Awareness of racial and ethnic 

health disparities has improved only modestly over a decade. Health Aff 

2011;30:1860-7.
U.S. Government Printing Office: 2013-623-210/XXXXX Region IV ISSN: 1546-0738

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