Measuring Healthy Days

Population Assessment of Health-Related Quality of Life

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Disease Control and Prevention

National Center for Chronic Disease Prevention and Health Promotion

Division of Adult and Community Health

November 2000

Measuring Healthy Days 1

Measuring Healthy Days

Population Assessment of Health-Related Quality of Life

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Disease Control and Prevention

National Center for Chronic Disease Prevention and Health Promotion

Division of Adult and Community Health

Atlanta, Georgia

November 2000

Suggested citation: Centers for Disease Control and Prevention. Measuring Healthy Days. Atlanta, Georgia:

CDC, November 2000.

For additional information: For general information about the Healthy Days measures, contact: Health Care and

Aging Studies Branch, Mailstop K-45, DACH, NCCDPHP, CDC, 4770 Buford Highway NE, Atlanta, Georgia

30341 (Tel: 770-488-5464). For information about public-domain Behavioral Risk Factor Surveillance System

(BRFSS) methods or data, see: http://www.cdc.gov/nccdphp/brfss/ or contact your State BRFSS Coordinator

(contact information available on this website).

Acknowledgments: Virginia Ross Taylor (writer-editor), Kerstin Weis (manuscript design), Behavioral Risk

Factor Surveillance System State Coordinators, CDC Health Care and Aging Studies Branch staff, reviewers of the

draft manuscript who provided comments and corrections, and developers, researchers, and users of the Healthy

Days measures, who provided the expert guidance and technical material on which this report was based. The

cover design has been adapted from the Mona Lisa by Leonardo da Vinci, which is in the permanent collection

of the Musee du Louvre in Paris, France.
EXECUTIVE SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . .4

INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5

Why quality of life? . . . . . . . . . . . . . . . . . . . . . . . . . . . .5

What is quality of life? . . . . . . . . . . . . . . . . . . . . . . . . . .5

What is health-related quality of life? . . . . . . . . . . . . . . .6

Why is it important to track HRQOL? . . . . . . . . . . . . . . . .6

How can HRQOL be measured? . . . . . . . . . . . . . . . . . . . .7

HEALTHY DAYS METHODS . . . . . . . . . . . . . . . . . . . . . . . . .8

How is the summary index of

unhealthy days calculated? . . . . . . . . . . . . . . . . . . . . . . .8

Why collect data about health perceptions? . . . . . . . . . . .9

What is the BRFSS? . . . . . . . . . . . . . . . . . . . . . . . . . . . .10

What is the advantage of having core 

HRQOL questions on the BRFSS? . . . . . . . . . . . . . . . . . . .10

Why ask “How many days . . . “ when other questions 

like rating your overall health are easier to answer? . . . .10

How can you measure HRQOL with only four questions? .10

Why are most of the measures oriented toward 

the negative side of health? . . . . . . . . . . . . . . . . . . . . . .11

How do the Healthy Days measures differ 

from QALYs, DALYs, and YHLs? . . . . . . . . . . . . . . . . . . .12

FINDINGS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12

What have been some of the findings from the 

Healthy Days core questions? (nationwide, 

state comparisons, seasonal patterns, and time trends) . .12

VALIDATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15

Do these questions accurately measure HRQOL? . . . . . . .15

What challenges in measurement have been identified? . .18

What is being done to address problems in accuracy? . . .19

PRACTICAL APPLICATIONS . . . . . . . . . . . . . . . . . . . . . . . .20

What are some of the cross-cultural uses of the 

Healthy Days measures? . . . . . . . . . . . . . . . . . . . . . . . . .20

How are the Healthy Days measures useful at the 

state and local levels? . . . . . . . . . . . . . . . . . . . . . . . . . .20

How are the HRQOL measures being used to 

identify and address the needs of special populations? . .21

2 Measuring Healthy Days

Measuring Healthy Days
POLICY IMPLICATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . .26

What are the policy implications of 

HRQOL surveillance? . . . . . . . . . . . . . . . . . . . . . . . . . . .26

Why is HRQOL surveillance important 

in Healthy People 2010? . . . . . . . . . . . . . . . . . . . . . . . .26

How do Healthy Days relate to the 

Healthy People 2010 Leading Health Indicators? . . . . . . .26

How can the Healthy Days measures support

epidemiologic and prevention research? . . . . . . . . . . . .28 

How can an HRQOL focus support 

community mobilization? . . . . . . . . . . . . . . . . . . . . . . . .28

CONCLUSIONS AND FUTURE DIRECTIONS . . . . . . . . . . . .29

What are the main advantages of the Healthy Days 

measures and population data? . . . . . . . . . . . . . . . . . . .29

What areas need future study and analysis? . . . . . . . . . .30

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .30

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .31

RESOURCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .33

TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .34

Table 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .34

Self-rated health, unhealthy days, and activity 

limitation days, adjusted for age—Behavioral Risk 

Factor Surveillance System, 1993–1997, by:

A. demographic group

B. health condition

C. behavioral risk factor

D. self-rated health

Table 2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .37

Health-related quality-of-life measures for 

states using the optional Quality of Life 

Module–Behavioral Risk Factor Surveillance 

System, 1995–1997 by:

A. state and year 

B. demographic group

C. health condition

D. behavioral risk factor

E. self-rated health

F. extent of activity limitation

G. main cause of activity limitation

Use of trade names and commercial sources is for identification only and does not imply

endorsement by the U.S. Department of Health and Human Services.

References to non-CDC sites on the Internet are provided as a service and do not constitute or imply endorsement of these organizations or their programs by CDC or the U.S.

Department of Health and Human Services. CDC is not responsible for the content of

pages found at these sites.

Measuring Healthy Days 3
4 Measuring Healthy Days

Executive Summar

y

EXECUTIVE SUMMARY

This technical report,

Measuring Healthy Days,

describes the origins, validity, and value of a set of survey measures developed by the Centers for

Disease Control and Prevention

(CDC) and its partners for use in

tracking population health status

and health-related quality of life

(HRQOL) in states and communities. The first four of these measures pertain to general

self-rated health and recent days of physical health,

mental health, and activity limitation. These measures

have been part of the full sample Behavioral Risk Factor

Surveillance System (BRFSS) core since 1993 and were

added, beginning in 2000, to the examination component of the National Health and Nutrition Examination

Survey (NHANES). An additional five measures of

activity limitation and five questions on recent days of

pain, depression, anxiety, sleeplessness, and vitality constitute an optional quality-of-life module added to the

BRFSS in 1995.

The primary target audiences for this report are public health professionals with a current stake or potential

interest in HRQOL measurement. The report identifies

the policy origins of the Healthy Days measures, discusses how HRQOL differs from other health and social

constructs, and summarizes several studies designed to

test the reliability, validity, and responsiveness of the

measures. It also describes surveillance findings to date

and provides methods and population reference data

from 1993–97 to assist states and others in the appropriate use and interpretation of their own Healthy Days

data.

In recent years, several organizations have found

these Healthy Days measures useful at the national level

for: 1) identifying health disparities, 2) tracking population trends, and 3) building broad coalitions around a

measure of population health compatible with the

World Health Organization’s definition of health. The demonstrated

value of these measures and the

continuous accumulation of public

domain data have resulted in support from the CDC Disability,

Women’s Health, and Arthritis

Programs. The HRQOL measures

and data have also been used for

research or program planning by

the CDC Cardiovascular Health,

Nutrition and Physical Activity, and

HIV/AIDS Programs as well as by the Public Health

Foundation, the Foundation for Accountability, the

American Cancer Society, and several other government

and academic programs.

One of the greatest anticipated uses of the BRFSS

Healthy Days measures and data is at the state and local

levels in support of the two major goals

of Healthy People 2010: Improving the

Quality and Years of Healthy Life and

Eliminating Health Disparities. Healthy

People 2010 identifies the BRFSS as a

key source for tracking several HRQOL

measures. As knowledge builds about

the value of HRQOL surveillance and

how to use it, these validated measures

and accumulating data give states and

communities a unique resource for

tracking adult physical and mental health over time,

identifying unmet health needs, and guiding broad

community efforts to improve population health.

Measuring Healthy Days

“Health is a state of complete physical,

mental, and social well-being—not merely

the absence of disease, or infirmity.”

—-World Health Organization, 1948
Measuring Healthy Days 5

Introduction

INTRODUCTION

This report is designed for use by public health

professionals who are involved or interested in

health- related quality of life (HRQOL) surveillance or measurement. The report identifies the policy

and conceptual origins of a set of Healthy Days HRQOL

measures that were developed for use as public health

outcome measures and summarizes the results of studies designed to test the accuracy and consistency of these

measures. It also describes surveillance findings to date

and provides analytical methods and population reference data from 1993–1997 to assist states and others in

the appropriate use and interpretation of the Healthy Days measures and data.

This report is organized

around answers to questions people commonly ask about HRQOL

and its measurement.

Why quality of life? 

Although the World Health

Organization (WHO)

defined health very broadly as long as a half century ago, health in the U.S. has traditionally been measured narrowly and in the negative.

What is measured is ill health in its severe manifestations, those which are verifiable through physical examination and other objective procedures or tests. These

measures have generally been done at the individual

level, at clinics and hospitals.

Such traditional measures of morbidity and mortality provide information about the lowest levels of

health, but they reveal little about other important

aspects of an individual’s or a community’s level of

health, including dysfunction and disability associated

with diseases, injuries, and other health problems.

Developing a composite index of overall health by combining data about the presence or absence of various

diseases and conditions is problematic.

In the 1980s, the search began for additional measures to supplement traditional measures of morbidity

and mortality. Health status is now seen by the public

health community as a multidimensional construct

(Patrick 1993). Some of the variables generally considered to be in the domain of health include premature

mortality and life expectancy, various symptoms and

physiologic states, physical functions, emotional and

cognitive functions, and perceptions about present and

future health.

This reframed definition of health also considered

quality of life. As medical and public health advances led

to cures and better treatments of existing diseases and

delayed mortality, it seemed logical that those who

measure health outcomes would begin to assess the population’s health not only on the basis of saving lives, but

also in terms of improving them. The public, too,

became aware that an important dimension was missing

from the traditional health paradigm: the dimension of

the quality of a person’s life. Although biochemical

measures and morbidity data may indicate the need for

treatment, they do not always correlate with the way

people feel (Gill 1994, NIH 1993).

What is quality of life?

Quality of life (QOL) is a popular term that conveys an overall sense of well-being, includingaspects of happiness and satisfaction with life as

a whole. It is broad and subjective rather than specific

and objective.What makes it so challenging to measure is

that, although the term “quality of life” has meaning for

nearly everyone and every academic discipline, what it

actually means is somewhat different for each individual

and group. How do you reach accord about a measure for

quality of life? Perhaps the strongest area of a consensus

is that quality of life is extraordinarily broad and conceptually complex, yet measures are most meaningful

when they measure key concepts in a logical way and are

as precise as possible.

Although health is an important domain of overall

quality of life, there are other domains as well—for

instance, jobs, housing, schools, and the neighborhood.

Aspects of culture, values, and spirituality are also key

aspects of overall quality of life that add to the complexity of its measurement. Nevertheless, researchers in the

fields of psychology and sociology have developed useful

techniques that have helped to conceptualize and measure these multiple domains and how they relate to each

other.

“The web of our life is of a mingled

yarn—good and ill together.”

—-Shakespeare, All’s Well That Ends Well
What is health-related quality of life?

The concept of health-related quality of life

(HRQOL) and its determinants have evolved

since the 1980s to encompass those aspects of

overall quality of life that can be clearly shown to affect

health—either physical or mental (McHorney 1999). On

the individual level, this includes physical and mental

health perceptions and their correlates, including health

risks and conditions, functional status, social support,

and socioeconomic status. However, some aspects of

health do not appear to have a direct bearing on quality

of life at the time of assessment. These include an illness,

exposure, or genetic predisposition that is unknown to

the individual without symptoms.

On the community level, HRQOL includes

resources, conditions, policies, and practices that influence a population’s health perceptions and functional

status. The construct of HRQOL broadens the traditional notion of health to meet the expressed physical and

mental health needs of the population. It also enables

health agencies to legitimately address broader areas of

healthy public policy around a common theme in collaboration with a wider circle of health partners, including social service agencies, community planners, and

commercial groups (Stokols 1992).

HRQOL is rapidly gaining acceptance as a measurable outcome. HRQOL questions about perceived physical

and mental health and function have become an important component of health surveillance and are generally

considered valid indicators of service needs and intervention outcomes. Self-assessed health status has proved

a more powerful predictor of mortality and morbidity

than many objective measures of health (Idler 1997).

HRQOL measures make it possible to demonstrate scientifically the impact of quality of life on health, going

well beyond the old paradigm that was limited to what

can be seen under a microscope.

Why is it important to track HRQOL?

Researchers and practitioners in fields outside public health are actively engaged in quality of lifemeasurement, especially those from sociology,

psychology, social work, aging, disability, environmental

sustainability, economics, marketing, and urban/rural

planning. Moreover, business and community leaders,

the media, and the public are interested in community

quality of life and appear willing to grant health agencies

6 Measuring Healthy Days

Introduction
a major responsibility for measuring and helping to sustain it. Focusing on HRQOL as a national health standard can thereby bridge artificial boundaries between

disciplines and between social, mental, and medical services (Pope 1991).

Several recent federal policy changes underscore the

need for measuring HRQOL to supplement public

health’s traditional measures of morbidity and mortality.

Healthy People 2000 and 2010 both identified quality of

life improvement as a central public health goal. In addition, increased awareness of the burden of chronic health

conditions and the links between quality of life and prevention led to a revision of the mission of the Centers for

Disease Control and Prevention (CDC). Further, the

CDC Chronic Disease, Disability, and Women’s Health

Programs have evolved to target quality of life as an

important health outcome.

HRQOL is related to both self-reported chronic diseases (diabetes, breast cancer, arthritis, and hypertension),

and their risk factors (body mass index, physical inactivity, and smoking status). Measuring HRQOL can help

determine the burden of preventable disease, injuries, and

disabilities, and it can provide valuable new insights into

the relationships between HRQOL and risk factors.

Measuring HRQOL will help monitor progress in

achieving the nation’s health objectives. Analysis of

HRQOL surveillance data can identify subgroups with

relatively poor perceived health and

help to guide interventions to improve

their situations and avert more serious

consequences. Interpretation and publication of these data can garner support for health policies and legislation,

help to allocate resources based on

unmet needs, guide the development of

strategic plans, and monitor the effectiveness of broad community interventions. HRQOL

assessment is a particularly important public health tool

for the elderly in an era when life expectancy is increasing, with the goal of improving the extra years in spite of

the cumulative health effects associated with normal

aging and pathological disease processes.

How can HRQOL be measured?

S

everal measures have been used to assess HRQOL

and related concepts of functional status. Among

them are the Medical Outcomes Study Short Forms

(SF-12 and SF-36), the Sickness Impact Profile, and the

Quality of Well-Being Scale. The SF-36 measures are now

used by the Health Care Financing Administration

(HCFA) and the National Committee for Quality

Assurance’s Health Plan Employer Data and Information

Set (HEDIS 3.0) to help evaluate the quality of care in

managed care plans and other health care applications.

While these measures have been widely used and extensively validated in clinical settings and special population

studies, their length often makes them impractical to use

in population surveillance.

Measuring Healthy Days 7

Introduction

Healthy People 2010 Goals

■ Increase the quality and years of healthy life

■ Eliminate health disparities

CDC Mission

“to promote health and quality of life by preventing, 

and controlling disease, injury and disability”
HEALTHY DAYS METHODS

To meet the need for a standard set of valid

HRQOL measures that could be used in our

national health surveillance system, a collaborative program was initiated in 1989 by the Division of

Adult and Community Health (DACH) in the CDC’s

National Center for Chronic Disease Prevention and

Health Promotion (NCCDPHP). This HRQOL surveillance program received its initial direction and guidance

from several planning meetings that included representatives of state and local chronic disease and health promotion programs, relevant academic disciplines, and

survey researchers (CDC 1993-1, CDC 1993-2).

During the next several years, the Division worked

with CDC’s Disability Prevention Program, Women’s

Health Program, National Center for Health Statistics

Questionnaire Development Research Lab, and

Epidemiology Program Office to develop and validate a

compact set of measures that states and communities

could use to measure HRQOL (Hennessy 1994). These

are the Healthy Days measures, an integrated set of

broad questions about recent perceived health status

and activity limitation. On the basis of a synthesis of the

scientific literature and advice from its public health

partners, the CDC has defined HRQOL as “an individual’s or group’s perceived physical and mental health

over time.”

The core Healthy Days measures assess a person’s perceived sense of well-being through four questions on: 1)

self-rated health, 2) number of recent days when physical

health was not good, 3) number of recent days when

mental health was not good, and 4) number of recent

activity limitation days because of poor physical or mental health (see BRFSS “Health Status” questions @

http://www.cdc.gov/nccdphp/brfss/). For the Healthy Days

measures, recent is defined as during the past 30 days.

The first item measures overall self-rated health on a

scale from poor through excellent. Question #2 on physical health is a global measure of recent physical symptoms, and question #3 is a global measure of recent

mental and emotional distress. Mental and physical

health are probed in separate questions in order to link

quality of life measurement to the medical, mental

health, and behavioral medicine fields. Question #4

about recent activity limitation is a global indicator of

perceived disability as well as an indicator of productivity and human capital.

How is the summary index of unhealthy days

calculated?

Unhealthy days are an estimate of the overall

number of days during the previous 30 days

when the respondent felt that either his or her

physical or mental health was not good. To obtain an

estimate of a person’s overall unhealthy days, responses

to questions #2 and #3 are added together, with a logical

maximum of 30 unhealthy days. For example, a person

who reports 4 physically unhealthy days and 2 mentally

unhealthy days is assigned a value of 6 unhealthy days

and someone who reports 30 physically unhealthy days

8 Measuring Healthy Days

Healthy Days Methods

Definition

Health-related quality of life

“An individual’s or group’s perceived 

physical and mental health over time”

Core Healthy Days Measures

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 how many days during

the past 30 days was your physical

health not good?

3. Now 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?

4. During the past 30 days, for about

how many days did poor physical or

mental health keep you from doing

your usual activities, such as self-care,

work, or recreation?
and 30 mentally unhealthy days is

assigned the maximum of 30

unhealthy days.

This method for estimating

unhealthy days is supported by the

actual pattern of survey responses

to the two individual questions.

The large majority of individuals

report substantially different numbers of physically unhealthy days

versus mentally unhealthy days,

e.g., in the 1998 BRFSS, 67.8% of

the 68,619 adults who reported

any unhealthy days, reported only

physically unhealthy days or only

mentally unhealthy days, while

only 4.5% reported equal numbers

for each measure. Additional evidence indicates that other reported

days do not overlap, e.g., 10.5% of

the 256 persons who reported both

15 physically unhealthy days and 15 mentally unhealthy

days also reported more than 15 days of recent activity

limitation due to poor physical or mental health. An

alternative calculation method that assumed a maximum

amount of overlap in the two responses (e.g., a person

who reports 4 physically unhealthy days and 2 mentally

unhealthy days is assigned a value of 4 unhealthy days)

was not as plausible from the overall response pattern.

Furthermore, this latter method resulted in only a 0.4 day

overall mean difference in unhealthy days compared with

the recommended method and showed similar demographic patterns and subgroup differences with aggregated population data.

Unhealthy days provides a

simple, yet comprehensive,

HRQOL summary measure that is

a valid and responsive index of

perceived physical and mental

health over time (Newschaffer

1998, Moum 1999) and that is

generally acceptable to public

health and social science

researchers, policy makers, and

practitioners. Healthy days—a

term coined by columnist Jane E.

Brody of the New York Times in

an article describing the first published comparisons of state

HRQOL (NYT, March 29,

1995)—was formerly called “good

health days” and is a positive complementary form of unhealthy days.

Healthy days estimates the number

of recent days when a person’s

physical and mental health was

good (or better) and is calculated

by subtracting the number of

unhealthy days from 30 days

(Hennessy 1994). These summary

measures are designed to assess

people’s overall perceptions about

their health over time and to identify groups in the general adult population with potentially unmet perceived health needs.

Why collect data about health

perceptions?

People’s self-perceptions about

their health are very important in the present as health

outcomes and can serve as proxy measures for the perceived symptom burden of both acute and chronic

health conditions. Also, because people generally seek

health care only when they feel unhealthy, self-perceptions are also predictive of the future burden on the

health care delivery system (Idler 1997, Pijls 1993). The

Healthy Days measures, then, work as both outcome

measures and predictors. The core set of Healthy Days

measures has been used continuously by all states as the

first four questions of the Behavioral Risk Factor

Surveillance System (BRFSS) since 1993.

Measuring Healthy Days 9

Healthy Days Methods
What is the BRFSS?

The BRFSS is a continuous, state-based, random

telephone survey of community-dwelling U.S.

adults aged 18 and older (see BRFSS website at

http://www.cdc.gov/nccdphp/brfss/). It is the largest, continuously conducted telephone health survey in the

world. It helps agencies monitor modifiable risk factors

for chronic diseases and other leading causes of death.All

50 states and the District of Columbia participate in the

BRFSS, and many specialized national, state, and local

surveys use both its methods and its measures. Therefore,

the BRFSS is an important public domain resource for

continuous, comparable data about population health.

What is the advantage of having core HRQOL 

questions on the BRFSS?

The BRFSS is the primary source of state-based

information on risk behaviors among adult populations. Data collection is flexible, timely, and

ongoing. CDC edits and processes data from each state’s

monthly interviews, then returns prevalence information

and selected reports to all states for their use, allowing for

state-to-state and within-state comparisons. The BRFSS

gathers information on age, gender, racial and ethnic

background, education, marital and employment status,

the county of residence, and other demographic factors

so that estimates can be made for specific population

groups. These data can be used as a benchmark to determine how perceived health and activity limitations vary

over time.

More than 900,000 adults have responded to the core

HRQOL questions as part of the BRFSS since their introduction in 1993. Adding the core HRQOL question to the

BRFSS has also stimulated interest in HRQOL as a public health outcome. Because the BRFSS is the survey that

most closely tracks geographical and temporal differences, using the core questions on other special population studies and assessments permits comparability with

general population data. For comparability with national surveys, the core Healthy Days questions were added to

the examination component of the National Health and

Examination Survey (NHANES) beginning in 2000.

The BRFSS was chosen as a vehicle for questions on

HRQOL because of its broad coverage that permits

state- and locality-based estimates and its high visibility

as a surveillance mechanism within the public health

community. The new annual survey format for

NHANES offers additional opportunities for both surveillance and prevention research. Moreover, including

the core questions in existing periodic surveys helps

minimize the costs of HRQOL surveillance.

Why ask “How many days . . . “ when other 

questions like rating your overall health are easier

to answer?

F

irst, there is a policy value of estimating the burden of disease or disability in days, months, or

years because it provides concrete measures that

can be understood by legislators and policy makers and

can be used in prevention effectiveness studies to assess

cost-effectiveness of alternative interventions. As they

go about and plan their lives, people tend to think in

terms of monthly intervals. High BRFSS response rates

for these questions show that most adults are able to

estimate the number of days—even if they are making

only rough estimates, e.g., in 1998 there was a 98.6%

response rate for recent physical health days, 98.5% for

recent mental health days, and 99.3% for recent activity

limitation days. In addition, quantifying estimates in

terms of days in the most recent month avoids the need

to use complex weights in aggregating and comparing

data that are based on multiple choice questions. Most

important, HRQOL is inherently a time-related phenomenon that is best measured over, or with reference

to, a specified period of time.

How can you measure HRQOL with only four 

questions?

Although the four basic questions may tell how

population subgroups rate next to the general

population, they do not provide enough information to identify specific public health interventions

because they only track general health needs.

10 Measuring Healthy Days

Healthy Days Methods
Therefore, CDC and several state and community

health agencies began in January 1995 to collect data 

on an additional 10-item set of health perception and

activity limitation questions. The additional set of

10 questions comprises an optional quality-of-life 

module that states and communities can choose to

include in their surveys (see “Quality of Life Optional 

Module” questions on the BRFSS questionnaire at

http://www.cdc.gov/nccdphp/brfss/). With support from

CDC’s Arthritis Program and Disability and Health

Program, about half of all states were using the additional 10-question set in 2000.

These questions include measures for pain, depression, anxiety, sleeplessness, and vitality. Data in response

to these measures provide more information on specific

potentially remediable causes of poor HRQOL indicated

by the first four more global measures. The expanded

HRQOL-14 questions measure the burden of both

short-term and persistent physical and mental health

problems in a manner that disease-specific health planners and legislators can use to allocate resources among

competing health programs and to guide health policy

by tracking important short- and long-term effects of

health programs.

The expanded set of questions will make it possible

to compare the perceived burdens of diseases and conditions as well as to differentiate health benefits that

alternative interventions yield. Use of the expanded set

of questions may be one of the most cost-effective ways

of assessing the population need or susceptibility for

health services, disease incidence, and death. HRQOL

may be a major determinant of many behavioral risks

and may be easier to directly modify than the risks

themselves. For example, treatment of anxiety and

depression among adults who smoke or are overweight

may reduce their risk of disease and death and lead

them to make and maintain healthy behavioral changes.

Why are most of the measures oriented toward the

negative side of health?

Unlike disease or death, HRQOL is a health concept that covers the full spectrum of health andis not inherently positive or negative in its orientation. In representative community populations

most persons tend be at the healthy end of the spectrum—for example, 85.6% of BRFSS adult respondents

reported that their overall health was good to excellent

(Table 1). However, to guide public health and social

policy, it was important to have HRQOL measures that

best identify and distinguish those at the lower end of

the health spectrum who have health conditions that

could most benefit from healthier environments, early

diagnosis, and appropriate treatment. Therefore, more

measures were developed that asked about negative

HRQOL qualities, such as pain, depression, and activity

limitation than about positive qualities such as feeling

very healthy and full of energy. Also, it was clear from

cognitive studies and field tests of the Healthy Days

measures that—because most respondents had fewer

recent days affected by these negative qualities—it was

easier for them to estimate the number of unhealthy versus healthy days.

Measuring Healthy Days 11

Healthy Days Methods

Additional Healthy Days Measures

1. any activity 

limitation

if yes...

2. major cause

3. how long

4. routine care

5. personal care

6. pain days

7. depression days

8. anxiety days

9. sleepless days

10. vitality days
How do the Healthy Days measures differ from

QALYs, DALYs, and YHLs?

Quality-adjusted life years (QALYs) are estimates of person-years lived at particular levels

of health. They are mostly used in cost-effectiveness analysis and clinical trials involving health

conditions that consider the quality as well as the

length of life. Quality is typically measured on a scale

of zero (death) to 1.0 (perfect health) by assigning

various weights to potential health states. There is also

a group of somewhat related measures, including

Disability-Adjusted Life Years (DALYs) and Years of

Healthy Life (YHL), that adjust life expectancy estimates with weighted estimates of health and function

(Murray 1998, Erickson 1995). On a population level,

these latter types of estimates are most useful for guiding health policy and for modeling what we know about

death, disease, and their burden, especially at the

national and multi-national level.

In contrast, the Healthy Days measures are direct

estimates of a population’s health over time derived

from asking a representative sample of people what

proportion of their recent days were spent at particular

levels of health. Although they could potentially be used

for estimating a population’s healthy years of life and

health-adjusted life expectancy, they were specifically

designed as HRQOL surveillance measures capable of

identifying disparities and trends and evaluating

changes based on broad population interventions.

Because they work well at the community and smallgroup level and also reflect population preferences for

quality as well as length of life, the Healthy Days measures complement the other more complex and comprehensive population health measures.

FINDINGS

What have been some of the findings from the

Healthy Days core questions?

Nationwide

So far, adult survey respondents have said they have an

average of 24.7 healthy days (or 5.3 unhealthy days) a

month, which means that 82.3% of all adult days are

reported as healthy (Table 1). Although healthy days

declined only modestly with increasing age, an interesting finding was that young adults reported consistently

worse mental health versus the oldest age groups,

whereas older adults reported considerably more physical health problems than younger adults.

Acquiring representative data about a population

can help identify significant health differences among

subgroups. For example, the highest average number of

healthy days was reported by college graduates, Asian

Americans (English-speaking), and persons with annual

household incomes above $50,000 (CDC 1994). The

least mean number of healthy days was reported in people who were unemployed, separated, aged 75 years or

older, or with less than a high school education. Fewer

healthy days were also reported by those smoking cigarettes or having a chronic health condition tracked by

the BRFSS (i.e., high blood pressure, diabetes, or breast

cancer) (Table 1).

Nearly one third of Americans say they suffer from

some form of mental or emotional health problem every

month—including 11 percent who said their mental

health was not good more than seven days a month.

Women had more poor mental health days than men

(Borawski 1998). Younger Americans, aged 18 to 24

years, suffered the most poor mental health days of all

adult age groups. The lowest average was for people over

age 65 years, with 1.9 poor mental health days a month.

The unemployed also had a high average of poor mental

health days, at 5.2 days, but people unable to work

because of disabilities fared worse at 8.9 poor mental

health days a month. People without health coverage

were considerably more likely to suffer poor mental

health days a month than people with health coverage

(4.2 versus 2.6 days a month).

Several factors seem to predict “frequent mental distress,” which is defined as 14 or more days during the

previous 30 days when mental health was not good.

These factors include being unable to work, being previously married but now separated, having an annual

household income of less than $15,000, having less than

12 Measuring Healthy Days

Healthy Days Methods/Findings
a high school education, and being an American Indian

or Alaska Native. Levels of stress and mental distress are

predictive of medical diseases and health services utilization, and data based on the Healthy Days questions

allow examination of the reciprocal influences of body

and mind.

These data have also shown how reported health differs by place of residence—by state or by proximity to an

urban center (CDC 1995). A benefit of tracing perceived

health status over time is that it allows study of seasonal

patterns and the effects of health-related events over

time.

State comparisons 

Comparisons of Healthy Days data at the state level and

among socioeconomic and demographic subgroups have

helped to identify potentially remediable geographic and

demographic disparities in health status. After adjusting

for age differences, one such comparison found HRQOL

differences among states for adults without a high-school

degree, but more importantly found notable HRQOL differences within each state (e.g., people with less education

typically had lower HRQOL than those with more education) (CDC 1998-2).

Measuring Healthy Days 13

Findings

Percentage of adults reporting frequent

mental distress (FMD) by selected 

sociodemographic group, 1997 BRFSS

Men 7.3

Women 10.5

Whites 8.7

African Amer. 10.2

Asian/Pac. Is. 6.9

Amer. Ind/Al. Natives 15.0

Hispanic 11.0

< H.S. Education 13.3

H.S. Graduates 9.4

College Graduates 5.8

Health Plan = Yes 8.2

Health Plan = No 13.7

Disabled = No 6.0

Disabled = Yes 20.1

Frequent Mental Distress = mental health was not good for

14 or more days in the past 30 days

See also: Centers for Disease Control and Prevention. 

Self-Reported Frequent Mental Distress Among Adults—

United States, 1993-1996. MMWR, 1998; 47:325-31 @

http://www.cdc.gov/epo/mmwr/mmwr.html

Mean number of “healthy days” among adults with less than a high school education,* 

by state—United States, Behavioral Risk Factor Surveillance System, 1993-1996

*Age-adjusted to the 1990 U.S. population aged ≥18 years.
Seasonal patterns

During the first 72 months of data collection with the

four core Healthy Days questions, a clear seasonal pattern was observed for each of the three “days in the past

30 days” measures. This was most evident in the

unhealthy days summary index.

When the 72 months of data were combined and

aggregated by calendar quarter, a striking 10% difference was noted between the winter months of January,

February, and March, and the summer months of July,

August, and September.

14 Measuring Healthy Days

Findings

Time trends

During the first six years of data collection, for the period 1993–1998, several trends have been noted at both

the national and state levels with the four Healthy Days

measures. The most striking nationwide trend has been

a significant increase in Frequent Mental Distress

(FMD) reported by women —both younger (aged <45

years) and older (aged ≥45 years). A similar trend was

not observed for men, although younger men (aged

<45 years) showed a non-significant tendency toward

higher levels of FMD during the last 2 years of analysis

(1997 and 1998).

Several significant upward and downward trends

related to the core Healthy Days measures have also

been detected at the state level and are now being analyzed.
VALIDATION

Do these questions accurately measure HRQOL?

S

pecifying HRQOL represents a unique effort on the

part of national policy makers to formally recognize quality of life as an important component of

health. However, establishing valid and reliable measures

of a subjective self-report is challenging. Validity is the

degree to which a set of questions measures what it is

supposed to measure. Validity can be assessed in several

ways. Construct validity is the ability of the question to

correlate with other measures that it should correlate

with. Criterion validity compares the performance of a

measure with some other measure of the condition

under study—ideally a “gold standard” accepted in the

field. Concurrent criterion validity means the measures

being evaluated are correlated with an established criterion measure, both of which are available at the same

time. Predictive criterion validity refers to the usefulness

of the measure in predicting future health-related events

and states (Streiner 1995).

Validation has been identified as an essential prerequisite for a useful set of measures by leaders in both of

the major international quality of life research societies:

ISQOLS, the professional society that concentrates on

general QOL studies, and ISOQOL, the organization

dedicated to HRQOL research. Therefore, the CDC

HRQOL measurement program has concentrated its initial efforts on validation.

Construct validity

HRQOL, though fundamentally subjective, can be validated by statistically correlating self-reported survey data

with other more objective or established health outcomes and measurements. Some studies have examined

how the Healthy Days HRQOL measures compare with

other established measures like the Medical Outcomes

Study Short Form 36 (Newschaffer 1998, Andresen 1999-

1). The SF-36, which is widely used in clinical studies of

HRQOL,was developed by the Rand Corporation during

the 1980s to measure the functional status and perceived

well-being of representative U.S. patient populations.

Analyses of the four core questions in representative surveys of adults found that the Healthy Days measures are

internally consistent and that they identify known or suspected population groups with unmet health-related

needs, including persons with reported chronic health

conditions, disabilities, and low socioeconomic status

(Diwan 1995, Nanda 1998, Andresen 1999-3, Moriarty

1999-2).

Measuring Healthy Days 15

V

Important HRQOL Validation issues

alidation

■ Can HRQOL be clearly defined?

■ Can HRQOL be accurately measured by

survey?

Will people answer?

Will their responses make sense?

■ Are responses merely personality-based?

■ Do interviewers influence responses?

■ Are responses consistent with other

accepted data?

■ What do results mean in practical policy

terms (i.e., why is HRQOL measurement

important)?

Validation of BRFSS HRQOL Measures

in a Statewide Sample 

St. Louis University School of Public Health

C. Newschaffer, J. Jackson-Thompson, M. Counte

Key HRQOL-14 Findings: N=588

1. Good construct validity in a statewide

adult population...

■ demography & socioeconomic status

■ reported chronic diseases

■ depression-screen positive

2. Acceptable correlation with related 

SF-36 scales

depression .55

pain .56

vitality .50

3. BRFSS items explain most variation in 

SF-36 summary scores

4 Qs -- >59% of PCS (physical)

3 Qs -- >64% of MCS (mental)
Concurrent validity

Several validation studies are being conducted to assess

the set of questions.A validation study performed by the

St. Louis University Prevention Research Center and the

Missouri Department of Health featured a simultaneous

comparison of the 14 HRQOL questions on the BRFSS

and the SF-36 in a statewide general population

(Newschaffer 1998). Results for both sets of measures

varied in similar fashion over sample characteristics.

The researchers found that the HRQOL-14 has good

construct validity and should be considered for both

surveillance and research applications. In addition, they

determined that the HRQOL-14 has acceptable criterion

and known-groups validity. In this study, the healthy

day’s summary measure was found to be the most valid

measure of a quality-of-life deficit in a mixed population with concurrent physical and mental health problems.

The unhealthy days measure also shows good concurrent validity when compared with the categorical

responses of the self-rated health measure for all adults

and for geographic and demographic subgroups. When

data are adjusted for age, there is a tenfold difference in

unhealthy days between adults reporting excellent versus poor general health (Table 1). This comparison also

illustrates the value of using a continuous measure such

as unhealthy days by clearly showing much larger differences in perceived mental and physical health at the

“poor” end versus the “excellent” end of the categorical

self-rated health measure. Further, it shows that some of

those who say their health is excellent still report some

unhealthy days and those who report poor health also

report some healthy days.

The unhealthy days measure also was found to be

directly related to a global life satisfaction question—a

frequently assessed construct in studies of overall (not

just health-related) quality of life. This relationship—

although not as strong as in the previous comparison

with general self-rated health—nevertheless shows that

HRQOL is a major component of overall QOL.

16 Measuring Healthy Days

Validation
Predictive validity

Studies are also underway to examine the ability of the

Healthy Days measures to predict morbidity and mortality. The Pennsylvania State University Department of

Biobehavioral Health is now tracking 82,000 low-income

elderly adults in a predictive validity study to link the

HRQOL-14 with prescriptions, health care utilization,

and mortality (Ahern 1999). This study has been funded

by the CDC Office on Women’s Health because about 3/4

of the respondents in this study are women and quality

of life is an important women’s health issue. Early results

of this study suggest that a mailed version of the questions has good construct validity and that self-rated

health and each of the “days” questions are valid predictors of short-term mortality.

Acceptability

Another study involving persons with known disabilities

in the community and in institutional settings, performed by the St. Louis University School of Public

Health, concluded that the 14 HRQOL questions have

Measuring Healthy Days 17

Validation

Measuring Health and Disability

with the CDC’s BRFSS 

St. Louis University School of Public Health

E. Andresen, B. Fouts, F. Wolinsky, 

C. Brownson, J. Romeis

Key HRQOL-14 Findings: N=513

1. Good construct validity in a population

of persons with disabilities

2. Good respondent acceptability ratings

IADL 93%

HRQOL-14 92%

SF-36 90%

QWB 87%

3. Acceptable correlation with related 

SF-36 scales

depression .71

vitality .69

anxiety .67

pain .61

Quality of Life, Medications, and

Health Among the Elderly 

Penn State University Department 

of Biobehavioral Health

College of Health and Human Development

F. Ahern, C. Gold, K. Dominick, 

L. Markovitz, D. Heller

Key HRQOL-14 Findings: N=82,853

1. Good construct validity in a statewide

population of older low-income 

adults...

■ demography & socioeconomic 

status

■ disease

■ residential status

■ prescription drug use

2. The core Healthy Days measures predict

mortality/hospitalization

criterion validity with respect to the SF-36 and are

acceptable for use with people with disabilities in both

surveillance and research (Andresen 1999-1).

Reliability and responsiveness

Reliability is the consistency or degree of dependability

of a measuring instrument. Responsiveness is the degree

to which a measure is capable of reflecting changes over

time. The University of Oslo studied both of these characteristics in a nationwide study of Norwegian adults

with a follow-up survey and found that the Healthy Days

measures had good internal consistency reliability and

that response changes on the follow-up survey were

indicative of actual changes in respondent health status

(Moum 1999). A telephone-based reliability study by St.

Louis University School of Public Health in a population

of persons with known disabilities (N=52) found substantial re-test reliability after about one week for the

eight “days” questions, but not as good reliability for the

six categorical response measures (Nanda 1998).

Another longitudinal validation study with older,

low-income, African American males conducted by
Columbia University School of Public Health found that

changes in respondents’ answers to the physical health

days question were consistent with reported medical

care utilization over a period of several months. In this

study, the mental health days and depression days measures showed impressive correlations with the Center for

Epidemiologic Studies Depression Scale (CES-D).

What challenges in measurement have been 

identified?

S

ome degree of response error is unavoidable when

complex concepts are being measured. Response

errors can occur if the respondent has difficulty

interpreting either what the question is really asking or

what a satisfactory response will be. Further confounding this potential difficulty are issues unique to surveying older respondents, differences in how cultural

groups view their health (Larson 1998), and issues

inherent in administering surveys by telephone, in-person, or by mail (Moum 1998).

Generally, the response format (in this case, number

of days) signals respondents about what sort of infor18 Measuring Healthy Days

Validation

mation is being requested and how to formulate an

answer. However, older people often provide narrative

answers and are less likely to respond within the format

specified by the question. A National Center for Health

Statistics (NCHS) cognitive study indicated that older

respondents may have trouble translating frequency

information recalled from memory into required survey

response categories (Beatty 1996, Schechter 1998). The

level of their health and activity limitations may not easily be described as occurring or not occurring for a given

number of discrete days, and memory problems may

further compromise the accuracy of their answers. Many

respondents appear to give a response that represents

their overall impression of their health over the recent

past versus an actual count of days.

Good results on reliability, responsiveness, and

validity studies, however, suggest that the response

error rate is not too great. For instance, in a recent

mixed-mode panel study of the reliability, responsiveness, and other measurement properties of the

Validation of BRFSS QOL Items:

Harlem Prostate Screening Project 

Columbia University College 

of Physicians and Surgeons

S. Albert

Key HRQOL-14 Findings: N=239

1. Acceptable construct validity in a population of low-income older African

American males

2. Good correlation with other scales

pain .60 SF-36 (pain)

days-mental .59 CESD

depression .58 CESD

2. Unhealthy physical days were responsive

to changes in reported doctor appointments and hospitalizations
Healthy Days measures in a nationwide sample of

2400 Norwegian working-aged adults, the measures

were found to have normal reliability and test-retest

correlations (Moum 1999). Almost all items show

responsiveness to change in respondent-defined

“health”; the summary measure of healthy days was

the best predictor.

The Norwegian-language study noted that there

were some expected mode effects with persons reporting somewhat more impaired mental health on the selfadministered mail version versus the telephone version

of the questions (Moum 1998). In the only U.S. study

that has examined mode effects, the St. Louis

University study of persons with known disabilities

found generally better HRQOL reported by respondents for the HRQOL-14 measures in the in-person

interviews versus the telephone interviews, with significantly better HRQOL reported in-person for the activity limitation days, any current activity limitation, and

needing help with personal care measures (Andresen

1999-1).

Measuring Healthy Days 19

Validation

Other Healthy Days Validation Studies 

Research Group Lead Scientist Key Features

COMPLETED:

St. Louis U. SPH C. Newschaffer SF-36, chron dis/depression

NCHS Cog. Lab S. Schechter cognitive interviews (elders)

Georgia State U. S. Diwan focus groups (elders)

U. of Michigan L. Verbrugge activity limitations

Case Western U. E. Borawski severe work disability

IN PROGRESS:

McMaster U. S. Ounpuu disease, demography

U. Oslo T. Moum psychometrics, mode, 

reliability & responsiveness

What is being done to address problems in 

accuracy?

I

nterviewers are trained to help respondents who

have difficulty with numbers to provide a numerical

response of symptom days. For example, if someone

answers “a few days” or “several days,” the interviewer

would then ask, “Was that for 3 days, 4 days, 5 days, or

more than that?” Also, research that correlates nonresponse and inconsistent responses for persons with

diagnosed physical and mental conditions, including

dementia, is being planned in longitudinal studies that

have incorporated the core Healthy Days measures.

Other studies are underway to examine statistical issues

of sampling, population weighting, and aggregation of

BRFSS estimates (Schulman 1999).
PRACTICAL APPLICATIONS

HRQOL is more likely than other health outcomes

to reflect broad consequences of disease or

injury. The Healthy Days questions may also

capture health-related factors that are underreported,

such as violence against women. In many cultures, a

quality-of-life focus could offer a unifying theme for programs as diverse as health services and economic development. Such a focus can also help to ensure that important mental health issues, often ignored or inadequately

considered in health status measures and productivity

indicators, will be addressed. Asking about recent physical and mental health appears to capture a large proportion of the concerns persons have about the quality of

their life related to health. This is a simple but powerful

way of broadening the scope of health services to include

psychosocial aspects that are now recognized as important to the health and productivity of a population

(Siegel 1994).

What are some of the cross-cultural uses of the

Healthy Days measures?

During the last few years, there has been increasing

interest in the use of the Healthy Days measures

by other countries, as the population surveillance value of the measures and data is becoming evident

(Lankenau 1995). The measures were first used outside

the U.S. in a population health survey in Hamilton,

Ontario, in December 1995 (N=1,042) (Ounpuu 2000).

This study found the four core Healthy Days measures

acceptably valid and supported their use in monitoring

the health of populations. This study also found that the

Healthy Days data provided a useful organizing framework for the measurement of HRQOL in a Canadian

city. On the basis of this success, a follow-up survey with

the expanded set of Healthy Days measures is under way.

In a late-1999 Canadian study designed by a different

research group, the four core Healthy Days measures

have been used in a province-wide British Columbia survey of adult health and well-being.

The full set of measures has been used in a Spanishlanguage version of the BRFSS and some of the questions

have been translated for use in Sweden and among various Asian immigrant groups in the U.S. The self-rated

health and the eight “days” measures have also been studied in depth by the University of Oslo in a nationwide

sample of Norwegian adults (N=2,481) in a mixed-mode

(telephone versus mail) panel study (Moum 1999). They

have been found to have good respondent acceptability

(low non- response), normal test-retest reliability, good

concurrent validity, and good responsiveness to change

over time. The summary measure of healthy days was

determined to be the best predictor of change.

How are the Healthy Days measures useful at the

state and local levels?

CDC,states, and others are now developing the utility of the HRQOL measures for state and localagencies to identify vulnerable subgroups

(Campbell 1999, Perry 1996). Further analysis is underway

at CDC—in collaboration with the Public Health

Foundation—to identify valid community-level HRQOL

indicators, such as rates of suicide, alcoholism, divorce,

and unemployment, that can be used to supplement

direct survey data available to communities (Kanarek

2000). Potential community indicators are being compared with aggregate Healthy Days data to determine

those that explain differing local levels of population

HRQOL.

Communities that incorporate the HRQOL measures in their local surveys will be better able to partner

with the growing number of state and local health

groups that use community health assessments.

Disseminating data at the local level is a powerful tool to

20 Measuring Healthy Days

Practical Applications
foster a sense of community ownership and participation

(Dever 1991, IOM 1997). CDC and state and local health

agencies benefit by learning about community indicators, concepts of sustainability, and the influences of the

environment and the economy on population health.

Adopting a quality of life approach to community

health assessment can offer health agencies a popular

and positive way to integrate diverse activities and contribute to the vitality of their communities (Moriarty

1996, 1999-1). Impaired health days may reflect poor

health days on the job and increased health care use as

well as diminished quality of life. These measures translate well to partners in public health such as the business

and education communities. Because data based on the

Healthy Days measures reflect the combined effects of

many groups’ actions in a community, successful interventions and healthy public policies require active partnerships with the community’s major players.

Measuring Healthy Days 21

Practical Applications

COST-EFFECTIVENESS ANALYSIS 

An economic evaluation in whichalternative programs, services, or

interventions are compared in terms of

the cost per unit of clinical effect (for

example, cost per life saved, cost per 

millimeter of mercury of blood pressure

lowered, or cost per quality-adjusted

life-year gained). The last form of measuring outcomes (and equivalents such 

as “healthy days of life gained”) gives

rise to what is also referred to as 

COST-UTILITY ANALYSIS.

From: Glossary of Methodologic Terms (American

Medical Association, Archives of Internal Medicine). 

http://archinte.ama-assn.org/info/auinst_term.html

How are the HRQOL measures being used to identify

and address the needs of special populations?

Uses in clinical medicine

Although the HRQOL-14 measures were designed for

public health surveillance, they may also prove useful in

helping to measure the medium- and long-term effects

of medical care. Recently, the Foundation for

Accountability (FACCT) in Portland, Oregon, has been

developing chronic disease outcome measures for diabetes, asthma, and coronary artery disease (See

FACCT|ONE at http://www.facct.org/ ). The initial version of these measures includes both the SF-12 (an

abbreviated form of the SF-36) and the core Healthy

Days questions. Inclusion of the core Healthy Days questions in clinical assessments provides useful data on how

patient populations differ from those in the broader geographic community.

Disability 

A study conducted by the St. Louis University School of

Public Health concluded that the HRQOL-14 questions

are acceptable to use with people with disabilities

(Nanda 1998). A study at Case Western Reserve

University studied the county-level prevalence of severe

work disability and found that the estimates of HRQOL

from the BRFSS were highly correlated with U.S. Census

and Social Security figures (Borawski 1999, Jia 1999).

Healthy Days measures are now used by all Disability

Prevention States as part of a standard set of questions
that includes the HRQOL-14 questions and nine questions that pertain specifically to disability and related

concepts. Because it includes the HRQOL module, the

22 Measuring Healthy Days

Practical Applications

Disability set of questions makes it possible to compare

persons with disability to the general population, and

this comparison clearly shows the large overall differences in HRQOL burden (CDC 1998-2). Information

can be culled from the BRFSS about preventive health

behaviors practiced by an important population at elevated risk for many adverse health outcomes. The

Disability measures are part of a 14-state program to

increase surveillance of disability.

CDC and others are now studying potential uses of

the HRQOL data in tracking disability in the adult population and in estimating state-level economic implications of reported activity limitation (Andresen 1999-2,

Andresen 2000, Verbrugge 1999). Public health disability

research has broadened to concentrate on outcomes of

disability, especially on secondary conditions that are

common and preventable. This surveillance will also be

helpful in targeting programs to prevent secondary conditions associated with disability and to identify environmental determinants of disability.

The Severe Work Disability Rate by County, 1995–1996 BRFSS
Racial and ethnic groups

According to results from the core HRQOL questions,

Native Americans and Alaskan Natives reported the

highest mean number of impairment days of any subpopulation (Table 1). Survey results may even be underreported because Native Americans have the highest percentage of phoneless households of any racial or ethnic

group (an estimated 23% in 1990)(Gilliland 1998). This

HRQOL disparity alerts program planners to the need to

focus efforts on Native Americans and Alaskan Natives.

Such findings of health deficits can be considered in

planning future Indian Health Service services (John

1999).

Puerto Ricans responding to a Spanish language version of the BRFSS also report high levels of HRQOL

impairment. These responses suggest the need for further study of this population and the potential value of

translating the Healthy Days questions into other languages.

Chronic disease epidemiology 

HRQOL surveillance is particularly relevant to the field

of chronic disease epidemiology. It provides direct evidence of the considerable population burden of longterm health conditions. The Healthy Days measures and

population data clearly show that persons who report a

current activity limitation because of a chronic health

condition also report much more symptom burden than

those with no limitation (CDC 1998-2)(Table 2). The

extent to which particular symptoms are reported tends

to reflect the expectations for each disease—for example,

high levels of pain for cancer and arthritis. In chronic

disease programs, HRQOL is useful both as a health outcome and a risk factor.

Measuring Healthy Days 23

Practical Applications

Mean symptom days for selected activity-limiting conditions 

1995-1997 BRFSS (13 states)

MAIN CAUSE OF LIMITATION

MEASURE NO LIMIT ARTHRITIS CANCER DEPR/ANX

Unhealthy days 4 12 19 21*

Low vitality 10 18 22 24*

Pain 1 13* 12 7

Depression 2 5 10 19*

Anxiety 5 8 11 19*

Sleeplessness 7 9 11 13*

Limitation of 1 5 13* 13*

usual activities

* highest mean for symptom
Arthritis

The Arthritis Program at CDC is interested in assessing

quality of life in a nationally representative sample of

persons with reported or diagnosed arthritis and related conditions—including the extent to which HRQOL

varies by level of physical fitness and lower body

strength. The National Arthritis Action Plan targets

HRQOL data as a key surveillance need (Arthritis

Foundation 1999).

Surveillance for

arthritis is critical

for understanding

the epidemiology of

this disease, targeting interventions,

developing policy,

and setting priorities for prevention

research. Specific

activities to improve arthritis surveillance include

encouraging states

to use the full set of

HRQOL-14 measures. Analyzing these data should help

quantify the impact of arthritis on quality of life.

Preliminary results from an analysis of 1996– 1998

BRFSS data showed that, compared with respondents

without arthritis, persons with arthritis (defined as having chronic joint symptoms or doctor-diagnosed arthritis) reported worse age-adjusted HRQOL for both

females and males on all four core BRFSS measures

(CDC 2000). HRQOL measures will be useful to states

for tracking arthritis- related Healthy People 2010

objectives and for measuring progress toward the plan’s

goal of increasing quality and years of healthy life.

Cardiovascular health

The Cardiovascular Health Branch of the CDC/NCCDPHP Division of Adult and Community Health has

recently begun to study the use of the Healthy Days

measures in its research. In recent presentations of data

derived from the BRFSS, the measures were shown to be

useful in characterizing the excess symptom burden of

self-reported heart disease and stroke. The apparent

association of HRQOL with cardiovascular disease risk

and protective factors is a promising area of research that

offers hope for new disease prevention and control

strategies (Greenlund 2000).

Aging 

The Healthy Days measures and population data are particularly useful for identifying disparities among vulnerable groups of older adults, because many of them have

chronic health conditions that are not easily assessed by

other available means. In a December 1999 MMWR

Surveillance Summary analysis, the CDC Health Care

and Aging Studies Branch reported notable state differences in mean unhealthy days reported by adults aged 75

24 Measuring Healthy Days

Practical Applications

National Arthritis Action Plan

Recommendations on Quality of 

Life Surveillance, 1999

■ Encourage states to use the BRFSS 

modules on arthritis and quality 

of life

■ Encourage the development and use

of modules on arthritis and quality of

life in national data sets (e.g., NHIS,

NHANES)

■ Analyze data on arthritis and quality

of life (e.g., from the BRFSS, NHIS, &

NHANES) to quantify the impact of

arthritis on quality of life

■ Monitor changes in the occurrence 

of arthritis and its impact (e.g., on 

disability and quality of life)
years and older in the 1993–1997

BRFSS (Campbell 1999). Of even

greater policy significance, substantially

more mean unhealthy days were reported by older men and women with low

annual household income than by those

with the highest levels of income—particularly for those aged 55–64 years who

were generally not yet eligible for Social

Security and Medicare benefits.

Women’s health

The Healthy Days measures and data are

important for women’s health, because

they reflect the differential burdens associated with many

pertinent health issues–-including child-bearing, parenting, violence, caregiver burden, arthritis, osteoporosis,

medication use, and level of health related to socioeconomic status. In August 2000, state means for BRFSS

mental health days and activity limitation days were

included as key indicators of women’s mental health and

overall health in the new Making the Grade on Women’s

Health: A National and State-by-State Report Card developed by the National Women’s Law Center (see:

http://www.nwlc.org/display.cfm?section=health ).

Significantly increasing trends of Frequent Mental

Distress can be observed from 1993–1998 for both young

and old women. A higher overall level of unhealthy days

and activity limitation days were also reported by women

than by men (Table 1). These results reveal how the measures highlight gender disparities.

Other chronic and acute health conditions

The value of the Healthy Days measures for assessing the

burden of other chronic and acute health conditions is

now being explored. As brief, validated measures with a

large amount of continuously collected population data,

they offer an attractive complement to surveys seeking to

assess HRQOL burden against a robust population standard. For example, the HIV/AIDS Survivors Survey is

planning to include the 14 Healthy Days measures in its

2000 survey, and the American Cancer Society plans to

include Healthy Days measures in a survey of persons

who have been diagnosed with cancer.

Obesity, physical activity, nutrition, smoking and

other behavioral risk factors 

In addition to their value in reflecting the burden of current health conditions, the Healthy Days measures offer

considerable promise for improving

understanding of how poor HRQOL

may be a determinant of behavioral

risks and protective behaviors (Ford

2000). Researchers have suggested the

potential value of studying how

potentially modifiable HRQOL-14

measures for recent physical health,

mental distress, activity limitation,

pain, depression, anxiety, sleeplessness, and lack of vitality correlate with

health risks (including hypertension,

hypercholesterolemia,smoking, physical activity, and obesity) and protective factors (including use of preventive services and safety gear) (CDC 1998-1). Those people with poor HRQOL may also be those who need

screening and other preventive services and who are

affected by other risk factors for chronic disease.

Measuring Healthy Days 25

Practical Applications
POLICY IMPLICATIONS

What are the policy implications of HRQOL 

surveillance?

The Healthy Days measures are beginning to show

their value for population surveillance and program planning and evaluation. Because the

Healthy Days measures consistently reflect population

differences in educational attainment, income, employment status, marital status, chronic diseases, and disability, and because they correlate with broader community

health status indicators such as the proportion of births

to adolescents, they offer health agencies a useful tool for

guiding healthy public policy and collaborating with

partners outside the health community (Institute of

Medicine 1997). These measures assess the burden of both

short-term and persistent physical and mental health

problems in a manner that is not disease-specific.

Therefore, health planners and legislators can use the

measures and resulting data to help allocate resources

among competing health programs and to guide health

policy by tracking important short- and long-term effects

of health programs. Because of their sensitivity to broad

influences such as seasonal patterns and time trends, the

Healthy Days measures are also likely to be useful in

detecting the impact of major population-based policy or

interventions.

Why is HRQOL surveillance important in Healthy

People 2010?

Healthy People 2010, developed with leadership of

the Department of Health and Human Services,

is the nation’s prevention agenda with a scorecard to assess progress toward meeting goals (DHHS

2000). It is a road map that can be used by states, communities, professional organizations, and others who are

concerned about increasing life expectancy and enhancing population health. Healthy People 2010 has two major

overall goals: 1) to increase the quality and years of

healthy life and 2) to eliminate health disparities. By continuously tracking population HRQOL in national and

state surveillance systems, the Healthy Days measures and

data will help to directly monitor the nation’s and states’

progress toward meeting the

first goal of improved

HRQOL. This will help to

assure that the net progress in

achieving targets set by the

Healthy People 2010 objectives

in specific focus areas is not

offset by unanticipated new

diseases, barriers, or health

problems.

The second major goal of Healthy People 2010 is to

eliminate health disparities among segments of the population. BRFSS and NHANES data obtained from socioeconomic and demographic questions and the Healthy

Days measures will help to quantify perceived physical

and mental health disparities among population subgroups on the basis of characteristics such as gender, race

or ethnicity, education, income, place of residence, and

sexual orientation. The Healthy Days population surveillance data also offer great promise as a tool to help identify more precisely which individual behaviors and community-level factors—such as physical activity and safe

neighborhoods—contribute to good health. Identifying

these factors in turn helps program planners to focus

their resources on the health improvement interventions

most likely to be effective in eliminating disparities.

How do Healthy Days relate to the Healthy People

2010 Leading Health Indicators?

The leading health indicators are a set of 10 measures intended to make Healthy People 2010 more

useful as a focus of national attention and as a tool

for monitoring the health of Americans. Although the

full set of objectives for Healthy People 2010 will be used

by health professionals, the leading health indicators are

26 Measuring Healthy Days

Policy Implications

“To affect the quality of the day—that

is the highest of arts.” —-Thoreau
intended to engage the public and

other health partners. There is

great potential for using the

Healthy Days measures and population data as a unifying theme that

links the Healthy People 2010

goals, leading health indicators,

and objectives. For example,

Healthy Days data can provide

valuable insights on the individual

and community determinants of

the leading health indicators and can demonstrate 

the overall population effects of improvements in the

indicators:

Physical Activity

Cross-sectional analyses show that Healthy Days measures are correlated in expected ways with leisure-time

physical activity and inactivity (Table 1). Healthy Days

could be useful outcome measures that change positively

in response to exercise programs and might also help to

predict whether persons will begin and maintain an exercise program.

Overweight and obesity

Studies comparing unhealthy days with Body Mass Index

(BMI) show that adults who are either underweight or

obese report higher levels of impaired HRQOL (Ford

2000)(Table 1). BMI is similarly associated with depression days and anxiety days (Table 2). This information

provides valuable insights into the causes and effects of

obesity, a major U.S. public health problem.

Tobacco use

Compared with adults who have never smoked, those who

are former smokers and current smokers report higher

levels of unhealthy days (Table 1). Most of these unhealthy

days are attributable to impaired mental rather than physical health,which should help focus exploration on potentially fruitful areas of prevention or health promotion.

Substance abuse 

Population data provide some support that HRQOL is

lower among those who report high levels of alcohol use

in the past month. Although the accuracy of self-reported data may be especially problematic among this group,

the collection of data about health perceptions of persons who use and abuse substances may yield new

insights into prevention and treatment approaches.

Responsible sexual behavior 

Although no such data are currently

available for analysis, the core Healthy

Days and sexual behavior measures now

being asked in the NHANES examination component will provide data for a

study of potential connections between

HRQOL and responsible sexual behavior. This is an important prevention

research area that needs exploration.

Mental health

Population surveillance of perceived mental distress—in

general as well as symptoms of depression, anxiety, sleeplessness, and lack of vitality—is an integral aspect of the

Healthy Days measures (Borawski 1998). Of all adults

who report a current activity limitation, those who say

that the major cause is depression, anxiety, or some other

emotional problem report the highest levels of recent

sleeplessness, the lowest levels of vitality, and the highest

levels of recent activity limitation (Table 2).

Injury and violence 

The Healthy Days measures are potentially useful in

measuring the perceived burden of injuries and disabilities—including the effects of partner violence. For example, in one study involving 13 states, the 1.7% of adults

who reported a current activity limitation due mainly to

a fracture, bone, or joint injury had these limitations for

an average of 5.9 years and reported an average of 11.8

recent pain days—10 times as much as adults with no

current activity limitation, who averaged only 1.1 pain

days (CDC 1998-1)(Table 2).

Environmental quality

Although no analyses have yet been made of the relationships between unhealthy days and environmental quality,

community residents who have asthma or similar respiratory conditions would likely report more unhealthy

days in periods of poor air quality than during periods of

clean air. Poor water and other environmental contaminants are also likely to be associated with reports of low

HRQOL.

Immunization

Some BRFSS data are available for analysis of relationships between HRQOL and influenza and pneumonia

immunization status. This is a prevention research area

that needs exploration.

Measuring Healthy Days 27

Policy Implications
Access to care 

Adults who report having no health care coverage or

insurance also report higher levels of unhealthy days

compared with those with coverage (Tables 1, 2), mostly

because of the higher levels of perceived mental distress

among those with no coverage.

How can the Healthy Days measures support 

epidemiologic and prevention research?

The validity, brevity, and comparability of the

Healthy Days measures make them ideal additions to existing and new survey instruments to

enhance opportunities for epidemiologic and prevention research.

The addition of the core Healthy Days measures to

the NHANES, beginning in 2000, supports public health

prevention goals. NHANES measures the national burden of preventable disease, injuries, and disabilities. It

will provide valuable new insights into the relationship

between HRQOL and clinically-measured health characteristics and conditions such as blood pressure, physical strength and endurance, oral health, and mental

health (http://www.cdc.gov/nchs/nhanes.htm).

Moreover, the addition of the HRQOL questions to

the NHANES survey is important because it is a nationally representative survey that is linked with other

national surveys and health outcomes. NHANES

Healthy Days data will help validate people’s self-reports

in comparison with the NHANES objective measures of

physical health and blood work for interviewed subjects.

NHANES studies of the levels of reported unhealthy

days and activity limitation days in relation to measured

body mass index and physical endurance, as well as to

reported nutritional and physical activity patterns, can

provide research-based messages to support related

health communication objectives. Adding the Healthy

Days questions to NHANES extends the HRQOL surveillance to adolescents as well as adults and will yield

important information on the nature and extent of

health disparities among geographic and socio-demographic groups.

How can an HRQOL focus support community 

mobilization?

The Healthy Days measures have been used effectively in Boone County, Missouri since 1993 tohelp identify health problems of vulnerable

groups and to justify additional funding for addressing

these disparities. The measures are also proving to be

valuable for population health assessment in Hamilton,

Ontario (Ounpuu 2000), and as community health indicators in King County, Washington (see Communities

Count 2000: Social and Health Indicators Across King

County at http://www.communitiescount.org). In addition, the utility of the core measures to identify health

needs and changes in communities is currently being

tested by both the CDC Division of Adult and

Community Health and the Urban Research Center

Program of the CDC Epidemiology Program Office.

The Healthy Days measures and data have attracted

the interest of the National Association of County and

City Health Officials (NACCHO), and their applicability to local health agencies has been described in articles in the NACCHO monthly newsletter (Moriarty

1996, Centra 1998). More recently, with support from

the Health Resources and Services Administration, the

Public Health Foundation and its partners, NACCHO

and ASTHO, have included county-level BRFSS data

from the four core Healthy Days measures in their July

2000 Community Health Status Indicator (CHSI)

reports (see: http://www.communityhealth.hrsa.gov/).

28 Measuring Healthy Days

Policy Implications
The PHF is also now collaborating with the CDC to

identify county-level indicators of HRQOL—using

mean unhealthy days as a summary measure of perceived population physical and mental health (Kanarek

2000). Initial findings from this study show that adults

residing in counties with the largest (i.e., ≥1 million)

and smallest (i.e., <25,000) populations report the

highest levels of unhealthy days while those in moderately large counties (i.e., between 500,000 and 999,999

residents) report the lowest level of mean unhealthy

days. This study also found that counties with the highest rates of poverty, unemployment, births to teens,

severe work disability, age-adjusted mortality, and lack

of a high school diploma also had the highest reported

levels of unhealthy days in comparison with counties

with lower prevalences of these indicators.

The Institute of Medicine (IOM) recognized the

value of tracking both self-rated health and the

unhealthy days measure in its recent book, Improving

Health in the Community: A Role for Performance

Monitoring, available in its entirety on the National

Academy of Sciences Press website (IOM 1997). The

IOM identified these BRFSS-based measures as two of

its recommended 25 Community Health Profile indicators that all communities should track. The book also

contains a practical set of appendices with specific

examples of how the BRFSS HRQOL measures can be

used in combination with other measures that individual communities identify as important. For example,

one appendix suggests the use of the Healthy Days

depressive symptoms measure for estimating the

prevalence of adult depression in the community, in

conjunction with information on the prevalence of

treatment for diagnosed depression and other community correlates of depression.

CONCLUSIONS AND FUTURE DIRECTIONS

The Healthy Days measures and accumulating population data offer considerable promise as part ofa new public health paradigm that looks at health

as a broad multidimensional construct, comprising both

physical and mental well-being.

What are the main advantages of the Healthy Days

measures and population data?

1. Relate directly to the Healthy People 2010 goals

2. Reflect known demographic and socioeconomic 

disparities and health patterns

3. Reflect the burden of physical and mental illness and

disability 

4. Are validated against other established measures

(e.g., MOS Short Form 36)

5. Predict short-term mortality and hospitalization 

6. Provide new insights into health behaviors

7. Indicate important new policy-relevant disparities

and trends 

8. Provide a focus for comprehensive community

health programs

9. Provide comparable population data from 1993

onward (nearly one million adults) 

10. Are simple to measure, calculate, interpret, and add

to other assessments 

Measuring Healthy Days 29

Conclusions
What areas need future study and analysis?

Areas in which future effort will extend the value of the

Healthy Days measures include:

1. Use of alternative weighting methods for aggregating multiple years of data and for estimating the

sub-state prevalence of HRQOL; use of other analytic methods, such as multilevel analyses, to

describe relationships between aggregated BRFSS

data and potential community indicators of population HRQOL.

2. Validation studies that simultaneously compare the

Healthy Days measures with “gold standard” measures of mental illness, such as the Diagnostic

Interview Schedule (DIS) for depression and anxiety; additional validation studies for persons with

known major chronic health conditions, such as

diabetes, cardiovascular disease, asthma, and cancer.

3. Measurement research, such as sensitivity/specificity analyses, to identify appropriate cutoff points for

analysis and population prevalence threshold levels

that indicate policy-relevant HRQOL changes for

each Healthy Days variable.

4. Studies to demonstrate that the Healthy Days measures are suitable for measuring the HRQOL effects

of public health interventions and are therefore

suitable for use on cost-effectiveness studies.

5. Studies of mode effects (self-administered versus

telephone versus in-person versus proxy) and feasibility in a variety of settings—including prisons,

homeless shelters, clinics, and assisted-living and

skilled nursing facilities.

6. Direct measure-to-measure (crosswalk) and riskadjustment studies that examine potential uses of

the continuous population data from the core

Healthy Days measures to predict expected HRQOL

results in other surveys that use both the Healthy

Days and other HRQOL measures.

7. Predictors of HRQOL in persons.

8. Studies of HRQOL among the working-age population, including its effects on productivity and the

costs of unhealthy days to employers and society.

9. Multi-variate analyses to quantify the associations of

specific individual, group, and environmental characteristics with population HRQOL.

Conclusion

As knowledge builds about HRQOL surveillance

and its potential uses, the Healthy Days measures

and accumulating population data give states and

communities a unique nationwide standard for identifying and tracking perceived unmet health needs and disparities. Focusing on HRQOL will help health agencies

bridge artificial distinctions between physical and mental

health and spur collaboration with a wider circle of

health partners toward shared goals.

30 Measuring Healthy Days

Conclusions

Lessons learned from HRQOL 

surveillance in the U.S. 

1. Consensus versus vision no one ideal set of

measures

2. Clarity is critical e.g., perceived good

mental and physical

health over time

3. Brevity is best four questions are easy

to add

4. Validity is essential comparisons with 

validated measures;

prediction studies

5. Continuously collected show group 

HRQOL data are useful differences, trends, 

& socio-environmental 

effects
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Measuring Healthy Days 33

Resources

RESOURCES

A. CDC Behavioral Risk Factor Surveillance System

Home Page (with links to BRFSS questionnaires,

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D. Healthy People 2010:

http://web.health.gov/healthypeople 

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34 Measuring Healthy Days

Tables

TABLES 

Table 1: Percentage of good-to-excellent self-rated health, mean unhealthy days, and mean activity limitation days among adults by demographic and health characteristics (age-adjusted*)—Behavioral Risk Factor

Surveillance System, 1993–1997

MEAN

%GOOD-TO- MEAN ACTIVITY 

EXCELLENT UNHEALTHY LIMITATION 

CHARACTERISTIC COUNT HEALTH DAYS DAYS

Overall 576,367 86.1 5.3 1.7

Sex

Male 239,094 86.7 4.3 1.6

Female 337,273 85.7 6.0 1.8

Age group

18-24 yr 53,074 93.5 5.2 1.1

25-34 yr 116,845 93.7 4.7 1.1

35-44 yr 131,184 91.6 5.1 1.4

45-54 yr 93,871 87.1 5.3 1.7

55-64 yr 64,920 79.7 5.6 2.1

65-74 yr 67,469 74.5 5.6 2.2

>= 75 yr 46,458 66.9 6.7 3.0

Self-reported race

White 492,134 87.2 5.2 1.6

Black 50,877 78.8 5.8 2.2

Asian/Pacific Islander 13,478 87.9 4.2 1.3

American Indian/Alaska Native 7,048 77.6 7.3 2.6

Other 10,985 76.9 6.1 2.1

Ethnicity

Hispanic 30,610 76.6 5.9 2.0

Non-Hispanic 543,600 86.7 5.2 1.7

Highest grade/year of school completed

Less than high school 79,505 69.7 7.5 3.0

High school grad 189,372 85.1 5.4 1.7

Some college 157,118 88.8 5.3 1.6

College grad 148,984 93.5 4.0 1.1

Annual household income

< $15,000 94,279 72.6 8.2 3.3

$15,000-$24,999 108,695 82.8 6.0 2.0

$25,000-$34,999 88,475 88.6 4.9 1.4

$35,000-$49,999 93,478 91.8 4.3 1.1

≥ $50,000 115,122 92.2 4.0 1.1

Refused 26,583 89.4 4.1 1.2
Measuring Healthy Days 35

Tables

TABLE 1 (continued)

MEAN

%GOOD-TO- MEAN ACTIVITY 

EXCELLENT UNHEALTHY LIMITATION 

CHARACTERISTIC COUNT HEALTH DAYS DAYS

Employment status

Employed for wages 306,804 90.5 4.2 0.9

Self-employed 49,523 91.4 4.2 0.9

Out of work one year or more 9,066 72.4 9.1 4.2

Out of work less than one year 12,401 82.1 7.2 2.7

Homemaker 46,317 83.8 5.7 1.5

Student 19,331 90.3 5.1 1.3

Retired 110,956 81.9 5.9 2.2

Unable to work 18,021 34.5 19.9 13.4

Marital status

Married 316,030 88.3 4.6 1.4

Divorced 71,805 83.1 6.8 2.3

Widowed 63,283 82.5 6.3 2.1

Separated 14,941 76.5 8.6 2.8

Never married 97,549 85.7 5.3 1.8

Unmarried couple 11,415 83.1 6.3 1.9

Reported diabetes mellitus

Yes 27,721 57.5 9.9 4.4

No 547,721 87.6 5.1 1.6

Told has high blood pressure

Never told 295,384 89.1 4.7 1.4

Told once 21,027 86.2 5.6 1.6

Told twice 71,518 73.6 7.9 3.0

Reported breast cancer†

Yes 3,634 73.7 8.5 3.0

No 333,639 85.2 6.1 1.8

Sex Body mass index category (WHO§ )

Male Underweight 2,213 73.7 7.6 3.5

Normal 92,910 87.9 4.2 1.5

Pre-Obese 105,590 88.6 4.0 1.3

Obese, Class 1 27,670 82.0 5.0 1.9

Obese, Class 2 5,947 74.5 6.3 2.8

Obese, Class 3 2,049 64.4 8.2 3.6

Female Underweight 12,882 83.8 6.8 2.1

Normal 173,209 89.6 5.3 1.5

Pre-Obese 85,455 85.6 6.0 1.8

Obese, Class 1 32,857 78.1 7.4 2.4

Obese, Class 2 10,334 69.2 9.2 3.3

Obese, Class 3 5,508 60.7 11.3 4.7
36 Measuring Healthy Days

Tables

TABLE 1 (continued)

MEAN

%GOOD-TO- MEAN ACTIVITY 

EXCELLENT UNHEALTHY LIMITATION 

CHARACTERISTIC COUNT HEALTH DAYS DAYS

Cigarette smoking status

Never smoked 299,931 88.2 4.5 1.4

Former smoker 143,657 86.0 5.4 1.7

Smokes <1 pack/day 62,793 83.3 6.4 2.1

Smokes ≥1 pack/day 65,679 80.2 7.3 2.5

Drank 5 or more alcoholic beverages 

once or more in the past month

Yes 51,571 87.4 5.5 1.6

No 355,560 86.0 5.3 1.7

Any leisure-time physical activity 

in past month

Yes 205,005 89.7 4.5 1.2

No 89,827 78.1 6.8 2.8

Uses seatbelt in car

Always 255,465 87.3 5.1 1.6

Nearly always 58,437 86.8 5.1 1.6

Sometimes 31,359 83.3 5.7 1.9

Seldom 15,878 82.3 6.0 1.9

Never 17,643 78.1 7.1 2.4

Never drive/ride in car 1,455 66.1 8.8 4.4

Has health insurance

Yes 505,188 87.2 5.1 1.6

No 69,742 78.6 6.6 2.3

Self-rated health

Excellent 139,565 100.0 2.3 0.4

Very good 194,685 100.0 3.5 0.7

Good 159,276 100.0 5.2 1.4

Fair 59,442 0.0 11.7 4.2

Poor 23,399 0.0 23.9 13.9

* Responses in all groups except the age groups were age-adjusted to the overall BRFSS sample for 1993–1997 for all characteristics

except age group. 

† Questions regarding breast cancer were posed to women only. 

§ Categories are underweight (< 18.5 kg/m2); normal (18.5 kg/m2-24.9 kg/m2); overweight (25.0 kg/m2-29.9 kg/m2); 

obese class I (30.0 kg/m2-34.9 kg/m2); obese class II (35.0 kg/m2-39.9 kg/m2); and obese class III ( ≥40 kg/m2).
Measuring Healthy Days 37

Tables

Overall 42,632 24.7 3.1 2.8 1.7 2.6 3.0 5.2 7.6 19.0

State Year

California 1996 4,003 24.5 3.1 3.0 1.3 2.4 2.9 4.9 6.9 18.9

Delaware 1995 2,112 24.9 3.0 2.9 1.8 3.0 2.8 5.0 7.4 19.9

Indiana 1995 2,412 23.7 3.5 3.6 1.7 3.4 3.4 6.1 9.0 18.4

Kansas 1995 2,009 24.7 3.1 3.0 1.7 2.8 2.9 5.7 7.8 18.8

Kansas 1997 2,004 26.6 2.1 1.7 0.8 1.9 1.8 3.6 6.3 19.7

Mississippi 1995 1,592 24.8 3.3 2.6 2.2 3.2 3.5 4.7 6.7 20.2

Missouri 1995 1,572 24.6 2.9 3.1 1.7 2.6 2.9 5.3 7.9 18.5

Missouri 1996 1,550 24.2 3.5 3.0 1.8 3.4 3.3 5.4 8.4 18.9

Missouri 1997 1,849 24.1 3.5 3.2 1.9 3.0 3.2 5.6 7.9 18.4

New Mexico 1995 1,298 24.1 3.7 2.8 2.1 2.3 3.0 5.4 7.2 20.3

New York 1996 4,312 24.6 3.1 2.9 1.7 2.6 3.3 5.7 8.2 19.0

New York 1997 3,403 24.6 3.1 3.0 1.8 3.0 3.7 6.2 8.3 19.9

North Carolina 1995 1,326 26.1 2.8 1.8 1.4 2.2 2.9 4.2 6.9 17.5

North Carolina 1996 2,794 25.9 2.7 2.0 1.8 2.5 2.6 4.4 7.2 19.7

Ohio 1997 3,166 25.8 2.6 2.2 1.4 2.4 2.2 3.7 7.5 19.2

Pennsylvania 1997 3,603 24.7 3.2 2.9 1.6 2.4 2.9 5.0 7.5 18.5

Rhode Island 1997 1,842 24.8 3.0 2.7 1.9 2.6 2.9 5.1 7.4 19.1

Tennessee 1995 1,785 24.8 3.6 2.5 1.8 2.5 3.5 5.6 7.6 17.7

Sex

Male 17,234 25.6 2.7 2.3 1.5 2.4 2.5 4.5 7.0 20.0

Female 25,398 24.0 3.5 3.3 1.8 2.8 3.6 5.8 8.2 18.1

Age group

18-24 yr 3,739 24.9 1.9 3.4 1.2 1.4 3.6 6.2 10.0 18.1

25-34 yr 8,648 25.2 2.1 3.0 1.2 1.7 3.0 5.9 9.6 18.5

35-44 yr 9,379 24.9 2.5 3.3 1.5 2.4 3.2 5.7 8.5 19.2

45-54 yr 7,056 24.7 3.2 2.9 1.7 3.0 3.2 5.2 7.4 19.1

55-64 yr 4,860 24.5 4.0 2.4 2.0 3.6 2.9 4.6 5.3 20.1

65-74 yr 5,122 24.4 4.8 1.7 2.0 3.7 2.4 3.3 4.1 19.9

≥ 75 yr 3,623 23.3 5.9 1.9 3.0 4.6 2.8 3.0 3.9 18.6

Race/ethnicity

White, non-Hispanic 33,910 24.8 3.1 2.7 1.6 2.7 2.8 5.2 7.7 19.0

Black, non-Hispanic 4,654 24.1 3.4 3.3 2.1 2.9 4.1 5.3 8.1 18.6

White, Hispanic 1,932 24.6 3.1 2.0 1.3 2.1 3.4 5.3 6.5 19.7

Black, Hispanic 208 23.5 3.5 4.2 2.7 3.1 4.7 7.9 7.9 19.9

Other, Hispanic 481 23.8 3.4 3.6 2.4 3.2 4.1 5.8 8.4 19.2

Asian/Pacific Isl. 696 26.0 1.7 2.5 0.6 1.4 2.3 4.1 6.3 18.4

Native Amer/Alaska Natv 273 23.4 4.0 3.8 1.9 3.5 4.3 6.7 7.7 18.5

Other 214 23.5 2.5 4.3 2.2 2.0 3.0 5.3 8.0 20.1

TABLE 2: Health-related quality of life among adults by demographic and health characteristics for 13 states 

using the full set of Healthy Days measures—Behavioral Risk Factor Surveillance System, 1995–1997 

MEAN NUMBER OF DAYS IN PAST 30 DAYS

Characteristic Count

Healthy Days

Not Good 

Physical Health

Not Good 

Mental Health

Activity

Limitation

Pain

Sad, Blue, 

Depressed

Anxious

Sleepless

Full of Energy
38 Measuring Healthy Days

Tables

Educational level

< High school grad 5,944 22.5 5.4 3.6 3.0 4.4 4.6 6.0 6.9 17.5

High school grad 14,243 24.7 3.1 2.9 1.6 2.8 3.2 5.1 7.6 19.2

Some college 11,233 24.8 2.8 3.0 1.6 2.5 3.0 5.4 8.3 18.9

College grad 11,073 25.9 2.1 2.3 1.0 1.7 2.1 4.6 7.4 19.8

Annual household income

< $15,000 5,666 21.6 5.5 4.4 3.4 4.6 5.1 6.9 8.0 17.0

$15,000-$24,999 8,216 23.7 4.0 3.3 2.3 3.4 3.8 5.5 7.5 18.2

$25,000-$34,999 6,533 25.0 2.7 2.8 1.4 2.4 2.9 5.4 7.8 19.3

$35,000-$49,999 6,864 25.8 2.0 2.4 1.0 1.9 2.4 4.8 7.6 19.4

≥$50,000 8,618 26.1 2.0 2.1 0.9 1.6 1.9 4.5 7.9 19.7

Missing/Unknown income 6,735 25.0 3.2 2.5 1.5 2.8 2.9 4.7 6.9 19.8

Employment status

Employed, wages 23,190 25.9 1.8 2.5 0.9 1.6 2.5 5.1 8.4 19.5

Self-employed 3,023 25.9 2.1 2.4 1.0 1.9 2.5 5.3 7.2 20.6

Unemployed ≥1 yr 696 20.8 5.4 5.3 3.9 5.4 5.8 7.8 9.2 16.5

Unemployed <1 yr 875 21.9 3.9 5.4 2.8 3.0 6.1 8.7 7.5 17.8

Homemaker 3,301 24.1 3.3 3.2 1.6 2.5 3.5 5.3 7.3 18.5

Student 1,324 25.0 1.7 3.4 0.9 1.4 3.3 6.2 9.6 18.3

Retired 8,705 24.2 5.0 1.8 2.2 4.0 2.5 3.1 4.0 19.7

Unable to work 1,473 10.7 16.1 9.6 12.9 14.5 10.6 12.2 11.7 9.1

Marital status

Currently married 22,589 25.3 2.9 2.4 1.5 2.5 2.5 4.6 7.3 19.6

Divorced 5,336 23.4 3.8 3.9 2.3 3.7 4.2 6.7 8.6 18.0

Widowed 4,876 22.7 5.7 2.8 2.8 4.7 3.9 4.3 4.9 18.4

Separated 1,346 21.9 4.4 5.0 2.6 4.0 5.8 8.2 9.3 17.1

Never married 7,511 24.7 2.3 3.3 1.4 1.8 3.5 6.0 8.8 18.4

Unmarried couple 861 24.1 2.4 3.9 1.4 2.1 3.5 5.9 8.9 18.3

Told has diabetes

Yes 2,115 19.7 8.5 3.8 4.7 6.6 5.0 6.2 7.3 15.3

No 40,462 25.0 2.8 2.8 1.5 2.4 2.9 5.1 7.6 19.2

Told has high blood pressure

Never told 28,920 25.3 2.5 2.7 1.3 2.1 2.9 5.1 7.7 19.4

Told once 1,953 24.0 3.5 3.3 1.6 2.8 3.3 5.6 7.9 18.7

Told twice 7,180 22.4 5.5 3.4 3.0 4.9 3.9 5.8 7.1 17.0

Reported breast cancer*

Yes 333 21.7 5.7 4.1 3.7 5.3 4.3 6.7 6.6 16.6

No 25,065 24.0 3.4 3.3 1.8 2.8 3.5 5.8 8.2 18.2

TABLE 2 (continued)

MEAN NUMBER OF DAYS IN PAST 30 DAYS

Characteristic Count

Healthy Days

Not Good 

Physical Health

Not Good 

Mental Health

Activity

Limitation

Pain

Sad, Blue, 

Depressed

Anxious

Sleepless

Full of Energy
Measuring Healthy Days 39

Tables

Body mass index by sex†

Underweight Male 140 22.0 6.1 3.1 4.1 3.8 5.3 7.7 7.4 16.9

Normal BMI Male 6,279 25.6 2.5 2.4 1.4 2.2 2.6 4.6 7.1 20.1

Overweight Male 7,723 26.2 2.3 1.9 1.3 2.2 2.2 4.1 6.6 20.4

Obese (class I) Male 2,172 24.7 3.2 2.7 1.8 2.9 2.7 5.0 7.7 19.1

Obese (class II) Male 504 23.3 4.7 3.8 3.1 5.1 4.0 5.8 9.0 16.8

Obese (class III) Male 156 20.3 7.4 3.4 4.3 6.2 4.0 5.1 7.2 17.2

Underweight Female 491 22.7 2.5 5.4 1.5 1.5 4.8 8.8 11.3 16.7

Normal BMI Female 6,260 24.8 2.1 3.5 1.2 1.5 3.2 6.0 9.7 18.5

Overweight Female 2,440 24.2 2.5 3.8 1.3 2.1 4.2 6.7 10.6 17.1

Obese (class I) Female 944 23.6 3.3 4.0 1.9 2.7 4.1 7.3 10.5 16.2

Obese (class II) Female 329 22.0 3.8 5.1 2.4 3.2 5.1 8.2 11.7 14.1

Obese (class III) Female 189 18.9 6.0 6.8 3.5 4.2 7.6 9.9 12.6 14.7

Cigarette smoking status

Never smoked 22,201 25.5 2.6 2.4 1.3 2.1 2.6 4.5 7.3 19.5

Former smoker 9,260 24.3 3.8 2.7 2.1 3.3 2.9 5.1 6.7 19.1

Smokes <1 pack/day 3,717 23.4 3.2 4.0 1.8 2.7 4.2 6.7 9.3 18.1

Smokes ≥1 pack/day 4,897 22.9 3.8 4.5 2.5 3.8 4.8 7.8 9.9 17.0

Drank ≥ 5 alcoholic beverages

at least once in past month

Yes 4,086 24.6 2.2 3.5 1.3 2.0 3.4 6.3 8.5 18.2

No 13,876 25.4 2.3 2.7 1.2 2.0 2.7 5.1 7.7 19.7

Leisure-time physical activity

during past month

Yes 14,026 25.5 2.3 2.6 1.1 1.9 2.7 4.8 7.4 20.2

No 6,984 23.2 4.7 3.4 2.7 4.2 3.9 6.0 8.1 17.1

Uses seatbelt in car

Always 20,004 25.1 3.0 2.5 1.6 2.6 2.8 4.9 7.5 19.5

Nearly always 4,284 25.1 2.8 2.7 1.5 2.5 2.8 4.9 7.5 18.4

Sometimes 2,624 24.3 2.9 3.5 1.8 2.7 3.7 6.0 8.7 18.4

Seldom 1,262 24.6 2.8 3.4 1.5 2.8 4.0 7.3 9.2 17.8

Never 1,580 22.3 4.8 4.4 3.0 4.6 4.9 7.4 10.2 16.5

Has health insurance

Yes 37,582 24.9 3.1 2.7 1.6 2.6 2.8 4.9 7.5 19.1

No 4,959 23.9 3.0 3.9 1.7 2.6 4.3 6.6 8.1 18.5

Self-rated health status

Excellent 9,772 27.6 0.9 1.6 0.4 0.9 1.8 3.9 6.9 22.7

Very good 14,357 26.5 1.5 2.1 0.7 1.4 2.2 4.3 7.3 20.3

Good 12,025 24.7 3.0 2.7 1.3 2.6 3.3 5.5 7.7 18.1

Fair 4,600 18.8 7.9 5.1 4.3 6.3 5.6 7.7 8.6 13.2

Poor 1,774 5.9 22.3 9.3 14.4 15.4 10.4 11.7 12.0 6.7

TABLE 2 (continued)

MEAN NUMBER OF DAYS IN PAST 30 DAYS

Characteristic Count

Healthy Days

Not Good 

Physical Health

Not Good 

Mental Health

Activity

Limitation

Pain

Sad, Blue, 

Depressed

Anxious

Sleepless

Full of Energy
40 Measuring Healthy Days

Tables

Extent of activity limitation

None 34,390 26.2 1.7 2.3 0.7 1.1 2.4 4.5 7.2 20.3

Limited, but no

help needed 5,037 19.6 7.4 4.6 4.1 7.8 5.4 7.7 9.1 14.0

Need help with 

routine care needs 1,585 12.5 14.9 7.3 10.3 15.2 8.4 10.9 11.4 9.1

Need help with 

personal care needs 837 9.8 17.9 8.6 15.1 17.9 10.5 12.4 11.7 7.7

Cause of activity limitation

Depression/Anxiety/

Emotional problem 170 9.0 9.8 15.7 13.1 7.2 18.6 19.0 13.4 6.1 7.8

Cancer 111 10.8 16.4 9.3 12.9 12.1 10.0 11.1 11.0 8.3 3.9

Diabetes 179 14.5 13.1 7.0 9.8 8.7 9.4 9.4 9.2 11.5 7.2

High blood pressure 75 14.8 9.9 6.3 7.5 7.4 8.8 7.7 7.9 11.7 10.0

Back or neck problem 1,372 16.9 10.2 6.1 7.1 13.5 7.0 10.0 12.0 11.8 7.3

Lung/Breathing 557 16.9 10.1 5.2 6.4 6.2 6.0 9.0 10.3 11.9 11.4

problem

Stroke problem 153 17.0 12.6 4.1 8.0 7.1 6.7 8.1 6.7 10.2 4.7

Other impairment/ 1,373 17.1 9.2 6.7 6.8 8.7 6.6 9.5 10.3 12.3 9.2

problem

Heart problem 633 17.1 11.2 4.5 6.7 7.4 5.5 7.2 8.3 12.2 8.5

Walking problem 526 17.8 10.8 3.7 6.3 11.8 5.1 6.5 7.3 12.7 7.4

Arthritis/Rheumatism 1,244 18.1 10.2 4.0 4.7 13.4 5.4 7.9 8.8 12.0 8.2

Fracture/Joint injury 710 18.9 8.9 3.9 5.8 11.8 4.8 7.4 9.4 15.0 5.9

Eye/Vision problem 252 21.8 5.8 4.2 2.9 4.1 5.1 5.4 7.7 18.0 14.4

Hearing problem 64 23.9 4.2 2.0 2.6 4.4 3.1 5.5 6.5 20.2 17.4

No limitation 34,390 26.2 1.7 2.3 0.7 1.1 2.4 4.5 7.2 20.3 _

* Questions regarding breast cancer were posed to women only. 

† Categories are underweight (< 18.5 kg/m2); normal (18.5 kg/m2-24.9 kg/m2); 

overweight (25.0 kg/m2-29.9 kg/m2); obese class I (30.0 kg/m2-34.9 kg/m2); obese class II (35.0 kg/m2-39.9 kg/m2); 

and obese class III (≥40 kg/m2).

TABLE 2 (continued)

MEAN NUMBER OF DAYS IN PAST 30 DAYS

Characteristic Count

Healthy Days

Not Good 

Physical Health

Not Good 

Mental Health

Activity

Limitation

Pain

Sad, Blue, 

Depressed

Anxious

Sleepless

Full of Energy

Years Limited