7: No. 4, July 2010
Ali H. Mokdad, PhD; Patrick L. Remington, MD, MPH
Suggested citation for this article: Mokdad AH, Remington PL. Measuring health behaviors in populations. Prev Chronic Dis 2010;7(4):A75.
http://www.cdc.gov/pcd/issues/2010/jul/10_0010.htm. Accessed [date].
Health behaviors are a leading cause of illness and death in the United States. Efforts to improve public health require information on the prevalence of health behaviors in populations — not only to target programs to areas of
most need but also to evaluate the effectiveness of intervention efforts. Telephone surveys, such as the Centers for Disease Control and Prevention’s Behavioral Risk Factor Surveillance System, are
a good way to assess health
behaviors in populations. These data provide estimates at the national and state level but often require multiple years of data to provide reliable estimates at the local level. With changes in telephone use (eg, rapid decline in the ownership of landlines), innovative methods to collect data on health behaviors, such as in health care settings or through Internet-based surveys, need to be developed.
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Efforts to improve community health at the national, state, and local level
require detailed and accurate information about the prevalence of health
behaviors (1-3). If existing data collection systems are to remain viable,
current approaches to measuring population health behaviors must be adapted.
Potential solutions address the challenges of nonresponse, coverage, data
quality, sample size, and costs.
McGinnis and Foege summarized the role of health behaviors as a leading
cause of death and labeled them the “actual causes of death” (4). Later updated
by Mokdad (5), these studies concluded that approximately half of all deaths in
the United States could be attributed to factors such as smoking, physical
inactivity, poor diet, and alcohol use (Table 1).
Public health campaigns were established that educated the public about the need
for healthy lifestyles
and supported health-promoting programs and policies. These changes contributed
to major declines in heart disease, stroke, and injury deaths (6).
Telephone surveys emerged as a feasible method to assess the prevalence
of many health risk behaviors among populations (7). In 1984, the Centers for
Disease Control and Prevention (CDC) implemented
the first state-based surveillance system for health behaviors, the Behavioral
Risk Factor Surveillance System (BRFSS) (8). BRFSS collects information on
health risk behaviors associated with the leading causes of illness and death (9).
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Reasons for Measuring Health Behaviors
The measurement of health behaviors in populations is useful for both program
planning and program evaluation. For program planning, estimates of the
prevalence of behavioral risk factors can be used to set priorities or to
compare rates across communities. For example, to provide more reliable
estimates, the Wisconsin County Health Rankings combines 7 years of data from BRFSS to
compare the rates of behaviors across all the counties in the state (10). In
contrast, more precise measures are needed when evaluating changes in health
behaviors over time. For example, a 95% confidence limit of plus or minus 3% may
be sufficient to estimate the prevalence of smoking in a population but is
insufficient to demonstrate changes in smoking rates over time. Efforts to
reward communities for improved health outcomes (11) would require precise
estimates of health behaviors so that incentives could be closely linked with
the implementation of programs or policies.
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Methods to Measure Health Behaviors in Populations
Several methods exist to assess behaviors in a target population. The choice
of methods is usually a function of cost due to time and personnel. Ideally, a
census would be the optimal means of collecting data. However, censuses are not
conducted frequently enough to enable timely data for planning. Hence, surveys
are often the best mode of data collection. Advances in sampling techniques and
software availability have rendered surveys the workhorse for behavioral
assessment. Several modes are useful for collecting survey data: 1) face to
face, 2) telephone, 3) mail, or 4) Web. The mode dictates whether the
data are self-reported, observed, or measured.
Five components determine the quality of a survey: 1) coverage, 2) sampling,
3) nonresponse, 4) measurements, and 5) data processing. Adequate coverage
is achieved when the sampling frame includes all units of the population of
interest. If the list of population units is incomplete, frame coverage errors
result. Challenges to coverage vary by survey mode. Usually, sampling frames for
face-to-face surveys are expensive to develop, whereas telephone sampling frames
are challenging because of the use of cellular phones and number portability
(area codes are no longer associated with a specific geographic location). The
US Postal Service’s sampling frames (mail surveys) are not complete,
but they are improving. On the other hand, Web sampling frames are not yet
Adequate sampling is achieved when each element on the sampling
frame has a known and nonzero probability of selection. This protects against
sampling bias and enables the researchers to quantify sampling error. Again,
this error varies by survey modes. Face-to-face and telephone surveys have
well-developed techniques for sampling. On the other hand, mail surveys do not
have a clear method for within-household selection, although some promising
findings have been reported. Researchers cannot control who will answer the
questionnaire once the letter is received.
Nonresponse errors occur when researchers are unable to obtain
data from selected respondents. This error has 2 aspects. Unit nonresponse means
that the selected person refuses to do the survey; item nonresponse means
that the respondent completes the survey but refuses to answer certain
questions. Again, this error varies by survey mode and questions. For example,
in face-to-face interviews, a respondent may be less likely to provide personal
information on sexual behaviors to an interviewer. However, the same person may
provide such answers via the Web or through a computer-assisted interview (ie,
researchers provide respondents a laptop during the household interview,
allowing them to self-administer sensitive questions).
Measurement errors occur when a respondent’s answer to a
question is inaccurate (departs from the “true” value). Several factors
contribute to this error, primarily, the wording of questions and their order in
the questionnaire. Therefore, it is crucial to cognitively test questionnaires
and pilot surveys before full implementation. Survey mode has
implications for measurement errors (interviewer vs self-administered). Indeed,
the interviewer stimuli and the manner in which the survey questions are
conveyed to respondents and responses are recorded will affect this error. For
example, asking “Are you trying to lose weight?” or “Weight loss is important for
your health; are you trying to lose weight?” will yield different estimates for
Data processing errors occur during data management, editing,
and recoding. Sometimes errors are made during imputations of certain missing
items or responses. Finally, errors could be made in the calculation of final
weights or poststratification adjustments. Hence, systems must be in place
during survey operation for quality assurance and control.
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Existing Surveys of Health Behaviors
Several US surveillance systems and surveys provide valuable information on
behavioral risk factors (Table 2).
Most of the surveys and surveillance systems are national; a few exceptions
provide data at the local and state levels. In addition, most of the surveys use
self-reported information on health behaviors because of the high cost of
face-to-face surveys and collecting physical measurements. Among self-reported
surveys, telephone surveys are the most common because they are the least
expensive. In addition, the development of computer-assisted telephone
interviewing software has allowed for a rapid
release of data.
The largest telephone survey in the United States is BRFSS, whereas the
National Health Nutrition Examination Survey (NHANES) is the main survey to
provide physical measurement. A brief description of some of the key surveys
The Behavioral Risk Factor Surveillance System
BRFSS is a state-based system of health surveys (9,12). The objective of BRFSS is to collect uniform, state-specific data on health risk behaviors,
clinical preventive health practices, and health care access that are associated
with the leading causes of death and illness in the United States. Currently,
data are collected monthly in all 50 states, the District of Columbia, Puerto
Rico, the Virgin Islands, and Guam. Health departments use the data to identify
demographic variations in health-related behaviors, target services, address
emergent and critical health issues, propose legislation for health initiatives,
measure progress toward state and national health objectives, and design evaluations of their programs and
policies. For most states and counties, BRFSS is the only
source of population-based health behavior data related to chronic disease.
National Health and Nutrition Examination Survey
NHANES is a series of national surveys of American health and nutrition that
have been conducted since the early 1960s (13). The surveys obtain both
interview and physical examination data from national samples of the US
population. Data collection for the current NHANES began in 1999 and is ongoing.
Each year, nearly 7,000 people of all ages in households across the United
States are randomly selected to participate. The study design includes
representative samples of people by age, sex, and income and oversamples
African Americans, Mexican Americans, adolescents, older people, and pregnant
women. Participants are interviewed in their homes. After the interview is
complete, they are asked to participate in a series of physical examinations.
Physical exams are conducted in specially equipped and designed Mobile
Examination Centers consisting of 4 trailers. NHANES data have been widely
used by policy makers at the national level.
Pregnancy Risk Assessment Monitoring System
The Pregnancy Risk Assessment Monitoring System (PRAMS) is a surveillance
project of CDC and state health
departments (14). PRAMS collects state-specific, population-based data on
maternal attitudes and experiences before, during, and shortly after pregnancy.
Research has indicated that maternal behaviors during pregnancy may influence
infant birth weight and mortality. The goal of the PRAMS project is to
improve the health of mothers and infants by reducing adverse outcomes such as
low birth weight, infant illness and death, and maternal illness. PRAMS
provides state-specific data for planning and assessing health programs and for
describing maternal experiences that may contribute to maternal and infant
The Youth Risk Behavior Surveillance System
The Youth Risk Behavior Surveillance System (YRBSS) monitors priority
health-risk behaviors and the prevalence of obesity and asthma among youth and
young adults (15). YRBSS includes a national school-based survey conducted by
CDC and state, territorial, tribal, and local surveys conducted by state,
territorial, and local education and health agencies and tribal governments. YRBSS monitors 6 categories of priority health-risk behaviors among youths and
young adults, including behaviors that contribute to unintentional injuries and
violence; tobacco use, alcohol and other drug use; sexual behaviors; and diet
and physical inactivity.
The National Survey on Drug Use and Health
The National Survey on Drug Use and Health (NSDUH) provides yearly national
and state-level data on the use of alcohol, tobacco, and illicit and nonmedical
prescription drugs in the United States (16). Other health-related questions
also appear from year to year, including questions about mental health. Many
state health agencies use NSDUH data to estimate the need for drug treatment
Other surveys and surveillance systems
Among other surveys and surveillance systems that states can use for their
public health activities are the Pediatric Nutrition Surveillance System,
Pregnancy Surveillance System, and the National Health Care Surveys (Table 2).
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Examples of Data Use at the State and Local Level
Trends in obesity by state
BRFSS provides valuable information about health behaviors at the state
and local level that is of interest not only to public health professionals but
also to the media. The use of a standard questionnaire in all states and over
time enables researchers to compare the health of communities. The best known
example of using data to communicate information about the obesity epidemic is
in a landmark article in 1999, followed by the posting of PowerPoint slides on the
CDC Web site (http://www.cdc.gov/obesity/data/trends.html). These slides
graphically show the spread of high rates of obesity across the entire United
States, from coast to coast (17-19).
The SMART Project
The need for prevalence estimates at the local level has led to the creation
of the Selected Metropolitan/Micropolitan Area Risk Trends (SMART) Project to
analyze the data of selected metropolitan and micropolitan statistical areas (MMSAs)
that have 500 or more respondents in BRFSS. Although BRFSS was designed
to produce state-level estimates, growth in the sample size has facilitated
production of smaller-area estimates. SMART showed that the prevalence of
certain behaviors varied across cities, not unlike the differences found across
states. Researchers were able to observe variation in prevalence by comparing
cities with their surrounding metropolitan areas and with the rest of their
state. This new use of BRFSS data fills a public health need for local area
surveillance data to support targeted program implementation and evaluation;
these data should help cities to better plan and direct their prevention
Mandating colorectal cancer screening insurance coverage
Data show that screening for colorectal cancer lags far behind screening for
other cancers. In 2006, BRFSS data showed that New Mexico’s colorectal cancer
screening rates were below the national median. Citing BRFSS data, which
indicated that states with mandatory coverage had better colorectal cancer
screening rates, New Mexico’s legislature passed a law requiring health
insurance providers to cover colorectal cancer screening for New Mexico residents aged
50 years or older, joining 22 other states with mandatory coverage.
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Data from surveys of health behaviors in populations will continue to play a
role in public health efforts at the national, state, and local level. During the
past 30 years, telephone surveys have become a standard approach to collect
information from adults and children. However, as response rates continue to
decline and costs to increase, other methods for collecting these data need to
Challenges of health behavior surveys and data
The challenge for surveys and surveillance systems is to effectively manage
increasingly complex systems that can serve the needs of multiple programs while
adapting to changes in communications technology such as the increased use of
cellular telephones and call screening devices, societal behaviors (concerns
about privacy and declining participation in surveys), and population diversity
(the growing number of languages spoken in the United States, more cultural
and ethnic diversity). As a result, all surveys are facing declining response
rates, especially those based on telephones. Hence, all surveys are focusing on
efforts to improve their data quality, reach populations previously not included
in their survey, and expand the usefulness of the surveillance data.
Many surveys have established expert panels to guide their system
improvements, to ensure the quality and validity of the data, and to reduce the
potential for bias in estimates. In addition, surveillance is becoming more
expensive and funding is becoming a major challenge. Indeed, many behavioral
surveillance surveys are receiving less funding at a time of more demand to
increase their sample sizes and add more questions.
Many surveys and surveillance systems face these challenges and are exploring
potential solutions (Appendix). Some provide incentives to increase response
rates. In addition, most large surveys are using prenotification to increase
participation in their systems. Multimode data collection can also increase
coverage and reduce cost. The systems maximize the collection of data using a
less expensive mode (eg, Web or landline telephones) and contacting fewer
respondents from more expensive modes, such as household interviews. The
combination would allow a more representative sample of the community at a lower
Moreover, different participants may prefer certain modes and will respond
better to such options. For example, young participants may prefer to respond to
a survey over the Internet and may be more accessible through their cellular
telephones. To address self-reported bias, surveys could consider conducting
physical measurements on a subsample of their respondents to examine and adjust
for this limitation. All surveys and surveillance systems should institute a
transparent data-quality report for their users to better describe the
limitation of the data and its generalizability. Finally, all surveys should
consider rotating questions every year or every several years; fewer questions
make better use of the
questionnaire’s limited space and reduces the burden on respondents.
Future directions for health behavior surveys and surveillance systems
Several issues should be considered in moving forward with data collection
and local needs. The survey and surveillance community should develop and
implement more innovative methods for data collection that will reduce
operational cost, hence allowing for an increase in sample size. The key factor
is how much detailed information is needed for monitoring trends and for action.
Unless the risk factor is very rare or prevalent only in a subgroup of the
population (eg, the percentage of people diagnosed with diabetes receiving a
yearly eye exam), a survey based on a sample size of 300 or more should be
adequate for action. On the other hand, monitoring a trend is more challenging,
especially if the purpose is to detect a small change in the prevalence of a
risk factor. In reality, the changes that we would expect in behaviors after a
program or policy change are very small. In such a case, researchers would need
a larger sample size to detect a significant difference from a baseline.
Several approaches are available for acquiring data for local communities.
The preference would be to increase the sample size of an ongoing survey in a
community. However, such an option can be very expensive. Perhaps using the
existing infrastructure of health care settings to collect data is worth
pursuing. This approach would involve developing new statistical methods to
combine data from different sources to inform decision makers. The use of
small-area estimates is the most promising alternative. Indeed, using existing
methods and a small sample size, it is possible to provide valid
estimates at the local level.
Showing the values of surveillance systems at the local level is the best way
to secure resources. Moreover, it is time to critically review our surveillance
systems to explore the possibility of combining efforts and systems to better
meet the needs of local data. For example, the National Immunization Survey
could be combined with BRFSS and NHANES could be combined with the National
Health Interview Survey (ie, measurements on a subsample of NHIS). Indeed, CDC is now better positioned
to implement such changes to improve surveillance, having recently created the
Office of Surveillance, Epidemiology, and Laboratory Services. The future of
health behavior surveys and surveillance systems depends on such improvements to
ensure adequate funding for data collection, more research on alternative
methods for data collection, and ongoing support for the use of these data.
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This manuscript was developed as part of the Mobilizing Action Towards
Community Health (MATCH) project funded by the Robert Wood Johnson
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Corresponding Author: Ali H. Mokdad, PhD, Institute for Health Metrics and Evaluation, University of Washington, 2301 Fifth Ave, Ste 600, Seattle, WA 98121. Telephone: 206-897-2800. E-mail:
Author Affiliation: Patrick Remington, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.
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