Environmental Health Perspectives Volume
103, Supplement 3, April 1995
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Issues Associated with the Design of a National Probability Sample for
Human Exposure Assessment
Trena M. Ezzati-Rice and Robert S. Murphy
National Center for Health Statistics, Centers for Disease Control and
Prevention, Hyattsville, Maryland
Abstract
Data obtained from national probability sample surveys provide important
information on the prevalence of various health conditions and distributions
of physical and biochemical characteristics of the U.S. population. The
sample design of a survey specifies how sampling from a designated population
over a stated period is to be accomplished. A survey's analytical objectives
and interests--in particular subpopulations--affect the sample design strategy.
Selected subdomains of the population often must be oversampled so that
estimates can be made with acceptable precision. This article addresses
sample design considerations for a national probability sample for human
tissue monitoring and specimen banking. Among the sampling issues addressed
are the oversampling of special populations e.g., minority groups and at-risk
groups such as low income or elderly persons; geographic coverage; and sample
size considerations. The sample design for a major health survey, the Third
National Health and Nutrition Examination Survey (NHANES III), is used to
illustrate a complex, multistage probability sample design and to highlight
some of the sampling issues discussed in this article. -- Environ Health
Perspect 103(Suppl 3):55-60 (1995)
Key words: survey design, oversampling, stratification, multistage
sampling
This paper was presented at the Conference on Human Tissue
Monitoring and Specimen Banking: Opportunities for Exposure Assessment,
Risk Assessment, and Epidemiologic Research held
30 March - 1 April 1993 in Research Triangle Park, North Carolina.
Address correspondence to Trena M. Ezzati-Rice, National
Center for Health Statistics, Centers for Disease Control and Prevention,
6525 Belcrest Road, Hyattsville, MD 20782. Telephone (301) 436-7022. Fax
(301) 436-7955.
Introduction
National probability sample surveys provide important information on
the prevalence of various health conditions and distributions of physical
and biochemical characteristics of the United States population as well
as providing data on the relationship between risk factors and selected
conditions (1,2). Recent advances in industrial technology have resulted
in the release of a diverse number of toxic substances into the environment
resulting in an increasing interest in monitoring and assessing the exposure
of the human population to environmental pollutants. The collection of human
tissue samples as part of national sample health surveys could provide a
unique source of data to help establish public health priorities in environmental
health and to help define guidelines for the prevention of environment-related
diseases. Further, tissue specimen banks could be useful for future epidemiologic
and risk assessment studies and for evaluation of public health prevention
efforts.
The design and conduct of current and previous national probability health
surveys could help guide the development of national surveys for human exposure
assessment. This article addresses sample design considerations for a national
probability sample for human tissue monitoring and specimen banking. Among
the sampling issues addressed are defining the sampling frame and sampling
units; stratification; sampling of special populations (e.g., minority groups
and at-risk groups); geographic coverage; and factors affecting sample size
and its allocation over regions, subdomains, and strata. The Third National
Health and Nutrition Examination Survey (NHANES III) is used to illustrate
a complex, multistage sample design for a major health survey and to highlight
some of the sampling considerations discussed in this article.
Survey Design Issues
The planning of national probability sample surveys requires survey designers
to address a number of complex issues. Sample design is only one of several
interrelated aspects involved in designing a sample survey. In an attempt
to achieve an efficient and economic sample design, the sample designer
must have knowledge of the objectives to be served by the survey's results.
Thus, the development of a well-defined set of goals and objectives is the
first, and perhaps, most critical, task of planning a national sample survey
for human exposure assessment because it determines the alternatives for
the sampling units and frame, the actual sample design and selection procedures,
and estimation methods.
Other important issues that must be addressed when designing a sample
survey include defining the target population about which estimates are
to be made, including any subdomains of the population and the level of
precision desired for the estimates (total and subdomain estimates). In
addition, the geographic coverage for the survey must be determined. A number
of operational issues also must be considered in the design of a survey,
including the types of data to be collected, the methods for obtaining the
needed survey data, the design and contents of questionnaires, and the time
frame in which the survey is to be conducted. Further, for environmental
risk assessment, issues surrounding the long-term storage and management
of biologic specimens must be addressed. Finally, sources of potential nonsampling
error (e.g., nonresponse and noncoverage) should also be anticipated and
efforts made to minimize their impact on survey estimates. After consideration
of these issues, along with the resources available to conduct the survey,
the sample design for the survey can be developed.
Sample Design Issues
The sample design for a survey includes the sampling plan; the set of
procedures by which the sample is selected from the target population; the
estimation procedures, the set of algorithms for estimating population values
from the sample and estimating the reliability of the estimates (3,4).
Development of the sample design for a survey involves not only developing
the sampling frame, but determining the total sample size and its allocation
by areas of the country (e.g., region or state); subdomains (e.g, the elderly,
children, Mexican Americans), and strata; and any clustering of the sample
to reduce survey costs. A good sample design produces estimates that are
unbiased and reliable. It also includes specification for feasible survey
operations.
The strategy for selection of an appropriate sample selection method
depends first on the survey's primary objectives. Other important considerations,
however, include the degree of precision required for estimates and the
sample size needed to meet the precision requirements. The sample size for
the survey directly affects the cost of the survey; therefore, the sample
size should be selected so as to maximize the reliability (accuracy) of
the survey results and at the same time to minimize the cost of the survey.
Other practical issues are the staff resources for the field operations,
the time frame for the survey, and the total budget.
In the absence of any convenient ready-made sample frame, a multistage
area sample is generally chosen for sampling people at large or for populations
whose members are widely scattered. A multistage area sample is typically
used for large-scale government-sponsored household surveys like the National
Health Interview Survey and the Third National Health and Nutrition Examination
Survey (1,2). This type of sampling provides a great deal of flexibility
in the kind, number, and size of the sampling units at each stage of selection
as well as the number of stages to use.
A national probability sample design typically involves stratification,
oversampling, and differential probabilities of selection. Stratification
is usually undertaken to improve sampling efficiency, but it also offers
some other benefits (5). In addition to improving the accuracy of
survey estimates, stratification can simplify survey planning and administration.
Further, it can help to form strata for which separate estimates are needed.
For example, in a national survey of human exposure assessment, separate
estimates by region may be desired; therefore, regions could be treated
as strata with sample selection from each so as to permit efficient comparisons.
Another distinct advantage of a multistage design is that it provides the
opportunity for stratification at various stages of the design for desired
subdomains of the population. Differences in the health status of racial
and ethnic minorities is a key public health concern. Although not excluded
from the target population, small numbers of selected subpopulations --
for example, blacks and Mexican Americans -- are included in population
surveys based on a probability proportional to size-sample design. Oversampling
specific population groups is often used to address this limitation. More
specifically, geographic stratification is one method often used to sample
racial or ethnic minorities that are clustered in the population to reduce
survey costs and for administrative efficiency. However, for many small
(rare) population subgroups (e.g., Native Americans or persons 65 years
and older), there are methodologic barriers to employing oversampling strategies.
These include geographic diffusion and the fact that screening for selected
subgroups is often costly. Some alternative methods for sampling special
populations (small subgroups of the population of analytical interest) are
addressed in the next section.
Sampling of Special Populations
One of the goals of national surveillance for human exposure would likely
be to assess the prevalence of exposure and levels of exposure of the United
States population to carcinogens and other toxic substances. Another possible
objective might be to identify population subgroups at increased risk of
exposure to carcinogens and other toxic substances (e.g., minorities, women,
children, elderly, or persons of lower socioeconomic status). Therefore,
in the early planning stage of a survey, specific subgroups of interest
should be identified and prioritized or given equal interest.
There are various sample selection techniques to ensure that various
subpopulations are adequately represented in a selected sample. Most of
the widely used methods give different probabilities of selection to various
subdomains. This results in a sample in which the proportions of particular
units vary from their occurrence in the population sampled. However, other
alternative methods deserve mention.
If a complete list of a special population exists, then it should be
used and no other sampling technique would be necessary. However, this is
rarely the case and when lists do exist, coverage is often incomplete or
unknown. Sometimes the use of multiple partial lists may provide some coverage
for subpopulations of interest. However, some screening would likely still
be necessary. In some instances, the use of previously collected data from
another sample survey can be used to provide a sample of a special population
(6). This linkage of surveys might be considered if the research
called for including a sample of persons with a selected health condition.
For example, information on chronic health conditions collected as part
of the National Health Interview Survey (NHIS) might provide a partial list
of persons with a selected target condition of interest. Another use of
this strategy might be applied if a previously conducted national health
survey had determined ethnicity for each sample person. A sample of, say,
Hispanics could then be selected from that sample for a new study. However,
there are some limitations to this approach, such as the quality of the
data, tracing of people who have moved, and privacy concerns of respondents
(7). Multi-plicity or network sampling, obtaining information from
respondents about persons with whom they are connected, can be effective
for some rare populations or conditions; but it is not appropriate in all
cases (8).
If special populations are clustered, there are several alternatives
for sampling that can reduce the survey costs. If geographic segments with
no members of a special population are known in advance, for example from
census data, screening in these segments could be eliminated, resulting
in a cost savings. However, for some special populations census data may
not be available, in which case telephone or mail screening could be used
to help determine the nonzero segments. Other methods discussed include
face-to-face screening and the use of lists to identify nonzero geographic
segments (7).
Many national surveys have an interest in producing separate estimates
for selected subgroups of the population (e.g., minorities, children, elderly,
women, and persons below the poverty level). Producing statistically reliable
estimates for small subdomains of the population often requires that these
subgroups of the population be oversampled. Oversampling blacks and especially
Hispanics poses a special challenge. An effective method for oversampling
subdomains of the population is to stratify geographic areas by concentration
of the minority population and to oversample those areas with high concentrations.
This design feature has been used in the Third National Health and Nutrition
Examina-tion Survey (NHANES III) and the National Survey of Family Growth,
Cycles II and III (1,9,10). The advantage of this procedure is that
it increases the reliability of statistics for the minorities and in most
cases will have a modest effect on estimates for the total population. The
disadvantage of this option is the extensive household screening still required
if minority statistics are desired for specific age domains that are particularly
rare. Research has shown that screening for sufficient numbers of black
and Hispanic Americans who are 65 years and older is very costly. Therefore,
another method to reduce screening is to obtain lists that contain a high
proportion of the special population domain. The list-frame sample can then
be used to supplement the area sample (dual-frame sampling). Potential list-frame
sources for the elderly include Medicare files maintained by the Health
Care Financing Administration and administrative files maintained by the
Social Security Administration (SSA). One complication in the use of these
list files for oversampling elderly Hispanics is that no specific Hispanic
identifier exists; therefore, Hispanic surname would need to be used to
identify elderly Hispanics from the list frame. A couple of other issues
must be considered before implementing a multiple frame sampling approach.
First, survey costs and complexity increase with a multiple frame survey,
and issues of coverage emerge. Second, complex estimation techniques are
required for appropriate statistical analyses. More specifically, the probability
of selecting a sample person from each frame used must be determined and
any duplication of persons on multiple frames must be accounted for. Therefore,
matching of frames becomes an important operation. A study to investigate
the use of SSA files to oversample elderly minorities in the National Health
Interview Survey has been previously described (11).
In many health surveys there is an interest in specific at-risk populations
such as persons whose income is below the poverty level. This might be the
case for environmental exposure as well. Therefore, oversampling geographic
areas by income class might be a consideration. However, research has shown
that poverty is not sufficiently concentrated for stratification and the
oversampling of high-density poverty areas to make it cost effective (12).
The primary reason is that low-income persons do not live in sufficiently
high areas of concentration. Furthermore, since the only source of data
for stratification by income is the most recent decennial census, these
data become outdated for intercensal years, especially towards the end of
a decade. A further complicating factor is the poor quality of income reporting
in screening interviews. Also, any oversampling procedure that has to rely
on screening to identify low-income persons, by definition would need to
be short and would then be subject to measurement error.
Previous research on sampling rare populations indicates that very high
concentrations of the rare population must exist for differential sampling
rates among strata to be effective (13). Further, it has been shown
that income class is an example of a subpopulation for which disproportionate
sampling is not particularly effective (3,14).
To illustrate some of the sample design issues discussed in the previous
sections of this article, the design of a major national health survey will
be described in the next section. Although no one study can serve as the
protocol for another since each has separate objectives, precision requirements,
and budgetary constraints, some features of previous national probability
sample surveys might be applicable to the design of a national survey for
human exposure assessment. The design of the Third National Health and Nutrition
Examination Survey (NHANES III) is therefore useful in illustrating a multistage
area probability sample design as well as the oversampling of selected subgroups
of the population.
Example of a National Random Sample Design
General Description of NHANES III
The National Health and Nutrition Examination Survey (NHANES) is a periodic
survey conducted by the National Center for Health Statistics (NCHS), Centers
for Disease Control and Prevention (CDC). The NHANES collects important
nutritional and health-related data on the civilian, noninstitutionalized
United States population and important subgroups. The Third National Health
and Nutrition Examination Survey (NHANES III) is the seventh in a series
of similar surveys
conducted by NCHS since the 1960s (15-22). NHANES III is designed
to provide national statistics on health and nutritional status for the
civilian noninstitutionalized population. Sociodemographic and medical history
data are obtained through personal household interviews, while physical
measurements, physiologic tests, and biochemical measurements are collected
through standardized physical examinations in specially equipped mobile
examination centers that are transported to each survey location. NHANES
III was partitioned into two 3-year surveys --Phase 1 (1988-1991) and Phase
2 (1991-1994)--to provide national estimates for each 3-year period as well
as for all 6 years.
The broad sample design specifications for NHANES III called for statistically
reliable estimates for 52 detailed age, sex, and race/ethnicity subdomains
(Table 1). The level of precision required for each subdomain was a relative
standard error of 30% or less for a 10% statistic. In addition, we wanted
to be able to detect differences of 10% with a Type I error of 0.05 or less
and a Type II error of 0.10 or less. A description of the target population,
expected sample sizes, stages of the hierarchical design, and estimation
procedures are described briefly below.
Target Population
The NHANES III sample was designed to cover the noninstitutionalized
population of the United States aged 2 months and older. Children under
5 years of age, adults aged 60 years and older, and both black and Mexican
American persons are sampled at higher rates than other persons to provide
reliable estimates for these important demographic subpopulations.
Sample Size
The sample size was fixed with regard to the resources available and
the time period for the conduct of the survey. The sample size was determined
in part from past NHANES experience taking into account patterns of nonresponse
and the time required to conduct the examination portion of the survey.
The desired sample size was 40,000 sample persons with 12,000 blacks, 12,000
Mexican Americans, and 16,000 whites and all others. Table 2 shows the sample
sizes, by race/ethnicity, that are expected to be achieved with geographic
stratification and oversampling and the corresponding sample sizes that
would have been obtained without any oversampling. Assuming a 75% response
rate, the sample is expected to yield a total of 30,000 examined persons.
Type of Design
Like most national random samples, NHANES III is a multistage stratified
design. The four stages of the design are shown in Table 3.
One of the operational advantages of a multistage national probability
survey design is that a relatively small number of areas can be designated
for the conduct of the survey, which limits the number of sample areas to
which survey personnel must travel. For example, in NHANES III the entire
United States (including Alaska and Hawaii) was divided into approximately
2812 geographic areas or primary sampling units (PSUs), most of which consisted
of individual counties. After the selection of 13 very large PSUs with certainty,
the remaining PSUs were grouped into 34 strata according to region, SMSA
status, race/ethnicity, and income and 2 PSUs were selected per strata.
These 81 PSUs were the first-stage units of selection. The noncertainty
PSUs were selected to take into account the need for reliable statistics
for black and Mexican-American persons. In each of the sample PSUs, successive
stages of sample selection included segments (census enumeration districts
or block groups), households, and sample individuals. To reduce the cost
of screening necessary to locate the desired Mexican Americans for the sample,
area segments consisting of census block groups and enumeration districts
are stratified by the percent of the population that is Mexican American,
with a higher rate of selection used in strata containing 3% or greater
Mexican American population. Households are also sampled at variable rates
depending on the concentration of Mexican Americans within the stratum.
Within households, children under 5 years, persons over 60, blacks, and
Mexican Americans are oversampled. A detailed description of the NHANES
III sample design has been previously published (1).
Estimation Procedures
The NHANES, like most sample surveys, experiences unit or total nonresponse
despite special procedures designed to maximize response rates. For NHANES
III, these procedures include extensive publicity in each survey location,
a home examination especially targeted for the older population, a remuneration
to all examined participants, and a report of major medical findings. Since
NHANES includes both an interview and an examination component, two levels
of unit nonresponse occur. That is, some persons randomly selected for the
survey refuse to be interviewed and some who are interviewed refuse the
examination portion of the survey. NHANES III-phase 1, conducted from 1988
to 1991, included 20,277 sample persons. In-person household interviews
were conducted with 17,464 persons (86%) and physical examinations were
conducted with 15,864 persons (78%). Table 4 shows the examination response
rates for males and females by age and race/ethnicity. The examination response
rate was highest for the two minority subgroups, and response rates decreased
with increasing age for both males and females.
Two features of the NHANES III design must be taken into account in any
analysis of the data collected. The first is the use of sample weights so
that correct national population estimates can be produced. For NHANES III,
the final analysis weights incorporate the selection probabilities and include
adjustments for nonresponse. The nonresponse adjusted weights are further
poststratified by age, gender, and race/ethnicity to account for noncoverage
and to bring the final national estimates in line with known population
counts. The weighting procedure for NHANES III has been previously described
(23,24). The second feature of the design that must be taken into
account is the strata and primary sampling units from the complex sample
design to estimate variances and test for statistical significance.
Discussion
Currently, no national data are available on the prevalence of exposure
of the United States population to various toxic substances. A limited number
of measurements for toxic substances have been done as part of the NHANES
including lead, cotinine, selected pesticides, cadmium, benzene, styrene,
and a few others. Clearly, national probability surveys to determine the
exposure to various toxic substances could help identify at-risk populations
and establish prevention programs. Furthermore, biologic specimen banking
could provide a valuable resource to permit future laboratory analyses for
the prevalence of toxic substances of emerging importance with respect to
disease risk and for determining the prevalence of environmental agents
as new techniques are developed to measure environmental exposure. This
article has addressed a number of sample design issues that need to be considered
in the design of possible surveys of human exposure assessment to ensure
that the survey goals and objectives can be met. Surveys designed for the
general population likely cannot address all of the risks of exposure for
numerous at-risk groups. Therefore, in addition to national probability
sample surveys to provide data on environmental exposures for the United
States population, consideration should also be given to conducting selected
special studies of potentially exposed populations, such as persons living
in certain agricultural areas or industrial areas.
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