Environmental Health Criteria 214

HUMAN EXPOSURE ASSESSMENT
population that the sample is designed to represent (Kish, 1965). For
these studies, one needs to (Sexton & Ryan, 1988):
* choose a population for investigation
* choose an appropriate unit for sampling and analysis (e.g., person,
household, neighbourhood, city, etc.)
* stratify as appropriate
* choose a sampling strategy (e.g., simple random sampling,
multistage sampling).
The results of a probability survey can be used to make general
statements about the population under investigation. The advantages
include having results that represent the population, taking into
account the possible error due to sampling. The disadvantages of this
scheme lie in the complicated sample selection, difficulty in
maintaining compliance from participants and the potentially complex
statistical analysis. In addition, randomized surveys of insufficient
sample size may miss rare hazardous events or small populations with
high exposure or risk.
Sampling strategies for survey studies include randomization
methods for choosing subjects to enroll in the study. Simple random
sampling is a scheme in which all sampling units of the same size have
equal probability of being selected. It can be difficult to implement
but relatively easy to generalize. Simple random sampling presents
logistic and fiscal constraints when considered for exposure surveys
that are large in geographic scope. For example, a national survey of
5000 personal exposures to respirable particulate matter that utilizes
simple random sampling may result in individuals selected from 1000
cities and towns. The travel and site preparation costs of such a
design may not be feasible in many situations.
A variety of alternatives to simple random sampling exist that
may be used to provide practical and efficient samples of large
populations (Callahan et al., 1995). Stratified sampling may be used
to obtain more precise survey results if exposures are more
homogeneous within strata than between them. Possible strata include
urban, suburban and rural populations, or occupationally exposed and
non-occupationally exposed individuals.
Oversampling of target populations or contaminants also may
yield substantial increases in the precision of results. Because the
individuals anticipated to have the highest exposures to a particular
pollutant may be rare in the population being studied, oversampling
can be considered to obtain more precise estimates of exposure. Before
committing substantial resources to oversampling, special care must be
taken to ensure that assumptions or data used to support a rationale
for selecting the oversampled population are accurate; otherwise
erroneous oversampling may decrease the precision of the study results
(Callahan et al., 1995).
Multistage sampling designs utilize clusters of sampling units
thereby limiting sampling locations to manageable areas. Depending on
the scope of the study, the stages of probability sampling necessary
may include:
* selection of primary sampling unit (e.g., a city)
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