Behavioural Surveillance Surveys - The Wisdom of Whores

The majority of sub-populations of
particular interest to BSS are precisely those
that are not easily accessible through
conventional household or institutional
sampling techniques. The primary challenge
to conducting meaningful BSS is to devise
sampling plans that are both feasible and
capable of producing unbiased estimates
(or, more realistically, estimates with minimal
levels of bias) for population sub-groups
that are not easily “captured” in household
surveys. As a practical matter, this will require
(1) the use of conventional, probability
sampling approaches in non-conventional
ways, (2) the use of different sampling
strategies for different sub-populations,
and (3) the occasional use of non-probability
sampling methods in situations where
probability methods are not feasible.
Devising the sampling plan
Once the step of selecting the sub-populations
has been completed, a sampling plan
must be devised before the actual mapping
and development of the sampling frame can
take place. During the process of mapping,
the original sampling plan is usually revised
several times, as the survey managers begin to
understand the patterns of the population, and
the effects of these patterns on operationalizing
the design. This is particularly true for hardto-reach
populations, such as sex workers,
injecting drug users, men who have sex with
men, and mobile populations, among whom
BSS is frequently conducted.
The first step in devising a sampling plan
is to select a sampling approach. In this
chapter, two sampling approaches are
proposed for hard-to-reach populations:
multi-stage sampling and a modified form
of snowball sampling known as “targeted”
sampling. Next, sample size requirements are
considered. The final section discusses a
number of key design issues and parameters
for the sampling strategies proposed.
Probability and non-probability
sampling methods:
issues and challenges
Sampling procedures may be thought
of as falling into two broad classes: formal
or probability methods, and informal or
non-probability methods. In a probability
sample, every person in the defined universe
may be selected into the sample, with a
known (non-zero) probability. Probability
sampling tends in practice to be characterized
by the use of lists or sampling frames to select
the sample, and by clearly defined sample
selection procedures. With a probability
sample, it is possible to use the data themselves
to estimate the sampling error, or the effect
of random fluctuations in sample selection
on the accuracy of the observed results.
Estimates of population characteristics derived
from surveys based upon probability sampling
methods may be expected to approximate
the “true” population value (i.e. proportion
or mean) within a specified margin of error
with a known probability.
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C H A PTER 4 B EHAV I OR A L S U R V EI L L A NC E S U R V EY S