Guidelines on surveillance among populations most at risk for HIV

Sampling of subgroups of interest may only recruit those who are at higher risk for HIV infection andonly later recruit those at lower risk (31).In sampling populations of interest, geographical areas may not be sampled in proportion to numbersof the population of interest. For example, drug users may not be sampled in proportion to the intensityof drug use in the region. The probability of selecting drug users may not be known.All of these issues are discussed further in the section on sampling.2.3. Step 3: Decide on a surveillance sampling strategySurveillance is conducted to allow comparisons of data from the same group over time. Thus, samplesshould be: representative of the population able to be repeated (or replicable) in a later survey.<strong>Guidelines</strong> on surveillance among populations most at risk for HIVFor most-at-risk populations, it is difficult to replicate surveys. Populations may be difficult to reach (hidden)or transient; that is, people move into or out of the population. To address these issues, HIV prevalencestudies among most-at-risk populations often use probability, convenience or snowball sampling methods,which are not always replicable over time.While selecting a sampling strategy, consider the following: It should be feasible to sample from the same population in future surveys. The sample should be as representative of the population as possible. The sampling strategy should match available resources for this round and future rounds of the survey. It should be possible to carry out the survey (reaching all persons in the sample) in a reasonably shortperiod of time. The cost involved should be affordable. Legal and ethical issues of implementing the sampling strategy should be taken into account.2.3.1 Simple random samplingDescriptionThe way a sample is selected determines the scientific viability of the study. A simple random sample is thegold standard for sampling considerations. Simple random sampling assumes that everyone in the targetpopulation has the same, known probability of being included in the sample, like drawing numbers ornames from a hat. More commonly, each member of the population is identified by a number and randomnumber tables or computer algorithms are used to select the required sample size.AdvantagesFrom a statistical perspective, simple random sampling: eliminates bias from a sample of volunteers who might have different characteristics than the completepopulation provides each member of the population with the same chance of being selected provides a sample that contains members with characteristics similar to the population as a whole.LimitationsAs straightforward as this seems, implementing a true simple random sample in fieldwork can be expensive,inconvenient or impossible: Implementing a random sample requires a sampling frame or list of the target population. Clearly, thisrequirement is not met for many populations at increased risk for HIV. (See Box 1.) Even if a list does exist, the sample population might be dispersed throughout a geographical area,making it expensive and time-consuming to carry out. A random sample may miss people or behaviours that are not common in the population.15