Guidelines on surveillance among populations most at risk for HIV

2.3.3 Cluster or targeted samplingDescriptionCluster or targeted sampling is also a modified simple random sampling. The target population maybe naturally divided into groups (as with stratified sampling). Within the groups there is diversity in thevariable of interest but, on average, one group is similar to the other. Each cluster should be a fairly goodrepresentation of the entire population on a smaller scale. Often, clusters are geographical areas, such as: villages regions of a rural area sections of a town or city physical units such as brothels, bars or streets.To conduct cluster sampling, randomly select the clusters and then perform simple random sampling withinthe clusters (37).AdvantagesAs with stratified sampling, weighting allows for calculation of the sampling error (38). Using the sex workerexample, if most of the population of interest is street-based, cluster sampling may be appropriate. Clustersampling commonly uses maps to guide the stratification. This allows for a greater generalizability to ageographical area.<strong>Guidelines</strong> on surveillance among populations most at risk for HIVLimitationsIt is usually easy to break groups into clusters when using geographical areas. Use mapping of the populationto be sure that a sample of the entire population in the region is included. A sampling frame from which todraw the sample is needed for each cluster.As described earlier, stratified and cluster samples can use weighting schemes to create statistical estimatesof variability and errors. This means that they can produce probability samples. Properly conducted, theseadaptations can produce estimates with as much or more accuracy as simple random sampling but with asubstantial saving of resources.However, these designs could lead to higher estimates of error compared with that from simple randomsampling. When surveys are structured with stratifications or clusters, they are described as having a designeffect. The design effect of the study needs to be considered when planning the sampling strategy andcalculating sample sizes. Involve a statistician on the surveillance team for these types of issues.The first three options described above are often not feasible when working with most-at-risk populations.There are usually no sampling frames if the entire most-at-risk population in a geographical area isconsidered the sampling universe. Therefore, non-probability sampling is often necessary for surveillanceof most-at-risk populations.2.3.4 Snowball samplingDescriptionSnowball sampling is one type of non-probability sampling. Persons already interviewed or measured areused to find study subjects. Generally, subjects first contacted are asked to name acquaintances who arethen approached, interviewed and asked for additional names. In this way, a sufficient number of subjectscan be accumulated to give a study adequate power (39).For example, if the goal is to study persons who inject drugs, sampling from needle exchange programmes,while accessible, will miss many women, youth and those who have recently started injecting. This wouldproduce a statistically representative sample of an unrepresentative part of the target population. Snowballsampling was developed to address this problem by reaching individuals in diverse social networks.AdvantageThe method allows the sampling of populations that are networked when there is no sampling frame.17