Guidelines on surveillance among populations most at risk for HIV

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In cases where a sampling frame and easy access to the population are not available, alternatives arenecessary. Excellent reviews of conventional sampling techniques can be found elsewhere (32,33,34). Severalvariants of classical sampling are important for surveillance among populations at increased risk for HIV (35).These variants are presented below.Box 1: What is a sampling frame?A sampling frame is simply a list of all of the individuals eligible to be in the survey. Persons participatingin the survey are selected from the sampling frame and are considered the survey sample. Anincomplete sampling frame can result in a biased sample and inaccurate results if the people excludedfrom the frame have different characteristics than those included in the sampling frame.2.3.2 Stratified samplingDescriptionStratified sampling is modified simple random sampling. Stratified sampling may provide an economicsaving over simple random sampling: Divide the target population into groups (strata) that are more similar within the group than the groupsare to each other. Now take a simple random sample from each group independently.Define strata by the nature of the sampling question. For example, if the variable of interest is condom use,and different types of sex workers used condoms at different frequencies, the strata could be defined as thevarious types of sex workers (street-based, brothel-based, home-based) in the population.AdvantagesWhen data are collected in this way, statistical methods allow weighting schemes to be used to estimate thevariability and potential error of the survey due to the sampling design (36). (See Box 2.)LimitationsIt is important to have a clear understanding of the population and the differences within the populationto be able to stratify it. In the example above, we would need to know the different types of sex workersand their behaviours. Once the strata are known, a sampling frame is needed for each stratum. Choose thesample from these lists.Box 2: How to weigh survey resultsWhen a sample is drawn from a sampling frame, it is common to draw more from one section of thepopulation than another. This is referred to as stratified or cluster sampling.When stratified sampling is used, it is possible to correct the bias in the sample by calculating avariable that truly represents the population of interest. For example: If one subgroup of the population is overrepresented in the survey, give this subgroup a weight ofless than one. If a subgroup is underrepresented in the survey, give them a weight greater than one. The overall weighted frequency is equal to the total number of people in the survey.All survey variables are then calculated using these weights to correct for the biases introduced fromthe sampling strategy. In addition, weights are used to calculate the precision of the estimate and thepotential error as a result of the survey design or the design effect.16
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
Page 1 and 2: UNAIDS/WHO Working Groupon Global H
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Page 5 and 6: ContentsAcknowledgements1 Introduct
Page 7 and 8: 1. IntroductionThe overall goal of
Page 9 and 10: Generalized The epidemic has mature
Page 12 and 13: Table 2.1. Potential stakeholders t
Page 14 and 15: Decide on a clear and specific defi
Page 16 and 17: In indirect sharing, one syringe is
Page 19: Sampling of subgroups of interest m
Page 23 and 24: Figure 2.2. Respondent-driven sampl
Page 25 and 26: Time-location sampling in Harare, Z
Page 27 and 28: Surveillance among men who have sex
Page 29 and 30: 3. Conduct surveillance of populati
Page 31 and 32: When constructing a questionnaire,
Page 33 and 34: 3.2.5 Maintain confidentiality and
Page 35 and 36: HIV incidence (recent infection) is
Page 37 and 38: 4.1.4 Use the results to improve pr
Page 39 and 40: Appendix A: List of additionalsurve
Page 41 and 42: Humanimmunodeficiencyvirus (HIV)HIV
Page 43 and 44: Appendix C: LinksUNAIDS/WHO Working
Page 45 and 46: 18 Topp L et al. Representativeness
Page 47 and 48: 59 Brookmeyer R, Stroup DF (eds). M
Page 49: For more information, contact:World

Guidelines on surveillance among populations most at risk for HIV

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