摘要：Objectives. We examined interconnections among runaway and homeless youths (RHYs) and how aggregated network structure position was associated with HIV risk in this population. Methods. We collected individual and social network data from 136 RHYs. On the basis of these data, we generated a sociomatrix, accomplished network visualization with a “spring embedder,” and examined k -cores. We used multivariate logistic regression models to assess associations between peripheral and nonperipheral network position and recent unprotected sexual intercourse. Results. Small numbers of nominations at the individual level aggregated into a large social network with a visible core, periphery, and small clusters. Female youths were more likely to be in the core, as were youths who had been homeless for 2 years or more. Youths at the periphery were less likely to report unprotected intercourse and had been homeless for a shorter duration. Conclusions. HIV risk was a function of risk-taking youths' connections with one another and was associated with position in the overall network structure. Social network–based prevention programs, young women's housing and health programs, and housing-first programs for peripheral youths could be effective strategies for preventing HIV among this population. The nearly 2 million runaway and homeless youths (RHYs) in the United States each year 1 are at increased risk for contracting HIV and other sexually transmitted infections. 2–4 Many RHYs are sexual minority youths, and most RHYs come from disorganized family environments filled with conflict, violence, and parental substance abuse. 5–10 When youths run away or are thrown out of the home, they often become embedded in small networks composed of other homeless peers who engage in high-risk behaviors, which normalizes heightened HIV risk taking, such as unprotected sexual intercourse and injection drug use. 5–14 This finding was based on studies of small qualitative samples or egocentric network studies (limited to perceived social ties of the surveyed individual) 5–14 rather than sociometric data that provide linkages among respondents in a sample. School-based sociometric data, however, have shown that a youth's position in broader, larger networks affects health (e.g., suicidal ideation, sexual risk, and smoking). 15–23 Few studies have examined how HIV risk behaviors are tied to sociometric position in larger social network structures, not just among RHYs but also among high-risk youths in general. 24 The shift in perspective from specific peer relationships to large social networks requires sociometric data and sociometric theorizing. On the basis of egocentric observations, Whitbeck 25 and Rice et al. 26–29 have suggested that there may be 2 distinct types of peer engagement among RHYs. Some youths become embedded in networks of other high-risk homeless youths, whereas other youths, who engage in relatively healthier behaviors, may never become fully embedded in such networks and instead maintain prosocial ties to home, primarily via the Internet and cell phones. 28,29 To move beyond egocentric findings that link risk-taking individuals to risk-taking peers, one must theoretically delineate the ways in which social network position can be related to HIV risk taking. In so doing, one must consider how macro-level social structures (such as housing) may affect the arrangement of social network ties for these youths. Previous work suggests that as youths are homeless for longer periods, they become increasingly likely to form relationships with other homeless youths. 5–14,25–29 Figure 1 provides a graphical representation of 4 possible arrangements of ties in which risk-taking youths are adjacent to one another and in which the average number of ties per youth is 3; however, the aggregate network structures and locations of risk are quite distinct among the arrangements. Open in a separate window FIGURE 1— Four equally plausible hypothetical networks—(a) clusters, (b) chain, (c) risk at the core, and (d) risk at the periphery—in which the average degree is 3, one third of youths are risk takers, and risk clusters from an egocentric perspective. Note . Circles indicate youths; lines indicate social network ties; gray indicates risk taker; white indicates non–risk taker. The small number of ties reported by RHYs might aggregate into a large number of relatively disconnected networks, or “clusters.” HIV risk occurs in isolated clusters (probably clusters comprising youths who have been homeless longer). Another possibility is that the small number of ties aggregate into long chains of relations that only connect 1 or 2 youths at a time but that span a large number of youths in the aggregate. Again, one might anticipate that risk occurs in particular clusters, primarily among youths who have been homeless longer. The third and fourth possibilities conceptualize ties as aggregating into large networks that have a core and periphery. In 1 case, youths in the core could be engaged in more risk taking. This scenario presents an interesting tension. The RHYs who have been homeless for longer periods of time (and who are thus relatively more marginal to housed society) become more central to street-based social networks. This marginalization may increase their affiliation with risk-taking peers and may further alienate them from prosocial influences, increasing their chances of engaging in a variety of risk behaviors. Conversely, risk could occur among members of the periphery, and youths who have been homeless for longer may be less well connected to one another (although this possibility seems less likely). To date, the data needed to assess network structures such as these have not been collected because RHYs are an unbounded population, and social network researchers have not reached consensus on how to collect sociometric data from unbounded populations. The event-based approach (EBA) proposed by Freeman and Webster 30 that samples the regulars of a sociophysical space (e.g., a beach) seems promising for RHYs. Freeman and Webster were interested in sampling from natural settings; that is, places where regular patterns of ongoing relationships occur and where entry and exit are common. In such settings, clusters of individuals interact over time, but strictly defined, mutually exclusive categories of individuals cannot be observed in any direct way. 30 Freeman and Webster tested this method with regular members of a beach community. They included in their sample those persons who attended the beach 3 times or more in the previous month, thus eliminating transient attendees who were unlikely to be a meaningful part of the social structure at that time. The beach was thus defined as a sociophysical space where interactions occurred among regular attendees over a specified time. Regular attendance at homeless youth drop-in centers fulfills the requirements of the EBA of Freeman and Webster. 30 Drop-in centers, which are common in urban settings, are physical locations where youths congregate to access services such as food, clothing, and case management, but they are also safe havens where youths can freely interact under the loose supervision of adult staff who ensure the safety and peace of the drop-in centers. Drop-in centers exhibit the basic criteria of the natural settings for which the technique of Freeman and Webster technique is appropriate: (1) drop-in centers are a natural social setting where patterns of ongoing social networks may be observed (i.e., a specific sociophysical space); (2) clusters of youths interact in these centers over time, but strictly defined, mutually exclusive categories of individuals are rare; and (3) entry into and exit from these spaces is routine and unregulated. We used the EBA strategy to record the social ties among a sample of 136 RHYs who received services multiple times from a single drop-in center. We used network visualization techniques 30 to examine the resulting network structure, and we performed a k -core analysis. 31,32 We then assessed the associations among relevant network positions and recent condom use.
