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Spatial, temporal and relational patterns in respondent-driven sampling: evidence from a social network study of rural drug users

Abstract

Background Respondent-driven sampling (RDS) has become a common tool for recruiting high-risk populations for HIV research. However, few studies have explored the influence of geospatial proximity and relationship-level characteristics on RDS recruitment, particularly among high-risk individuals residing in rural areas of the US.

Methods In a social network study of 503 drug users in rural Central Appalachia, interviewer-administered questionnaires were used to collect relationship-level data (eg, duration of relationship, frequency of communication, kinship, social/financial support, trust, drug use and sex) and residential location. Demographic and drug-use similarity were also evaluated. Residential data were geocoded and road distance (km) between participants and (1) their network members and (2) the study site were computed. Seasonal patterns were assessed using node-level analysis, and dyadic analyses were conducted using generalised linear mixed models. Adjusted ORs (AORs) and 95% CIs are reported.

Results Differences in distance to the study office by season and order of study entry were not observed (F=1.49, p=0.209 and β=0.074, p=0.050, respectively). Participants with transportation lived significantly further from the interview site than their counterparts (p<0.001). Dyadic analyses revealed no association between RDS recruitment likelihood and geographic proximity. However, kinship (AOR 1.62; CI 1.02 to 2.58) and frequency of communication (AOR 1.63; CI 1.25 to 2.13) were significantly associated with RDS recruitment.

Conclusions In this sample, recruitment from one's network was likely non-random, contradicting a core RDS assumption. These data underscore the importance of formative research to elucidate potential recruitment preferences and of quantifying recruitment preferences for use in analysis.

  • Epidemiological methods
  • HIV
  • Research Design in Epidemiology
  • SEXUALLY TRANS DIS
  • SPATIAL ANALYSIS
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