Open AccessSpatial Recruitment Bias in Respondent-Driven Sampling: Implications for HIV Prevalence Estimation in Urban Heterosexuals
Author(s) -
Samuel M. Jenness,
Alan Neaigus,
Travis Wendel,
Camila Gelpí-Acosta,
Holly Hagan
Publication year - 2013
Publication title -
aids and behavior
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.994
H-Index - 106
eISSN - 1573-3254
pISSN - 1090-7165
DOI - 10.1007/s10461-013-0640-8
Subject(s) - sampling bias , respondent , statistics , metric (unit) , sampling frame , demography , sampling (signal processing) , estimation , geography , econometrics , sample size determination , environmental health , medicine , mathematics , computer science , population , economics , operations management , management , filter (signal processing) , sociology , political science , law , computer vision
Respondent-driven sampling (RDS) is a study design used to investigate populations for which a probabilistic sampling frame cannot be efficiently generated. Biases in parameter estimates may result from systematic non-random recruitment within social networks by geography. We investigate the spatial distribution of RDS recruits relative to an inferred social network among heterosexual adults in New York City in 2010. Mean distances between recruitment dyads are compared to those of network dyads to quantify bias. Spatial regression models are then used to assess the impact of spatial structure on risk and prevalence outcomes. In our primary distance metric, network dyads were an average of 1.34 (95 % CI 0.82–1.86) miles farther dispersed than recruitment dyads, suggesting spatial bias. However, there was no evidence that demographic associations with HIV risk or prevalence were spatially confounded. Therefore, while the spatial structure of recruitment may be biased in heterogeneous urban settings, the impact of this bias on estimates of outcome measures appears minimal.