Assessing the geographic coverage and spatial clustering of illicit drug users recruited through respondent-driven sampling in New York City.
Assessing the geographic coverage and spatial clustering of illicit drug users recruited through respondent-driven sampling in New York City.
复制标题
评估纽约市通过受访者驱动抽样招募的非法药物使用者的地理覆盖范围和空间聚集情况。
DOI：
10.1007/s11524-015-9937-4
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Lewis,CrystalFuller
中科院分区：
文献类型：
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作者：
Rudolph,AbbyE;Young,AprilM;Lewis,CrystalFuller
We assess the geographic coverage and spatial clustering of drug users recruited through respondent-driven sampling (RDS) and discuss the potential for biased RDS prevalence estimates. Illicit drug users aged 18–40 were recruited through RDS (N= 401) and targeted street outreach (TSO)(N= 210) in New York City. Using the Google Maps API™, we calculated travel distances and times using public transportation between each participant’s recruitment location and the study office and between RDS recruiter–recruit pairs. We used K function analysis to evaluate and compare spatial clustering of (1) RDS vs. TSO respondents and (2) RDS seeds vs. RDS peer recruits. All participant recruitment locations clustered around the study office; however, RDS participants were significantly more likely to be recruited within walking distance of the study office than TSO participants. The TSO sample was also less spatially clustered than the RDS sample, which likely reflects (1) the van’s ability to increase the sample’s geographic heterogeneity and (2) that more TSO than RDS participants were enrolled on the van. Among RDS participants, individuals recruited spatially proximal peers, geographic coverage did not increase as recruitment waves progressed, and peer recruits were not less spatially clustered than seeds. Using a mobile van to recruit participants had a greater impact on the geographic coverage and spatial dependence of the TSO than the RDS sample. Future studies should consider and evaluate the impact of the recruitment approach on the geographic/spatial representativeness of the sample and how spatial biases, including the preferential recruitment of proximal peers, could impact the precision and accuracy of estimates.
