AIDS and Behavior

, Volume 18, Issue 12, pp 2366–2373 | Cite as

Spatial Recruitment Bias in Respondent-Driven Sampling: Implications for HIV Prevalence Estimation in Urban Heterosexuals

  • Samuel M. Jenness
  • Alan Neaigus
  • Travis Wendel
  • Camila Gelpi-Acosta
  • Holly Hagan
Original Paper

Abstract

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.

Keywords

Respondent-driven sampling Survey sampling HIV/AIDS Heterosexual 

Notes

Acknowledgments

This work was supported by a cooperative agreement between the New York City Department of Health and the Centers for Disease Control and Prevention (#U62/CCU223595-03-1). We would like to thank the NYC NHBS field staff for all their efforts on the study. We also greatly appreciate the insightful comments on an earlier version of this manuscript from three anonymous reviewers.

References

  1. 1.
    Heckathorn D. Extensions of respondent-driven sampling: analyzing continuous variables and controlling for differential recruitment. Sociol Methodol. 2007;37(1):151–207.CrossRefGoogle Scholar
  2. 2.
    Magnani R, Sabin K, Saidel T, Heckathorn D. Review of sampling hard-to-reach and hidden populations for HIV surveillance. AIDS. 2005;19(Suppl 2):S67–72.PubMedCrossRefGoogle Scholar
  3. 3.
    Des Jarlais DC, Arasteh K, Hagan H, McKnight C, Perlman DC, Friedman SR. Persistence and change in disparities in HIV infection among injection drug users in New York City after large-scale syringe exchange programs. Am J Public Health. 2009;99(Suppl 2):S445–51.PubMedCrossRefGoogle Scholar
  4. 4.
    Hathaway AD, Hyshka E, Erickson PG, et al. Whither RDS? An investigation of respondent driven sampling as a method of recruiting mainstream marijuana users. Harm Reduct J. 2010;7:15.PubMedCentralPubMedCrossRefGoogle Scholar
  5. 5.
    Johnston LG, Malekinejad M, Kendall C, Iuppa IM, Rutherford GW. Implementation challenges to using respondent-driven sampling methodology for HIV biological and behavioral surveillance: field experiences in international settings. AIDS Behav. 2008;12(4 Suppl):S131–41.PubMedCrossRefGoogle Scholar
  6. 6.
    Simic M, Johnston LG, Platt L, et al. Exploring barriers to ‘respondent driven sampling’ in sex worker and drug-injecting sex worker populations in Eastern Europe. J Urban Health. 2006;83(6 Suppl):i6–15.PubMedCrossRefGoogle Scholar
  7. 7.
    Malekinejad M, Johnston LG, Kendall C, Kerr LR, Rifkin MR, Rutherford GW. Using respondent-driven sampling methodology for HIV biological and behavioral surveillance in international settings: a systematic review. AIDS Behav. 2008;12(4 Suppl):S105–30.PubMedCrossRefGoogle Scholar
  8. 8.
    Goel S, Salganik MJ. Assessing respondent-driven sampling. Proc Natl Acad Sci USA. 2010;107(15):6743.PubMedCentralPubMedCrossRefGoogle Scholar
  9. 9.
    Wejnert C, Heckathorn DD. Web-based network sampling efficiency and efficacy of respondent-driven sampling for online research. Sociol Methods Res. 2008;37(1):105–34.CrossRefGoogle Scholar
  10. 10.
    Rothenberg R, Muth SQ, Malone S, Potterat JJ, Woodhouse DE. Social and geographic distance in HIV risk. Sex Transm Dis. 2005;32(8):506–12.PubMedCrossRefGoogle Scholar
  11. 11.
    Toledo L, Codeco CT, Bertoni N, Albuquerque E, Malta M, Bastos FI. Putting respondent-driven sampling on the map: insights from Rio de Janeiro, Brazil. J Acquir Immune Defic Syndr. 2011;57(Suppl 3):S136–43.PubMedCrossRefGoogle Scholar
  12. 12.
    McCreesh N, Frost SDW, Seeley J, et al. Evaluation of respondent-driven sampling. Epidemiology. 2012;23(1):138–47.PubMedCentralPubMedCrossRefGoogle Scholar
  13. 13.
    McCreesh N, Johnston LG, Copas A, et al. Evaluation of the role of location and distance in recruitment in respondent-driven sampling. Int J Health Geogr. 2011;10:56.PubMedCentralPubMedCrossRefGoogle Scholar
  14. 14.
    Shepard CW, Gortakowski HW, Nasrallah H, Cutler BH, Begier EM. Using GIS-based density maps of HIV surveillance data to identify previously unrecognized geographic foci of HIV burden in an urban epidemic. Public Health Rep. 2011;126(5):741–9.PubMedCentralPubMedGoogle Scholar
  15. 15.
    Centers for Disease Control and Prevention. HIV infection among heterosexuals at increased risk—United States, 2010. MMWR Morb Mortal Wkly Rep. 2013;62(10):183–8.Google Scholar
  16. 16.
    Reilly KH, Neaigus A, Jenness SM, Hagan H, Wendel T, Gelpi-Acosta C. High HIV prevalence among low-income, Black Women in New York city with self-reported HIV negative and unknown status. J Womens Health. 2013;22(9):745–54.CrossRefGoogle Scholar
  17. 17.
    Jenness SM, Neaigus A, Murrill CS, Wendel T, Forgione L, Hagan H. Estimated HIV incidence among high-risk heterosexuals in New York City, 2007. J Acquir Immune Defic Syndr. 2011;56(2):193–7.PubMedCrossRefGoogle Scholar
  18. 18.
    Bivand RS, Pebesma EJ, Gomez-Rubio V. Applied spatial data analysis with R. New York: Springer; 2008.Google Scholar
  19. 19.
    Pearl J. Causality: models, reasoning and inference. Cambridge: Cambridge University Press; 2000.Google Scholar
  20. 20.
    Wood SN. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J R Stat Soc Ser B. 2011;73(1):3–36.CrossRefGoogle Scholar
  21. 21.
    Heckathorn D. Respondent-driven sampling: a new approach to the study of hidden populations. Soc Probl. 1997;44:174–99.CrossRefGoogle Scholar
  22. 22.
    Johnston LG, Whitehead S, Simic-Lawson M, Kendall C. Formative research to optimize respondent-driven sampling surveys among hard-to-reach populations in HIV behavioral and biological surveillance: lessons learned from four case studies. AIDS Care. 2010;22(6):784–92.PubMedCrossRefGoogle Scholar
  23. 23.
    Salganik MJ. Variance estimation, design effects, and sample size calculations for respondent-driven sampling. J Urban Health. 2006;83(6 Suppl):i98–112.PubMedCrossRefGoogle Scholar
  24. 24.
    Ott M, Gile KJ, Uuskula A, Johnston LG. Spatial dependencies in respondent-driven sampling data. Redondo Beach: Sunbelt XXXII; 2012.Google Scholar
  25. 25.
    Kendall C, Kerr LR, Gondim RC, et al. An empirical comparison of respondent-driven sampling, time location sampling, and snowball sampling for behavioral surveillance in men who have sex with men, Fortaleza, Brazil. AIDS Behav. 2008;12(4 Suppl):S97–104.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Samuel M. Jenness
    • 1
  • Alan Neaigus
    • 2
  • Travis Wendel
    • 3
  • Camila Gelpi-Acosta
    • 4
  • Holly Hagan
    • 5
  1. 1.Department of EpidemiologyUniversity of WashingtonSeattleUSA
  2. 2.New York City Department of HealthHIV Epidemiology ProgramNew YorkUSA
  3. 3.Department of AnthropologyJohn Jay College of Criminal JusticeNew YorkUSA
  4. 4.Department of SociologyThe New SchoolNew YorkUSA
  5. 5.College of NursingNew York UniversityNew YorkUSA

Personalised recommendations