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Assessing Respondent-Driven Sampling in the Estimation of the Prevalence of Sexually Transmitted Infections (STIs) in Populations Structured in Complex Networks

  • Elizabeth M. Albuquerque
  • Cláudia T. Codeço
  • Francisco I. Bastos
Part of the Communications in Computer and Information Science book series (CCIS, volume 116)

Abstract

When a sampling frame for a given population cannot be defined, either because it requires expensive/time-consuming procedures or because it targets a stigmatized or illegal behavior that may compromise the identification of potential interviewees, traditional sampling methods cannot be used. Examples include “hidden populations” of special relevance for public health, such as men who have sex with men (MSM), sex workers and drug users. Since the late 1990s, a network-based method, called Respondent-Driven Sampling (RDS) has been used to assess such “hidden populations”.This paper simulates data from hidden populations, in order to assess the performance of prevalence estimators in different scenarios built after different combinations of social network structures and disease spreading patterns. The simulation models were parameterized using empirical data from a previous RDS study conducted on Brazilian MSM. Overall, RDS performed well, showing it is a valid strategy to assess hidden populations. However, the proper analysis of underlying network structures and patterns of disease spread should be emphasized as a source of potential estimate biases.

Keywords

Social Network Structure Hide Population Heterogeneous Chain Underlying Network Structure Traditional Sampling Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Baral, S., Sifakis, F., Cleghorn, F., Beyrer, C.: Elevated risk for hiv infection among men who have sex with men in low- and middle-income countries 2000-2006: a systematic review. PLoS Med. 4(12), 339 (2007), http://dx.doi.org/10.1371/journal.pmed.0040339, doi:10.1371/journal.pmed.0040339CrossRefGoogle Scholar
  2. 2.
    Boily, M.C., Bastos, F.I., Desai, K., Chesson, H., Aral, S.: Increasing prevalence of male homosexual partnerships and practices in britain 1990-2000: but why? AIDS 19(3), 352–4; author reply 354–5 (2005)Google Scholar
  3. 3.
    Gile, K.J., Handcock, M.S.: Respondent-driven sampling:: an assessment of current methodology. Sociological Methodology, 1467–9531 (2010)Google Scholar
  4. 4.
    Goel, S., Salganik, M.J.: Assessing respondent-driven sampling. Proc. Natl. Acad. Sci. USA 107(15), 6743–6747 (2010), http://dx.doi.org/10.1073/pnas.1000261107, doi:10.1073/pnas.1000261107CrossRefGoogle Scholar
  5. 5.
    Heckathorn, D.: Respondent-driven sampling: a new approach to the study of hidden populations. Social Problems 44, 174–199 (1997)CrossRefGoogle Scholar
  6. 6.
    Heckathorn, D.: Respondent-driven sampling ii: deriving valid population estimates from chain-referral samples of hideen populations. Social Problems 49, 11–34 (2002)Google Scholar
  7. 7.
    Hosmer, D., Lemeshow, S.: Applied Logistic Regression. Jonh Wiley & Sons, Chichester (2000)CrossRefzbMATHGoogle Scholar
  8. 8.
    Malekinejad, M., Johnston, L.G., Kendall, C., Kerr, L.R.F.S., Rifkin, M.R., Rutherford, G.W.: Using respondent-driven sampling methodology for hiv biological and behavioral surveillance in international settings: a systematic review. AIDS Behav. 12(4 Suppl.), S105–S130 (2008), http://dx.doi.org/10.1007/s10461-008-9421-1 CrossRefGoogle Scholar
  9. 9.
    Malta, M., Magnanini, M.M.F., Mello, M.B., Pascom, A.R.P., Linhares, Y., Bastos, F.I.: Hiv prevalence among female sex workers, drug users and men who have sex with men in brazil: a systematic review and meta-analysis. BMC Public Health 10, 317 (2010), http://dx.doi.org/10.1186/1471-2458-10-317, doi:10.1186/1471-2458-10-317CrossRefGoogle Scholar
  10. 10.
    Mello, M., A, A.P., M, M.C., Tun, W., Júnior, A.B., Ilário, M., Reis, P., Salles, R., Westman, S., Díaz: Assessment of risk factors for hiv infection among men who have sex with men in the metropolitan area of campinas city, brazil, using respondent-driven sampling. Tech. rep., Population Council (2008)Google Scholar
  11. 11.
    Morris, M.: Network Epidemiology: A handbook for survey design and data collection. Okford University (2004)Google Scholar
  12. 12.
    Poon, A.F.Y., Brouwer, K.C., Strathdee, S.A., Firestone-Cruz, M., Lozada, R.M., Pond, S.L.K., Heckathorn, D.D., Frost, S.D.W.: Parsing social network survey data from hidden populations using stochastic context-free grammars. PLoS One 4(9), e6777 (2009), http://dx.doi.org/10.1371/journal.pone.0006777, doi:10.1371/journal.pone.0006777CrossRefGoogle Scholar
  13. 13.
    Romano, C.M., de Carvalho-Mello, I.M.V.G., Jamal, L.F., de Melo, F.L., Iamarino, A., Motoki, M., Pinho, J.R.R., Holmes, E.C., de Andrade Zanotto, P.M., V. G. D. N. Consortium: Social networks shape the transmission dynamics of hepatitis c virus. PLoS One 5(6), e11,170 (2010), http://dx.doi.org/10.1371/journal.pone.0011170, doi:10.1371/journal.pone.0011170CrossRefGoogle Scholar
  14. 14.
    Salganik, M., Heckathorn, D.: Sampling and estimation in hidden populations using respondent-driven sampling. Sociological Methodology 34, 193–239 (2004)CrossRefGoogle Scholar
  15. 15.
    Salganik, M.J.: Variance estimation, design effects, and sample size calculations for respondent-driven sampling. J. Urban Health 83(6 suppl), 98–112 (2006), http://dx.doi.org/10.1007/s11524-006-9106-x, doi:10.1007/s11524-006-9106-xCrossRefGoogle Scholar
  16. 16.
    R Development Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing (2008)Google Scholar
  17. 17.
    Volz, E., Heckathorn, D.: Probability based estimation theory for respondent-driven sampling. Journal of Official Statistics 24, 79–97 (2008)Google Scholar
  18. 18.
    Strogatz, S.H.: Exploring complex networks. Nature 410(6825), 268–276 (2001), http://dx.doi.org/10.1038/35065725, doi:10.1038/35065725.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Elizabeth M. Albuquerque
    • 1
  • Cláudia T. Codeço
    • 2
  • Francisco I. Bastos
    • 1
    • 3
  1. 1.Oswaldo Cruz FoundationRio de JaneiroBrazil
  2. 2.Oswaldo Cruz FoundationRio de JaneiroBrazil
  3. 3.CAPES/Fulbright Visiting Scholar at Brown UniversityUSA

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