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AIDS and Behavior
Erschienen in: AIDS and Behavior 7/2018

28.06.2017 | Original Paper

Overlooked Threats to Respondent Driven Sampling Estimators: Peer Recruitment Reality, Degree Measures, and Random Selection Assumption

verfasst von: Jianghong Li, Thomas W. Valente, Hee-Sung Shin, Margaret Weeks, Alexei Zelenev, Gayatri Moothi, Heather Mosher, Robert Heimer, Eduardo Robles, Greg Palmer, Chinekwu Obidoa

Erschienen in: AIDS and Behavior | Ausgabe 7/2018

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Abstract

Intensive sociometric network data were collected from a typical respondent driven sample (RDS) of 528 people who inject drugs residing in Hartford, Connecticut in 2012–2013. This rich dataset enabled us to analyze a large number of unobserved network nodes and ties for the purpose of assessing common assumptions underlying RDS estimators. Results show that several assumptions central to RDS estimators, such as random selection, enrollment probability proportional to degree, and recruitment occurring over recruiter’s network ties, were violated. These problems stem from an overly simplistic conceptualization of peer recruitment processes and dynamics. We found nearly half of participants were recruited via coupon redistribution on the street. Non-uniform patterns occurred in multiple recruitment stages related to both recruiter behavior (choosing and reaching alters, passing coupons, etc.) and recruit behavior (accepting/rejecting coupons, failing to enter study, passing coupons to others). Some factors associated with these patterns were also associated with HIV risk.
Literatur
1.
Heckathorn DD. Respondent-driven sampling: a new approach to the study of hidden populations. Soc Problems. 1997;44(2):174–99 PubMed PMID: ISI:A1997XE13900003.CrossRef
2.
Heckathorn DD. Respondent-driven sampling II: deriving valid population estimates from chain-referral samples of hidden populations. Soc Problems. 2002;49(1):11–34.CrossRef
3.
Abdul-Quader AS, Heckathorn DD, McKnight C, Bramson H, Nemeth C, Sabin K, et al. Effectiveness of respondent-driven sampling for recruiting drug users in New York City: findings from a pilot study. J Urban Health. 2006;83(3):459–76 PubMed PMID: 16739048. Pubmed Central PMCID: 2527186. Epub 2006/06/02. eng.CrossRefPubMedCentralPubMed
4.
Johnston LG, Sabin K, Mai TH, Pham TH. Assessment of respondent driven sampling for recruiting female sex workers in two Vietnamese cities: reaching the unseen sex worker. J Urban Health. 2006;83(6 Suppl):i16–28 PubMed PMID: 17031567. Pubmed Central PMCID: 1705478. Epub 2006/10/13. eng.CrossRefPubMed
5.
Kral AH, Malekinejad M, Vaudrey J, Martinez AN, Lorvick J, McFarland W, et al. Comparing respondent-driven sampling and targeted sampling methods of recruiting injection drug users in San Francisco. J Urban Health. 2010;87(5):839–50 PubMed PMID: 20582573. Epub 2010/06/29. Eng.CrossRefPubMedCentralPubMed
6.
Iguchi M, Ober A, Berry S, Fain T, Heckathorn D, Gorbach P, et al. Simultaneous recruitment of drug users and men who have sex with men in the United States and Russia using respondent-driven sampling: sampling methods and implications. J Urban Health. 2009;86:5–31.CrossRefPubMedCentralPubMed
7.
Yeka W, Maibani-Michie G, Prybylski D, Colby D. Application of respondent driven sampling to collect baseline data on FSWs and MSM for HIV risk reduction interventions in two urban centres in Papua New Guinea. J Urban Health. 2006;83(6 Suppl):i60–72 PubMed PMID: 17066328. Pubmed Central PMCID: 1705546. Epub 2006/10/27. eng.CrossRefPubMed
8.
Malekinejad M, Johnston L, Kendall C, Kerr L, Rifkin M, Rutherford G. Using respondent-driven sampling methodology for HIV biological and behavioral surveillance in international settings: a systematic review. AIDS Behav. 2008;12:105–30.CrossRef
9.
Heckathorn DD. Extensions of respondent-driven sampling: analyzing continuous variables and controlling for differential recruitment. Sociol Methodol. 2007;37(1):151–207.CrossRef
10.
Salganik MJ, Heckathorn DD. Sampling and estimation in hidden populations using respondent-driven sampling. Sociol Methodol. 2004;34(1):193–240.CrossRef
11.
Volz E, Heckathorn DD. Probability based estimation theory for respondent driven sampling. J Off Stat. 2008;24(1):19.
12.
Gile KJ. Improved inference for respondent-driven sampling data with application to HIV prevalence estimation. J Am Stat Assoc. 2011;106(493):135–46.CrossRef
13.
Gile KJ, Handcock MS. Network model-assisted inference from respondent-driven sampling data. J R Stat Soc. 2015;178(3):619–39.CrossRef
14.
Heimer R. Critical issues and further questions about respondent-driven sampling: comment on Ramirez-Valles, et al. (2005). AIDS Behav. 2005;8(4):403–8.CrossRef
15.
Frost SDW, Brouwer K, Firestone Cruz M, Ramos R, Ramos ME, Lozada R, et al. Respondent-driven sampling of injection drug users in two U.S.—Mexico border cities: recruitment dynamics and impact on estimates of HIV and Syphilis prevalence. J Urban Health. 2006;83:83–97.CrossRefPubMedCentral
16.
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):131–41.CrossRef
17.
Li J, Moorthi G, Mosher H, Obidoa C, Weeks M, Valente T. Skepticism about RDS inference validity: preliminary empirical findings from a recruitment dynamic and network study. In: American Public Health Association Annual Conference, November 2–6, Boston, MA, 2013.
18.
19.
20.
Volz E, Wejner C, Degani I, Heckathorn DD. Respondent-driven sampling analysis tool (RDSAT) Version 5.6. Ithaca, NY: Cornell University; 2007.
21.
Lansky A, Abdul-Quader AS, Cribbin M, Hall T, Finlayson TJ, Garfein RS, et al. Developing an HIV behavioral surveillance system for injecting drug users: the National HIV behavioral surveillance system. Public Health Rep. 2007;122(1 Suppl):48–55 PubMed PMID: 17354527. Pubmed Central PMCID: 1804107. Epub 2007/03/16. eng.CrossRefPubMedCentralPubMed
22.
Douglas DH, Cameron CJ. Network sampling: from snowball and multiplicity to respondent-driven sampling. Annu Rev Sociol. Forth coming.
23.
24.
Handcock MS, Fellows IE, Gile KJ. RDS analyst: software for the analysis of respondent-driven sampling data. 0.42 ed: UCLA Statistics; 2014.
25.
26.
Gregoire TG. Design-based and model-based inference in survey sampling: appreciating the difference. Can J For Res. 1998;28(10):1429–47.CrossRef
27.
Sarndal C-E. Design-based and model-based inference in survey sampling1. Scand J Stat. 1978;5:27–52.
28.
29.
Gile KJ, Johnston LG, Salganik MJ. Diagnostics for respondent-driven sampling. J R Stat Soc. 2015;178(1):241–69.CrossRef
30.
Skvoretz J, Agneessens F. Reciprocity, multiplexity, and exchange: measures. Qual Quant. 2007;41(3):341–57.CrossRef
31.
Krohn MD. The web of conformity: a network approach to the explanation of delinquent behavior. Soc Probl. 1986;33(6):s81–93.CrossRef
32.
Krohn MD, Massey JL, Zielinski M. Role overlap, network multiplexity, and adolescent deviant behavior. Social Psychol Q. 1988;51:346–56.CrossRef
33.
Fujimoto K, Valente TW. Multiplex congruity: friendship networks and perceived popularity as correlates of adolescent alcohol use. Soc Sci Med. 2015;125:173–81.CrossRefPubMed
34.
Ramirez-Valles J, Douglas DH, Vazquez R, Diaz RM, Campbell RT. The fit between theory and data in respondent-driven sampling: response to Heimer. AIDS Behav. 2005;9(4):409–14.CrossRef
35.
McCreesh N, Frost S, Seeley J, Katongole J, Tarsh MN, Ndunguse R, et al. Evaluation of respondent-driven sampling. Epidemiology (Cambridge, Mass). 2012;23(1):138.CrossRef
36.
Abdul-Quader AS, Heckathorn DD, Sabin K, Saidel T. Implementation and analysis of respondent driven sampling: lessons learned from the field. J Urban Health. 2006;83(6 Suppl):i1–5 PubMed PMID: 17058119. Pubmed Central PMCID: 1705522. Epub 2006/10/24. eng.CrossRefPubMed
37.
Uuskula A, Johnston LG, Raag M, Trummal A, Talu A, Des Jarlais DC. Evaluating recruitment among female sex workers and injecting drug users at risk for HIV using respondent-driven sampling in Estonia. J Urban Health. 2010;87(2):304–17 PubMed PMID: 20131018. Pubmed Central PMCID: 2845833. Epub 2010/02/05. eng.CrossRefPubMedCentralPubMed
38.
Gwadz MV, Leonard NR, Cleland CM, Riedel M, Banfield A, Mildvan D. The effect of peer-driven intervention on rates of screening for AIDS clinical trials among African Americans and Hispanics. Am J Public Health. 2011;101(6):1096–102.CrossRefPubMedCentralPubMed
39.
Liu H, Li J, Ha T, Li J. Assessment of random recruitment assumption in respondent-driven sampling in egocentric network data. Soc Netw. 2012;1(2):13.CrossRefPubMedCentralPubMed
40.
Friedman SR, Kottiri BJ, Neaigus A, Curtis R, Vermund SH, Des Jarlais DC. Network-related mechanisms may help explain long-term HIV-1 seroprevalence levels that remain high but do not approach population-group saturation. Am J Epidemiol. 2000;152(10):913–22 PubMed PMID: 11092433.CrossRefPubMed
41.
Neaigus A, Friedman SR, Curtis R, Jarlais DCD, Furst RT, Jose B, et al. The relevance of drug injectors’ social and risk networks for understanding and preventing HIV infection. Soc Sci Med. 1994;38(1):67–78.CrossRefPubMed
42.
Latkin CA. A personal network approach to AIDS prevention: an experimental peer group intervention for street-injecting drug users: the SAFE study. NIDA Res Monogr. 1995;151:181–95.PubMed
43.
El-Bassel N, Cooper DK, Chen DR, Schilling RF. Personal social networks and HIV status among women on methadone. AIDS Care. 1998;10(6):735–49.CrossRefPubMed
44.
Lakon CM, Ennett ST, Norton EC. Mechanisms through which drug, sex partner, and friendship network characteristics relate to risky needle use among high risk youth and young adults. Soc Sci Med. 2006;63:2489–99.CrossRefPubMed
45.
Latkin CA, Mandell W, Oziemkowska M, Vlahov D, Celentano D. The relationships between sexual behavior, alcohol use, and personal network characteristics among injecting drug users in Baltimore, Maryland. Sex Transm Dis. 1994;21(3):161–7.CrossRefPubMed
46.
Shaw SY, Shah L, Jolly AM, Wylie JL. Determinants of injection drug user (IDU) syringe sharing: the relationship between availability of syringes and risk network member characteristics in Winnipeg, Canada. Addiction. 2007;102(10):1626–35.CrossRefPubMed
47.
Sherman SG, Latkin CA, Gielen AC. Social factors related to syringe sharing among injecting partners: a focus on gender. Subst Use Misuse. 2001;36(14):2113–36.CrossRefPubMed
48.
Li J, Weeks MR, Convey M, editors. The social relationship context and cultural perspective of injection risk, a comparison between US and Chinese injection drug users. In: Society for Prevention Research 16th annual meeting: context and prevention science; 27–30, May 2008, San Francisco, CA.
49.
Valente TW, Vlahov D. Selective risk taking among needle exchange participants: implications for supplemental interventions. Am J Public Health. 2001;91(3):406–11.CrossRefPubMedCentralPubMed
50.
Lansky A, Drake A, Pham H. HIV-associated behaviors among injecting-drug users-23 cities, United States, May 2005–February 2006. Morb Mortal Wkly Rep. 2009;58(13):329–32.
51.
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 PubMed PMID: 20467937. Epub 2010/05/15. eng.CrossRefPubMed
52.
Magnani R, Sabin K, Saidel T, Douglas DH. Review of sampling hard-to-reach and hidden populations for HIV suveillance. AIDS. 2005;19(suppl 2):S67–72.CrossRefPubMed
53.
Trotter RT II, Bowen AM, Potter JM Jr. Network models for HIV outreach and prevention programs for drug users. NIDA Res Monogr Ser. 1995;151:144–80 PubMed PMID: 8742765.
54.
Mitchell JC. The concept and use of social networks. In: Mitchell JC, editor. Social networks in Urban Situations: analysis of personal relationships in Central African Towns. Manchester: Manchester University Press; 1969. p. 1–50.
55.
Granovetter MS. The strength of weak ties. Am J Sociol. 1973;78(6):1360–80.CrossRef
56.
Maguire L. Understanding social networks. Beverly Hills: Sage; 1983. p. 119.
57.
Marsden PV, Campbell CE. Measuring tie strength. Soc Forces. 1984;63(2):482–501.CrossRef
58.
Weeks MR, Li J, Dickson-Gomez J, Convey M, Martinez M, Radda K, et al. Outcomes of a peer HIV prevention program with injection drug and crack users: the risk avoidance partnership. Subst Use Misuse. 2009;44(2):253–81.CrossRefPubMedCentralPubMed
59.
Weeks MR, Clair S, Borgatti SP, Radda K, Schensul JJ. Social networks of drug users in high-risk sites: finding the connections. AIDS Behav. 2002;6(2):193–206.CrossRef
60.
Li J, Weeks MR, Borgatti SP, Clair S, Dickson-Gomez J. A social network approach to demonstrate the diffusion and change process of intervention from peer health advocates to the drug using community. Subst Use Misuse. 2012;47(5):474–90 Epub March 18.CrossRefPubMedCentralPubMed
61.
Duke M, Li J, Singer. M. Drug use, syringe sharing and HIV risk in the People’s Republic of China. In: Pope C, White R, Malow R, editors. HIV/AIDS: Global frontiers in prevention/intervention. New York, London: Rutledge; 2008. pp. 40–49.
62.
Bell DC, Belli-McQueen B, Haider A. Partner naming and forgetting: recall of network members. Soc Netw. 2007;29(2):279–99.CrossRef
63.
Brewer DD, Garrett SB, Kulasingam S. Forgetting as a cause of incomplete reporting of sexual and drug injection partners. Sex Transm Dis. 1999;26(3):166–76.CrossRefPubMed
64.
Brewer DD, Webster CM. Forgetting of friends and its effects on measuring friendship networks. Soc Netw. 2000;21(4):361–73.CrossRef
65.
Young AM, Rudolph AE, Su AE, King L, Jent S, Havens JR. Accuracy of name and age data provided about network members in a social network study of people who use drugs: implications for constructing sociometric networks. Ann Epidemiolo. 2016;26(11):802–9.
66.
Bartunov S, Korshunov A, Park S-T, Ryu W, Lee H, editors. Joint link-attribute user identity resolution in online social networks. In: Proceedings of the 6th international conference on knowledge discovery and data mining, workshop on social network mining and analysis ACM, 2012.
67.
Vosecky J, Hong D, Shen VY, editors. User identification across multiple social networks. Networked Digital Technologies, 2009 NDT’09 First International Conference on, 2009: IEEE.
68.
Bilgic M, Licamele L, Getoor L, Shneiderman B, editors. D-dupe: An interactive tool for entity resolution in social networks. Visual analytics science and technology, 2006 IEEE Symposium On, 2006: IEEE.
69.
Borgatti SP, Everett MG, Freeman LC. Ucinet for windows: software for social network analysis. Harvard: Analytic Technologies; 2002.
70.
Borgatti SP. NetDraw: graph visualization software. Harvard: Analytic Technologies; 2002.
71.
Center for Disease Control and Prevention. National Health Behavior Suvilliance Injection Drug User Cycle Survey Instrument. 2005.
72.
Rudolph AE, Young AM, Lewis CF. Assessing the geographic coverage and spatial clustering of illicit drug users recruited through respondent-driven sampling in New York City. J Urban Health. 2015;92(2):352–78.CrossRefPubMedCentralPubMed
73.
Young AM, Rudolph AE, Quillen D, Havens JR. Spatial, temporal and relational patterns in respondent-driven sampling: evidence from a social network study of rural drug users. J Epidemiol Community Health. 2014;68(8):792–8.CrossRefPubMedCentralPubMed
74.
Salganik M. Variance estimation, design effects, and sample size calculations for respondent-driven sampling. J Urban Health. 2006;83:98–112.CrossRefPubMedCentral
75.
Moorthi G, Li J, Mosher H, Weeks M. An ethnographic exploration of RDS coupon distribution dynamics: motativation constraints and contexual factors. In: International network for social network analysis annual meeting, 18–23 Feburary, St. Pete Beach, Florida2014.
76.
Valente TW, Zogg JB, Christensen S, Richardson J, Kovacs A, Operskalski E. Using social networks to recruit an HIV vaccine preparedness cohort. J Acq Immun Def Synd. 1999;52(4):514.
77.
Mosher HI, Moorthi G, Li J, Weeks MR. A qualitative analysis of peer recruitment pressures in respondent driven sampling: are risks above the ethical limit? Int J Drug Policy. 2015;26(9):832–42.CrossRefPubMedCentralPubMed
78.
Semaan S, Santibanez S, Garfein RS, Heckathorn DD, Des Jarlais DC. Ethical and regulatory considerations in HIV prevention studies employing respondent-driven sampling. Int J Drug Policy. 2009;20(1):14–27 PubMed PMID: 18243679. Epub 2008/02/05. eng.CrossRefPubMed
79.
DeJong J, Mahfoud Z, Khoury D, Barbir F, Afifi RA. Ethical considerations in HIV/AIDS biobehavioral surveys that use respondent-driven sampling: illustrations from Lebanon. Am J Public Health. 2009;99(9):1562–7.CrossRefPubMedCentralPubMed
80.
Verdery AM, Mouw T, Bauldry S, Mucha PJ. Network structure and biased variance estimation in respondent driven sampling. PLoS ONE. 2015;10(12):e0145296.CrossRefPubMedCentralPubMed
81.
Crawford FW, Aronow PM, Zeng L, Li J. Identification of homophily and preferential recruitment in respondent-driven sampling. Am J Epidemiol. 2017. doi:10.​1093/​aje/​kwx208.
82.
Lu X. Linked ego networks: improving estimate reliability and validity with respondent-driven sampling. Soc Netw. 2013;35(4):669–85.CrossRef
83.
Mouw T, Verdery AM. Network sampling with memory a proposal for more efficient sampling from social networks. Sociol Methodol. 2012;42(1):206–56.CrossRefPubMedCentralPubMed
Metadaten
Titel
Overlooked Threats to Respondent Driven Sampling Estimators: Peer Recruitment Reality, Degree Measures, and Random Selection Assumption
verfasst von
Jianghong Li
Thomas W. Valente
Hee-Sung Shin
Margaret Weeks
Alexei Zelenev
Gayatri Moothi
Heather Mosher
Robert Heimer
Eduardo Robles
Greg Palmer
Chinekwu Obidoa
Publikationsdatum
28.06.2017
Verlag
Springer US
Erschienen in
AIDS and Behavior / Ausgabe 7/2018
Print ISSN: 1090-7165
Elektronische ISSN: 1573-3254
DOI
https://doi.org/10.1007/s10461-017-1827-1

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