Advertisement

Feasibility of using respondent-driven sampling to recruit participants in superdiverse neighbourhoods for a general health survey

  • Florence Samkange-ZeebEmail author
  • Ronja Foraita
  • Stefan Rach
  • Tilman Brand
Original Article
  • 46 Downloads

Abstract

Objectives

Respondent-driven sampling (RDS), a modified chain-referral system, has been proposed as a strategy for reaching ‘hidden’ populations. We applied RDS to assess its feasibility to recruit ‘hard-to-reach’ populations such as migrants and the unemployed in a general health survey and compared it to register-based sampling (RBS).

Methods

RDS was applied parallel to standard population RBS in two superdiverse neighbourhoods in Bremen, Germany. Prevalences of sample characteristics of interest were estimated in RDS Analyst using the successive sampling estimator. These were then compared between the samples.

Results

Only 115 persons were recruited via RDS compared to 779 via RBS. The prevalence of (1) migrant background, (2) unemployment and (3) poverty risk was significantly higher in the RDS than in the RBS sample. The respective estimates were (1) 51.6 versus 32.5% (95% CIRDS 40.4–62.7), (2) 18.1 versus 7.5% (95% CIRDS 8.4–27.9) and (3) 55.0 versus 30.4% (95% CIRDS 41.3–68.7).

Conclusions

Although recruitment was difficult and the number of participants was small, RDS proved to be a feasible method for reaching migrants and other disadvantaged persons in our study.

Keywords

Respondent-driven sampling Feasibility Superdiverse Hard-to-reach Migrants 

Notes

Acknowledgements

We would like to thank the Field Work Unit at the BIPS, all interviewers and other project staff as well as our cooperation partners in the two neighbourhoods for their support during the preparation and conduction of the study.

Funding

The UPWEB project was funded by NORFACE (Grant No. 462-14-091), and the respondent-driven sampling arm was financed through internal funding of the Leibniz Institute for Prevention Research and Epidemiology—BIPS.

Compliance with ethical standards

Conflict of interest

All authors declare no conflict of interest.

Ethical approval

Ethical approval was obtained from the University of Bremen ethics committee.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. Demarest S, Van der Heyden J, Charafeddine R, Tafforeau J, Van Oyen H, Van Hal G (2013) Socio-economic differences in participation of households in a Belgian national health survey. Eur J Public Health 23(6):981–985CrossRefGoogle Scholar
  2. European Social Survey (2012) ESS Round 6 Source Questionnaire. Centre for Comparative Social Surveys, City University London, LondonGoogle Scholar
  3. Frere-Smith T, Luthra R, Platt L (2014) Sampling recently arrived immigrants in the UK: Exploring the effectiveness of respondent driven sampling. CreAM Discussion Paper Series. London, UK, Center for Research and Analysis of Migration. CPS 32/14Google Scholar
  4. George S, Duran N, Norris K (2014) A systematic review of barriers and facilitators to minority research participation among African Americans, Latinos, Asian Americans, and Pacific Islanders. Am J Public Health 104(2):e16–e31CrossRefGoogle Scholar
  5. Gile KJ (2011) Improved inference for respondent-driven sampling data with application to HIV prevalence estimation. J Am Stat Assoc 106(493):135–146CrossRefGoogle Scholar
  6. Górny A, Napierała J (2016) Comparing the effectiveness of respondent-driven sampling and quota sampling in migration research. Int J Soc Res Methodol 19(6):645–661CrossRefGoogle Scholar
  7. Handcock MS, Fellows IE, Gile KJ (2014) RDS Analyst: software for the analysis of respondent-driven sampling data, Version 0.42. http://hpmrg.org. Accessed Jan 2018
  8. Heckathorn D (1997) Respondent-driven sampling: a new approach to the study of hidden populations. Soc Probl 44(Suppl 2):174–199CrossRefGoogle Scholar
  9. Hu J, Wong KC, Wang Z (2015) Recruiting migrants for health research through social network sites: an online survey among Chinese migrants in Australia. JMIR Res Protoc 4(2):e46CrossRefGoogle Scholar
  10. Johnston LG (2014) Sampling migrants: how respondent driven sampling works. In: Tydum G, Johnston LG (eds) Applying respondent driven sampling to migrant populations: lessons from the field. Palgrave Macmillan, Basingstoke, pp 9–16Google Scholar
  11. Johnston LG, Luthra R (2014) Analyzing data in RDS. In: Tydum G, Johnston LG (eds) Applying respondent driven sampling to migrant populations: lessons from the field. Palgrave Macmillan, Basingstoke, pp 84–99Google Scholar
  12. Johnston LG, Thurman TR, Mock N, Nano L, Carcani V (2010) Respondent-driven sampling: a new method for studying street children with findings from Albania. Vulnerable Child Youth Stud 5(1):1–11.  https://doi.org/10.1080/17450120903193923 CrossRefGoogle Scholar
  13. Johnston LG, Luthra L, Napierala J (2014) Measuring personal network size in RDS. In: Tydum G, Johnston LG (eds) Applying respondent driven sampling to migrant populations: lessons from the field. Palgrave Macmillan, Basingstoke, pp 27–36Google Scholar
  14. Johnston LG, Hakim AJ, Dittrich S, Burnett J, Kim E, White RG (2016) A systematic review of published respondent-driven sampling surveys collecting behavioral and biologic data. AIDS Behav 20(8):1754–1776CrossRefGoogle Scholar
  15. Kubal A, Shvab I, Wojtynska A (2014) Initiation of the RDS recruitment process: seed selection and role. In: Tyldum G, Johnston LG (eds) Applying respondent driven sampling to migrant populations: lessons from the field. Palgrave Macmillan, Basingstoke, pp 37–47Google Scholar
  16. Montealegre JR, Risser JM, Selwyn BJ, McCurdy SA, Sabin K (2013) Effectiveness of respondent driven sampling to recruit undocumented Central American immigrant women in Houston, Texas for an HIV behavioral survey. AIDS Behav 17(2):719–727CrossRefGoogle Scholar
  17. Mühlau P, Kaliszewska M, Röder A (2011) Polonia in Dublin: preliminary report of survey findings. Dublin: Trinity College Dublin. https://www.tcd.ie/sociology/assets/pdf/PoloniainDublinReportI[1].pdf. Accessed Oct 2018
  18. Organisation for Economic Co-operation and Development (2011) Divided we stand: Why inequality keeps rising. OECD Publishing.  https://doi.org/10.1787/9789264119536-en
  19. Pelikan JM, Röthlin F, Ganahl K (2014) Measuring comprehensive health literacy in general populations: validation of instrument, indices and scales of the HLS-EU study. In: 6th Annual Health Literacy Research Conference. Bethesda, Maryland, USAGoogle Scholar
  20. Phillimore J, Bradby H, Knecht M, Padilla B, Brand T, Cheung SY, Pemberton S, Zeeb H (2015) Understanding healthcare practices in superdiverse neighbourhoods and developing the concept of welfare bricolage: protocol of a cross-national mixed-methods study. BMC Int Health Hum Rights 15:16CrossRefGoogle Scholar
  21. Platt L, Luthra L, Frere-Smith T (2015) Adapting chain referral methods to sample new migrants: possibilities and limitations. Demogr Res 33:665–700CrossRefGoogle Scholar
  22. Quay TA, Frimer L, Janssen PA, Lamers Y (2017) Barriers and facilitators to recruitment of South Asians to health research: a scoping review. BMJ Open 7(5):e014889CrossRefGoogle Scholar
  23. Redwood S, Gill PS (2013) Under-representation of minority ethnic groups in research–call for action. Br J Gen Pract 63(612):342–343CrossRefGoogle Scholar
  24. Reineke A, Pigeot I, Ahrens W, Rach S (2018) MODYS—a modular control and documentation system for epidemiological studies. In: Bammann K, Lissner L, Pigeot I, Ahrens W (eds) Instruments for health surveys in children and adoloscents. Springer Nature, Zürich, pp 25–45Google Scholar
  25. Reiss K, Dragano N, Ellert U, Fricke J, Greiser KH, Keil T, Krist L, Moebus S, Pundt N, Schlaud M, Yesil-Jurgens R, Zeeb H, Zimmermann H, Razum O, Jockel KH, Becher H (2014) Comparing sampling strategies to recruit migrants for an epidemiological study. Results from a German feasibility study. Eur J Public Health 24(5):721–726CrossRefGoogle Scholar
  26. Salganik MJ (2006) Variance estimation, design effects, and sample size calculations for respondent-driven sampling. J Urban Health 83(6 Suppl):i98–i112CrossRefGoogle Scholar
  27. Senatorin für Arbeit, Frauen, Gesundheit, Jugend und Soziales (2009) Lebenslagen in Bremen: Armuts- und Reichtumsbericht für das Land Bremen 2009. BremenGoogle Scholar
  28. Shaghaghi A, Bhopal RS, Sheikh A (2011) Approaches to recruiting ‘hard-to-reach’ populations into research: a review of the literature. Health Promot Perspect 1(2):86–94PubMedPubMedCentralGoogle Scholar
  29. Statistisches Landesamt Bremen (2015) Bremen Kleinräumig Infosystem. https://www.statistik.bremen.de/. Accessed Jan 2018
  30. Statistisches Landesamt Bremen (2016) Bremen Kleinräumig Infosystem. https://www.statistik.bremen.de/. Accessed Jan 2018
  31. Strathdee SA, Lozada R, Ojeda VD, Pollini RA, Brouwer KC, Vera A, Cornelius W, Nguyen L, Magis-Rodriguez C, Patterson TL, El Proyecto C (2008) Differential effects of migration and deportation on HIV infection among male and female injection drug users in Tijuana, Mexico. PLoS ONE 3(7):e2690CrossRefGoogle Scholar
  32. Tyldum G, Johnston LG (eds) (2014) Applying respondent driven sampling to migrant populations: lessons from the field. Palgrave Macmillan, BasingstokeGoogle Scholar
  33. United Nations (2016) International Migration Report 2015—Highlights. United Nations. ST/ESA/SER.A/375Google Scholar
  34. Vertovec S (2007) Super-diversity and its implications. Ethnic Racial Stud 30(6):1024–1054CrossRefGoogle Scholar
  35. Ware JE Jr, Sherbourne CD (1992) The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med Care 30(6):473–483CrossRefGoogle Scholar
  36. Weber MF, Banks E, Smith DP, O’Connell D, Sitas F (2009) Cancer screening among migrants in an Australian cohort; cross-sectional analyses from the 45 and Up Study. BMC Public Health 9:144CrossRefGoogle Scholar

Copyright information

© Swiss School of Public Health (SSPH+) 2019

Authors and Affiliations

  1. 1.Leibniz Institute for Prevention Research and Epidemiology – BIPSBremenGermany

Personalised recommendations