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

Florence Samkange-Zeeb (), Ronja Foraita, Stefan Rach and Tilman Brand
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Florence Samkange-Zeeb: Leibniz Institute for Prevention Research and Epidemiology – BIPS
Ronja Foraita: Leibniz Institute for Prevention Research and Epidemiology – BIPS
Stefan Rach: Leibniz Institute for Prevention Research and Epidemiology – BIPS
Tilman Brand: Leibniz Institute for Prevention Research and Epidemiology – BIPS

International Journal of Public Health, 2019, vol. 64, issue 3, No 17, 459 pages

Abstract: 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 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s00038-018-1191-6

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