V Anna Gyarmathy1, Lisa G Johnston2, Irma Caplinskiene3, Saulius Caplinskas3, Carl A Latkin4. 1. Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. Electronic address: vgyarmat@jhsph.edu. 2. University of California, San Francisco, Global Health Sciences, San Francisco, CA, USA; Tulane University, School of International Public Health and Tropical Medicine, New Orleans, LA, USA. 3. Centre for Communicable Diseases and AIDS, Vilnius, Lithuania; M. Romeris University, Social Policy Faculty, Vilnius, Lithuania. 4. Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Abstract
BACKGROUND: Respondent driven sampling (RDS) and incentivized snowball sampling (ISS) are two sampling methods that are commonly used to reach people who inject drugs (PWID). METHODS: We generated a set of simulated RDS samples on an actual sociometric ISS sample of PWID in Vilnius, Lithuania ("original sample") to assess if the simulated RDS estimates were statistically significantly different from the original ISS sample prevalences for HIV (9.8%), Hepatitis A (43.6%), Hepatitis B (Anti-HBc 43.9% and HBsAg 3.4%), Hepatitis C (87.5%), syphilis (6.8%) and Chlamydia (8.8%) infections and for selected behavioral risk characteristics. RESULTS: The original sample consisted of a large component of 249 people (83% of the sample) and 13 smaller components with 1-12 individuals. Generally, as long as all seeds were recruited from the large component of the original sample, the simulation samples simply recreated the large component. There were no significant differences between the large component and the entire original sample for the characteristics of interest. Altogether 99.2% of 360 simulation sample point estimates were within the confidence interval of the original prevalence values for the characteristics of interest. CONCLUSIONS: When population characteristics are reflected in large network components that dominate the population, RDS and ISS may produce samples that have statistically non-different prevalence values, even though some isolated network components may be under-sampled and/or statistically significantly different from the main groups. This so-called "strudel effect" is discussed in the paper.
BACKGROUND: Respondent driven sampling (RDS) and incentivized snowball sampling (ISS) are two sampling methods that are commonly used to reach people who inject drugs (PWID). METHODS: We generated a set of simulated RDS samples on an actual sociometric ISS sample of PWID in Vilnius, Lithuania ("original sample") to assess if the simulated RDS estimates were statistically significantly different from the original ISS sample prevalences for HIV (9.8%), Hepatitis A (43.6%), Hepatitis B (Anti-HBc 43.9% and HBsAg 3.4%), Hepatitis C (87.5%), syphilis (6.8%) and Chlamydia (8.8%) infections and for selected behavioral risk characteristics. RESULTS: The original sample consisted of a large component of 249 people (83% of the sample) and 13 smaller components with 1-12 individuals. Generally, as long as all seeds were recruited from the large component of the original sample, the simulation samples simply recreated the large component. There were no significant differences between the large component and the entire original sample for the characteristics of interest. Altogether 99.2% of 360 simulation sample point estimates were within the confidence interval of the original prevalence values for the characteristics of interest. CONCLUSIONS: When population characteristics are reflected in large network components that dominate the population, RDS and ISS may produce samples that have statistically non-different prevalence values, even though some isolated network components may be under-sampled and/or statistically significantly different from the main groups. This so-called "strudel effect" is discussed in the paper.
Authors: S R Friedman; A Neaigus; B Jose; R Curtis; M Goldstein; G Ildefonso; R B Rothenberg; D C Des Jarlais Journal: Am J Public Health Date: 1997-08 Impact factor: 9.308
Authors: Jichuan Wang; Robert G Carlson; Russel S Falck; Harvey A Siegal; Ahmmed Rahman; Linna Li Journal: Drug Alcohol Depend Date: 2004-12-22 Impact factor: 4.492
Authors: Alex H Kral; Mohsen Malekinejad; Jason Vaudrey; Alexis N Martinez; Jennifer Lorvick; Willi McFarland; H Fisher Raymond Journal: J Urban Health Date: 2010-09 Impact factor: 3.671
Authors: Lucy Platt; Martin Wall; Tim Rhodes; Ali Judd; Matthew Hickman; Lisa G Johnston; Adrian Renton; Natalia Bobrova; Anya Sarang Journal: J Urban Health Date: 2006-11 Impact factor: 3.671
Authors: Danielle Horyniak; Peter Higgs; Rebecca Jenkinson; Louisa Degenhardt; Mark Stoové; Thomas Kerr; Matthew Hickman; Campbell Aitken; Paul Dietze Journal: Harm Reduct J Date: 2013-06-21
Authors: Andrea L Wirtz; Shruti H Mehta; Carl Latkin; Carla E Zelaya; Noya Galai; Alena Peryshkina; Vladimir Mogilnyi; Petr Dzhigun; Irina Kostetskaya; Chris Beyrer Journal: PLoS One Date: 2016-06-01 Impact factor: 3.240
Authors: Mark B Ulanja; Carrie Lyons; Sosthenes Ketende; Shauna Stahlman; Daouda Diouf; Abo Kouamé; Rebecca Ezouatchi; Amara Bamba; Fatou Drame; Ben Liestman; Stefan Baral Journal: BMC Int Health Hum Rights Date: 2019-03-05
Authors: Allison E Aiello; Amanda M Simanek; Marisa C Eisenberg; Alison R Walsh; Brian Davis; Erik Volz; Caroline Cheng; Jeanette J Rainey; Amra Uzicanin; Hongjiang Gao; Nathaniel Osgood; Dylan Knowles; Kevin Stanley; Kara Tarter; Arnold S Monto Journal: Epidemics Date: 2016-02-01 Impact factor: 4.396