Author information
1Department of Epidemiology and Biostatistics, Temple University College of Public Health, 1301 Cecil B Moore Avenue, Ritter Annex 905, Philadelphia, PA, USA.
2Harborview Medical Center, University of Washington, 325 9th Ave, Box 359931, Seattle, WA, 98106, USA.
3RTI International, 3040 East Cornwallis Road, P.O. Box 12194, Research Triangle Park, NC, 27709-2194, USA.
4Division of Epidemiology, College of Public Health, Ohio State University, Columbus, Ohio, 43210, USA.
5University of Wisconsin-Madison, Population Health Institute, 610 Walnut Street, 575 WARF, Madison, WI, 53726, USA.
6General Internal Medicine and Geriatrics, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA.
7Rollins School of Public Health, Emory University, Grace Crum Rollins Building, 1518 Clifton Road, Atlanta, Georgia, 30322, USA.
8Baystate Medical Center-University of Massachusetts, Office of Research, UMass Chan Medical School - Baystate, 3601 Main Street, 3rd Floor, Springfield, MA, 01199, USA.
9Microsoft Premonition, Microsoft Building 99, 14820 NE 36th St. Redmond, Seattle, WA, 98052, USA.
10Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
11University of North Carolina-Chapel Hill, 363 Rosenau Hall, CB# 7440, Chapel Hill, NC, 27599, USA.
12Southern Illinois University, 201 E Madison Street, Springfield, IL, 62702, USA.
13Oregon Health & Science University, 3270 Southwest Pavilion Loop OHSU Physicians Pavilion, Suite 350, Portland, OR, 97239, USA.
14The Ohio State University, 302 Cunz Hall, 1841 Neil Ave, Columbus, OH, 43210, USA.
15University of Chicago, 5841 S. Maryland Avenue, Chicago, IL, 60637, USA.
16Tulane University, 1440 Canal Street, Suite 2210, New Orleans, LA, 70112, USA.
17Tufts University School of Medicine, Public Health and Community Medicine, 136 Harrison Avenue, Boston, MA, 02111, USA.
18University of Wisconsin-Madison, 1685 Highland Avenue, 5th Floor, Madison, WI, 53705-2281, USA.
19University of Kentucky, 760 Press Avenue, Suite 280, Lexington, KY, 40536, USA.
20Harborview Medical Center, University of Washington, 325 9th Ave, Box 359931, Seattle, WA, 98106, USA. hcrane@uw.edu.
21Harborview Medical Center, University of Washington and University of Manitoba, University of Washington, 325 9th Ave, Box 359931, Seattle, WA, 98106, USA.
Abstract
Background: Accurate prevalence estimates of drug use and its harms are important to characterize burden and develop interventions to reduce negative health outcomes and disparities. Lack of a sampling frame for marginalized/stigmatized populations, including persons who use drugs (PWUD) in rural settings, makes this challenging. Respondent-driven sampling (RDS) is frequently used to recruit PWUD. However, the validity of RDS-generated population-level prevalence estimates relies on assumptions that should be evaluated.
Methods: RDS was used to recruit PWUD across seven Rural Opioid Initiative studies between 2018-2020. To evaluate RDS assumptions, we computed recruitment homophily and design effects, generated convergence and bottleneck plots, and tested for recruitment and degree differences. We compared sample proportions with three RDS-adjusted estimators (two variations of RDS-I and RDS-II) for five variables of interest (past 30-day use of heroin, fentanyl, and methamphetamine; past 6-month homelessness; and being positive for hepatitis C virus (HCV) antibody) using linear regression with robust confidence intervals. We compared regression estimates for the associations between HCV positive antibody status and (a) heroin use, (b) fentanyl use, and (c) age using RDS-1 and RDS-II probability weights and no weights using logistic and modified Poisson regression and random-effects meta-analyses.
Results: Among 2,842 PWUD, median age was 34 years and 43% were female. Most participants (54%) reported opioids as their drug of choice, however regional differences were present (e.g., methamphetamine range: 4-52%). Many recruitment chains were not long enough to achieve sample equilibrium. Recruitment homophily was present for some variables. Differences with respect to recruitment and degree varied across studies. Prevalence estimates varied only slightly with different RDS weighting approaches, most confidence intervals overlapped. Variations in measures of association varied little based on weighting approach.
Conclusions: RDS was a useful recruitment tool for PWUD in rural settings. However, several violations of key RDS assumptions were observed which slightly impacts estimation of proportion although not associations.