Evaluating Respondent-Driven Sampling in a Major ... - nastad

Appendix: 3.7.2

Evaluating Respondent-Driven Sampling in a Major Metropolitan Area: Comparing Injection Drug Users in the 2005 Seattle Area National HIV Behavioral Surveillance System Survey with Participants in the RAVEN and Kiwi Studies RICHARD D. BURT, PHD, HOLLY HAGAN, PHD, KEITH SABIN, PHD, AND HANNE THIEDE, DVM, MPH

PURPOSE: To empirically evaluate respondent-driven sampling (RDS) recruitment methods, which have been proposed as an advantageous means of surveying hidden populations. METHODS: The National HIV Behavioral Surveillance system used RDS to recruit 370 injection drug users (IDU) in the Seattle area in 2005 (NHBS-IDU1). We compared the NHBS-IDU1 estimates of participants’ area of residence, age, race, sex, and drug most frequently injected to corresponding data from two previous surveys, the RAVEN and Kiwi Studies, and to persons newly diagnosed with HIV/AIDS and reported from 2001 through 2005. RESULTS: The NHBS-IDU1 population was estimated to be more likely to reside in downtown Seattle (52%) than participants in the other data sources (22%–25%), be older than 50 years of age (29% vs. 5%– 10%), and report multiple races (12% vs. 3%–5%). The NHBS-IDU1 population resembled persons using the downtown needle exchange in age and race distribution. An examination of cross-group recruitment frequencies in NHBS-IDU1 suggested barriers to recruitment across different areas of residence, races, and drugs most frequently injected. CONCLUSIONS: The substantial differences in age and area of residence between NHBS-IDU1 and the other data sources suggest that RDS may not have accessed the full universe of Seattle area injection networks. Further empirical data are needed to guide the evaluation of RDS-generated samples. Ann Epidemiol 2010;20:159–167. Ó 2010 Elsevier Inc. All rights reserved. KEY WORDS:

Respondent-Driven Sampling, Injection Drug Users, HIV, Hepatitis C.

INTRODUCTION Injection drug users (IDU) are a population at elevated risk for several infections of public health importance, including HIV, hepatitis B, and hepatitis C (1, 2). Epidemiologic surveys of drug-injecting populations are important in measuring the prevalence of these diseases, evaluating public health measures to control them, identifying unmet needs and noting opportunities for prevention efforts. However, obtaining an unbiased sample of IDU has proved problematic because of the illegal nature of drug injection and the social marginalization of many IDU. Most methods of IDU recruitment contain well-recognized sources of bias, the effects of which are difficult or impossible to quantify (3, 4). From the Public Health – Seattle & King County, Seattle, WA (R.D.B., H.H., H.T.) and the Division of HIV/AIDS Prevention, Centers for Disease Control and Prevention, Atlanta, GA (K.S.). Address correspondence to: Richard D. Burt, PhD, Public Health – Seattle & King County, 400 Yesler Way, Seattle WA 98104. Tel.: (206) 296-4580; Fax: (206) 205-5281. E-mail: [email protected]. Current affiliations: New York University, New York City (H.H.), and the World Health Organization, Geneva, Switzerland (K.S.). Received April 3, 2009; accepted October 18, 2009. Ó 2010 Elsevier Inc. All rights reserved. 360 Park Avenue South, New York, NY 10010

Respondent-driven sampling (RDS) has been proposed as an advantageous means of accessing hidden populations (5–7). In RDS, participants are given coupons with which to recruit their peers and offered payments when new recruits bring in the coupons. Theoretical reasoning and mathematical modeling propose that RDS methods can produce a study population from which unbiased estimates of the properties of a target population can be calculated. RDS offers further advantages in the ease of its implementation and the standardization of its methods, advantages which have made RDS attractive in international studies with limited resources (8, 9). While RDS is being used in a growing number of settings (10–24), there remains a need for empirical data evaluating how well RDS fulfills its promise in practice. One means of evaluating RDS is to compare RDS results for a population with data on the same population obtained through other means (25). In 2005, IDU were surveyed for the National HIV Behavioral Surveillance system using RDS in 23 U.S. cities (the NHBS-IDU1 survey), including Seattle (25). Two previous studies, the Risk Activity Variables, Epidemiology and Network Study (RAVEN) 1047-2797/10/$–see front matter doi:10.1016/j.annepidem.2009.10.002

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Burt et al. EVALUATING AN RDS-RECRUITED SEATTLE IDU POPULATION

Selected Abbreviations and Acronyms IDU Z injection drug users RDS Z respondent-driven sampling NHBS-IDU1 Z National HIV/AIDS Behavioral Survey’s first cycle survey of IDU MSM Z men reporting male-to-male sex MSM/IDU Z persons reporting male-to-male sex and injection drug use HARS Z the HIV/AIDS Reporting System

and the Kiwi study, recruited Seattle area IDU using differing sampling strategies. Data on IDU diagnosed with HIV/AIDS during the period 2001–2005 were available through the HIV/AIDS Reporting System (HARS). We compared these different populations in terms of area of residence, age, race, gender, and drug most frequently injected, variables commonly used to characterize IDU populations (15, 18, 20, 26).

METHODS Recruitment The methodology for surveying IDU in NHBS-IDU1 has been described (25). Participants were required to be 18 years of age or older; have injected in the previous 12 months; reside in King, Snohomish, or Island counties; and be able to communicate in English. In Seattle, 19 initial IDU (seeds) were each given three coupons to pass on to their injecting peers. Participants who completed a survey questionnaire were paid $20 and offered in turn coupons to distribute to their IDU peers. They received a payment of $10 for each eligible study participant they referred. Recruitment began on May 25, 2005 and was terminated Jan. 31, 2006. In-person interviews were conducted using hand-held computers in a storefront office in Seattle’s south downtown, which was open for interviews 3 to 4 days per week, or at one of two sites in south King County, open 1 to 2 days a week. The RAVEN study recruited IDU from June 1994 through May 1997 from among persons entering four methadone treatment centers (39% of participants), a drug treatment evaluation agency (17%), a drug detoxification center (15%), two social service agencies (19%), and entering the King County jail in Seattle on drug-related charges (10%) (27). In each of these settings, candidates were selected for recruitment on the basis of a random number algorithm. Among persons potentially eligible for the study, 10% refused participation. The 2,538 RAVEN participants meeting the criteria described below for inclusion in this analysis constitute 16% of the approximately 16,000 IDU estimated to reside in the Seattle area (28). The Kiwi study recruited IDU from among persons incarcerated in the two main King County jails, in Seattle and

AEP Vol. 20, No. 2 February 2010: 159–167

Kent, from September 1998 through December 2002 (29). Participants were recruited by screening all persons booked into jail during randomly selected time intervals in the Seattle jail, or from inmates visiting the Kent jail health clinic for a regular 14-day physical exam, or from persons requesting HIV counseling and testing in both the Seattle and Kent jails. Among persons screened and determined to be eligible, 60% completed an interview. The 1,567 Kiwi Study participants constituted about 10% of the estimated IDU in the Seattle area (28). HARS collects data on all reported cases of HIV/AIDS. We present results from HARS on persons newly diagnosed with HIV/AIDS between 2001 and 2005 with residence in King, Snohomish, or Island counties. Analysis is restricted to the 263 cases whose exposure category was recorded as IDU (48% of cases) or male-to-male sex and injection drug use (MSM/IDU) (52%). Our findings are supplemented by a survey of Seattle area needle exchangers. Public Health Seattle-King County staff attempted to briefly interview every person exchanging needles at a needle exchange in King County from November 11, 2006 through November 24, 2006. Overall, 49% of persons approached completed a survey. Informed consent was obtained from participants in RAVEN, Kiwi Study, and NHBS-IDU1 before administration of the questionnaire. For RAVEN, study procedures were approved by the institutional review boards of the State of Washington and the University of Washington; for Kiwi by the state of Washington and the Centers for Disease Control and Prevention (CDC); and for NHBS-IDU1 by the State of Washington. Data Analysis To obtain consistency among study populations, analysis was restricted to participants in the three studies who were 18 years of age or older and residents of King, Snohomish and Island counties. For the NHBS-IDU1 data, the RDS Analysis Tool (RDSAT) was used to calculate RDS-adjusted population proportion estimates, their 95% confidence intervals, RDS-adjusted standard errors, sample population and equilibrium proportions, and cross-group recruitment probabilities (30). The RDS-adjusted estimates incorporate adjustments based on network size, cross-group recruitment probabilities, and group-specific recruitment efficiency. Network size was based on a question about the number of injectors that a participant knew and whom they had seen at least once in the previous 6 months. Recruitment chains were diagrammed by means of NETDRAW (31). While a number of different approaches to the statistical evaluation of RDS-recruited populations have been published, no one method has found general acceptance (8, 17, 18, 32–34). We used a test based on chi-square methods,


Appendix: 3.7.2

after adjustment for RDS design effects according to a method proposed by Heckathorn (personal communication, 2007). RDSAT generates standard errors for population proportion estimates by a bootstrap method (S.E.bootstrap) (35). The design effect quantifies the proportionate difference between the variance of RSDAT bootstrap method and the variance that would be expected were the RDS estimates normally distributed (35). It was calculated as (S.E.bootstrap)2/(p(1–p)/n), where p is the RDS-adjusted population proportion estimate. For the RAVEN, Kiwi and HARS populations, the chi-square tests used the actual numbers of participants in each category of interest. For NHBS-IDU1, we used the population proportion estimate times the number of NHBS-IDU1 participants (i.e., the expected number of participants), then divided this by the design effect. This adjusts for the weaker power in an RDS sample (as reflected in the wider bootstrap confidence intervals). Even so, these methods may overestimate the precision of the estimates in the RDS sample (36). Analyses were conducted by using SPSS and Epistat (37, 38).

RESULTS


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population and the distributions of other IDU populations (Table 2). The RDS estimate indicated that 52% of the NHBS-IDU1 population resided in downtown Seattle, compared to 24% in both RAVEN and Kiwi and 25% in HARS. Age The NHBS-IDU1 age estimates indicated an IDU population older than the other data sources (see Table 2). Twenty-nine percent of the NHBS-IDU1 study population was estimated to be over 50 years old compared to 6% in RAVEN, 5% in Kiwi, and 10% in the HARS data. To investigate the extent to which the NHBS-IDU1 study population represents an aging cohort of IDU, we performed a linear regression analysis of participants’ age against the date of interview within the RAVEN and Kiwi studies. There was a modest trend toward decreasing age within both RAVEN (b Z –0.06; p Z 0.001) and Kiwi (b Z –0.04; p Z 0.14). Similar analyses revealed no evidence of a temporal trend toward an increasing proportion of IDU residing in downtown Seattle within RAVEN or Kiwi.

Measures of RDS Recruitment

Race

Of the 19 seeds interviewed, 10 recruited at least one study participant. Sixty percent of the sample population derived from one seed. Fifty-eight percent of participants recruited at least one new study participant; 31% of the coupons distributed were returned by new participants. The rate of recruitment was substantially slower than anticipated. At the fixed time the survey was terminated (as mandated by CDC protocol), data from 370 eligible participants were available for analysis; this was less than the initial target sample size of 500. Despite concerted efforts to plant seeds in south King County, these seeds generated few participants. Ninety-two percent of study interviews were conducted at the downtown Seattle site. Only 10 participants reported being recruited by a stranger. There were 28 waves of recruitment (Fig. 1). Ninety-one percent of the sample population was recruited at the fourth wave or higher. All categories of area of residence, age, race, sex, and drug most frequently injected were represented in the NHBS-IDU1 sample population within 2% of their calculated equilibrium estimates, except for residence in south King County and south Seattle where the deviation was 4% (Table 1). There was cross-recruitment across each of the three interview sites.

The proportion of participants reporting multiple races was higher in NHBS-IDU1 than in the other data sources (13% vs. 3%–5%). Among participants reporting multiple races, 77% in NHBS-IDU1 and 75% in Kiwi reported Native American as one of the races selected. The percentage of Hispanics rose in the successive interview studies over time.

Area of Residence There were substantial differences between the RDS estimates of the geographic distribution of the NHBS-IDU1

Gender RAVEN had a significantly higher proportion of females (37%) than the other data sources (14%–24%). Females were more likely than males to have been in drug or alcohol treatment in the previous year in both the Kiwi (45% vs. 38%; p Z 0.02) and the NHBS-IDU1 populations (54% vs. 42%; p Z 0.05). Drug Most Frequently Injected Heroin was by far the most commonly reported drug in each of the interview studies (HARS collected no data on use of specific drugs). The proportion of participants reporting amphetamines was higher in Kiwi (26%) than in RAVEN (6%) and at an intermediate level in the NHBSIDU1 estimate (18%). Among participants from south King County, a region currently affected by high levels of amphetamine injection (39). NHBS-IDU1 estimated a lower proportion injected amphetamines (23%) than found in Kiwi participants (50%).

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Chain 1

Chain 6 Chain 9 Chain 10

Chain 5

Chain 8 Chain 4

Chain 3 Chain 7

Chain 2

Seattle outside of downtown King County outside Seattle

Downtown Seattle Other area or Missing data

FIG 1. Seattle area NHBS-IDU1 recruitment chains incorporating data on area of residence.

Demographic Characteristics of Seattle Area Needle Exchange Users The distribution of age, race, and sex among needle exchange users participating in the 2006 Seattle needle exchange survey at sites in three different Seattle neighborhoods is shown in Table 3. Needle exchange users at the downtown site differed from those at the other two sites in having a higher proportion over 50 years of age (25% vs. 6% and 11%) and reporting black race (17% vs. 3% and 9%) and, with respect to these variables, most closely resembled the NHBS-IDU1 population. NHBS-IDU1 Recruitment Across and Within Groups We evaluated recruitment probabilities across groups of participants defined by differing areas of residence, age, race, sex, and drug most frequently injected in order to assess differential recruitment patterns on the basis of these characteristics (Table 4). A pronounced pattern of preferential recruitment of participants from the same area of residence was seen for participants from south King County (62% did so) and south Seattle (54%). Participants tended to recruit persons broadly similar in age to themselves; recruitment probabilities were substantially higher among persons in the same or adjacent age categories of participants than for those farther apart in age.

Black participants showed a marked tendency to recruit other black participants (55% did so) and were themselves recruited by very low proportions of persons of other races (from 0% to 8%). Amphetamine injectors most frequently recruited other amphetamine injectors (65%).

DISCUSSION The NHBS-IDU1 results stand out from RAVEN, Kiwi, and HARS data in the high estimated proportion of participants from downtown Seattle residents and in the markedly older age distribution. Lacking a definitive gold standard, we cannot determine with assurance which of these populations, if any, accurately reflects the characteristics of Seattle area IDU. The various data sources in this report have strengths and weaknesses. RAVEN’s random number–based sampling reduced volunteer bias, and the multiple recruitment settings reduced the influence of any single site. IDU who had no contact with the institutions where RAVEN recruitment occurred, however, would have been missed. With its jail-based recruitment, Kiwi provided access to the substantial proportion of IDU experiencing incarceration (40), but would have missed IDU who were not arrested. As HIV/AIDS is a reportable infection, HARS data would be expected to identify essentially all persons diagnosed with HIV/AIDS (41), but is likely to be affected

Appendix: 3.7.2



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TABLE 1. RDS-adjusted population proportion estimates, 95% confidence intervals, standard errors, design effects, sample population proportions, equilibrium proportions, and network sizes among 370 Seattle participants in the 2005 NHBS-IDU1 survey

Area of residence North Seattle Downtown Seattle South Seattle South King County East King County Snohomish, Island counties Age 18–29 30–39 40–49 >50 Race White Black Hispanic Native American Other race Multiple races Sex Male Female Drug most frequently injected Heroin Speedballs Cocaine Amphetamines Other drug

RDSadjusted estimate

95% Confidence interval

RDS standard error*

RDS design effecty

Sample population proportion

Equilibrium estimatez

Unadjusted mean network size

.07 .52 .25 .11 .06 –

(.03–.11) (.41–.65) (.16–.36) (.03–.29) (.004–.12) –

.021 .060 .052 .046 .030 –

2.67 5.32 5.31 8.02 6.16 –

.07 .51 .24 .16 .02 –

.08 .53 .28 .12 .02 –

30 45 39 47 29 –

.12 .21 .38 .29

(.06–.21) (.13–.31) (.29–.50) (.19–.38)

.032 .040 .043 .042

3.60 3.54 2.92 3.20

.13 .19 .40 .29

.12 .18 .40 .30

30 37 46 42

.53 .20 .12 .02 .01 .13

(.42–.64) (.10–.30) (.04–.22) (.003–.06) (.002–.02) (.07–.19)

.058 .050 .046 .014 .003 .030

5.00 5.78 7.47 3.70 0.56 3.05

.56 .18 .08 .02 .01 .16

.57 .16 .09 .02 .01 .16

44 25 59 18 13 45

.77 .24

(.67–.82) (.16–.33)

.045 .045

4.17 4.17

.76 .24

.77 .23

43 37

.66 .08 .07 .18 .01

(.50–.78) (.04–.12) (.03–.12) (.07–.35) (.001–.03)

.071 .022 .025 .071 .007

7.32 2.27 3.05 11.13 1.37

.71 .09 .07 .11 .01

.72 .09 .06 .11 .01

43 52 29 19 19

RDS Z respondent-driven sampling; NHBS-IDU1 Z National HIV/AIDS Behavioral Survey’s first cycle survey of injection drug users. *Derived by bootstrap methods. y Measures the proportionate difference between the RDS bootstrap variance and the variance expected on the basis of a normal distribution of the population proportion estimate, as calculated from p(1–p)/n. z Equilibrium refers to the calculated proportion to which the study population characteristic would be expected to converge as the number of recruitment waves increases.

by patterns of HIV testing and to oversample IDU at higher risk for HIV transmission (such as MSM/IDU) and older IDU. The RDS methodology of NHBS-IDU1 has a body of theory-based investigations asserting its capacity to produce unbiased estimates of population characteristics, but this has not been convincingly verified by empirical data. The closer concordance among the three other sources of data constitutes an argument that the NHBS estimates for age and area of residence are the less representative portrayal of Seattle area IDU. We offer the hypothesis that RDS coupon distribution did not effectively penetrate the full universe of injector networks in the Seattle area. The similarity in age and racial distribution between the NHBS-IDU1 population and downtown needle exchangers suggests that NHBS-IDU1 recruitment occurred disproportionately among networks of downtown IDU. Our data on recruitment probabilities within and across groups support the idea of incomplete network penetration in NHBSIDU1 by documenting lower recruitment probabilities,

and hence network barriers, between IDU across differing areas of residence, races, and injection drugs. The criteria generally considered important for valid RDS recruitment appear to have been fulfilled in the Seattle NHBS-IDU1 survey: recruitment chains were long and the preponderance of participants were recruited in the fourth or higher waves, the sample population approached estimated equilibrium values for the characteristics analyzed, recruitment occurred across interviewing sites, and few participants reported being recruited by strangers. The numbers of participants in the present report and the number of survey sites are comparable to what has been published in other studies. Our findings raise the question whether the efficient penetration of the injector networks throughout a large metropolitan area might require a wider dispersal of interview sites and larger numbers of participants than has been the practice in RDS studies. In addition to differing recruiting methods, the four sources of data were conducted over an 11-year time span. The differing patterns of drug preference in the different studies

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TABLE 2. Comparison between RAVEN, Kiwi, and NHBS-IDU1 population distributions of area of residence, age, race, sex, and drug most frequently injected, with statistical tests for differences between studies P values

Number Area of residence North Seattle Downtown Seattle South Seattle South King County East King County Snohomish, Island counties Age 18–29 30–39 40–49 >50 Race White Black Hispanic Native American Other race Multiple races Sex Male Female Drug most frequently injected* Heroin Speedballs Cocaine Amphetamines Other drug

NHBS-IDU1 RDS-adjusted estimate

RAVEN sample proportion

Kiwi sample proportion

HARS reported cases 2002–2005

370

2538

1567

263

.07 .52 .25 .11 .06 0

.14 .22 .34 .16 .07 .07

.13 .23 .25 .23 .11 .05

.12 .21 .38 .29

.22 .39 .33 .06

.53 .20 .12 .02 .01 .13

NHBS-IDU1 vs. RAVEN

NHBS-IDU1 vs. Kiwi

NHBS-IDU1 vs. HARS

.12 .25 .34 .11 .09 .09

!106

1.91 106

!106

.27 .40 .30 .05

.20 .43 .28 .10

2.26 106

!106

!106

.65 .23 .04 .04 .02 .03

.64 .16 .08 .05 .03 .05

.69 .13 .08 .05 .02 .03

!106

1.61 106

6.38 106

.77 .24

.63 .37

.77 .24

.86 .14

1.14 102

.92

.07

.66 .08 .07 .18 .01

.67 .14 .12 .06 .01

.50 .12 .12 .26 .004

6.67 104

4.23 106

d

HARS Z HIV/AIDS Reporting System; for other abbreviations, see Table 1. *HARS collected no data on the drugs injected by HIV/AIDS cases.

may reflect increasing amphetamine use in the Seattle area over the time period of our data (42), as has been seen in other areas (43). The increasing proportions of Hispanic participants across the studies likely reflects the continuing growth of the Hispanic population in the King County, increasing from 2.9% in 1990 to 6.7% in 2005 (44). The higher proportion of females in RAVEN may be a product of a higher likelihood that females take part in the drug treatment programs that were a source of a substantial proportion of RAVEN participants. Other reports have found discrepancies between RDSrecruited IDU study populations and those recruited by other methods. RDS-derived IDU populations were compared with contemporaneous samples derived from targeted sampling methods in Detroit, Houston, and New Orleans, finding no significant difference between the different sample populations in gender or age distributions but differences in racial distributions in Houston and New

Orleans (18). An RDS-generated study population of drug users in New York City found similar age and gender distributions to those in two previous studies but a different racial makeup, possibly as a result of neighborhood demographic changes over time (26). Outside the United States, RDSrecruited IDU study populations were compared with participants recruited by earlier studies by indigenous field worker sampling in Volgograd and Barnaul, Russia (15). In both locations, significant differences were found between the sample populations in age, gender, education, and needlesharing. A St. Petersburg study found a substantially higher proportion of females than seen in two previous studies (20). In addition, a Web-based RDS survey of Cornell University students found substantial discrepancies between RDSadjusted estimates for race and gender proportions and their true distribution (24). Two RDS studies in MSM provide data relevant to our findings. Ma et al. (13) reported on MSM in Beijing

Appendix: 3.7.2



TABLE 3. Demographic characteristics of participants* in the 2006 survey of Seattle needle exchange users in different neighborhoods Needle exchange area

Age, yr 18–29 30–39 40–49 >50 Race White Black Hispanic Native American Other race Sex Male Female No. of participants (total)

Downtown (%)

Capitol Hill (%)

University District (%)

16 25 34 25

40 34 20 6

40 24 24 11

63 17 7 12 2

79 3 7 7 4

78 9 4 9 0

74 26

78 22

76 24

393

196

46

*Data expressed as percentages, except as noted.

surveyed by RDS in three consecutive years. Substantial differences were observed among the surveys in a number of key study variables. While these differences may be

165

a product of rapid changes over time, or a change in how network size data were elicited, it is also possible they reflect material variation in repeat samples recruited by RDS methodology. Kendall et al. (12) compared an RDS-generated MSM study population in Fortaleza, Brazil with previous surveys which used snowball sampling and venue-based recruitment. The RDS population had characteristics, most notably lower socioeconomic status, less consistent with the other study populations than those populations were with one another. To explain this, the authors noted that the club venues at which recruitment occurred might have biased the sample to MSM of higher SES and noted that the RDS sample more closely resembled the census characteristics of Fortaleza. On the other hand, they remark that higher SES MSM may have hesitated to travel to the interview sites, which were in the central city. The discrepancies among the different sources of data on Seattle area IDU make it difficult to determine the degree to which of these, if any, accurately reflects the characteristics of the underlying IDU universe. We offer a hypothesis that incomplete penetration of injector networks lies behind the observed differences between the NHBS-IDU1 estimates for age and area of residences and those of the other sources of data. We acknowledge that there are other plausible sources of bias among the sources of data we examined. Given the limited empirical evidence of RDS functioning currently

TABLE 4. Recruitment probabilities across areas of residence, age, race, sex, and drug most frequently injected, among 370 Seattle participants in the 2005 NHBS-IDU1 survey Recruit Recruiter* Area of residence North Seattle Downtown South Seattle South King County East King County Age 18–29 30–39 40–49 >50 Race White Black Hispanic Multiple races Sex Male Female Drug most frequently injected Heroin Speedballs Cocaine Amphetamines

North Seattle .25 .09 .04 .02 0 18–29 .36 .20 .08 .04 White .68 .23 .81 .46 Male .82 .60 Heroin .79 .92 .62 .27

Downtown .50 .67 .37 .21 .86 30–39 .32 .22 .18 .09 Black .05 .55 .06 .27 Female .18 .40 Speedballs .10 .08 .10 .05

South Seattle .15 .18 .54 .11 0 40–49 .25 .40 .44 .40 Hispanic .10 .06 .08 .07

South King County 0 .07 .03 .62 0 >50 .07 .18 .30 .47 Multiple races .16 .16 .06 .20

Cocaine .07 0 .24 .05

Amphetamines .05 0 .05 .64

East King County .10 0 .03 .04 .14

*Because of low numbers, Native Americans and participants reporting a race or injection drug other than those specified in the questionnaire were excluded from this analysis.

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available, we would recommend caution in drawing overly broad conclusions from any individual study. Claims that RDS efficiently accesses all but the most isolated networks need to be validated in practice. Further empirical data on RDS functioning in a variety of settings are called for. Funding for this work came from the National Institute on Drug Abuse (1RO1DA08023) and Centers for Disease Control and Prevention (U62/CCU006260). The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention.

14. McKnight C, Des JD, Bramson H, Tower L, Abdul-Quader AS, Nemeth C, et al. Respondent-driven sampling in a study of drug users in New York City: notes from the field. J Urban Health. 2006;83:i54–i59.

We wish to acknowledge the contributions of Nadine Snyder (Project Coordinator), Carrie Shriver, Susan Nelson and Jef St. De Lore (interviewers), and study participants, without whose efforts this study would not have been possible.

18. Robinson WT, Risser JMH, McGoy S, Becker AB, Rehman H, Jefferson M, et al. Recruiting injection drug users: a three-site comparison of results and experiences with respondent-driven and targeted sampling procedures. J Urban Health. 2007;87:i29–i37.

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