AIDS Behav (2014) 18:S305–S315 DOI 10.1007/s10461-013-0512-2
ORIGINAL PAPER
Substance Abuse Treatment Utilization, HIV Risk Behaviors, and Recruitment Among Suburban Injection Drug Users in Long Island, New York Carol-Ann Watson • Charlene Xuelin Weng • Tyler French • Bridget J. Anderson • Chris Nemeth • Louise-Anne McNutt • Lou C. Smith
Published online: 25 May 2013 Ó Springer Science+Business Media New York 2013
Abstract Prevention and treatment of injection drug use remains a public health concern. We used data from the 2005 Centers for Disease Control and prevention National HIV Behavioral Surveillance system to assess substance abuse treatment utilization, risk behaviors, and recruitment processes in a respondent driven sample of suburban injectors. Twelve service utilization and injection risk variables were analyzed using latent class analysis. Three latent classes were identified: low use, low risk; low use, high risk; and high use, moderate/high risk. In multivariate analysis, annual income\$15,000 (adjusted odds ratio (aOR) = 8.19 [95 % confidence interval (CI), 3.83–17.51]) and self-reported hepatitis C virus infection (aOR = 4.32, 95 % CI (1.84–10.17)) were significantly associated with class membership. Homophily, a measure of preferential C.-A. Watson (&) T. French B. J. Anderson L. C. Smith Bureau of HIV/AIDS Epidemiology, Division of Epidemiology, Evaluation and Research, AIDS Institute, New York State Department of Health, ESP, Corning Tower, Albany, NY 12237, USA e-mail:
[email protected] C. X. Weng Statistical Unit, Division of Epidemiology, New York State Department of Health, Albany, NY, USA C. Nemeth Commissioner’s Office, New York State Office for People with Developmental Disabilities, Albany, NY, USA L.-A. McNutt L. C. Smith Department of Epidemiology and Biostatistics, School of Public Health University at Albany, State University of New York, Albany, NY, USA L. C. Smith School of Public Health, University at Albany, State University of New York, Albany, NY, USA
recruitment showed that injectors with recent treatment utilization appear a more cohesive group than out-of-treatment injectors. Preferentially reaching injection drug users with high risk behaviors and no recent drug treatment history via respondent driven sampling will require future research. Keywords Injection drug users HIV Respondent driven sampling Latent class analysis
Introduction The incidence of HIV among injection drug users (IDUs) in the United States has declined steadily since the early 1990’s. However, injection drug use continues to account for a significant proportion of infections. According to the Centers for Disease Control and Prevention (CDC), injection drug use was a risk for 13 % and 26 % of prevalent HIV infections in 2009 among males and females age 13 years and older, respectively [1]. In addition, survival after diagnosis with HIV or AIDS was lower for IDUs than other groups at risk for HIV in the first 3 years after diagnosis [1], which is likely attributable to drug overdose [2]. Suburban communities share common public health concerns with their contiguous urban neighbors, including issues related to the prevention and treatment of illicit drug injection. According to the National Survey on Drug Use and Health, illicit drug use in the past month among individuals aged 12 years and older was comparable in large, small, and nonmetropolitan areas (*8 %) [3]. Further, the epidemiology of illicit drug use outside of urban areas appears to have evolved such that injection has become a common administration route [4, 5]. This change will impact local public health jurisdictions through higher health care expenditures and increased parenteral transmission of communicable diseases like HIV.
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A consistent finding in studies of suburban IDUs is the comparatively high proportion that engage in receptive sharing of injection paraphernalia [6, 7]. While studies of urban injectors in the U.S. report that as many as half of IDUs engage in receptive syringe sharing [8, 9], a study of suburban injectors found that almost two-thirds reported sharing syringes in the past 6 months [6]. Notably, suburban users were less likely to have used needle exchange programs and were more likely to report difficulty obtaining new, sterile needles compared to their urban counterparts [6]. This finding is most likely related to limited availability of syringe exchange and other harm reduction programs in suburban areas [10]. Similar findings have been reported among IDUs in rural areas where these programs are even less available [11–13]. Access to effective substance abuse treatment programs is also a concern as distance from urban centers increase. Participation in drug treatment programs is associated with reductions in HIV risk behaviors, drug use, and HIV transmission [14–20]. In addition, participation in different types of programs may have different treatment related outcomes [21–23]. For example, Mark et al. [24] compared IDUs in methadone maintenance, detoxification, and needle exchange programs and found that participants in methadone maintenance were the least likely to have shared paraphernalia while those in needle exchange programs were more likely to have done so. Therefore, treatment outcomes among suburban IDUs may differ because of differing access to specific types of treatment. Given the significant challenges in accessing harm reduction programs outside of urban centers, the prevalence of injection drug use in these areas, and the associated risky injection practices, a focused look at the variation in IDU substance abuse treatment utilization and concomitant risk behaviors in nonurban IDUs is warranted. Techniques for study of complex social behaviors such as injection drug use have evolved. Latent class analysis (LCA) is an analytic tool that may help delineate the intertwined relationships of treatment utilization and risk behaviors, as opposed to examination of the relationship between individual risks and/or treatment modalities; such advanced understanding may prompt improved intervention and prevention strategies for injectors at highest risk for HIV infection. Moreover, respondent driven sampling (RDS) [25], an adapted chain-referral sampling method, is being used to reach members of hard-to-reach populations [26–30] such as injection drug users. An advantage of RDS is that peers are uniquely suited to identify and recruit other members of their cohort. RDS analytic software is available that generates weights to adjust for specific biases of chain referral samples, namely network size and differential participant recruitment [31– 33]. RDS appears a promising and cost efficient innovation
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with potential use in both research and service delivery, but additional data is needed to better understand recruitment outcomes. Understanding recruitment patterns tied to underlying similarities or dissimilarities between individuals in IDU networks may be informative for effectively tailoring peer-driven programs. This understanding could serve as a tool to assess whether samples have met outreach and recruitment goals in specific populations. Thus, the specific goals of this study are to (1) characterize treatment utilization and injection risk behavior among suburban injectors using LCA, (2) assess the correlation of demographic and other characteristics with treatment utilization and injection risk behaviors, and (3) determine if participant recruitment via RDS is related to treatment utilization and injection risk behaviors.
Methods Between July and December 2005, residents of Nassau and Suffolk Counties, New York, also known as Long Island, were enrolled in a health survey conducted by the New York State Department of Health (NYSDOH) with funding from the CDC. Data were collected among IDUs as part of the National HIV Behavioral Surveillance (NHBS) system. NHBS is an annual cross-sectional risk behavior survey that is conducted among men who have sex with men, IDUs, and heterosexuals in high HIV/AIDS prevalence areas [30]. Participants were recruited using RDS. Project staff selected 12 individuals known as ‘‘seeds’’ to begin recruitment. Once interviewed, seeds were asked to invite up to three peers to participate in the health survey. Recruitment continued in successive waves until the target sample size was reached. Participation in all components of the health survey was anonymous. Eligible participants were 18 years of age or older, resided on Long Island, and injected illicit drugs in the past 12 months. Evidence of recent injection such as visible track marks was required to satisfy the enrollment criteria. An electronic standardized questionnaire that took approximately 35 minutes to complete was administered by trained interviewers. HIV status was obtained by selfreport. All participants provided consent prior to the interview, and all eligible participants were compensated for the survey ($30) and for recruiting eligible peers ($10 for each eligible recruit). The study was approved by the NYSDOH Institutional Review Board. Seven independent variables (Table 3) and twelve outcome variables (Table 1) were used in the analysis; among the twelve outcome variables were eight alcohol and drug treatment utilization variables and four injection risk
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Table 1 Substance abuse treatment utilization, injection risk behaviors, and latent class item responses Prevalence of latent class items
Probability of class membership and item responsesb
na
Unweighted percent
Weighted percentb
Low use, low risk
Low use, high risk
High use, moderate/ high risk
485
n/a
n/a
259a (65 %)
120a (20 %)
106a (15 %)
Detoxification program Drug free outpatient clinic
70 66
14 % 14 %
10 % 11 %
1.2 % 1.9 %
0.3 % 0.8 %
61.6 % 63.1 %
Methadone maintenance
83
17 %
12 %
2.7 %
15.0 %
46.6 %
Narcotics anonymous
62
13 %
8%
0.0 %
2.8 %
50.9 %
Alcoholic anonymous
51
11 %
7%
0.2 %
0.0 %
46.4 %
Inpatient drug treatment
48
10 %
7%
0.6 %
1.3 %
38.8 %
HIV prevention counseling
113
23 %
16 %
7.0 %
14.2 %
59.1 %
HIV prevention materials
84
17 %
13 %
5.0 %
7.6 %
55.6 %
Sample size Treatment utilization
Risk behaviors Inject once a day or more
305
63 %
58 %
54.8 %
55.4 %
71.7 %
Binge drinkingc
299
62 %
62 %
59.4 %
66.5 %
68.1 %
Share drug paraphernaliad
171
35 %
27 %
0.0 %
92.1 %
55.6 %
Share needle(s)
96
20 %
16 %
0.0 %
59.7 %
27.2 %
Unless otherwise indicated utilization and risk behaviors occurred in the 12 months prior to the interview a
Actual count
b
Respondent-driven sample weighted proportions are presented
c
Defined as C4 alcoholic beverages for females and C5 for males in one sitting
d
Defined as sharing cooker, cotton, water, or syringe
variables. These variables encompass a wide range of treatment utilization options and were selected based on a review of the literature [18, 19, 34, 35]. All behaviors occurred in the 12 months prior to the interview.
drugs?’’ and ‘‘Have you gotten any free new sterile needles?’’.
Utilization of Alcohol or Drug Treatment Programs and Risk Reduction Materials
Binge drinking was defined as four or more alcoholic beverages for females and five or more for males in one sitting. Sharing drug paraphernalia was defined as any episode of receptive sharing of a cooker, cotton or water. Sharing needles was defined as any episode of receptive sharing. Daily injection was defined as injecting once per day or more frequently.
Participation (Yes/No) in detoxification, drug free outpatient clinic, methadone maintenance, narcotics/cocaine anonymous, alcoholics anonymous or inpatient drug treatment was ascertained by asking ‘‘Which of the following types of programs were you in?’’ HIV prevention counseling was defined as an affirmative answer to either of the following two questions: ‘‘Have you had a one-on-one conversation with an outreach worker, counselor or prevention program worker about ways to prevent HIV? Don’t count the times when you had a conversation like this as part of an HIV test.’’ and ‘‘Not including discussions with friends, have you been a participant in any organized session(s) involving a small group of people to discuss ways to prevent HIV?’’ Receipt of prevention materials (Yes/No) was ascertained by asking ‘‘Have you gotten any free kits that have items like cookers, cotton or water for rinsing needles or preparing
Risk Behaviors
Statistical Analyses The data were weighted using sampling weights generated from RDS Analysis Tool (RDSAT) version 5.6 [36]. Weighting was done to account for sampling biases related to participant network size and recruitment patterns [37]. Latent class analysis was used to identify the number and composition of IDU subgroups. [38, 39]. LCA incorporates the heterogeneity of treatment utilization and HIV injection risk behaviors to identify distinct subgroups based on responses from categorical variables. Latent class model
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assumptions stipulate that associations between observed categorical variables arise because the population is composed of unobserved factor(s) rather than a causal association between variables [38–40]. The model also assumes that there is no association between the observed categorical variables within the latent classes, such that within latent classes the association between the observed categorical variables equals one. The best fit latent class model was determined by assessing the value of the akaike information criterion (AIC) and bayes information criterion (BIC), two goodness of fit measures, and by assessing the entropy value and change in the likelihood ratio statistic (G2). The AIC and BIC values take model parsimony into account and penalize the likelihood for less parsimonious models with large numbers of parameters. Models with lower AIC and BIC values are preferred to those with higher values and indicate a better fitted model. Entropy ranges from 0 to 1. Values closer to 1 indicate better distinction between latent classes [41]. Within the latent class model unconditional and conditional probabilities are parameters that provide information about the distribution of the unobserved latent variable. The unconditional probability indicates how many classes were identified and what proportion of the population resides in each. The conditional probability indicates the likelihood that an individual in a latent class will respond in a particular way on an observed variable or the likelihood of endorsing each item. The association between the latent classes and independent covariates was assessed using chi-squared tests for categorical variables. Only variables significant at a p value \ 0.2 in bivariate analyses were considered in the multivariate model [42]. The RDS weights for the latent classes were applied to the regression model [43, 44]. SAS Proc LCA version 1.2.7 [45] was used to analyze the 12 items. The LCA procedure utilizes logistic regression modeling to analyze covariates. Adjusted odds ratios and 95 % confidence intervals were computed to examine differences between latent classes. Analyses were conducted in SAS version 9.3 [46]. Finally, the latent class composition of injecting networks, represented by separate recruitment chains for each seed were created using netdraw software [47].
Results A total of fourteen recruitment waves were attained, exceeding the seven waves required to reach equilibrium, which is the point at which the sample composition becomes independent of seeds with respect to key characteristics. Independence from non-randomly selected
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seeds is required for valid RDS estimators [32]. Twentythree individuals were excluded from final analysis 11 due to missing data or technical error that occurred during data and 12 seeds who were selected by project staff to begin recruitment. A total of 556 individuals were screened for eligibility, resulting in 508 completed surveys. The final sample consisted of 485 injection drug users. Heroin was the preferred drug for more than two-thirds (68 %). The mean age was 43.2 years. The weighted proportions indicate that participants were predominantly male (56 %), non-Hispanic black (48 %) and poor, with slightly less than half (46 %) reporting an annual household income less than $15,000. The average years of injection for sample participants was 21 years (SD ± 10.6). Latent Class Analysis The distribution of the 12 dichotomized variables is presented in Table 1. Forty-three percent of the sample reported ever participating in alcohol or drug treatment programs (data not shown). However, participation in the past 12 months was relatively low (range 7 %–16 %). The fit of five latent class models was estimated (Table 2). The goodness of fit value (BIC = 1114.4) was smallest for the three-class model indicating that three classes fit the data best. This conclusion was supported by the entropy value (0.86) and change in the likelihood ratio statistic, which indicated that the percent change in the G2 statistic was substantial up to three classes. The probability of membership in each class (the unconditional probability) is shown in Table 1 as is the likelihood of endorsing each item (the conditional probability). Each class was assigned a label relative to the other two classes in the model. The three classes were characterized as (1) ‘‘low use, low risk’’ indicating infrequent use of treatment programs and low risk behaviors, (2) ‘‘low use, high risk’’ indicating infrequent use of treatment programs and high risk behaviors, and (3) ‘‘high use, moderate to high risk’’ indicating high use of treatment programs and moderate to high levels of risk behavior. There was a marked difference in shared drug paraphernalia and injection needles across the three classes. The reported prevalence of shared paraphernalia among those with low use, high risk (92 %) was 1.6 times greater than those who reported high use, moderate to high risk behaviors (56 %). Similarly, twice as many IDUs in the low use, high risk class (60 %) reported receptive needle sharing compared to the high use, moderate to high risk class (27 %). Gender, age, annual household income, homelessness in the past 12 months, and self-reported HIV and hepatitis C positive status were associated with latent class membership in bivariate analysis (Table 3). Further, we observed
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Table 2 Latent class model selection Number of classes
Likelihood ratio, G2
AICa
BICb
Degrees of freedom
Entropy
2
1019.7
1069.7
1174.3
4,070
0.9
3
879.4
955.4
1114.4
4,057
0.86
13.8 %
4
822.7
924.7
1138.1
4,044
0.79
6.4 %
5
783.7
911.7
1179.4
4,031
0.83
4.7 %
6
748.3
902.32
1224.5
4,018
0.86
4.5 %
a
AIC Akaike information criterion
b
BIC Bayes information criterion
% Change in G2
Table 3 Sample characteristics by latent class Actual count
Latent classa
Percent Unweighted
Weighteda
Low use, low risk
Low use, high risk
High use, moderate/ high risk
p valueb
Gender Male
282
58 %
56 %
52 %
55 %
70 %
Female Race
203
42 %
44 %
48 %
45 %
30 %
Black, non-hispanic
236
49 %
48 %
51 %
44 %
43 %
Other
249
51 %
52 %
49 %
56 %
57 %
0.0196
0.3072
Age (in years) 18–42
237
49 %
50 %
52 %
54 %
36 %
43 and older
248
51 %
50 %
43 %
46 %
64 %
\$15,000
250
52 %
46 %
35 %
45 %
83 %
C$15,000
227
47 %
53 %
65 %
55 %
17 %
Yes
66
14 %
13 %
10 %
19 %
20 %
No
419
84 %
87 %
90 %
81 %
80 %
34
7%
5%
3%
7%
14 %
93 %
95 %
97 %
93 %
86 %
0.0240
Annual household income \0.0001
Homeless, past 12 months 0.0148
Self-reported HIV positive Yes
No 451 Self-reported hepatitis C diagnosis Yes
131
27 %
22 %
14 %
27 %
49 %
No
354
73 %
78 %
86 %
73 %
51 %
0.0003
\0.0001
Final sample size = 485 injection drug users a
Respondent-driven sample weighted proportions
b
Represents the association between the sample characteristic and latent class membership
that as injection risk behaviors increased so did the proportion of respondents who reported low income, homelessness, HIV positive status, and hepatitis C positive status. For example, 35 % of IDUs in the low use, low risk class reported less than $15,000 annual household income. In contrast, more than 80 % of IDUs in the high use, moderate to high-risk class earned less than $15,000 annual household income.
The factors most strongly associated with latent class membership in multivariate analysis were annual household income and self-reported hepatitis C virus diagnosis (Table 4). Compared to IDUs who reported earning $15,000 or more annual household income, the odds of reporting less than $15,000 annual household income was significantly greater for those in the high use, moderate to high risk class (aOR, 8.19 [95 %CI, 3.83–17.51])
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Table 4 Adjusted odds ratios (aORs) and 95 % confidence intervals (CIs) for selected covariates by latent class
Low use, high risk vs. low use, low riska OR (95 % CI)
High use, moderate/high risk vs. low use, low riska OR (95 % CI)
0.91 (0.40–2.07)
1.18 (0.44–3.17)
Gender Male Female
–
–
Age (in years) 18–42
1.13 (0.44–2.92)
43 and older Annual household income \$15,000
–
–
2.14 (0.95–4.81)
C$15,000
0.64 (0.28–1.49)
–
8.19 (3.83–17.51) –
Homeless, past 12 months Each covariate is adjusted for all others in the model; the model is weighted by RDSAT generated weights to adjust for biases in the sampling method. ‘‘–’’ indicates referent group for covariate a
Low use, low risk was used as the referent group in the latent class regression model
Yes
2.22 (0.63–7.85)
No
–
–
Self-reported HIV positive Yes
2.01 (0.56–7.22)
No
–
1.34 (0.39–4.54) –
Self-reported hepatitis C diagnosis Yes
2.17 (0.82–5.71)
No
compared to IDUs in the low use, low risk class. A similar trend was observed when low use, high risk IDUs were compared to low use, low risk IDUs; however, the finding was not statistically significant. For hepatitis C virus diagnosis, the odds of being in the high use, moderate to high risk class vs. the low use, low risk class was significantly greater for those who self-reported hepatitis C diagnosis compared to those who did not report a hepatitis C virus diagnosis (aOR, 4.32 [95 %CI, 1.84–10.17]). The trend was similar but not statistically significant when low use, high risk IDUs and low use, low risk IDUs were compared. IDU Networks Five of the twelve seeds (42 %) successfully recruited more than two other IDUs from their injecting network generating five recruitment chains. Of these, three were heterogeneous or composed of individuals from all latent classes and two were homogeneous or composed of individuals primarily from the same latent class. Two-hundred and sixty-seven IDUs comprised the largest recruitment chain and the majority (72 %) belonged to the low use, low risk class (Fig. 1). The recruitment chain depicted in Fig. 2 was also homogeneous with respect to latent class with all recruits from the high use, moderate to high risk class. We observed wide variation in homophily—the extent to which individuals with a given characteristic preferentially recruit others with similar characteristics—by latent
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1.07 (0.41–2.81)
–
4.32 (1.84–10.17) –
class. A homphily value of -1 indicates that members of a particular group will always select non-group members (heterophily) while a value of ?1 signifies that group members will always select from within their group [48]; zero indicates no recruitment preference. Homophily for the three classes was as follows: 0.38 for high use, moderate to high risk IDUs, 0.12 for low use, low risk IDUs and 0.036 for low use, high risk IDUs. Preferential recruitment was strongest for those with high use, moderate to high risk behaviors indicating that 38 % of recruitment in this class was non-random with respect to latent class while 62 % was independent of latent class through random mixing. In contrast, IDUs in the low use, high risk class showed little recruitment preference by latent class (3.6 %).
Discussion A primary aim of this study was to characterize treatment utilization and injection risk among suburban injectors. The latent class analysis indicated that suburban IDUs are not a homogeneous group. We identified three distinct subgroups of IDUs, the majority of whom had low utilization of both drug treatment and HIV prevention services in the recent past. Over one-third of the injectors fell within two groups distinguished by high injection risk behaviors that included daily injection and frequent sharing of drug paraphernalia and injection needles. Of these, about half accessed drug
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1= Low Use, High Risk 2= Low Use, Low Risk 3= High Use, Moderate/ High Risk - 1 = Missing Data Square=Seed * Arrow indicates direction of recruitment
Fig. 1 Distribution of latent classes in the primary IDU recruitment chain
treatment and HIV prevention services in the past 12 months; little contact with drug treatment or HIV prevention services was reported in the other group. The discordance between risk behaviors and treatment utilization in the low use, high risk class is of particular concern given that few reported any program use during the past 12 months (range 0 %–15 %). Substance abuse has broad impact on health and social functioning [49] and dependent users of heroin and other opioids have more chronic conditions and mental health concerns than the general population [50, 51]. They are also at greater risk of death during out-of-treatment periods than during in-treatment periods [51]. Participation in drug treatment programs mitigates these risks and results in significant reductions in substance use and improvements in health and social function for many recipients of treatment [51, 52]. Unfortunately, recruitment homophily (3.6 %) indicates that low use high risk IDUs in this sample are not a cohesive group, and
specific targeting of group members with peer recruitment mechanisms will be challenging. Consistent with previous literature, we found that low income was associated with increased behavioral risk [53, 54]. IDUs with less than $15,000 annual household income were more likely to report moderate to high risk behaviors, irrespective of whether their reported recent substance abuse treatment service utilization was low or high. For a substantial proportion of IDUs, risky practices continue even in environments with long-standing access to low cost syringes. In 2001, New York State implemented the expanded syringe access program (ESAP) which allowed legal sale of small quantities of syringes without prescription by pharmacies and certain other providers [55]. Over time, syringe sales in NYS have increased significantly [56, 57] and the program is associated with a decline in receptive sharing of needles among IDUs [57]. Nonetheless, pharmacies remain an inconsistent source of sterile syringes
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Fig. 2 Distribution of latent classes in a homogeneous network
1= Low Use, High Risk 2= Low Use, Low Risk 3= High Use, Moderate/ High Risk - 1 = Missing Data Square= Seed *arrow indicates direction of recruitment
among IDUs [58], and pharmacists often lack appropriate training to administer such public health programs [59]. The first syringe exchange program on Long Island was implemented in 2012, and it will be some time before the combined effects of syringe exchange and ESAP are known. We observed that one in five reported a hepatitis C infection diagnosis and 25 % of these reported being told of their hepatitis C virus positive status within the past year. Not surprisingly, infection was concentrated among IDUs who reported higher prevalence of sharing paraphernalia and needles. In addition to corroborating previous research [9], the proportion of recently identified HCV infection highlights the continued need for targeted prevention strategies that might lead infected IDUs to modify risk behaviors and thereby reduce the risk of transmitting to uninfected others. It may also indicate a lack of adequate penetration of HCV screening for at risk populations. Multiple factors influence the composition of samples recruited using peer referral mechanisms. Among these are race/ethnicity, HIV positive status, and location and distance to interview sites [60, 61]. This analysis provides empirical evidence that treatment history and injection practices require similar considerations. The previously unidentified latent structure was influential in determining recruitment outcomes for some IDUs, particularly members of high use, high-risk class; 38 % of recruitment occurred non-randomly based on latent class. In-treatment populations appear a more cohesive group than out-oftreatment injectors where the tendency to recruit others like
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themselves was comparatively low with homophily of 12 % and 3 %, respectively. Network recruitment patterns and homophily suggest that some IDUs are more easily targeted with peer referral mechanisms than others. For example, membership in the cohort of IDUs receiving treatment appears to facilitate in-group recruitment while there was a marked lack of cohesion among IDUs with the low use, high-risk profile. Implications for Prevention Programs These findings indicate that effective targeting and retention of IDUs who are not currently engaged in care is needed. A substantial proportion of these injectors have recent high-risk behaviors comparable to and in some instances exceeding those of IDUs who are engaged in treatment programs. Given that low use, high-risk IDUs are not a cohesive group, interventions relying on peer recruitment may not achieve the desired results. Programs organized around other characteristics such as low income and HCV status may be more helpful in reaching these high-risk, out-of care IDUs. Limitation All variables were dichotomized in the multivariate model due to the limited sample size. In addition, the SAS procedure for conducting latent class analysis is relatively new and limited primarily to linear and logistic regression. To
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our knowledge, no latent class analysis program allows for log-binomial regression, the most appropriate analysis for the data given that the outcome is common [62]. In some cases, the prevalence odds ratios presented here slightly overestimate the prevalence ratios. We carefully assessed the models to determine their fit to the data. The analyses appear robust to analysis decisions (e.g., variable cutpoints). Assessment of covariate patterns showed that the model results explained the observed data across participant characteristics. We also compared the weighted models with unweighted regression models. In unweighted analyses, income, hepatitis C virus status, and gender were significant predictors of latent class membership. However, gender was not a significant predictor of latent class membership in the weighted analyses. In their assessment of RDS assumptions in the NHBS, Lansky et al. noted that cross-recruitment of peers in jurisdictions with multiple field sites indicate networked IDU populations [63]. Interviews with IDUs on Long Island were conducted at two recruitment sites and cross recruitment between sites was low (\1 %). If a higher threshold for determining cross recruitment is indeed required for networked IDUs then alternative analytic techniques such as separate analysis for each independent IDU population may be warranted. The frequency of injection and sharing of drug paraphernalia was obtained by self-report as was utilization of drug treatment programs. Self-reported data is subject to recall errors and under-reporting of less socially acceptable behaviors. However, all data were collected anonymously from study participants, likely reducing bias related to social desirability. In addition, compensation provided to participants may have influenced the composition of the sample since our sample was predominately poor; the financial incentives were likely more attractive to individuals with fewer resources. Thus, our findings may not be generalizable to IDUs with higher incomes. Finally, our data are cross-sectional and no causal relationships can be established from the data presented.
Conclusion Previous studies of IDUs have focused primarily on urban populations. Our study is among the few that assess the context of HIV risk in suburban populations. In addition, our data provides greater depth to the discussion around heterogeneity of RDS samples beyond the often-described diversity in demographic characteristics. Our sample was diverse with respect to factors such as treatment engagement, risk taking behaviors, and hepatitis C infection status. Further, our findings suggest that LCA is a valuable tool for assessing and understanding peer recruitment as it relates to recruitment patterns in social networks and
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respondent types within communities of interest. How people are organized with regard to their social networks, behaviors, and access to and engagement in HIV prevention and substance use services is important for understanding behavioral risks and for understanding the ways in which peers can be reached or mobilized for HIV prevention. We observed that multiple factors impacted recruitment in this network of suburban IDUs and that preferentially reaching those with high-risk behaviors and no recent drug treatment history will require future research with additional focus during ethnographic work and seed selection. Public health policy makers have vested RDS methodology with a major role in disease surveillance and prevention [29]. As such, important study of RDS application and development is ongoing and has provided further advances in analytic methods, social network insights, and design effect which quantifies differences between samples accrued using simple random sampling and respondent driven sampling [61, 64, 65]. However, the high stakes afforded this applied research method requires that field researchers also do their part to assure meaningful data are obtained for decision-making and programmatic interventions. Better understanding of RDS participant typologies is a useful complement to the usual sample quality assurance measures of post hoc delineation of recruitment of strangers, chain length, and equilibrium. The success or failure of current peer-driven outreach and research methods could have significant future impact on health care costs and the transmission of bloodborne communicable diseases such as HIV. Acknowledgments This research was supported by cooperative agreement 1U62-PS00958 from the Centers for Disease Control and Prevention. Its contents are solely the responsibility of the authors and do not represent the official views of the Centers for Disease Control and Prevention.
References 1. Division of HIV/AIDS Prevention NCFHA, Viral Hepatitis, STD and TB Prevention, Centers for Disease Control and Prevention (CDC), U.S. Department of Health and Human Services, Atlanta, Georgia. HIV surveillance report. Surveillance report. 2010. 2. Pfeiffer MR, Hanna DB, Begier EM, Sepkowitz KA, Zimmerman R, Sackoff JE. Excess mortality among injection drug users with AIDS, New York City (1999–2004). Subst Use Misuse. 2011; 46(2–3):245–53. 3. Administration SAaMHS. Results from the 2011 national survey on drug use and health: summary of national findings. Rockville: Substance Abuse and Mental Health Services Administration. http://www.samhsa.gov/data/NSDUH/2k11Results/NSDUHresults 2011.htm(2012). Accessed 2 Aug 2012. 4. Trends in injection drug use among persons entering addiction treatment–New Jersey, 1992–1999. MMWR. 2001;50(19): 378–81. 5. Novak SP, Kral AH. Comparing injection and non-injection routes of administration for heroin, methamphetamine, and
123
S314
6.
7.
8.
9.
10. 11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
cocaine users in the United States. J Addict Dis. 2011;30(3): 248–57. Thorpe LE, Bailey SL, Huo D, Monterroso ER, Ouellet LJ. Injection-related risk behaviors in young urban and suburban injection drug users in Chicago (1997–1999). J Acquir Immune Defic Syndr. 2001;27(1):71–8. Lawrinson P, Copeland J, Indig D. Regional differences in injecting practices and other substance use-related behaviour among entrants into opioid maintenance pharmacotherapy treatment in New South Wales, Australia. Drug Alcohol Depend. 2006;82(Suppl 1):S95–102. Bailey SL, Ouellet LJ, Mackesy-Amiti ME, Golub ET, Hagan H, Hudson SM, et al. Perceived risk, peer influences, and injection partner type predict receptive syringe sharing among young adult injection drug users in five U.S. cities. Drug Alcohol Depend. 2007;91(Suppl 1):S18–29. Hagan H, Pouget ER, Williams IT, Garfein RL, Strathdee SA, Hudson SM, et al. Attribution of hepatitis C virus seroconversion risk in young injection drug users in 5 US cities. J Infect Dis. 2010;201(3):378–85. Network NASE. http://www.nasen.org(2013). Accessed 2 Nov 2013. Havens JR, Walker R, Leukefeld CG. Prevalence of opioid analgesic injection among rural nonmedical opioid analgesic users. Drug Alcohol Depend. 2007;87(1):98–102. Day C, Conroy E, Lowe J, Page J, Dolan K. Patterns of drug use and associated harms among rural injecting drug users: comparisons with metropolitan injecting drug users. Aust J Rural Health. 2006;14(3):120–5. Parker J, Jackson L, Dykeman M, Gahagan J, Karabanow J. Access to harm reduction services in Atlantic Canada: implications for non-urban residents who inject drugs. Health Place. 2012;18(2):152–62. Booth RE, Crowley TJ, Zhang Y. Substance abuse treatment entry, retention and effectiveness: out-of-treatment opiate injection drug users. Drug Alcohol Depend. 1996;42(1):11–20. Metzger DS, Woody GE, O’Brien CP. Drug treatment as HIV prevention: a research update. J Acquir Immune Defic Syndr. 2010;55(Suppl 1):S32–6. Gibson DR, Flynn NM, McCarthy JJ. Effectiveness of methadone treatment in reducing HIV risk behavior and HIV seroconversion among injecting drug users. AIDS. 1999;13(14):1807–18. McLellan AT, Woody GE, Metzger D, McKay J, Durrell J, Alterman AI, et al. Evaluating the effectiveness of addiction treatments: reasonable expectations, appropriate comparisons. Milbank Q. 1996;74(1):51–85. Booth RE, Campbell BK, Mikulich-Gilbertson SK, Tillotson CJ, Choi D, Robinson J, et al. Reducing HIV-related risk behaviors among injection drug users in residential detoxification. AIDS Behav. 2011;15(1):30–44. Sorensen JL, Copeland AL. Drug abuse treatment as an HIV prevention strategy: a review. Drug Alcohol Depend. 2000; 59(1):17–31. Sullivan LE, Moore BA, Chawarski MC, Pantalon MV, Barry D, O’Connor PG, et al. Buprenorphine/naloxone treatment in primary care is associated with decreased human immunodeficiency virus risk behaviors. J Subst Abuse Treat. 2008;35(1):87–92. Heimer R, Clair S, Grau LE, Bluthenthal RN, Marshall PA, Singer M. Hepatitis-associated knowledge is low and risks are high among HIV-aware injection drug users in three US cities. Addiction. 2002;97(10):1277–87. Bailey SL, Huo D, Garfein RS, Ouellet LJ. The use of needle exchange by young injection drug users. J Acquir Immune Defic Syndr. 2003;34(1):67–70. Des Jarlais DC, Arasteh K, McKnight C, Hagan H, Perlman DC, Torian LV, et al. HIV infection during limited versus combined
123
AIDS Behav (2014) 18:S305–S315
24.
25.
26.
27.
28.
29.
30.
31. 32.
33. 34.
35.
36.
37.
38. 39. 40.
41.
42. 43.
HIV prevention programs for IDUs in New York City: the importance of transmission behaviors. Drug Alcohol Depend. 2010;109(1–3):154–60. Mark HD, Nanda J, Davis-Vogel A, Navaline H, Scotti R, Wickrema R, et al. Profiles of self-reported HIV-risk behaviors among injection drug users in methadone maintenance treatment, detoxification, and needle exchange programs. Public Health Nurs. 2006;23(1):11–9. Cohen M, Deamant C, Barkan S, Richardson J, Young M, Holman S, et al. Domestic violence and childhood sexual abuse in HIV-infected women and women at risk for HIV. Am J Public Health. 2000;190(4):560–5. Montealegre JR, Risser JM, Selwyn BJ, McCurdy SA, Sabin K. Effectiveness of respondent driven sampling to recruit undocumented central american immigrant women in Houston, texas for an HIV behavioral survey. AIDS Behav. 2012;17(2):719–27. Hladik W, Barker J, Ssenkusu JM, Opio A, Tappero JW, Hakim A, et al. HIV infection among men who have sex with men in Kampala, Uganda—a respondent driven sampling survey. PLoS One. 2012;7(5):e38143. McKnight C, Des Jarlais D, 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(Suppl 6):i54–9. Lansky A, Abdul-Quader AS, Cribbin M, Hall T, Finlayson TJ, Garfein RS, et al. Developing an HIV behavioral surveillance system for injecting drug users: the national HIV behavioral surveillance system. Public Health Rep. 2007;122(Suppl 1): 48–55. Gallagher KM, Sullivan PS, Lansky A, Onorato IM. Behavioral surveillance among people at risk for HIV infection in the U.S.: the national HIV behavioral surveillance system. Public Health Rep. 2007;122(Suppl 1):32–8. Heckathorn DD. Snowball versus respondent-driven sampling. Sociol Methodol. 2011;41(1):355–66. Heckathorn DD. Respondent-driven sampling ii: deriving valid population estimates from chain-referral samples of hidden populations. Soc Probl. 2002;49(1):11–34. Heckathorn D. Respondent-driven sampling: a new approach to the study of hidden populations. Soc Probl. 1997;44(2):174–99. Kuramoto SJ, Bohnert AS, Latkin CA. Understanding subtypes of inner-city drug users with a latent class approach. Drug Alcohol Depend. 2011;118(2–3):237–43. Falck RS, Siegal HA, Wang J, Carlson RG, Draus PJ. Nonmedical drug use among stimulant-using adults in small towns in rural Ohio. J Subst Abuse Treat. 2005;28(4):341–9. Volz E, Wejnert C, Degani I, Heckathorn, DD. Respondent-driven sampling analysis tool (RDSAT). Version 5.6. Ithaca, NY: Cornell University; 2007. Heckathorn DD. Extensions of respondent-driven sampling: analyzing continuous variables and controlling for differential recruitment. Sociol Methodol. 2007;37(1):151–207. Lazarsfeld. Latent structure analysis. Boston: Houghton Mifflin; 1986. McCutcheon. Latent Class Analysis. John L. Sullivan RGN, editor: Sage Publications, Inc.; 1987. Stephanie T, Lanza LMC, Lemmon Davis R, Schafer Joseph L. PROC LCA: a SAS procedure for latent class analysis. Struct Equ Model. 2007;14(4):671–94. Ramaswamy V, Desarbo WS, Reibstein DJ, Robinson WT. An empirical pooling approach for estimating marketing mix elasticities with PIMS data. Mark Sci. 1993;12(1):20. David W, Hosmer SL. Applied logistic regression. 2nd ed. New York: Wiley; 2000. Risser JM, Padgett P, Wolverton M, Risser WL. Relationship between heterosexual anal sex, injection drug use and HIV
AIDS Behav (2014) 18:S305–S315
44.
45.
46. 47. 48.
49.
50.
51.
52.
53.
54.
55.
infection among black men and women. Int J STD AIDS. 2009;20(5):310–4. Johnston L, O’Bra H, Chopra M, Mathews C, Townsend L, Sabin K, et al. The associations of voluntary counseling and testing acceptance and the perceived likelihood of being HIV-infected among men with multiple sex partners in a South African township. AIDS Behav. 2010;14(4):922–31. Lanza ST, Dziak JJ, Huang L, Xu S, & Collins LM. PROC LCA & PROC LTA users’ guide 1.2.7. University Park: The Methodology Center, Penn State; 2011. SAS Institute Inc. SCD, Cary; SAS. 2011. Borgatti SP. NetDraw: graph visualization software. 2.22. Harvard: Analytic Technologies; 2002. Ramirez-Valles J, Heckathorn DD, Vazquez R, Diaz RM, Campbell RT. From networks to populations: the development and application of respondent-driven sampling among IDUs and Latino gay men. AIDS Behav. 2005;9(4):387–402. Degenhardt L, Hall W. Extent of illicit drug use and dependence, and their contribution to the global burden of disease. Lancet. 2012;379(9810):55–70. Grella CE, Lovinger K. Gender differences in physical and mental health outcomes among an aging cohort of individuals with a history of heroin dependence. Addict Behav. 2010;37(3): 306–12. Degenhardt L, Bucello C, Mathers B, Briegleb C, Ali H, Hickman M, et al. Mortality among regular or dependent users of heroin and other opioids: a systematic review and meta-analysis of cohort studies. Addiction. 2010;106(1):32–51. McLellan AT, Lewis DC, O’Brien CP, Kleber HD. Drug dependence, a chronic medical illness: implications for treatment, insurance, and outcomes evaluation. JAMA. 2000;284(13): 1689–95. Stein JA, Nyamathi A. Gender differences in behavioural and psychosocial predictors of HIV testing and return for test results in a high-risk population. AIDS Care. 2000;12(3):343–56. Beck DW, Lalota M, Metsch LR, Cardenas GA, Forrest DW, Lieb S, et al. HIV prevention and transmission myths among heterosexually active adults in low-income areas of South Florida. AIDS Behav. 2011;16(3):751–60. Health NYSDo. Expanded Syringe Access Program (ESAP): overview of the law and regulations. http://www.health.ny.gov/
S315
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
diseases/aids/harm_reduction/needles_syringes/esap/overview. htm#comments. Accessed Sept 2012. Tesoriero JM, Battles HB, Klein SJ, Kaufman E, Birkhead GS. Expanding access to sterile syringes through pharmacies: assessment of New York’s expanded syringe access program. J Am Pharm Assoc (2003). 2009;49(3):407–16. Pouget ER, Deren S, Fuller CM, Blaney S, McMahon JM, Kang SY, et al. Receptive syringe sharing among injection drug users in HArlem and the Bronx during the New York state expanded syringe access demonstration program. J Acquir Immune Defic Syndr. 2005;39(4):471–7. Battles HB, Rowe KA, Ortega-Peluso C, Klein SJ, Tesoriero JM. Who purchases nonprescription syringes? characterizing customers of the expanded syringe access program (ESAP). J Urban Health. 2009;86(6):946–50. Eades CE, Ferguson JS, O’Carroll RE. Public health in community pharmacy: a systematic review of pharmacist and consumer views. BMC Public Health. 2011;11:582. doi:10.1186/ 1471-2458-11-582. Abramovitz D, Volz EM, Strathdee SA, Patterson TL, Vera A, Frost SD. Using respondent-driven sampling in a hidden population at risk of HIV infection: who do HIV-positive recruiters recruit? Sex Transm Dis. 2009;36(12):750–6. McCreesh N, Johnston LG, Copas A, Sonnenberg P, Seeley J, Hayes RJ, et al. Evaluation of the role of location and distance in recruitment in respondent-driven sampling. Int J Health Geogr. 2011;10:56. doi:10.1186/1476-072X-10-56. McNutt LA, Wu C, Xue X, Hafner JP. Estimating the relative risk in cohort studies and clinical trials of common outcomes. Am J Epidemiol. 2003;157(10):940–3. Lansky A, Drake A, Wejnert C, Pham H, Cribbin M, Heckathorn DD. Assessing the assumptions of respondent-driven sampling in the national HIV behavioral surveillance system among injecting drug users. Open AIDS J. 2012;6:77–82. Wejnert C. An empirical test of respondent-driven sampling: point estimates, variance, degree measures, and out-of-equilibrium data. Sociol Methodol. 2009;39(1):73–116. Johnston LG, Chen YH, Silva-Santisteban A, Raymond HF. An empirical examination of respondent driven sampling design effects among HIV risk groups from studies conducted around the world. AIDS Behav. 2013. doi:10.1007/s10461-012-0394-8.
123
