Respondent-Driven Sampling of Injection Drug Users in Two U.S.–Mexico Border Cities: Recruitment Dynamics and Impact on Estimates of HIV and Syphilis Prevalence

Respondent-driven sampling (RDS), a chain referral sampling approach, is increasingly used to recruit participants from hard-to-reach populations, such as injection drug users (IDUs). Using RDS, we recruited IDUs in Tijuana and Ciudad (Cd.) Juárez, two Mexican cities bordering San Diego, CA and El Paso, TX, respectively, and compared recruitment dynamics, reported network size, and estimates of HIV and syphilis prevalence. Between February and April 2005, we used RDS to recruit IDUs in Tijuana (15 seeds, 207 recruits) and Cd. Juárez (9 seeds, 197 recruits), Mexico for a cross-sectional study of behavioral and contextual factors associated with HIV, HCV and syphilis infections. All subjects provided informed consent, an anonymous interview, and a venous blood sample for serologic testing of HIV, HCV, HBV (Cd. Juárez only) and syphilis antibody. Log-linear models were used to analyze the association between the state of the recruiter and that of the recruitee in the referral chains, and population estimates of the presence of syphilis antibody were obtained, correcting for biased sampling using RDS-based estimators. Sampling of the targeted 200 recruits per city was achieved rapidly (2 months in Tijuana, 2 weeks in Cd. Juárez). After excluding seeds and missing data, the sample prevalence of HCV, HIV and syphilis were 96.6, 1.9 and 13.5% respectively in Tijuana, and 95.3, 4.1, and 2.7% respectively in Cd. Juárez (where HBV prevalence was 84.7%). Syphilis cases were clustered in recruitment trees. RDS-corrected estimates of syphilis antibody prevalence ranged from 12.8 to 26.8% in Tijuana and from 2.9 to 15.6% in Ciudad Juárez, depending on how recruitment patterns were modeled, and assumptions about how network size affected an individual’s probability of being included in the sample. RDS was an effective method to rapidly recruit IDUs in these cities. Although the frequency of HIV was low, syphilis prevalence was high, particularly in Tijuana. RDS-corrected estimates of syphilis prevalence were sensitive to model assumptions, suggesting that further validation of RDS is necessary

Illicit opioid use and its key characteristics: A select overview and evidence from a Canadian multi-site cohort of illicit opioid users (OPICAN)

OBJECTIVES: To summarize key characteristics and consequences of illicit opioid use from the literature and to present corresponding data from a multisite sample of illicit opioid users in 5 Canadian cities (OPICAN study). METHOD: We undertook an overview of recent literature from North America, Australia, and Europe. We obtained data from the multicity OPICAN cohort study, which consisted of an interviewer-administered questionnaire, a standardized mental health instrument (the Composite International Diagnostic Interview Short Form for depression), and saliva-antibody tests for infectious disease (that is, HIV and hepatitis C virus). The baseline sample (n=679) was collected in 2002. RESULTS: Illicit opioid use in Canada and elsewhere is becoming increasingly heterogeneous in terms of opioid drugs used, with heroin playing an increasingly minor role; further, it predominantly occurs in a context of polydrug use (for example, cocaine-crack or benzodiazepines). Large proportions of illicit opioid users have physical and (or) mental health comorbidities, including infectious disease and (or) depression, and therefore require integrated interventions. Finally, morbidity risks among illicit opioid users are often predicted by social marginalization factors, for example, housing status or involvement in CONCLUSIONS: Given the epidemiologic profile and high disease burden associated with contemporary forms of illicit opioid use, more effective treatment approaches are urgently needed in Canada and elsewhere. Specifically, treatment must adjust to the extensive polysubstance use realities, yet it must also more effectively address the complex physical and (or) mental health comorbidities presented by this high-risk population

Parsing social network survey data from hidden populations using stochastic context-free grammars.

BACKGROUND:Human populations are structured by social networks, in which individuals tend to form relationships based on shared attributes. Certain attributes that are ambiguous, stigmatized or illegal can create a OhiddenO population, so-called because its members are difficult to identify. Many hidden populations are also at an elevated risk of exposure to infectious diseases. Consequently, public health agencies are presently adopting modern survey techniques that traverse social networks in hidden populations by soliciting individuals to recruit their peers, e.g., respondent-driven sampling (RDS). The concomitant accumulation of network-based epidemiological data, however, is rapidly outpacing the development of computational methods for analysis. Moreover, current analytical models rely on unrealistic assumptions, e.g., that the traversal of social networks can be modeled by a Markov chain rather than a branching process. METHODOLOGY/PRINCIPAL FINDINGS:Here, we develop a new methodology based on stochastic context-free grammars (SCFGs), which are well-suited to modeling tree-like structure of the RDS recruitment process. We apply this methodology to an RDS case study of injection drug users (IDUs) in Tijuana, México, a hidden population at high risk of blood-borne and sexually-transmitted infections (i.e., HIV, hepatitis C virus, syphilis). Survey data were encoded as text strings that were parsed using our custom implementation of the inside-outside algorithm in a publicly-available software package (HyPhy), which uses either expectation maximization or direct optimization methods and permits constraints on model parameters for hypothesis testing. We identified significant latent variability in the recruitment process that violates assumptions of Markov chain-based methods for RDS analysis: firstly, IDUs tended to emulate the recruitment behavior of their own recruiter; and secondly, the recruitment of like peers (homophily) was dependent on the number of recruits. CONCLUSIONS:SCFGs provide a rich probabilistic language that can articulate complex latent structure in survey data derived from the traversal of social networks. Such structure that has no representation in Markov chain-based models can interfere with the estimation of the composition of hidden populations if left unaccounted for, raising critical implications for the prevention and control of infectious disease epidemics

Correlates of Incarceration Among Young Methamphetamine Users in Chiang Mai, Thailand

Objectives. We examined correlates of incarceration among young methamphetamine users in Chiang Mai, Thailand in 2005 to 2006