The complex interplay of social networks, geography and HIV risk among Malaysian Drug Injectors: Results from respondent-driven sampling

Abstract

Background

HIV is primarily concentrated among people who inject drugs (PWID) in Malaysia, where currently HIV prevention and treatment coverage is inadequate. To improve the targeting of interventions, we examined HIV clustering and the role that social networks and geographical distance play in influencing HIV transmission among PWID.

Methods

Data were derived from a respondent-driven survey sample (RDS) collected during 2010 of 460 PWID in greater Kuala Lumpur. Analysis focused on socio-demographic, clinical, behavioural, and network information. Spatial probit models were developed based on a distinction between the influence of peers (individuals nominated through a recruitment network) and neighbours (residing a close distance to the individual). The models were expanded to account for the potential influence of the network formation.

Results

Recruitment patterns of HIV-infected PWID clustered both spatially and across the recruitment networks. In addition, HIV-infected PWID were more likely to have peers and neighbours who inject with clean needles were HIV-infected and lived nearby (<5 km), more likely to have been previously incarcerated, less likely to use clean needles (26.8% vs 53.0% of the reported injections, p < 0.01), and have fewer recent injection partners (2.4 vs 5.4, p < 0.01). The association between the HIV status of peers and neighbours remained significantly correlated even after controlling for unobserved variation related to network formation and sero-sorting.

Conclusion

The relationship between HIV status across networks and space in Kuala Lumpur underscores the importance of these factors for surveillance and prevention strategies, and this needs to be more closely integrated. RDS can be applied to identify injection network structures, and this provides an important mechanism for improving public health surveillance, accessing high-risk populations, and implementing risk-reduction interventions to slow HIV transmission.

Introduction

With over 36.7 million people infected worldwide and 1.1 million deaths in 2015 alone, the HIV pandemic is the one of the most significant public health challenges of the 21st century (Joint United Nations Programme on HIV/AIDS (UNAIDS), 2016). Many countries struggle with developing and implementing effective HIV prevention and treatment strategies that target high-risk and hidden populations, including people who inject drugs (PWID), sex workers, transgender women and men who have sex with men (MSM). Illicit drug use, in particular, has a profound effect on the global burden of disease: among the 12 million PWID globally (United Nations Office on Drugs and Crime (UNODC), 2016), injection drug use as a risk factor for HIV accounts for 2.1 million Disability Life Adjusted Years (DALYs) (Degenhardt and Hall, 2012, Degenhardt et al., 2013). Even in concentrated HIV epidemics, where total HIV prevalence in the population is <1%, effective prevention strategies are needed due to the salience of the “bridging ties” that create opportunities for HIV transmission from high-risk individuals to the lower-risk general population, increasing the odds that the HIV epidemic may become generalized (Doherty, Shiboski, Ellen, Adimora, & Padian, 2006).

Bio-behavioural surveillance studies are often used to assess HIV prevalence and risk-behaviours in high-risk, hidden populations, and typically rely on either respondent-driven sampling (RDS) or time-space venue-based sampling (Kendall et al., 2008; Magnani, Sabin, Saidel, & Heckathorn, 2005) recruitment strategies. Each method, however, is fraught with challenges that undermine its ability to represent the intended population. Such limitations include non-response and selection bias due to differential recruitment (Amber & Gile, 2011), homophily (Mills et al., 2012), variability in geographical location (Bazazi, Crawford et al., 2015, McCreesh et al., 2012, Toledo et al., 2011), and seed selection (i.e. who is initially recruited) (Heimer, 2005). Despite these methodological limitations, RDS remains a primary recruitment strategy for PWID by international public health authorities due to its efficiency in reaching hidden populations (Centers for Disease Control and Prevention, 2007, Goel and Salganik, 2010, Malekinejad et al., 2008).

Malaysia, a polycultural Southeast Asian country with a population of over 30 million, is home to an estimated 200,000 PWID, most of whom inject opioids (Bachireddy et al., 2011; United Nations Office on Drugs and Crime (UNODC), 2016). HIV was primarily concentrated in PWID and HIV prevention and treatment efforts first focused on the introduction of needle/syringe exchange programs (NSPs) and in 2006 opioid agonist therapies (OAT) with buprenorphine and methadone (Kamarulzaman, 2009; Reid, Kamarulzaman, & Sran, 2007). Though there is nascent evidence of an emerging transitional epidemic, including transmission from PWID to their heterosexual partners (Ministry of Health Malaysia, 2014; UNGASS, 2010), the majority of people living with HIV (PLH) are PWID. Yet, HIV prevention and treatment in Malaysia remains inadequately scaled to need (Degenhardt et al., 2014, Kamarulzaman, 2009) with preventive measures reaching only a small fraction of the most-at-risk populations (Reid et al., 2007). Based on recent 2013 surveillance data, Malaysia had a cumulative number of more than 100,000 HIV cases, including more than 85,000 PLH and more than 16,000 deaths related to HIV/AIDS (Ministry of Health Malaysia, 2014).

In 2010, we conducted a bio-behavioural surveillance study in greater Kuala Lumpur using RDS to recruit opioid-dependent PWID (Bazazi, Crawford et al., 2015, Bazazi, Zelenev et al., 2015). We analysed how the spatial proximity of PWID to their peer network, influence HIV status and HIV risk behaviours in order to: (a) inform improvements in sampling methods and (b) guide the discussion for designing more optimal prevention strategies. Previous studies have demonstrated the importance of networks (Friedman, Curtis, Neaigus, Jose, & Des Jarlais, 2002; Friedman et al., 1997; Latkin, Forman, Knowlton, & Sherman, 2003; Mustanski, Birkett, Kuhns, Latkin, & Muth, 2014; Rothenberg et al., 2000) for HIV transmission and geography for recruitment of populations most-at-risk for HIV (Jenness, Neaigus, Wendel, Gelpi-Acosta, & Hagan, 2014; Rothenberg, Muth, Malone, Potterat, & Woodhouse, 2005; Toledo et al., 2011), yet none of these studies have accounted for the influence of the network formation process, which can induce a non-causal pattern of observed correlations in the HIV outcomes. Findings from these analyses are relevant for future interventions that aim to target individuals most-at-risk and explore the potential for incorporating network, structural and spatial strategies in reducing HIV transmission.