Affiliations 

  • 1 Department of Internal Medicine, Section of Infectious Diseases, AIDS Program, Yale School of Medicine, 135 College St., Suite 323, New Haven, CT 06510, USA. Electronic address: alexei.zelenev@yale.edu
  • 2 UCLA Anderson School of Management, Decisions, Operations & Technology Management Department, Los Angeles, CA, USA
  • 3 Department of Internal Medicine, Section of Infectious Diseases, AIDS Program, Yale School of Medicine, 135 College St., Suite 323, New Haven, CT 06510, USA; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
  • 4 Department of Internal Medicine, Section of Infectious Diseases, AIDS Program, Yale School of Medicine, 135 College St., Suite 323, New Haven, CT 06510, USA; Centre of Excellence for Research in AIDS (CERiA), Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
  • 5 Department of Internal Medicine, Section of Infectious Diseases, AIDS Program, Yale School of Medicine, 135 College St., Suite 323, New Haven, CT 06510, USA; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA; Centre of Excellence for Research in AIDS (CERiA), Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
Int. J. Drug Policy, 2016 11;37:98-106.
PMID: 27639995 DOI: 10.1016/j.drugpo.2016.08.008

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 (<5km), 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.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.