Population mixing patterns can greatly inform allocation of HIV prevention interventions such as treatment as prevention or preexposure prophylaxis. Characterizing contact patterns among subgroups can help identify the specific combinations of contact expected to result in the greatest number of new infections.
Baseline data from an intervention to reduce HIV-related risk behaviors in male persons who inject drugs (PWID) in the Northern Vietnamese province of Thai Nguyen were used for the analysis.
Egocentric network data were provided by PWID who reported any drug-injection equipment sharing in the previous 3 months. Age-dependent mixing was assessed to explore its epidemiological implications on risk of HIV transmission risk (among those HIV-infected) and HIV acquisition risk (among those not infected) in PWID.
A total of 1139 PWID collectively reported 2070 equipment-sharing partnerships in the previous 3 months. Mixing by age identified the 30–34 and 35–39 years age groups as the groups from whom the largest number of new infections was transmitted, making them primary targets for treatment as prevention. Among the uninfected, 25–29, 30–35, and 35–39 years age groups had the highest HIV acquisition rate, making them the primary targets for preexposure prophylaxis.
Collection and analysis of contact patterns in PWID is feasible and can greatly inform infectious disease dynamics and targeting of appropriate interventions. Results presented also provide much needed empirical data on mixing to improve mathematical models of disease transmission in this population.
*Division of Infectious Diseases, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC;
†Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam;
‡Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD; and
§Department of Health Education, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC.
Correspondence to: M. Kumi Smith, PhD, Institute for Global Health and Infectious Diseases, School of Medicine, University of North Carolina at Chapel Hill, 130 Mason Farm Road, Chapel Hill, NC 27599 (e-mail: firstname.lastname@example.org).
Supported by the National Institute on Drug Abuse (NIDA R01 DA032217) and the National Institute on Allergies and Infectious Diseases (NIAID; P30 AI094189). CAL received support from NIDA (R01DA031030, R01DA032217, and R01DA040488); M.K.S. from NIAID (T32AI102623) and M.G. by the Bill and Melinda Gates Foundation (OPP1106427).
The authors have no funding or conflicts of interest to disclose.
M.K.S., C.A.L., and V.L.G. conceptualized the original study. M.K.S. and M.G. conducted the computational analyses. M.K.S. composed the first draft of the manuscript, and substantial improvements were made by the other 3 coauthors.
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Received July 11, 2017
Accepted December 01, 2017