We develop new statistical methods for investigation and scale-up of HIV prevention interventions. Challenges arise from the complex dependencies that characterize data from HIV prevention research studies, reflecting the spread of HIV along the sexual contact networks we consider. The data generated from both randomized and observational studies include HIV incidence in different subpopulations, self-reported behavior regarding partner selection, and viral genetic sequences. Such data are likely to be incomplete because of failure to locate intended participants, denial of consent, and dropout. Furthermore even complete data would not allow networks to be fully characterized, or viral genetic linkage analyses to be conducted with certainty. Our methods are intended to make maximal use of incomplete information to estimate quantities that will be useful in guiding scale-up of successful interventions. These include not only the randomized effects of interventions, but also expected effects under policies of delivering them in ways that are likely to be used practice. Optimal scale-up requires knowledge of where and in which populations the interventions succeeded (fully or partially) and to identify factors, such as network features, that predict success. To investigate these questions, we extend methods developed in the first grant period for investigation of viral genetic linkage of incident and prevalent HIV infections. Such linkage provides information about the extent to which new infections arise from strains circulating within or across subpopulations defined by demographic factors, such as age, gender, and residence. It also provides insight into HIV transmission dynamics, such as identifying bridge populations that facilitate entry of HIV into new populations and the impact of interventions on such spread. During the first grant period, we focused on cross-sectional data collected in single villages; here we consider data collected across multiple villages over time that permit investigation of spatio-temporal HIV dynamics. Building on existing network theory and our own prior work, we also propose new methods to investigate how best to use prevention interventions to functionally fragment sexual contact networks and thereby impede epidemic spread.

Public Health Relevance

Development of analytical methods is targeted to estimate quantities that will help guide policies regarding how best to target HIV prevention interventions, choose among them, and prioritize resources for their scale-up. We also describe application of new methods to data from HIV prevention research studies that may be incomplete because of denial of consent, dropout, or other mechanisms.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Method to Extend Research in Time (MERIT) Award (R37)
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Special Emphasis Panel (NSS)
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Gezmu, Misrak
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Harvard University
Biostatistics & Other Math Sci
Schools of Public Health
United States
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