Faculty Bibliography

Early treatment, soon after infection, reduces HIV transmissions and benefits patients. The Transmission Reduction Intervention Project (TRIP) evaluated a network intervention to detect individuals recently infected (in the past 6 months). TRIP was conducted in Greece (2013-2015) and focused on drug injector networks. Based on HIV status, testing history, and the results of an assay to detect recent infections, TRIP classified drug injector "Seeds" into groups: Recent Seeds (RS), and Control Seeds with Long-term HIV infection (LCS). The network members of RS and LCS were traced for two steps. The analysis included 23 RS, 171 network members of the RS, 19 LCS, and 65 network members of the LCS. The per-seed number of recents detected in the network of RS was 5 times the number in the network of LCS (Ratio RS vs. LCS: 5.23; 95% Confidence Interval (CI): 1.54-27.61). The proportion of recents among HIV positives in the network of RS (27%) was approximately 3 times (Ratio RS vs. LCS: 3.30; 95% CI: 1.04-10.43) that in the network of LCS (8%). Strategic network tracing that starts with recently infected persons could support public health efforts to find and treat people early in their HIV infection.

BACKGROUND:Little is known on how perceived availability of marijuana is associated with medical marijuana laws. We examined the relationship between medical marijuana laws (MML) and the prevalence of past-month marijuana use, with perceived availability of marijuana. METHODS:Data were from respondents included in the National Survey of Drug Use and Health restricted use data portal 2004-2013. Multilevel logistic regression of individual-level data was used to test differences between MML and non-MML states and changes in prevalence of past-month marijuana use and perceived availability from before to after passage of MML among adolescents, young adults and older adults controlling for demographics. RESULTS:Among adults 26+, past-month prevalence of marijuana use increased from 5.87% to 7.15% after MML passage (Adjusted Odds Ratio (AOR): 1.24 [1.16-1.31]), but no change in prevalence of use was found for 12-17 or 18-25 year-olds. Perceived availability of marijuana increased after MML was enacted among those 26+ but not in younger groups. Among all age groups, prevalence of marijuana use and perception of it being easily available was higher in states that would eventually pass MML by 2013 compared to those that had not. Perceived availability was significantly associated with increased risk of past-month marijuana use in all age groups. CONCLUSION:Evidence suggests perceived availability as a driver of change in use of marijuana due to MML. To date, this has only occurred in adults 26+ and different scenarios that could explain this change need to be further explored.

The number of public health applications for molecular epidemiology and social network analysis has increased rapidly since the improvement in computational capacities and the development of new sequencing techniques. Currently, molecular epidemiology methods are used in a variety of settings: from infectious disease surveillance systems to the description of disease transmission pathways. The latter are of great epidemiological importance as they let us describe how a virus spreads in a community, make predictions for the further epidemic developments, and plan preventive interventions. Social network methods are used to understand how infections spread through communities and what the risk factors for this are, as well as in improved contact tracing and message-dissemination interventions. Research is needed on how to combine molecular and social network data as both include essential, but not fully sufficient information on infection transmission pathways. The main differences between the two data sources are that, firstly, social network data include uninfected individuals unlike the molecular data sampled only from infected network members. Thus, social network data include more detailed picture of a network and can improve inferences made from molecular data. Secondly, network data refer to the current state and interactions within the social network, while molecular data refer to the time points when transmissions happened, which might have happened years before the sampling date. As of today, there have been attempts to combine and compare the data obtained from the two sources. Even though there is no consensus on whether and how social and genetic data complement each other, this research might significantly improve our understanding of how viruses spread through communities.