Data analysis framework.

<p>(<b>a</b>) YRBSS data was processed for each question, including conversion from categorical to binary, data weighting, reverse coding and the removal of missing values. (<b>b</b>) Odds ratios (ORs) for each question combination were calculated and stored as a branching odds ratio matrix. ORs comparing the same question (i.e. Q1xQ1, Q2xQ2, etc) are indicated as infinity (Inf) and replaced by the maximum value in the row for all subsequent analysis. (<b>c</b>) The global odds ratio matrix was then normalized by dividing by the median value of each row (Row 1 = 6.62, Row2 = 1.46, etc.) and LOG2 transformation. (<b>D</b>) The normalized global odds ratio matrix was then used in hierarchical clustering of each risk behavior, and shown as a heatmap (with colors indicating associated normalized odds ratios) and dendrogram (indicating the similarity of each behavior to one another).</p

Hierarchical clustering of 2011 comprehensive odds ratios identifies six distinct behavior groupings.

<p>Hierarchical clustering, dendogram and heat map based on normalized odds ratios for each permutation of the 55 risk questions. Each row corresponds to questions ordered (1–55), grouped by <i>a priori</i> categories listed in order in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111893#pone.0111893.s009" target="_blank">Table S1A</a>.</b> Data was median centered across rows, log2 normalized, clustered along columns and rotated for better visualization (+ in the past week; * in the past month; ∧ in the past year).</p

YRBSS Odds Ratio Calculation.

<p>YRBSS Odds Ratio Calculation.</p

Gun possession odds ratio hierarchical clustering identifies highest and lowest risk behavior associations.

<p>Hierarchical clustering, dendrogram and heat map based on odds ratios for weapon carrying and each of the 55 risk questions for each survey year between 2001–2011. Each row corresponds to a survey year. Data was median centered across rows, log2 normalized, clustered along columns and rotated for better visualization (+ in the past week; * in the past month; ∧ in the past year).</p

Overall risk behavior participation is increased in subjects reporting gun possession.

<p>The number of risk behaviors each subject has participated in was determined by finding the number of positive risk behavior responses for the 55 survey questions considered across all subjects in all years, split by those answering yes to gun carrying versus those who have not carried a gun in the past 30 days. ANOVA comparison between the two groups indicated the difference between groups to be highly significant p<0.000001).</p

Gun possession is positively associated with the majority of risk behaviors assessed by the YRBSS.

<p>Plotting of raw odds ratio (OR) values for 2001, 2003, 2005, 2007 2009 and 2011 for each risk behavior and gun possession, excluding behaviors with infinite OR values (“Carried a weapon”). Behaviors are binned and colored based on their minimum OR across time (>10, red; <5, orange; <2, green; >1, blue; <1, grey). Question labels are indicated in corresponding boxes in order of OR value in 2011 (+ in the past week; * in the past month; ∧ in the past year).</p

Clinical trial interventions and associated costs and effects considered in HIV transmission simulation model.

<p>Uniform distributions were used for all costs and lognormal distribution for all effects in probabilistic analyses. Intervention costs were derived from India-specific sources.[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0184179#pone.0184179.ref035" target="_blank">35</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0184179#pone.0184179.ref049" target="_blank">49</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0184179#pone.0184179.ref051" target="_blank">51</a>] Cost in 2012 USD.</p

Betting on the fastest horse: Using computer simulation to design a combination HIV intervention for future projects in Maharashtra, India

<div><p>Objective</p><p>To inform the design of a combination intervention strategy targeting HIV-infected unhealthy alcohol users in Maharashtra, India, that could be tested in future randomized control trials.</p><p>Methods</p><p>Using probabilistic compartmental simulation modeling we compared intervention strategies targeting HIV-infected unhealthy alcohol users on antiretroviral therapy (ART) in Maharashtra, India. We tested interventions targeting four behaviors (unhealthy alcohol consumption, risky sexual behavior, depression and antiretroviral adherence), in three formats (individual, group based, community) and two durations (shorter versus longer). A total of 5,386 possible intervention combinations were tested across the population for a 20-year time horizon and intervention bundles were narrowed down based on incremental cost-effectiveness analysis using a two-step probabilistic uncertainty analysis approach.</p><p>Results</p><p>Taking into account uncertainty in transmission variables and intervention cost and effectiveness values, we were able to reduce the number of possible intervention combinations to be used in a randomized control trial from over 5,000 to less than 5. The most robust intervention bundle identified was a combination of three interventions: long individual alcohol counseling; weekly Short Message Service (SMS) adherence counseling; and brief sex risk group counseling.</p><p>Conclusions</p><p>In addition to guiding policy design, simulation modeling of HIV transmission can be used as a preparatory step to trial design, offering a method for intervention pre-selection at a reduced cost.</p></div

Analyses methodology.

<p>Pipeline workflow for intervention bundle prioritization. a, Creation of efficient frontier for all combinations of 15 interventions and filtering out 8 interventions that were never found on the frontier. b, For the remaining 7 interventions, completion of 100 probabilistic runs varying intervention costs and effects and filtering out intervention bundles that were never found on the frontier. c, Completion of a full probabilistic analyses (run N = 1000) varying intervention cost and effect as well as 96 input variables. All analysis was run for a 20-year simulation.</p

Efficient frontier for HIV interventions during a 20-year simulation of HIV epidemic in Maharashtra, India.

<p>a, Graphical representation efficient frontier for all permutations of 12 interventions (4096 total combinations). Blue circles represent packages of interventions on the frontier, red represent packages off the frontier. b, focused graphical representation of efficient frontier for the lower end of discounted cost (0.888–0.898 Billion USD). c, Interventions contained within each efficient frontier package.</p