108 research outputs found
Public-health impact of 10 years of consistent PrEP use by 50% of the population projected by the model parameterized with the assumptions extracted from published papers.
<p>The outcomes presented are A) the cumulative fraction of prevented infections (CPF); B) resistance prevalence due to PrEP (RP); C) cumulative fraction of infections in which resistance is transmitted (TRF) and D) resistance contribution to CPF. The boxplots (median, 2.5th, 25th, 75th, 97.5th percentiles) reflect the variation in impact estimates based on 10,000 simulations (10 per preselected epidemic set). In D, the contribution of resistance to CPF is calculated as the percentage change in CPF from simulations in which the resistance is disregarded. Intervention parameters are fixed on their baseline values from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080927#pone-0080927-t001" target="_blank">Table 1</a>, part C.</p
Parameters and ranges used in the analysis.
<p>Parameters and ranges used in the analysis.</p
Partial rank correlation coefficients (PRCC) between resistance-related parameters and intervention outcomes, relative 10-year CPF (green) and resistance prevalence after 10 years (blue) based on 10, 000 simulations (10 per preselected epidemic set).
<p>The intervention parameters are fixed on their baseline values from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080927#pone-0080927-t001" target="_blank">Table 1</a>, part C. Relative CPF is calculated as the ratio of the 10-year CPF for scenarios with resistance over baseline scenario (no resistance).</p
Model fit to HIV and HCV prevalence.
<p>This figure shows the range of model estimates for HIV (blue) and HCV (green) prevalence among PWID in the shaded regions, with the estimate from the best-fit parameter set represented by the dashed line. Data estimates and corresponding confidence intervals to which the model was calibrated are represented by circles and error bars.</p
Diagram of model flows.
<p>This figure shows a graphical representation of the model pathways. The model has a layer for each set of dynamics (PWID, HIV, HCV), and each layer is composed of compartments with flows between them. For example, in the PWID layer, individuals in the active user compartment move into the ex-PWID and Methadone Maintenance compartments at different rates. Each PWID compartment in turn is broken up into compartments for each disease stage, so the total number of compartments is number of PWID compartments multiplied by the number of HIV compartments multiplied by the number of HCV compartments. Full equations and descriptions can be found in Section 5 of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177195#pone.0177195.s001" target="_blank">S1 File</a>.</p
ART scale-up: Incidence and deaths changes over time.
<p>Each panel in this figure shows a plot of reductions in HIV and HCV incidence, prevalence or deaths with varying ART scale-up, with scale-up percentages representing the proportion of patients newly initiated on ART each year (See Sections 5 and 7 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177195#pone.0177195.s001" target="_blank">S1 File</a> for details).</p
HCV treatment: Reductions in deaths from disease over time.
<p>Each panel in this figure shows a plot of reductions in deaths from disease 10 years after roll-out of HCV treatment coverage, with maximum previous scale up of ART and MMT coverage (80% and 50%).</p
MMT scale-up: Incidence and deaths changes over time.
<p>Each panel in this figure shows a plot of reductions in HIV and HCV incidence, prevalence or deaths with varying MMT scale-up with scale-up percentages representing the proportion of patients newly initiated on MMT each year (See Sections 5 and 7 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177195#pone.0177195.s001" target="_blank">S1 File</a> for details).</p
ART and MMT scale-up: Reductions in deaths from disease over time.
<p>Each bar in this figure shows a plot of reductions in deaths from disease 10 years after intervention scale-up with varying ART and MMT scale-up.</p
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