Summary of key variables and main influences in model.

<p>Effects of vaccine are illustrated in grey.</p

Characteristics of the population up to and at baseline (year 2025).^*

<p>*all values relate to 2025 unless stated.</p><p>Characteristics of the population up to and at baseline (year 2025).^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107214#nt101" target="_blank">*</a>}</p

Predicted outcomes 2025–2060 of eight vaccine introduction scenarios in 2025.

<p>(i) prevention efficacy 0.0, viral load efficacy 0.0 log₁₀, (ii) prevention efficacy 30%, viral load efficacy 0.0 log₁₀, (iii) prevention efficacy 50%, viral load efficacy 0.0 log₁₀, (iv) prevention efficacy 90%, viral load efficacy 0.0 log₁₀, (v) prevention efficacy 0.0, viral load efficacy 1.0 log₁₀, (vi) prevention efficacy 0.0, viral load efficacy 2.0 log₁₀, (vii) prevention efficacy 50%, viral load efficacy 1.0 log₁₀, (viii) prevention efficacy 90%, viral load efficacy 2.0 log₁₀. All in the context of vaccination at 15, with a rate of vaccination per 3 months of 0.3 amongst those age 15–17 (and a 5 year catch-up program amongst adults age 18–30 covering 50% of the population of that age), with a maximum coverage (in 15–17 year olds) of 70%, and with regular boosters every 5 years (the assumed duration of vaccine effect) with 80% of people being adherent to these boosts.</p

Mean over 2040–2060 of on-going vaccine effect, HIV incidence (per 1000 person years and death rate (/100 person years) in the whole population, according to variations in vaccine implementation characteristics.

<p>Base scenario: Coverage 70%, boosting to age 50, with 80% completion rates to boosters, no tapering in effect over time, and with an adult catch-up program in 18–30 years covering 50% of the population of that age, rate vaccination  = 0.3/3mths, vaccine effect on VL in 100% of people, duration of vaccine effect 5 years, age of introduction 15. For each row, one characteristic is made different from the base scenario. 95% CI shown in Table S1, along with further comparisons.</p><p>Mean over 2040–2060 of on-going vaccine effect, HIV incidence (per 1000 person years and death rate (/100 person years) in the whole population, according to variations in vaccine implementation characteristics.</p

Multivariable uncertainty analysis based on 500 runs.

<p>Variation in effect of vaccine on HIV incidence under parameter variation, sampling from distributions of parameter values given in Supplementary Methods and Results in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107214#pone.0107214.s001" target="_blank">File S1</a>.</p><p>Multivariable uncertainty analysis based on 500 runs.</p

Mean over 2040–2060 of the following outcomes: HIV incidence (per 1000 person years), prevalence (%), % of whole population on ART (not only HIV infected), death rate (in whole population; per 100 person years), % of population age 15–65 with on-going vaccine effect (i.e. vaccinated and up to date with boosters), 2040–2060, for base implementation characteristics.

<p>Base scenario: Coverage 70%, boosting to age 50, with 80% completion rates to boosters, no tapering in effect over time, and with an adult catch-up program in 18–30 years covering 50% of the population of that age, rate vaccination  = 0.3/3mths, vaccine effect on VL in 100% of people, duration of vaccine effect 5 years, age of introduction 15. 95% CI shown in italics.</p>+<p>of entire population, including HIV uninfected.</p><p>Mean over 2040–2060 of the following outcomes: HIV incidence (per 1000 person years), prevalence (%), % of whole population on ART (not only HIV infected), death rate (in whole population; per 100 person years), % of population age 15–65 with on-going vaccine effect (i.e. vaccinated and up to date with boosters), 2040–2060, for base implementation characteristics.</p

Receiver operating characteristic curve for the ability of plasma hyaluronic acid to predict liver-related events.

<p>Receiver operating characteristic curve for the ability of plasma hyaluronic acid to predict liver-related events.</p

Crude mortality rate across the six different regions.

<p>Cause of death are reported by the site investigator and, since 2004, a Coding of Death in HIV (CoDe) case report form is additionally completed for each fatal case and CoDe methods used to determine the underlying cause of death.</p

Results from univariate and multivariate Poisson regression analysis investigating mortality rates by region (South is the reference region).

a<p>adjusted for baseline variables gender, age, HIV exposure group, hepatitis B and C status, prior AIDS diagnosis, prior non-AIDS diagnosis, hypertension, diabetes, anaemia, smoking status, CD4 count, baseline date and on cART viral load.</p>b<p>adjusted as in a but age, hepatitis B and C status, hypertension, diabetes, anaemia, smoking status, CD4 count, year of follow-up and on cART viral load were included as time-updated covariates.</p>c<p>adjusted for baseline variables gender, age, HIV exposure group, hepatitis B and C status, prior AIDS diagnosis, prior non-AIDS diagnosis, hypertension, diabetes, anaemia, CD4 count, baseline date and on cART viral load.</p>d<p>adjusted as in a but age, hepatitis B and C status, hypertension, diabetes, anaemia, CD4 count, year of follow-up and on cART viral load were included as time-updated covariates.</p

Distribution of plasma hyaluronic acid levels and any-liver related events during follow-up.

<p>Distribution of plasma hyaluronic acid levels and any-liver related events during follow-up.</p