HIV prevalence and change in incidence for leading cancers.

<p>Note: IP, inverse probability; 95%CI, 95% confidence interval</p><p>^a Quadratic term was significant for non-Hodgkin’s lymphoma among HIV-infected individuals—4.5% (95%CI -10.1 to 23.9%) per year and -9.45% (95%CI -19.5 to -1.2%) per year².</p><p>HIV prevalence and change in incidence for leading cancers.</p

HIV prevalence and change in incidence for leading cancers.

<p>Note: IP, inverse probability; 95%CI, 95% confidence interval</p><p>^a Quadratic term was significant for non-Hodgkin’s lymphoma among HIV-infected individuals—4.5% (95%CI -10.1 to 23.9%) per year and -9.45% (95%CI -19.5 to -1.2%) per year².</p><p>HIV prevalence and change in incidence for leading cancers.</p

ART treatment coverage and median CD4 at ART initiation during the study period.

<p>Note: ART, combination antiretroviral therapy.</p

Trend in standardized incidence ratio (SIR) of cancer comparing HIV infected and HIV uninfected populations during ART expansion.

<p>Analyses utilized the IPW population. Note: ART, combination antiretroviral therapy.</p

Trends in incidence for leading cancers among HIV-infected population.

<p>Estimates from IPW population accounting for changes in overall and age-specific HIV prevalence. Shaded 95% confidence bands from 1000 bootstrap samples. Note: NHL, non-Hodgkin’s lymphoma</p

Annual number of cancer diagnoses among HIV-infected and HIV-uninfected in Botswana.

<p>Analyses used the IPW population.</p

Overall cancer age-adjusted incidence among HIV-infected (solid) and HIV-uninfected (dotted) individuals.

<p>Analyses utilized the IPW population.</p

Phylogenetic relatedness of HIV-1C <i>env</i> sequences based on the ML_FastTree2 + ML_PhyML analysis.

<p>The identified clusters with bootstrap support of ≥80% are collapsed. The collapsed clusters shown in red represent Mochudi-unique clusters; in blue, Mochudi mixed clusters with other Botswana sequences; in orange, non-Mochudi clusters with Botswana sequences; and in green, non-Botswana clusters. Clusters with mother-infant pairs from two MTCT studies in Malawi are shown by asterisks <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080589#pone.0080589-Russell1" target="_blank">[78]</a> and green circle <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080589#pone.0080589-Kumar1" target="_blank">[79]</a>.</p

HIV-1C <i>env</i> sequences included in analysis.

<p>A total of 11,934 non-recombinant HIV-1C <i>env</i> sequences spanning at least 1,000 bp within the gp120 V1–C5 region were selected from the LANL HIV Database. After initial on-line filtering, 3,170 sequences were selected. The applied manual filtering left 1,334 sequences. Together with 885 new Botswana sequences including 785 sequences known to be from Mochudi, a total of 2,219 HIV-1C <i>env</i> sequences were included in analysis.</p

Flow of cluster analysis.

<p>A total of 2,219 HIV-1C <i>env</i> sequences were included. After re-alignment of variable loops, the first step ML analysis was implemented by FastTree2, which selected 969 sequences based on the Shimodira-Hasegawa test for splits support ≥0.98. Two ML analyses implemented by PhyML and RAxML, and ME analysis using MCL model, were performed in the second step. Clusters were identified based on bootstrap support of ≥80% in at least 2 of 3 methods in the second step.</p