METHODS: We developed a linear optimisation model to estimate efficiency gains that could be achieved based on current procurement of OAT. We also developed a dynamic, compartmental population model of HIV transmission that included both injection and sexual risk to estimate the effect of OAT scale-up on HIV infections and mortality over a 10-year horizon. The compartmental population model was calibrated to HIV prevalence and incidence among PWID for 23 administrative regions of Ukraine. Sources for regional data included the SyrEx database, the Integrated Biological and Behavioral Survey, the Ukrainian Center for Socially Dangerous Disease Control of the Ministry of Health of Ukraine, the Public Health Center of the Ministry of Health of Ukraine, and the Ukrainian Census.
FINDINGS: Under a status-quo scenario (OAT coverage of 2·7% among PWID), the number of new HIV infections among PWID in Ukraine over the next 10 years was projected to increase to 58 820 (95% CI 47 968-65 535), with striking regional differences. With optimum allocation of OAT without additional increases in procurement, OAT coverage could increase from 2·7% to 3·3% by increasing OAT doses to ensure higher retention levels. OAT scale-up to 10% and 20% over 10 years would, respectively, prevent 4368 (95% CI 3134-5243) and 10 864 (7787-13 038) new HIV infections and reduce deaths by 7096 (95% CI 5078-9160) and 17 863 (12 828-23 062), relative to the status quo. OAT expansion to 20% in five regions of Ukraine with the highest HIV burden would account for 56% of new HIV infections and 49% of deaths prevented over 10 years.
INTERPRETATION: To optimise HIV prevention and treatment goals in Ukraine, OAT must be substantially scaled up in all regions. Increased medication procurement is needed, combined with optimisation of OAT dosing. Restricting OAT scale-up to some regions of Ukraine could benefit many PWID, but the regions most affected are not necessarily those with the highest HIV burden.
FUNDING: National Institute on Drug Abuse.
METHODS: Blips were defined as detectable VL (≥ 50 copies/mL) preceded and followed by undetectable VL (<50 copies/mL). Virological failure (VF) was defined as two consecutive VL ≥50 copies/ml. Cox proportional hazard models of time to first VF after entry, were developed.
RESULTS: 5040 patients (AHOD n = 2597 and TAHOD n = 2521) were included; 910 (18%) of patients experienced blips. 744 (21%) and 166 (11%) of high- and middle/low-income participants, respectively, experienced blips ever. 711 (14%) experienced blips prior to virological failure. 559 (16%) and 152 (10%) of high- and middle/low-income participants, respectively, experienced blips prior to virological failure. VL testing occurred at a median frequency of 175 and 91 days in middle/low- and high-income sites, respectively. Longer time to VF occurred in middle/low income sites, compared with high-income sites (adjusted hazards ratio (AHR) 0.41; p<0.001), adjusted for year of first cART, Hepatitis C co-infection, cART regimen, and prior blips. Prior blips were not a significant predictor of VF in univariate analysis (AHR 0.97, p = 0.82). Differing magnitudes of blips were not significant in univariate analyses as predictors of virological failure (p = 0.360 for blip 50-≤1000, p = 0.309 for blip 50-≤400 and p = 0.300 for blip 50-≤200). 209 of 866 (24%) patients were switched to an alternate regimen in the setting of a blip.
CONCLUSION: Despite a lower proportion of blips occurring in low/middle-income settings, no significant difference was found between settings. Nonetheless, a substantial number of participants were switched to alternative regimens in the setting of blips.
METHODS: Ninety-four HIV-infected patients were recruited to the study; a longitudinal cohort of patients recruited just prior to commencing cART and followed up for 48 weeks (n = 27), and a cross-sectional cohort (n = 67) consisting of patients with sIR (CD4 T-cell count < 350 cells/μL) and oIR (CD4 T-cell count > 500 cells/μL) after a minimum of 2 years on cART. Controls (n = 29) consisted of HIV-negative individuals. IFN-γ ELISPOT responses against HPV16 and HPV52 E6 were correlated to clinical characteristics, anal and oral HPV carriage, T-cell maturational subsets, markers of activation, senescence and T-regulatory cells.
RESULTS: HPV16 and HPV52 E6-specific T-cell responses were detected in only one of 27 patients (3.7%) during the initial phase of immune recovery. After at least 2 years of cART, those who achieved oIR had significantly higher E6-specific responses (9 of 34; 26.5%) compared with those with sIR (2 of 32; 6.3%) (P = 0.029). Apart from higher CD4 T-cell counts and lower CD4 T-cell activation, no other immunological correlates were associated with the detection of HPV16 and HPV52 E6-specific responses.
CONCLUSIONS: HPV16 and HPV52 E6-specific IFN-γ T-cell responses, a correlate of protective immunity, were detected more frequently among HIV-infected patients who achieved optimal immune recovery on cART (26.5%) compared with those with suboptimal recovery (6.3%).
METHODS: Randomized trials, assessing the efficacy of antiviral drugs for HBV and HIV co-infected patients were searched in health-related databases. The methodological quality of the included trials was evaluated using the Cochrane risk of bias tool. Main outcome in this meta-analysis study was the success of treatment by antivirals as determined by virologic response. We performed pairwise and network meta-analysis of these trials and assessed the quality of evidence using the GRADE approach.
RESULTS: Seven randomized trials (329 participants) were included in this network meta-analysis study. A network geometry was formed with six treatment options including four antiviral drugs, adefovir (ADV), emtricitabine (FTC), lamivudine (LMV) and tenofovir disoproxil fumarate (TDF), combination treatment of TDF plus LMV, and placebo. The weighted percentage contributions of each comparison distributed fairly equally in the entire network of evidence. An assumption of consistency required for network meta-analysis was not violated (the global Wald test for inconsistency: Chi2(4) = 3.63, p = 0.46). The results of estimates showed no differences between the treatment regimens in terms of viral response for treating HBV and HIV co-infected patients, which spanned both benefit and harm (e.g. LMV vs TDF plus LMV: OR: 0.37, 95%CI: 0.06-2.41). Overall, the certainty of evidence was very low in all comparisons (e.g. LMV vs TDF plus LMV: 218 fewer per 1000,121 more to 602 fewer, very low certainty). Therefore, we remained uncertain to the true ranking of the antiviral treatments in HBV/ HIV co-infected patients.
CONCLUSIONS: The findings suggest that the evidence is insufficient to provide guidance to the relative effectiveness of currently available antiviral drugs with dual activity in treating co-infection of HBV/HIV. Well-designed, large clinical trials in this field to address other important outcomes from different epidemiological settings are recommended.
METHODS: HIV+ patients from the Australian HIV Observational Database (AHOD) and the TREAT Asia HIV Observational Database (TAHOD) meeting specific criteria were included. In these analyses Asian and Caucasian status were defined by cohort. Factors associated with a low CD4:CD8 ratio (cutoff <0.2) prior to ART commencement, and with achieving a normal CD4:CD8 ratio (>1) at 12 and 24 months post ART commencement were assessed using logistic regression.
RESULTS: There were 591 patients from AHOD and 2,620 patients from TAHOD who met the inclusion criteria. TAHOD patients had a significantly (P<0.001) lower odds of having a baseline (prior to ART initiation) CD4:CD8 ratio greater than 0.2. After 12 months of ART, AHOD patients were more than twice as likely to achieve a normal CD4:CD8 ratio compared to TAHOD patients (15% versus 6%). However, after adjustment for confounding factors there was no significant difference between cohorts in the odds of achieving a CD4:CD8 ratio >1 (P=0.475).
CONCLUSIONS: We found a significantly lower CD4:CD8 ratio prior to commencing ART in TAHOD compared to AHOD even after adjusting for confounders. However, after adjustment, there was no significant difference between the cohorts in odds of achieving normal ratio. Baseline CD4+ and CD8+ counts seem to be the main driver for this difference between these two populations.
METHODS: Prospectively collected longitudinal data from patients in Thailand, Hong Kong, Malaysia, Japan, Taiwan, and South Korea were provided for analysis. Covariates included demographics, hepatitis B and C coinfections, baseline CD4 T lymphocyte count, and plasma HIV-1 RNA levels. Clinical deterioration (a new diagnosis of Centers for Disease Control and Prevention category B/AIDS-defining illness or death) was assessed by proportional hazards models. Surrogate endpoints were 12-month change in CD4 cell count and virologic suppression post therapy, evaluated by linear and logistic regression, respectively.
RESULTS: Of 1105 patients, 1036 (93.8%) infected with CRF01_AE or subtype B were eligible for inclusion in clinical deterioration analyses and contributed 1546.7 person-years of follow-up (median: 413 days, interquartile range: 169-672 days). Patients >40 years demonstrated smaller immunological increases (P = 0.002) and higher risk of clinical deterioration (hazard ratio = 2.17; P = 0.008). Patients with baseline CD4 cell counts >200 cells per microliter had lower risk of clinical deterioration (hazard ratio = 0.373; P = 0.003). A total of 532 patients (48.1% of eligible) had CD4 counts available at baseline and 12 months post therapy for inclusion in immunolgic analyses. Patients infected with subtype B had larger increases in CD4 counts at 12 months (P = 0.024). A total of 530 patients (48.0% of eligible) were included in virological analyses with no differences in response found between genotypes.
CONCLUSIONS: Results suggest that patients infected with CRF01_AE have reduced immunologic response to therapy at 12 months, compared with subtype B-infected counterparts. Clinical deterioration was associated with low baseline CD4 counts and older age. The lack of differences in virologic outcomes suggests that all patients have opportunities for virological suppression.
METHODS: Data from two regional cohort observational databases were analyzed for trends in median CD4 cell counts at ART initiation and the proportion of late ART initiation (CD4 cell counts <200 cells/mm(3) or prior AIDS diagnosis). Predictors for late ART initiation and mortality were determined.
RESULTS: A total of 2737 HIV-positive ART-naïve patients from 22 sites in 13 Asian countries and territories were eligible. The overall median (IQR) CD4 cell count at ART initiation was 150 (46-241) cells/mm(3). Median CD4 cell counts at ART initiation increased over time, from a low point of 115 cells/mm(3) in 2008 to a peak of 302 cells/mm(3) after 2011 (p for trend 0.002). The proportion of patients with late ART initiation significantly decreased over time from 79.1% before 2007 to 36.3% after 2011 (p for trend <0.001). Factors associated with late ART initiation were year of ART initiation (e.g. 2010 vs. before 2007; OR 0.40, 95% CI 0.27-0.59; p<0.001), sex (male vs. female; OR 1.51, 95% CI 1.18-1.93; p=0.001) and HIV exposure risk (heterosexual vs. homosexual; OR 1.66, 95% CI 1.24-2.23; p=0.001 and intravenous drug use vs. homosexual; OR 3.03, 95% CI 1.77-5.21; p<0.001). Factors associated with mortality after ART initiation were late ART initiation (HR 2.13, 95% CI 1.19-3.79; p=0.010), sex (male vs. female; HR 2.12, 95% CI 1.31-3.43; p=0.002), age (≥51 vs. ≤30 years; HR 3.91, 95% CI 2.18-7.04; p<0.001) and hepatitis C serostatus (positive vs. negative; HR 2.48, 95% CI 1.-4.36; p=0.035).
CONCLUSIONS: Median CD4 cell count at ART initiation among Asian patients significantly increases over time but the proportion of patients with late ART initiation is still significant. ART initiation at higher CD4 cell counts remains a challenge. Strategic interventions to increase earlier diagnosis of HIV infection and prompt more rapid linkage to ART must be implemented.
SETTING: An Asian cohort in 16 pediatric HIV services across 6 countries.
METHODS: From 2005 to 2014, patients younger than 20 years who achieved virologic suppression and had subsequent viral load testing were included. Early virologic failure was defined as a HIV RNA ≥1000 copies per milliliter within 12 months of virologic suppression, and late virologic as a HIV RNA ≥1000 copies per milliliter after 12 months following virologic suppression. Characteristics at combination antiretroviral therapy initiation and virologic suppression were described, and a competing risk time-to-event analysis was used to determine cumulative incidence of virologic failure and factors at virologic suppression associated with early and late virologic failure.
RESULTS: Of 1105 included in the analysis, 182 (17.9%) experienced virologic failure. The median age at virologic suppression was 6.9 years, and the median time to virologic failure was 24.6 months after virologic suppression. The incidence rate for a first virologic failure event was 3.3 per 100 person-years. Factors at virologic suppression associated with late virologic failure included older age, mostly rural clinic setting, tuberculosis, protease inhibitor-based regimens, and early virologic failure. No risk factors were identified for early virologic failure.
CONCLUSIONS: Around 1 in 5 experienced virologic failure in our cohort after achieving virologic suppression. Targeted interventions to manage complex treatment scenarios, including adolescents, tuberculosis coinfection, and those with poor virologic control are required.
METHODS: Regional Asian data (2001-2016) were analyzed to describe PHIVA who experienced ≥2 weeks of lamivudine or emtricitabine monotherapy or treatment interruption and trends in CD4 count and HIV viral load during and after episodes. Survival analyses were used for World Health Organization (WHO) stage III/IV clinical and immunologic event-free survival during monotherapy or treatment interruption, and a Poisson regression to determine factors associated with monotherapy or treatment interruption.
RESULTS: Of 3,448 PHIVA, 84 (2.4%) experienced 94 monotherapy episodes, and 147 (4.3%) experienced 174 treatment interruptions. Monotherapy was associated with older age, HIV RNA >400 copies/mL, younger age at ART initiation, and exposure to ≥2 combination ART regimens. Treatment interruption was associated with CD4 count <350 cells/μL, HIV RNA ≥1,000 copies/mL, ART adverse event, and commencing ART age ≥10 years compared with age <3 years. WHO clinical stage III/IV 1-year event-free survival was 96% and 85% for monotherapy and treatment interruption cohorts, respectively. WHO immunologic stage III/IV 1-year event-free survival was 52% for both cohorts. Those who experienced monotherapy or treatment interruption for more than 6 months had worse immunologic and virologic outcomes.
CONCLUSIONS: Until challenges of treatment adherence, engagement in care, and combination ART durability/tolerability are met, monotherapy and treatment interruption will lead to poor long-term outcomes.
METHODS: Data collected 2001 to 2016 from PHIVA 10-19 years of age within a regional Asian cohort were analyzed using competing risk time-to-event and Poisson regression analyses to describe the nature and incidence of morbidity events and hospitalizations and identify factors associated with disease-related, treatment-related and overall morbidity. Morbidity was defined according to World Health Organization clinical staging criteria and U.S. National Institutes of Health Division of AIDS criteria.
RESULTS: A total 3,448 PHIVA contributed 17,778 person-years. Median age at HIV diagnosis was 5.5 years, and ART initiation was 6.9 years. There were 2,562 morbidity events and 307 hospitalizations. Cumulative incidence for any morbidity was 51.7%, and hospitalization was 10.0%. Early adolescence was dominated by disease-related infectious morbidity, with a trend toward noninfectious and treatment-related morbidity in later adolescence. Higher overall morbidity rates were associated with a CD4 count <350 cells/µL, HIV viral load ≥10,000 copies/mL and experiencing prior morbidity at age <10 years. Lower overall morbidity rates were found for those 15-19 years of age compared with 10-14 years and those who initiated ART at age 5-9 years compared with <5 or ≥10 years.
CONCLUSIONS: Half of our PHIVA cohort experienced a morbidity event, with a trend from disease-related infectious events to treatment-related and noninfectious events as PHIVA age. ART initiation to prevent immune system damage, optimize virologic control and minimize childhood morbidity are key to limiting adolescent morbidity.
METHODS: CLHIV aged <18 years, who were on first-line cART for ≥12 months, and had virological suppression (two consecutive plasma viral load [pVL] <50 copies/mL) were included. Those who started treatment with mono/dual antiretroviral therapy, had a history of treatment interruption >14 days, or received treatment and care at sites with a pVL lower limit of detection >50 copies/mL were excluded. LLV was defined as a pVL 50 to 1000 copies/mL, and VF as a single pVL >1000 copies/mL. Baseline was the time of the second pVL
SETTING: Asian regional cohort incorporating 16 pediatric HIV services across 6 countries.
METHODS: Data from PHIVA (aged 10-19 years) who received combination antiretroviral therapy 2007-2016 were used to analyze LTFU through (1) an International epidemiology Databases to Evaluate AIDS (IeDEA) method that determined LTFU as >90 days late for an estimated next scheduled appointment without returning to care and (2) the absence of patient-level data for >365 days before the last data transfer from clinic sites. Descriptive analyses and competing-risk survival and regression analyses were used to evaluate LTFU epidemiology and associated factors when analyzed using each method.
RESULTS: Of 3509 included PHIVA, 275 (7.8%) met IeDEA and 149 (4.3%) met 365-day absence LTFU criteria. Cumulative incidence of LTFU was 19.9% and 11.8% using IeDEA and 365-day absence criteria, respectively. Risk factors for LTFU across both criteria included the following: age at combination antiretroviral therapy initiation <5 years compared with age ≥5 years, rural clinic settings compared with urban clinic settings, and high viral loads compared with undetectable viral loads. Age 10-14 years compared with age 15-19 years was another risk factor identified using 365-day absence criteria but not IeDEA LTFU criteria.
CONCLUSIONS: Between 12% and 20% of PHIVA were determined LTFU with treatment fatigue and rural treatment settings consistent risk factors. Better tracking of adolescents is required to provide a definitive understanding of LTFU and optimize evidence-based models of care.
METHODS: Data on children with perinatally acquired HIV aged <18 years on first-line, non-nucleoside reverse transcriptase inhibitor-based cART with viral suppression (two consecutive pVL <400 copies/mL over a six-month period) were included from a regional cohort study; those exposed to prior mono- or dual antiretroviral treatment were excluded. Frequency of pVL monitoring was determined at the site-level based on the median rate of pVL measurement: annual 0.75 to 1.5, and semi-annual >1.5 tests/patient/year. Treatment failure was defined as virologic failure (two consecutive pVL >1000 copies/mL), change of antiretroviral drug class, or death. Baseline was the date of the second consecutive pVL <400 copies/mL. Competing risk regression models were used to identify predictors of treatment failure.
RESULTS: During January 2008 to March 2015, there were 1220 eligible children from 10 sites that performed at least annual pVL monitoring, 1042 (85%) and 178 (15%) were from sites performing annual (n = 6) and semi-annual pVL monitoring (n = 4) respectively. Pre-cART, 675 children (55%) had World Health Organization clinical stage 3 or 4, the median nadir CD4 percentage was 9%, and the median pVL was 5.2 log10 copies/mL. At baseline, the median age was 9.2 years, 64% were on nevirapine-based regimens, the median cART duration was 1.6 years, and the median CD4 percentage was 26%. Over the follow-up period, 258 (25%) CLWH with annual and 40 (23%) with semi-annual pVL monitoring developed treatment failure, corresponding to incidence rates of 5.4 (95% CI: 4.8 to 6.1) and 4.3 (95% CI: 3.1 to 5.8) per 100 patient-years of follow-up respectively (p = 0.27). In multivariable analyses, the frequency of pVL monitoring was not associated with treatment failure (adjusted hazard ratio: 1.12; 95% CI: 0.80 to 1.59).
CONCLUSIONS: Annual compared to semi-annual pVL monitoring was not associated with an increased risk of treatment failure in our cohort of virally suppressed children with perinatally acquired HIV on first-line NNRTI-based cART.