We modeled global time trends in median CD4 cell counts at combination antiretroviral therapy initiation in human immunodeficiency virus–infected adults. These counts have increased in all country income groups since 2002 but generally remained below 350/μL in 2015.

Background:Migrant populations are overrepresented among persons diagnosed with HIV in the European Union and the European Economic Area. Understanding the timing of HIV acquisition (premigration or postmigration) is crucial for developing public health interventions and for producing reliable estimates of HIV incidence and the number of people living with undiagnosed HIV infection. We summarize a recently proposed method for determining the timing of HIV acquisition and apply it to both real and simulated data.Methods:The considered method combines estimates from a mixed model, applied to data from a large seroconverters' cohort, with biomarker measurements and individual characteristics to derive probabilities of premigration HIV acquisition within a Bayesian framework. The method is applied to a subset of data from the European Surveillance System (TESSy) and simulated data.Findings:Simulation study results showed good performance with the probabilities of correctly classifying a premigration case or a postmigration case being 87.4% and 80.4%, respectively. Applying the method to TESSy data, we estimated the proportions of migrants who acquired HIV in the destination country were 31.9%, 37.1%, 45.3%, and 45.2% for those originating from Africa, Europe, Asia, and other regions, respectively.Conclusions:Although the considered method was initially developed for cases with multiple biomarkers' measurements, its performance, when applied to data where only one CD4 count per individual is available, remains satisfactory. Application of the method to TESSy data, estimated that a substantial proportion of HIV acquisition among migrants occurs in destination countries, having important implications for public health policy and programs.

A total of 1387 patients were eligible. Median time between seroconversion and enrolment was 1 month (range 0-3). At enrolment, 202 of 1387 (15%) harboured an X4/DM-tropic virus. CD4 decrease slopes were not significantly different according to HIV-1 tropism during the first 30 months after seroconversion. No marked change in these results was found after adjusting for age, year of seroconversion and baseline HIV viral load. Time to antiretroviral treatment initiation was not statistically different between patients harbouring an R5 (20.76 months) and those harbouring an X4/DM-tropic virus (22.86 months, logrank test P = 0.32). Conclusions: In this large cohort collaboration, 15% of the patients harboured an X4/DM virus close to HIV seroconversion. Patients harbouring X4/DM-tropic viruses close to seroconversion did not have an increased risk of disease progression, estimated by the decline in CD4 T cell count or time to combined ART initiation.

Introduction Model‐based estimates of key HIV indicators depend on past epidemic trends that are derived based on assumptions about HIV disease progression and mortality in the absence of antiretroviral treatment (ART). Population‐based HIV Impact Assessment (PHIA) household surveys conducted between 2015 and 2018 found substantial numbers of respondents living with untreated HIV infection. CD4 cell counts measured in these individuals provide novel information to estimate HIV disease progression and mortality rates off ART. Methods We used Bayesian multi‐parameter evidence synthesis to combine data on (1) cross‐sectional CD4 cell counts among untreated adults living with HIV from 10 PHIA surveys, (2) survival after HIV seroconversion in East African seroconverter cohorts, (3) post‐seroconversion CD4 counts and (4) mortality rates by CD4 count predominantly from European, North American and Australian seroconverter cohorts. We used incremental mixture importance sampling to estimate HIV natural history and ART uptake parameters used in the Spectrum software. We validated modelled trends in CD4 count at ART initiation against ART initiator cohorts in sub‐Saharan Africa. Results Median untreated HIV survival decreased with increasing age at seroconversion, from 12.5 years [95% credible interval (CrI): 12.1–12.7] at ages 15–24 to 7.2 years (95% CrI: 7.1–7.7) at ages 45–54. Older age was associated with lower initial CD4 counts, faster CD4 count decline and higher HIV‐related mortality rates. Our estimates suggested a weaker association between ART uptake and HIV‐related mortality rates than previously assumed in Spectrum. Modelled CD4 counts in untreated people living with HIV matched recent household survey data well, though some intercountry variation in frequencies of CD4 counts above 500 cells/mm 3 was not explained. Trends in CD4 counts at ART initiation were comparable to data from ART initiator cohorts. An alternate model that stratified progression and mortality rates by sex did not improve model fit appreciably. Conclusions Synthesis of multiple data sources results in similar overall survival as previous Spectrum parameter assumptions but implies more rapid progression and longer survival in lower CD4 categories. New natural history parameter values improve consistency of model estimates with recent cross‐sectional CD4 data and trends in CD4 counts at ART initiation.

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Objective:The aim of this study was to propose a unified continuum-of-care (CoC) analysis combining cross-sectional and longitudinal elements, incorporating time spent between stages.Design:The established 90-90-90 target follows a cross-sectional four-stage CoC analysis, lacking information on timing of diagnosis, antiretroviral therapy (ART) initiation, and viral suppression durability.Methods:Data were derived from the Athens Multicenter AIDS Cohort Study (AMACS). In the cross-sectional CoC, we added stratification of diagnosed people with HIV (PWH) by estimated time from infection to diagnosis; of those who ever initiated ART or achieved viral suppression by corresponding current status (in 2018); and cumulative incidence function (CIF) of ART initiation and viral suppression, treating loss-to-follow-up (LTFU) as competing event. Viral suppression was defined as viral load less than 500 copies/ml. Viral suppression durability was assessed by the CIF of viral load rebound.Findings:About 89.1% of PWH in 2018 were diagnosed (range of diagnoses: 1980–2018). Median time to diagnosis was 3.5 years (IQR: 1.1–7.0). Among diagnosed, 89.1% were ever treated, of whom 86.7% remained on ART. CIF of ART initiation and LTFU before ART initiation were 80.9 and 6.0% at 5 years since diagnosis, respectively. Among treated, 89.4% achieved viral suppression, of whom 87.4% were currently virally suppressed. The CIF of viral load rebound was 24.2% at 5 years since first viral suppression but substantially reduced in more recent years.Interpretation:The proposed analysis highlights time gaps in CoC not evident by the standard cross-sectional approach. Our analysis highlights the need for early diagnosis and identifies late presenters as a key population for interventions that could decrease gaps in the CoC.

Introduction The continuum of care (CoC) model has been used to describe the main pillars of HIV care. This study aims to systematically review methods and elucidate gaps in the CoC analyses, especially in terms of the timing of the progression through steps, recognized nowadays as a critical parameter for an effective response to the epidemic. Methods A PubMed and EMBASE databases search up to December 2019 resulted in 1918 articles, of which 209 were included in this review; 84 studies presented in major HIV conferences were also included. Studies that did not provide explicit definitions, modelling studies and those reporting only on metrics for subpopulations or factors affecting a CoC stage were excluded. Included articles reported results on 1 to 6 CoC stages. Results Percentage treated and virally suppressed was reported in 78%, percentage diagnosed and retained in care in 58%, percentage linked to care in 54% and PLHIV in 36% of the articles. Information for all stages was provided in 23 studies. Only 6 articles use novel CoC estimates: One presents a dynamic CoC based on multistate analysis techniques, two base their time-to-next-stage estimates on a risk estimation method based on the cumulative incidence function, weighted for confounding and censoring and three studies estimated the HIV infection time based on mathematical modelling. Conclusion A limited number of studies provide elaborated time analyses of the CoC. Although time analyses lack the straightforward interpretation of the cross-sectional CoC, they provide valuable insights for the timely response to the HIV epidemic. A future goal would be to develop a model that retains the simplicity of the cross-sectional CoC but also incorporates timing between stages.