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Understanding the reasons for and consequences of bodyweight change in people living with HIV initiating antiretroviral therapy (ART) is crucial to optimising long-term health and wellbeing. We aimed to examine bodyweight trends and associated factors among individuals with well estimated dates of HIV-1 seroconversion.

Measures of CD4 T-cell count and HIV-1 plasma viral load before antiretroviral therapy are proxies for virulence. Whether these proxies are changing over time has implications for prevention and treatment. The aim of this study was to investigate those trends.

Supplemental Digital Content is available in the text

Supplemental Digital Content is available in the text

Supplemental Digital Content is available in the text.It is important not only to collect epidemiologic data on HIV but to also fully utilize such information to understand the epidemic over time and to help inform and monitor the impact of policies and interventions. We describe and apply a novel method to estimate the size and characteristics of HIV-positive populations. The method was applied to data on men who have sex with men living in the UK and to a pseudo dataset to assess performance for different data availability. The individual-based simulation model was calibrated using an approximate Bayesian computation-based approach. In 2013, 48,310 (90% plausibility range: 39,900–45,560) men who have sex with men were estimated to be living with HIV in the UK, of whom 10,400 (6,160–17,350) were undiagnosed. There were an estimated 3,210 (1,730–5,350) infections per year on average between 2010 and 2013. Sixty-two percent of the total HIV-positive population are thought to have viral load <500 copies/ml. In the pseudo-epidemic example, HIV estimates have narrower plausibility ranges and are closer to the true number, the greater the data availability to calibrate the model. We demonstrate that our method can be applied to settings with less data, however plausibility ranges for estimates will be wider to reflect greater uncertainty of the data used to fit the model.

Recommendations have differed nationally and internationally with respect to the best time to start antiretroviral therapy (ART). We compared effectiveness of three strategies for initiation of ART in high-income countries for HIV-positive individuals who do not have AIDS: immediate initiation, initiation at a CD4 count less than 500 cells per μL, and initiation at a CD4 count less than 350 cells per μL.

Many studies in various research areas have designs that involve repeated measurements over time of a continuous variable across a group of subjects. A frequent and serious problem in such studies is the occurrence of missing data. In many cases, missing data are caused by an event that leads to a premature termination of the series of repeated measurements on some subjects. When the probability of the occurrence of this event is related to the subject-specific underlying trend of the variable of interest, this missingness process is called informative censoring or informative dropout. Standard likelihood-based methods (for example, linear mixed models) fail to give consistent estimates. In such cases, one needs to apply methods that simultaneously model the observed data and the missingness process. In this article, we review a method proposed by Touloumi et al. (1999, Statistics in Medicine18: 1215–1233) to adjust for informative dropout in longitudinal data analysis. We also present the jmre1 command, which can be used to fit the proposed model. The estimation method combines the restricted iterative generalized least-squares method with a nested expectation-maximization algorithm. The method is implemented mainly using Stata's matrix programming language, Mata. Our example is derived from the epidemiology of the HIV infection.

To investigate the association between Clostridioides difficileinfection (CDI), antibiotic use, and infection control interventions, during an antimicrobial stewardship program (ASP) implemented in a tertiary-care hospital in Greece from 2013 to 2018.

The T cell receptor excision DNA circle (TREC) level is an independent predictor of HIV-1 disease prognosis. We studied the temporal changes in TREC levels prior to and after highly active antiretroviral therapy (HAART) in a cohort of 131 Greek men with hemophilia who were followed up for up to 20 years since seroconversion (SC). TREC levels were determined in all available cryopreserved samples of peripheral blood mononuclear cells (PBMCs) using a multiplex real-time polymerase chain reaction (PCR) assay. Trends in log10 TREC values were described using random effects models. Prior to HAART initiation TREC levels tended to decrease over time (mean rate of drop 19% per year; 95% CI: 16-22%). Initial TREC values were higher with younger age at SC, but the subsequent rate of drop did not differ significantly by age at SC. There was a monotonic relationship between baseline HIV-RNA levels and TREC slopes with steeper slopes at higher levels of HIV-RNA. The TREC slopes differed significantly by clinical outcome being steeper in subjects who progressed to AIDS sooner. After HAART initiation, TREC values tended to increase on average by 35% per year (95% CI: -7-94%) but the increase was evident only in subjects with a pre-HAART CD4 count below 80 cells/μl. TREC values, which likely represent a simple indicator of naive T-lymphocyte reserve, may be a clinically useful marker for long-term prognosis of HIV-1 infection and for immune reconstitution after successful HAART.

ABSTRACTThere are several forms of human immunodeficiency virus type 1 (HIV-1) DNA in peripheral blood T cells and lymph nodes in untreated HIV-1-infected individuals and in patients whose plasma HIV-1 RNA levels are suppressed by long-term combination antiretroviral therapy. However, it remains to be established whether the concentration of HIV-1 DNA in cells predicts the clinical outcome of HIV-1 infection. In this report, we measured the concentration of HIV-1 DNA forms which has undergone the second template switch (STS DNA) and 2-long-terminal-repeat DNA circles in peripheral blood mononuclear cell (PBMC) samples. To do this, we used molecular-beacon-based real-time PCR assays and studied 130 patients with hemophilia in the Multicenter Hemophilia Cohort Study. We assessed the influence of baseline HIV-1 STS DNA levels on the progression of HIV-1 disease in the absence of combination antiretroviral therapy by Kaplan-Meier and Cox regression analysis. Among the patients who progressed to AIDS, the median levels (interquartile ranges) of STS HIV-1 DNA in PBMC were significantly higher than those of patients who remained AIDS free during the 16 years of follow-up (1,017 [235 to 6,059] and 286 [31 to 732] copies per 106PBMC, respectively; P< 0.0001). Rates of progression to death and development of AIDS varied significantly (log rank P< 0.001) by quartile distribution of HIV-1 STS DNA levels. After adjustment for age at seroconversion, baseline CD4+T-cell counts, plasma viral load, and T-cell-receptor excision circles, the relative hazards (RH) of death and AIDS were significantly increased with higher HIV-1 STS DNA levels (adjusted RH, 1.84 [95% confidence interval {CI}, 1.30 to 2.59] and 2.62 [95% CI, 1.75 to 3.93] per 10-fold increase per 106PBMC, respectively). HIV-1 STS DNA levels in each individual remained steady in longitudinal PBMC samples during 16 years of follow-up. Our findings show that the concentration of HIV-1 STS DNA in PBMC complements the HIV-1 RNA load in plasma in predicting the clinical outcome of HIV-1 disease. This parameter may have important implications for understanding the virological response to combination antiretroviral therapy.