Not All Are Lost: Interrupted Laboratory Monitoring, Early Death, and Loss to Follow-Up (LTFU) in a Large South African Treatment Program

Background: Many HIV treatment programs in resource-limited settings are plagued by high rates of loss to follow-up (LTFU). Most studies have not distinguished between those who briefly interrupt, but return to care, and those more chronically lost to follow-up. Methods: We conducted a retrospective cohort study of 11,397 adults initiating antiretroviral therapy (ART) in 71 Southern African Catholic Bishops Conference/Catholic Relief Services HIV treatment clinics between January 2004 and December 2008. We distinguished among patients with early death, within the first 7 months on ART; patients with interruptions in laboratory monitoring (ILM), defined as missing visits in the first 7 months on ART, but returning to care by 12 months; and those LTFU, defined as missing all follow-up visits in the first 12 months on ART. We used multilevel logistic regression models to determine patient and clinic-level characteristics associated with these outcomes. Results: In the first year on ART, 60% of patients remained in care, 30% missed laboratory visits, and 10% suffered early death. Of the 3,194 patients who missed laboratory visits, 40% had ILM, resuming care by 12 months. After 12 months on ART, patients with ILM had a 30% increase in detectable viremia compared to those who remained in care. Risk of LTFU decreased with increasing enrollment year, and was lowest for patients who enrolled in 2008 compared to 2004 [OR 0.49, 95%CI 0.39–0.62]. Conclusions: In a large community-based cohort in South Africa, nearly 30% of patients miss follow-up visits for CD4 monitoring in the first year after starting ART. Of those, 40% have ILM but return to clinic with worse virologic outcomes than those who remain in care. The risk of chronic LTFU decreased with enrollment year. As ART availability increases, interruptions in care may become more common, and should be accounted for in addressing program LTFU

Conducting High-Value Secondary Dataset Analysis: An Introductory Guide and Resources

Secondary analyses of large datasets provide a mechanism for researchers to address high impact questions that would otherwise be prohibitively expensive and time-consuming to study. This paper presents a guide to assist investigators interested in conducting secondary data analysis, including advice on the process of successful secondary data analysis as well as a brief summary of high-value datasets and online resources for researchers, including the SGIM dataset compendium (www.sgim.org/go/datasets). The same basic research principles that apply to primary data analysis apply to secondary data analysis, including the development of a clear and clinically relevant research question, study sample, appropriate measures, and a thoughtful analytic approach. A real-world case description illustrates key steps: (1) define your research topic and question; (2) select a dataset; (3) get to know your dataset; and (4) structure your analysis and presentation of findings in a way that is clinically meaningful. Secondary dataset analysis is a well-established methodology. Secondary analysis is particularly valuable for junior investigators, who have limited time and resources to demonstrate expertise and productivity

WHO 2010 Guidelines for Prevention of Mother-to-Child HIV Transmission in Zimbabwe: Modeling Clinical Outcomes in Infants and Mothers

The Zimbabwean national prevention of mother-to-child HIV transmission (PMTCT) program provided primarily single-dose nevirapine (sdNVP) from 2002-2009 and is currently replacing sdNVP with more effective antiretroviral (ARV) regimens.Published HIV and PMTCT models, with local trial and programmatic data, were used to simulate a cohort of HIV-infected, pregnant/breastfeeding women in Zimbabwe (mean age 24.0 years, mean CD4 451 cells/µL). We compared five PMTCT regimens at a fixed level of PMTCT medication uptake: 1) no antenatal ARVs (comparator); 2) sdNVP; 3) WHO 2010 guidelines using "Option A" (zidovudine during pregnancy/infant NVP during breastfeeding for women without advanced HIV disease; lifelong 3-drug antiretroviral therapy (ART) for women with advanced disease); 4) WHO "Option B" (ART during pregnancy/breastfeeding without advanced disease; lifelong ART with advanced disease); and 5) "Option B+:" lifelong ART for all pregnant/breastfeeding, HIV-infected women. Pediatric (4-6 week and 18-month infection risk, 2-year survival) and maternal (2- and 5-year survival, life expectancy from delivery) outcomes were projected.Eighteen-month pediatric infection risks ranged from 25.8% (no antenatal ARVs) to 10.9% (Options B/B+). Although maternal short-term outcomes (2- and 5-year survival) varied only slightly by regimen, maternal life expectancy was reduced after receipt of sdNVP (13.8 years) or Option B (13.9 years) compared to no antenatal ARVs (14.0 years), Option A (14.0 years), or Option B+ (14.5 years).Replacement of sdNVP with currently recommended regimens for PMTCT (WHO Options A, B, or B+) is necessary to reduce infant HIV infection risk in Zimbabwe. The planned transition to Option A may also improve both pediatric and maternal outcomes

Assessing the Performance of a Computer-Based Policy Model of HIV and AIDS

BACKGROUND. Model-based analyses, conducted within a decision analytic framework, provide a systematic way to combine information about the natural history of disease and effectiveness of clinical management strategies with demographic and epidemiological characteristics of the population. Among the challenges with disease-specific modeling include the need to identify influential assumptions and to assess the face validity and internal consistency of the model. METHODS AND FINDINGS. We describe a series of exercises involved in adapting a computer-based simulation model of HIV disease to the Women's Interagency HIV Study (WIHS) cohort and assess model performance as we re-parameterized the model to address policy questions in the U.S. relevant to HIV-infected women using data from the WIHS. Empiric calibration targets included 24-month survival curves stratified by treatment status and CD4 cell count. The most influential assumptions in untreated women included chronic HIV-associated mortality following an opportunistic infection, and in treated women, the 'clinical effectiveness' of HAART and the ability of HAART to prevent HIV complications independent of virologic suppression. Good-fitting parameter sets required reductions in the clinical effectiveness of 1st and 2nd line HAART and improvements in 3rd and 4th line regimens. Projected rates of treatment regimen switching using the calibrated cohort-specific model closely approximated independent analyses published using data from the WIHS. CONCLUSIONS. The model demonstrated good internal consistency and face validity, and supported cohort heterogeneities that have been reported in the literature. Iterative assessment of model performance can provide information about the relative influence of uncertain assumptions and provide insight into heterogeneities within and between cohorts. Description of calibration exercises can enhance the transparency of disease-specific models.National Institute of Allergy and Infectious Diseases (R37 AI042006, K24 AI062476

Measurement of the inclusive isolated-photon cross section in pp collisions at √s = 13 TeV using 36 fb−1 of ATLAS data

The differential cross section for isolated-photon production in pp collisions is measured at a centre-of-mass energy of 13 TeV with the ATLAS detector at the LHC using an integrated luminosity of 36.1 fb. The differential cross section is presented as a function of the photon transverse energy in different regions of photon pseudorapidity. The differential cross section as a function of the absolute value of the photon pseudorapidity is also presented in different regions of photon transverse energy. Next-to-leading-order QCD calculations from Jetphox and Sherpa as well as next-to-next-to-leading-order QCD calculations from Nnlojet are compared with the measurement, using several parameterisations of the proton parton distribution functions. The predictions provide a good description of the data within the experimental and theoretical uncertainties. [Figure not available: see fulltext.

A communal catalogue reveals Earth's multiscale microbial diversity

Our growing awareness of the microbial world's importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project. Coordinated protocols and new analytical methods, particularly the use of exact sequences instead of clustered operational taxonomic units, enable bacterial and archaeal ribosomal RNA gene sequences to be followed across multiple studies and allow us to explore patterns of diversity at an unprecedented scale. The result is both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth's microbial diversity.Peer reviewe

A communal catalogue reveals Earth’s multiscale microbial diversity

Our growing awareness of the microbial world’s importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project. Coordinated protocols and new analytical methods, particularly the use of exact sequences instead of clustered operational taxonomic units, enable bacterial and archaeal ribosomal RNA gene sequences to be followed across multiple studies and allow us to explore patterns of diversity at an unprecedented scale. The result is both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth’s microbial diversity