Scaling Up Towards International Targets for AIDS, Tuberculosis, and Malaria: Contribution of Global Fund-Supported Programs in 2011–2015

OBJECTIVE: The paper projects the contribution to 2011-2015 international targets of three major pandemics by programs in 140 countries funded by the Global Fund to Fight AIDS, Tuberculosis and Malaria, the largest external financier of tuberculosis and malaria programs and a major external funder of HIV programs in low and middle income countries. DESIGN: Estimates, using past trends, for the period 2011-2015 of the number of persons receiving antiretroviral (ARV) treatment, tuberculosis case detection using the internationally approved DOTS strategy, and insecticide-treated nets (ITNs) to be delivered by programs in low and middle income countries supported by the Global Fund compared to international targets established by UNAIDS, Stop TB Partnership, Roll Back Malaria Partnership and the World Health Organisation. RESULTS: Global Fund-supported programs are projected to provide ARV treatment to 5.5-5.8 million people, providing 30%-31% of the 2015 international target. Investments in tuberculosis and malaria control will enable reaching in 2015 60%-63% of the international target for tuberculosis case detection and 30%-35% of the ITN distribution target in sub-Saharan Africa. CONCLUSION: Global Fund investments will substantially contribute to the achievement by 2015 of international targets for HIV, TB and malaria. However, additional large scale international and domestic financing is needed if these targets are to be reached by 2015

Using health surveillance systems data to assess the impact of AIDS and antiretroviral treatment on adult morbidity and mortality in Botswana

Introduction: Botswana's AIDS response included free antiretroviral treatment (ART) since 2002, achieving 80% coverage of persons with CD450% and >30% through 2011, while continuing to increase in older women. Conclusions: Adult mortality in Botswana fell markedly as ART coverage increased. HIV prevalence declines may reflect ART-associated reductions in sexual transmission. Triangulation of surveillance system data offers a reasonable approach to evaluate impact of HIV/AIDS interventions, complementing cohort approaches that monitor individual-level health outcomes

Lives saved by Global Fund-supported HIV/AIDS, tuberculosis and malaria programs: estimation approach and results between 2003 and end-2007

<p>Abstract</p> <p>Background</p> <p>Since 2003, the Global Fund has supported the scale-up of HIV/AIDS, tuberculosis and malaria control in low- and middle-income countries. This paper presents and discusses a methodology for estimating the lives saved through selected service deliveries reported to the Global Fund.</p> <p>Methods</p> <p>Global Fund-supported programs reported, by end-2007, 1.4 million HIV-infected persons on antiretroviral treatment (ARV), 3.3 million new smear-positive tuberculosis cases detected in DOTS (directly observed TB treatment, short course) programs, and 46 million insecticide-treated mosquito nets (ITNs) delivered. We estimated the corresponding lives saved using adaptations of existing epidemiological estimation models.</p> <p>Results</p> <p>By end-2007, an estimated 681,000 lives (95% uncertainty range 619,000-774,000) were saved and 1,097,000 (993,000-1,249,000) life-years gained by ARV. DOTS treatment would have saved 1.63 million lives (1.09 - 2.17 million) when compared against no treatment, or 408,000 lives (265,000-551,000) when compared against non-DOTS treatment. ITN distributions in countries with stable endemic <it>falciparum </it>malaria were estimated to have achieved protection from malaria for 26 million of child-years at risk cumulatively, resulting in 130,000 (27,000-232,000) under-5 deaths prevented.</p> <p>Conclusions</p> <p>These results illustrate the scale of mortality effects that supported programs may have achieved in recent years, despite margins of uncertainty and covering only selected intervention components. Evidence-based evaluation of disease impact of the programs supported by the Global Fund with international and in-country partners must be strengthened using population-level data on intervention coverage and demographic outcomes, information on quality of services, and trends in disease burdens recorded in national health information systems.</p

Ending AIDS as a public health threat by 2030: Time to reset targets for 2025.

Paul De Lay and co-authors introduce a Collection on the design of targets for ending the AIDS epidemic

A Surprising Prevention Success: Why Did the HIV Epidemic Decline in Zimbabwe?

Daniel Halperin and colleagues examine reasons for the remarkable decline in HIV in Zimbabwe, in the context of severe social, political, and economic disruption

Uganda's HIV Prevention Success: The Role of Sexual Behavior Change and the National Response

There has been considerable interest in understanding what may have led to Uganda's dramatic decline in HIV prevalence, one of the world's earliest and most compelling AIDS prevention successes. Survey and other data suggest that a decline in multi-partner sexual behavior is the behavioral change most likely associated with HIV decline. It appears that behavior change programs, particularly involving extensive promotion of “zero grazing” (faithfulness and partner reduction), largely developed by the Ugandan government and local NGOs including faith-based, women’s, people-living-with-AIDS and other community-based groups, contributed to the early declines in casual/multiple sexual partnerships and HIV incidence and, along with other factors including condom use, to the subsequent sharp decline in HIV prevalence. Yet the debate over “what happened in Uganda” continues, often involving divisive abstinence-versus-condoms rhetoric, which appears more related to the culture wars in the USA than to African social reality

GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19

Data availability: Downloadable summary data are available through the GenOMICC data site (https://genomicc.org/data). Summary statistics are available, but without the 23andMe summary statistics, except for the 10,000 most significant hits, for which full summary statistics are available. The full GWAS summary statistics for the 23andMe discovery dataset will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. For further information and to apply for access to the data, see the 23andMe website (https://research.23andMe.com/dataset-access/). All individual-level genotype and whole-genome sequencing data (for both academic and commercial uses) can be accessed through the UKRI/HDR UK Outbreak Data Analysis Platform (https://odap.ac.uk). A restricted dataset for a subset of GenOMICC participants is also available through the Genomics England data service. Monocyte RNA-seq data are available under the title ‘Monocyte gene expression data’ within the Oxford University Research Archives (https://doi.org/10.5287/ora-ko7q2nq66). Sequencing data will be made freely available to organizations and researchers to conduct research in accordance with the UK Policy Framework for Health and Social Care Research through a data access agreement. Sequencing data have been deposited at the European Genome–Phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001007111.Extended data figures and tables are available online at https://www.nature.com/articles/s41586-023-06034-3#Sec21 .Supplementary information is available online at https://www.nature.com/articles/s41586-023-06034-3#Sec22 .Code availability: Code to calculate the imputation of P values on the basis of SNPs in linkage disequilibrium is available at GitHub (https://github.com/baillielab/GenOMICC_GWAS).Acknowledgements: We thank the members of the Banco Nacional de ADN and the GRA@CE cohort group; and the research participants and employees of 23andMe for making this work possible. A full list of contributors who have provided data that were collated in the HGI project, including previous iterations, is available online (https://www.covid19hg.org/acknowledgements).Change history: 11 July 2023: A Correction to this paper has been published at: https://doi.org/10.1038/s41586-023-06383-z. -- In the version of this article initially published, the name of Ana Margarita Baldión-Elorza, of the SCOURGE Consortium, appeared incorrectly (as Ana María Baldion) and has now been amended in the HTML and PDF versions of the article.Copyright © The Author(s) 2023, Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte–macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).GenOMICC was funded by Sepsis Research (the Fiona Elizabeth Agnew Trust), the Intensive Care Society, a Wellcome Trust Senior Research Fellowship (to J.K.B., 223164/Z/21/Z), the Department of Health and Social Care (DHSC), Illumina, LifeArc, the Medical Research Council, UKRI, a BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070 and BBS/E/D/30002275) and UKRI grants MC_PC_20004, MC_PC_19025, MC_PC_1905 and MRNO2995X/1. A.D.B. acknowledges funding from the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z), the Edinburgh Clinical Academic Track (ECAT) programme. This research is supported in part by the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant MC_PC_20029). Laboratory work was funded by a Wellcome Intermediate Clinical Fellowship to B.F. (201488/Z/16/Z). We acknowledge the staff at NHS Digital, Public Health England and the Intensive Care National Audit and Research Centre who provided clinical data on the participants; and the National Institute for Healthcare Research Clinical Research Network (NIHR CRN) and the Chief Scientist’s Office (Scotland), who facilitate recruitment into research studies in NHS hospitals, and to the global ISARIC and InFACT consortia. GenOMICC genotype controls were obtained using UK Biobank Resource under project 788 funded by Roslin Institute Strategic Programme Grants from the BBSRC (BBS/E/D/10002070 and BBS/E/D/30002275) and Health Data Research UK (HDR-9004 and HDR-9003). UK Biobank data were used in the GSMR analyses presented here under project 66982. The UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government, British Heart Foundation and Diabetes UK. The work of L.K. was supported by an RCUK Innovation Fellowship from the National Productivity Investment Fund (MR/R026408/1). J.Y. is supported by the Westlake Education Foundation. SCOURGE is funded by the Instituto de Salud Carlos III (COV20_00622 to A.C., PI20/00876 to C.F.), European Union (ERDF) ‘A way of making Europe’, Fundación Amancio Ortega, Banco de Santander (to A.C.), Cabildo Insular de Tenerife (CGIEU0000219140 ‘Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19’ to C.F.) and Fundación Canaria Instituto de Investigación Sanitaria de Canarias (PIFIISC20/57 to C.F.). We also acknowledge the contribution of the Centro National de Genotipado (CEGEN) and Centro de Supercomputación de Galicia (CESGA) for funding this project by providing supercomputing infrastructures. A.D.L. is a recipient of fellowships from the National Council for Scientific and Technological Development (CNPq)-Brazil (309173/2019-1 and 201527/2020-0)