HIV Treatment and Care
Fact Sheet
July 2018
Regional snapshot: Eastern Europe and Central Asia
December 2018
https://data.unicef.org/wp-content/uploads/2018/11/EECA-regional-snapshot-2018.pdf
This document describes the key areas that national governments should consider for the introduction and scale-up of point-of-care (POC) diagnostics within national programmes, as new innovative POC technologies are being introduced into the market. The next steps taken to include these new innovati...ons within the broader context of national diagnostic networks of conventional laboratories could influence the achievement of the 2030 Fast Track targets for ending the AIDS epidemic.
POC diagnostics, when strategically introduced and integrated into national diagnostic networks, may help catalyse changes that improve the way diagnostics and clinical services are delivered. This document distils this understanding based on programmatic and market experiences of introducing POC diagnostics through catalytic investments in POC HIV technologies across numerous countries in sub-Saharan Africa.
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This checklist will help child psychosocial support and Child Friendly Space supervisors adhere to quality standards during program implementation. This checklist was developed by consultant Nicole Bohl, based on her previous work with Plan International and Save the Children. It has been adapted wi...th input from CRS EMECA Core Team members.
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Frontiers in Public Health | www.frontiersin.org 1 June 2017 | Volume 5 | Article 127
Special Report
This report of the European Centre for Disease Prevention and Control (ECDC) was coordinated by Teymur Noori. Report review was provided by Andrew J. Amato-Gauci, Anastasia Pharris, Annabelle Gourlay, Amanda Mocroft, Jan C. Semenza, Denis Coulombier and Piotr Kramarz.
This report investigates the impact of potential misclassification of samples on HIV prevalence estimates for 23 surveys conducted from 2010-2014. In addition to visual inspection of laboratory results, we examined how accounting for potential misclassification of HIV status through Bayesian latent ...class models affected the prevalence estimates. Two types of Bayesian models were specified: a model that only uses the individual dichotomous test results and a continuous model that uses the quantitative information of the EIA (i.e., the signal-to-cutoff values). Overall, we found that adjusted prevalence estimates matched the surveys’ original results, with overlapping uncertainty intervals. This suggested that misclassification of HIV status should not affect the prevalence estimates in most surveys. However, our analyses suggested that two surveys may be problematic. The prevalence could have been overestimated in the Uganda AIDS Indicator Survey 2011 and the Zambia Demographic and Health Survey 2013-14, although the magnitude of overestimation remains difficult to ascertain. Interpreting results from the Uganda survey is difficult because of the lack of internal quality control and potential violation of the multivariate normality assumption of the continuous Bayesian latent class model. In conclusion, despite the limitations of our latent class models, our analyses suggest that prevalence estimates from most of the surveys reviewed are not affected by sample misclassification.
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Research Article
PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002374 August 8, 2017
Supplement Article
J Acquir Immune Defic Syndr Volume 75, Supplement 2, June 1, 2017 www.jaids.com
World Health Organization Department of Reproductive Health and Research
Brocher Foundation, Hermance, Geneva, Switzerland, 27–29 April 2016
Policy Brief
HIV testing services
December 2016
WHO/HIV/2016.21
HIV testing services
Policy Brief
December 2016
Biennial Report. SUBMITTED TO THE UNITED NATIONS GENERAL ASSEMBLY SPECIAL SESSION ON HIV AND AIDS
Reporting period: January 2012 – December 2013
DHS Working Papers No. 119
Policy Brief
November 2014