SDG target 3.3: by 2030, end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, waterborne diseases and other communicable diseases.
PQDx 0181-031-00
WHO PQ Public Report
March/2017, version 3.0
Published:December 21, 2018
DOI:https://doi.org/10.1016/S2352-3018(18)30289-3
July 2018
This fourth edition of the Unitaid/WHO market and technology landscape: HIV rapid diagnostic tests for self-testing report summarizes the current HIV testing gap; the challenges facing efforts to scale up; and the potential role HIV self-testing (HIVST) could play to achieve the United... Nation’s 90-90-90 targets. In particular, the report synthesises the existing and emerging market demand and supply of kits.
The information in this report is intended for manufacturers, donors, national programmes, researchers and other global health stakeholders who are exploring the potential role of HIVST.
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Original Research
African Journal of Primary Health Care & Family Medicine
ISSN: (Online) 2071-2936, (Print) 2071-2928
Open Access
The European Journal of Public Health, Vol. 28, No. 1, 145–149
The Author 2017. Published by Oxford University Press on behalf of the European Public Health Association.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:...//creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
doi:10.1093/eurpub/ckx122 Advance Access published on 31 August 2017
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UNAIDS 2017 / Reference
Generating evidence for policy and action on HIV and social protection
WHO Progress Brief
Progress Brief
July 2017
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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Accessed November 2, 2017
Research Article
Hindawi
BioMed Research International
Volume 2018, Article ID 9619684, 10 pages https://doi.org/10.1155/2018/9619684