Assessment in action series
Key Findings from Azerbaijan, Georgia, Kyrgyzstan, Russia, and Ukraine
Writing by Katya Burns
Editing by Paul Silva and Roxanne Saucier
Kassa BMC Infectious Diseases (2018) 18:216 https://doi.org/10.1186/s12879-018-3126-5
Evaluation report
September 2014
Journal of Microbiology and Infectious Diseases / 2015; 5 (3): 110-113
JMID, doi: 10.5799/ahinjs.02.2015.03.0187
A Cost-Efficiency Analysis for the Kyrgyz Republi
The Guide to operationalize HIV viral load testing HIV presents 60 lessons learnt from the project in a systemic approach including: viral load strategy, laboratories, procurement and supply management, patient care and economy.
· Relevant interventions
· HIV country profiles
· Adolescents country profiles
Technical Update
HIV Treatment
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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Provide guidance to HIV care practitioners on the optimal use of antiretroviral (ARV) agents for the treatment of HIV infection in adults and adolescents.
Accessed November 2, 2017
Module 1q
PrEP users
July 2017
Module 11: PrEP users. This module provides information for people who are interested in taking PrEP to reduce their risk of acquiring HIV and people who are already taking PrEP – to support them in their choice and use of PrEP. This module gives ideas for cou...ntries and organizations implementing PrEP to help them develop their own tools.
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October 2018
HIV testing services
Research Article
PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002374 August 8, 2017
Participant Manual
February 2011
Edition 3.0