Directions in Development
Human Development
Practical Guidance for collaborative interventions
Saudi Journal of Biological Sciences. http://dx.doi.org/10.1016/j.sjbs.2016.03.006
Open Access
The five thematic discussion papers in this collection were prepared by members of the Global Prevention Coalition Steering Group and other experts from various institutions and countries. Contributors are listed in alphabetical order. The five papers are meant to inform country consultations and th...e development of a Global HIV Prevention Roadmap. They do not reflect the views of UNAIDS or any other agency or organization.
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The intended purpose of this compendium is to provide program managers, organizations, and policy makers with a menu of indicators to better “know their HIV epidemic/know their response” from a gender perspective. The indicators in the compendiu...m are all either part of existing indicators used in studies or by countries or have been adapted from existing indicators to address the intersection of gender and HIV. The indicators can be measured through existing data collection and information systems (e.g. routine program monitoring, surveys) in most country contexts, though some may require special studies or research.
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Training leaders in public health
IMDP 2016 Training Catalogue
Guidance | Preparedness - Response and early recovery - Recovery and reconstruction
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 miscl...assification 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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During the implementation of the National Strategic Plan 2009–2012 on HIV and AIDS, Rwanda has continued its progress towards universal access to HIV and AIDS services. The new ...ute-to-highlight medbox">HIV and AIDS National Strategic Plan July 2013–June 2018 (thereafter referred to as ‘the NSP’) presented here is set on pursuing the same objective, with inspiration from the global targets of “zero new HIV infections, zero HIV-related deaths and zero stigma and discrimination due to HIV”.
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