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Accessed November 2, 2017
Practical Guidance for collaborative interventions
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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Saudi Journal of Biological Sciences. http://dx.doi.org/10.1016/j.sjbs.2016.03.006
Open Access
The SPPCHS project worked with the Ministry of Home Affairs to design a peer education program for prisons to provide knowledge, skills, and tools to prevent and respond to tuberculosis (TB) and HIV in prisons. AIDSFree held two training of trainers
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(TOT) in Dodoma to train 55 prison officers from 26 prison facilities to train prison staff and inmates on the new peer education program.
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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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