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Publication Years
1302
1763
85
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Category
861
314
283
204
157
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30
2
Toolboxes
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263
202
183
170
158
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147
139
134
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48
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26
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JOURNAL OF THE ASSOCIATION OF NURSES IN AIDS CARE, Vol. 28, No. 2, March/April 2017, 186-198
http://dx.doi.org/10.1016/j.jana.2015.09.003
Key Populations Brief
Accessed November 2017
Health care workers
M. Smelyanskaya, J. Duncan (The Focus Group Consulting); C. Daniels, et al.
Stop TB Partnership; UNOPS; END TB
(2016)
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Key populations brief.
Chapter 8, Prison and Health, published
Prisoners
M. Smelyanskaya, J. Duncan (The Focus Group Consulting); C. Daniels, et al.
Stop TB Partnership; UNOPS; END TB
(2016)
C1
Key population brief.
DHS Analytical Studies No. 55.
Background paper for the Oslo Summit on Education for Development
CBM and the Global Campaign for Education 2014
AN ANALYSIS OF UNICEF MICS 3 SURVEY DATA FROM BANGLADESH, LAO PDR, MONGOLIA AND THAILAND
A Manual for Maternity Unit Personnel - Part of the CBM Prevention Toolkit on Birth Impairments
Updated March 2018 | Accessed Online March 2018
Pan African Medical Journal 2017;27:215. doi: 10.11604/pamj.2017.27.215.12994
Int Health. 2012 December 1; 4(4): 253–259. doi:10.1016/j.inhe.2012.07.001
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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At the threshold of Sustainable Development Goals (SDG) era, this document captures the remarkable achievements by Member States towards achieving MDGs 4 and 5. It acknowledges new opportunities in the post-2015 phase shaped by the SDGs and the Global Strategy for women’s, children’s and adoles
...
cents’ health and presents an advanced state of preparedness in the Region. This also highlights the region’s renewed commitment for a more inclusive and more dynamic flagship action for ending preventable maternal, newborn and child mortality as well as to improve women’s, children’s and adolescents’ health and wellbeing in the South-East Asia Region.
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