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1
Publication Years
1165
2052
106
1
1
1
Category
878
317
296
199
116
38
29
3
2
Toolboxes
372
364
275
197
183
175
159
146
133
119
98
48
45
40
34
34
20
13
12
6
3
2
2
2
1
Contraceptive Dynamics Following HIV Testing - DHS Analytical Studies No. 47
MacQuarrie, Kerry L.D., Sarah E.K. Bradley, Alison Gemmill, and Sarah Staveteig
Rockville, Maryland, USA: ICF International
(2014)
CC
Contraceptive Dynamics Following HIV Testing
Child Survival by HIV Status of the Mother: Evidence from DHS and AIS Surveys. DHS Comparative Reports No. 35
Fishel, Joy D., Ruilin Ren, Bernard Barrère, and Trevor N. Croft
Maryland, USA: ICF International
(2014)
C2
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)
C1
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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