PLOS ONE | https://doi.org/10.1371/journal.pone.0185526 September 28, 2017
Global Health and Tropical Medicine, GHTM, Instituto de Higiene e Medicina Tropical, IHMT, Universidade Nova de Lisboa, UNL, Lisboa, Portugal
PLOS ONE | https://doi.org/10.1371/journal.pone.0210937
February 5, 2019
DHS Working Papers No. 85
DHS Working Papers No. 111 | Zimbabwe Working Papers No. 12
DHS Analytical Studies No. 40
Journal of Microbiology and Infectious Diseases / 2015; 5 (3): 110-113
JMID, doi: 10.5799/ahinjs.02.2015.03.0187
Tokar A, et al. Sex Transm Infect 2019;95:193–200. doi:10.1136/sextrans-2018-053684
Clinical Infectious Diseases
1586 - 1594 • CID 2016:62 (15 June) • HIV/AIDS
A Systematic Review and Meta-analysis
Clinical Infectious Diseases® 2016;62(12):1586–94
General fact sheet in booklet form about the 2014-2015 Demographic and Health Survey conducted in Rwanda. The 2014-15 Rwanda Demographic and Health Survey (RDHS) provides data for monitoring the health situation of the population in Rwanda. The 2014-15 RDHS is the 5th Demographic and Health Survey c...onducted in the country. The survey is based on a nationally representative sample. It provides estimates at the national and provincial levels, as well as for urban and rural areas, and for some, at the district level.
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HIV Prevalence: Data from the 2010 Rwanda Demographic and Health Survey.
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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Since 2001, several Demographic and Health Surveys (DHS) include HIV
testing. For many countries, in particular in sub-Saharan Africa, DHS are
the only national source of data in general population. Several DHS collect
latitude and longitude of surveyed clusters but the sampling method is not
ap...propriate to derive local estimates: sample size is not large enough for a
direct spatial interpolation.
We developed a generic approach to map spatial regional trends of HIV
prevalence from DHS. We present how our results from Burkina Faso 2003
DHS shed new light on HIV epidemics.
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