PLoS ONE 9(6): e99880. doi:10.1371/journal.pone.0099880
Published June 17, 2014
Data from the 2000, 2005, and 2011 Ethiopia Demographic and Health Surveys | This report examines trends in key demographic indicators among youth from the findings of the 2000, 2005, and 2011 Ethiopia Demographic and Health Surveys (EDHS).
Data from the 2011 Ethiopia Demographic and Health Survey
Data from the 2011 Ethiopia Demographic and Health Survey
Data from the 2011 Ethiopia Demographic and Health Survey
Further Analysis of the 2000, 2005, and 2011 Demographic Health Surveys. DHS Further Analysis Reports No. 72
Data from the 2000, 2005, and 2011 Demographic and Health Surveys. DHS Trend Reports No. 7
DHS Working Papers No. 119
DHS Working Papers No. 105 - Rwanda has developed and implemented many strategies at the national level to reduce the incidence of HIV in the general population. One of the main objectives of such interventions is to improve the general level of knowledge of HIV, with the hypothesis that increasing... HIV knowledge will reduce risky sexual behavior. However, there has been a concern that HIV knowledge may not necessarily reduce risky sexual behavior. Only a limited number of population-based studies describe the results of these interventions in terms of how HIV knowledge affects risky sexual behavior. Therefore, the aim of this paper is to fill in this gap, by exploring HIV knowledge and its effect on risky sexual behavior among men in Rwanda.
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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 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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The provision of safe and efficacious blood and blood components for transfusion or manufacturing use involves a number of processes, from the selection of blood donors and the collection, processing and testing of blood donations to the testing of patient samples, the issue of compatible blood and ...its administration to the patient. There is a risk of error in each process in this “transfusion chain” and a failure at any of these stages can have serious implications for the recipients of blood and blood products. Thus, while blood transfusion can be life-saving, there are associated risks, particularly the transmission of bloodborne infections.
Screening for transfusion-transmissible infections (TTIs) to exclude blood donations at risk of transmitting infection from donors to recipients is a critical part of the process of ensuring that transfusion is as safe as possible. Effective screening for evidence of the presence of the most common and dangerous TTIs can reduce the risk of transmission to very low levels.
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