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Publication Years
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Category
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Toolboxes
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7
3
Measles outbreak guide
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The refugee exodus from South Sudan continues at an alarming rate, even as the crisis is entering its fifth year. Close to 2.4 million South Sudanese have fled to neighbouring countries mostly to Uganda—the largest host country in sub-Saharan Africa—followed by Sudan, Ethiopia, Kenya, the Democr
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UNICEF Strategic Plan 2018-2021. Draft Theory of Change Paper
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gladesh refers to the Rohingya as “Forcibly Displaced Myanmar Nationals”, in the present context. The UN system refers to this population as refugees, in line with the applicable international framework for protection and solutions, and the resulting accountabilities for the country of origin and asylum as well as the international community as a whole. In support of these efforts, the humanitarian community has rapidly scaled up its operations as well. Over a two-month period, the refugee population in Cox’s Bazar more than quadrupled.
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This report presents the findings of the Estimating the Size of Populations through a Household Survey (ESPHS) study that took place in 2011. The study utilized a single household survey to estimate the size of several key populations, including sex workers, men who have sex with men (MSM), injectin
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Recent Trends in HIV-Related Knowledge and Behaviors in Rwanda, 2005-2010: Further Analysis of the Demographic and Health Surveys.
Hong, Rathavuth, Jean de Dieu, Jeanine Umutesi Condo, Muhayimpundu Ribakare, and Egidie Murekatete
Calverton, Maryland, USA: ICF International
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t last sexual intercourse. The
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HIV Knowledge and Risky Sexual Behavior among Men in Rwanda
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Rockville, Maryland, USA: ICF International
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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
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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
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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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