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Y. Taneli
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Yale, G.
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Yan I.
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Yaron, Gil
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Yebyo, Henock G., Mussie A. Gebreselassie, and Alemayehu B. Kahsay
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Yimer, G. et al.
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Yoder, P. Stanley, Joe L.P. Lugalla, and Richard F. Sambaiga
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Yolaine Glèlè-Ahanhanzo, Alphonse Kpozèhouen, Noël Moussiliou Paraïso, Patrick Makoutodé, Chabi O. Alphonse Biaou, Eric Remacle, Edgard-Marius Ouendo, Alain Levêque
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Yotsu,R.R.
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Young, H., Marshak, A.
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Yourkavitch, Jennifer
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Youth Lead
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Yuyun, M.F.
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Z. Qiao
1
Z. T. Gavry
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Zachary, Blake, Zhuzhi Moore, and Pav Govindasamy
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Zaidi, Shehla
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Zambia National Public Health Institute
1
Zanzibar AIDS Commission (ZAC)
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Zaw Win
1
Ziaggi, G.
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Zimbabwe National Statistics Agency (Z IMSTAT) and ICF International. 20
1
Zimbabwe National Statistics Agency (ZIMSTAT)
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Zimbabwe National Statistics Agency (ZIMSTAT) & The DHS Program
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Zimbabwe National Statistics Agency (ZIMSTAT) and ICF International
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Zimbabwe National Statistics Agency and ICF International
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Zimbabwe, Ministry of Health and Child Care
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Zinsou, C.
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ВОЗ
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ВОЗ и Международная организация по проблемам болезни Альцгеймера
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Всемирная Организация Здравоохранения
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Всемирную организацию здравоохранения
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Е.А. Катунина, Ю.Н. Бездольный
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Российский государственный медицинский университет им. Н.И. Пирогова Кафедра неврологии и нейрохирургии лечебного факультета д.м.н., доцент Е.А. Катунина, к.м.н. Ю.Н. Бездольный
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Союз охраны психического здоровья, Issa, world council for psychotheraphy et al.
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 miscl
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assification 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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DHS Further Analysis Reports No. 89 - The 2010 Rwanda Demographic and Health Survey shows that 3 percent of Rwandan adults age 15-49 have been infected with HIV. The prevalence was much higher in urban areas, among women, and among adults who had mu
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ltiple lifetime sexual partners and used a condom at last sexual intercourse. The
level of and differences in HIV prevalence in Rwanda in 2010 are very similar to those observed in 2005. Using data from the two recent Rwanda Demographic and Health Surveys, implemented in 2005 and
2010, this study examined changes in key HIV-related knowledge, attitudes, and sexual behavior indicators. Significant changes in selected indicators during 2005 and 2010 were determined by Student ttest with p-values less than 0.05.
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DHS Further Analysis Reports No. 88 - This further analysis examines levels, trends, and determinants of neonatal mortality in Rwanda, using data from the 2000, 2005, and 2010 Rwanda Demographic and Health
...Surveys (RDHS).
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The government of Rwanda conducted the 2010 Rwanda Demographic and Health Survey (RDHS) to gather up-to-date information for monitoring progress on healthcare programs and policies in Rwanda, including the Economic Development and Poverty Reduction
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Strategy (EDPRS), the Millennium Development Goals (MDGs),
and Vision 2020. The 2010 RDHS is a follow-up to the 1992, 2000, 2005, and 2007-08 RDHS surveys. Each survey provides data on background characteristics of the respondents, demographic and health indicators, household health expenditures, and domestic violence. The target groups in these surveys were women age 15-49 and men age 15-59
who were randomly selected from households across the country. Information about children age 5 and under also was collected, including the weight and height of the children.
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The survey highlights changes that have taken place in Bangladesh’s demographic and health situation since the previous BDHS surveys. The survey provides important information for policymakers and
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program personnel in addressing the monitoring and evaluation needs of the 4th Health, Population and Nutrition Sector Program (4th HPNSP) of the Ministry of Health Family Welfare (MOHFW).
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Data from the 2000, 2005, and 2011 Ethiopia Demographic and Health Surveys | This report examines trends in key demographic indicators among youth
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from the findings of the 2000, 2005, and 2011 Ethiopia Demographic and Health Surveys (EDHS).
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This report provides an overview of the Key findings of the Rwanda 2014-2015 Demographic and Health Survey (RDHS). The 2014-15 Rwanda Demographic and Health Survey (RDHS) was designed to provide da
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ta for monitoring the population and health situation in Rwanda. The 2014-15 RDHS is the fifth Demographic and Health Survey
conducted in Rwanda since 1992. The objective of the survey was to provide reliable estimates of fertility levels, marriage, sexual activity, fertility preferences, family planning methods, breastfeeding practices, nutrition, childhood and maternal mortality, maternal and child health, early childhood development, malaria, domestic violence, and HIV/AIDS and other sexually transmitted infections (STIs) that can be used by program managers and policymakers to evaluate and improve existing programs.
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