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The Global Reference List of 100 Core Health Indicators is a standard set of core indicators prioritized by the global community to provide concise information on the health situation and trends, including responses at national and global levels.
This second (2018) edition builds on the previous
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work of the inter-agency working group that was commissioned by global health leaders to reduce reporting burden. The 2018 list of indicators contains modifications and additions to indicators and metadata elements to reflect the recommended health and health-related indicators of the Sustainable Development Goals, including universal health coverage.
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Pour répondre au besoin urgent de lits requis pour traiter les patients atteints d'Ebola, plusieurs établissements ont été reconfigurés pour prendre en charge, isoler et traiter les patients. Nombre d’entre eux ont été construits au sein d’hôpitaux, d’écoles ou de bâtiments existants
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utilisés pour d’ autres activités avant l’épidémie.
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WHO-SEARO in partnership with WHOCC AIIMS, UNICEF, UNFPA and USAID has prepared a training package for building capacity of healthcare teams in health facilities for continous quality improvement of maternal and newborn healthcare. The focus is on the care of mothers and newborns at the time of chil
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d birth since a large proportion of maternal deaths, newborn deaths and stillbirths happen around that time.
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The main objective of the 2014-15 RDHS was to obtain current information on demographic and health indicators, including family planning; maternal mortality; infant and child mortality; nutrition status of mothers and children; prenatal care, delivery, and postnatal care; childhood diseases; and ped
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iatric immunization. In addition, the survey was designed to measure indicators such as domestic violence, the prevalence of anemia and malaria among women and children, and the prevalence of HIV infection in Rwanda. For the first time, this 2014-15 RDHS also includes indicators to monitor HIV testing among children age 0-14 as well as domestic violence for males age 15-59.
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Trends in Neonatal Mortality in Rwanda, 2000-2010
Winter, Rebecca, Thomas Pullum, Anne Langston, Ndicunguye V. Mivumbi, Pierre C. Rutayisire, Dieudonne N. Muhoza, and Solange Hakiba
Calverton, Maryland, USA: ICF International.
(2013)
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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).
World Health Organisation Report on the global Tobacco Epidemic Rwanda Country profile (2017)
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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The National Institute of statistics of Rwanda (NISR) in collaboration with the worldwide Demographic and Health Surveys Program implemented the 2014-15 Rwanda Demographic and Health Survey (RDHS) to collect data for monitoring progress on health programs and policies in Rwanda. This publication ill
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ustrates the profile of Kigali City
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The National Institute of statistics of Rwanda (NISR) in collaboration with the worldwide Demographic and Health Surveys Program implemented the 2014-15 Rwanda Demographic and Health Survey (RDHS) to collect data for monitoring progress on health programs and policies in Rwanda. This publication ill
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ustrates the profile of Eastern Province.
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he National Institute of statistics of Rwanda (NISR) in collaboration with the worldwide Demographic and Health Surveys Program implemented the 2014-15 Rwanda Demographic and Health Survey (RDHS) to collect data for monitoring progress on health programs and policies in Rwanda. This publication illu
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strates the profile of Northern Province.
more
The National Institute of statistics of Rwanda (NISR) in collaboration with the worldwide Demographic and Health Surveys Program implemented the 2014-15 Rwanda Demographic and Health Survey (RDHS) to collect data for monitoring progress on health programs and policies in Rwanda. This publication ill
...
ustrates the profile of Southern province
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This report provides an update on the level of poverty based on 2013/14 Integrated Household Living Conditions Survey (EICV4) focusing on poverty as measured in consumption terms. The report also highlights other trend dimensions of living conditions captured in other surveys that complement and pro
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vide a holistic understanding of poverty and living conditions.
Rwanda’s economy has been growing steadily at about 8% since 2001 with GDP per capita more than tripling from US$ 211 in 2001 to US$ 718 in 2014. Food crop production growth was more than twice that of population growth between 2007 and 2014.
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Levels and Inequities
DHS Further Analysis Reports No. 110
This study shows large variations in maternal health indicators across high-priority counties in Kenya. Nairobi exceeds the national average on all maternal health indicators in this study, while other highpriority counties consist ... ently are disadvantaged compared with Kenya as a whole in most maternal health indicators. Kisumu exceeds the national average in use of antenatal care, delivery in a health facility, and postnatal care, but not other indicators. Nakuru has fewer women with fertility risk and fewer women who report that the distance they must travel to reach a health facility is a problem.
This study identifies a number of inequities in maternal health indicators across socio-demographic characteristics in the high-priority counties—most in the distribution of delivery care and least in antenatal care. Inequities are also observed in fertility risk and postnatal care. more
DHS Further Analysis Reports No. 110
This study shows large variations in maternal health indicators across high-priority counties in Kenya. Nairobi exceeds the national average on all maternal health indicators in this study, while other highpriority counties consist ... ently are disadvantaged compared with Kenya as a whole in most maternal health indicators. Kisumu exceeds the national average in use of antenatal care, delivery in a health facility, and postnatal care, but not other indicators. Nakuru has fewer women with fertility risk and fewer women who report that the distance they must travel to reach a health facility is a problem.
This study identifies a number of inequities in maternal health indicators across socio-demographic characteristics in the high-priority counties—most in the distribution of delivery care and least in antenatal care. Inequities are also observed in fertility risk and postnatal care. more
