The 2013 RMIS is a nationally representative, household-based survey that provides data on malaria indicators, which are used to assess the progress of a malaria control program. The primary objective of the 2013 Rwanda Malaria Indicator Survey (2013 RMIS) was to provide up-to date information on th...e prevention of malaria to policymakers, planners, and researchers.
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Census Report Volume 4-A
This thematic report presents findings on fertility and nuptiality in Myanmar. The analysis hows that the total fertility rate is 2.5 children per woman at the Union level, 1.9 children per woman for urban areas, and 2.8 children per woman for rural areas. Total fertili...ty for States and Regions varies from a high of 5.0 children per woman for Chin State to a low of 1.8 children per woman for Yangon Region. Total fertility appears to have declined at a rate of at least one child per woman per decade between 1970 and 2000. This relatively rapid decline apparently ceased sometime during the 1990s or 2000s. Estimates from the 2001 and 2007 surveys suggest that the level of fertility may have fluctuated between 2000 and 2014, but with no overall trend up or down. The marital status data shows an exceptionally high proportion of women remaining never married at age 50.
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Safe disposal of children’s feces is as essential as the safe disposal of adults’ feces. Th is brief provides an overview of the available data on child feces disposal in Burkina Faso and concludes with ideas to strengthen safe disposal practices, based on emerging good practice. Th e Joint Mon...itoring Programme for Water Supply and Sanitation (JMP) tracks progress toward the Millennium Development Goal 7 target to halve, by 2015, the proportion of people without sustainable access to safe drinking water and basic sanitation. Th e JMP standardized defi nition for an improved sanitation facility is one that hygienically separates human excreta from human contact.
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Further Analysis of the 2000, 2005, and 2011 Demographic and Health Surveys. DHS Further Analysis Reports No. 80
Further Analysis of the 2014 Cambodia Demographic and Health Survey | DHS Further Analysis Reports No. 105
2015-16 Demographic and Health Survey and Malaria Indicator Survey
Further Analysis of the 2000, 2005, 2010, and 2014 Cambodia Demographic and Health Surveys | DHS Further Analysis Reports No. 106
Further Analysis of the 2010 and 2014 Cambodia Demographic and Health Surveys | DHS Further Analysis Reports No. 104
Further analysis of the 2011 Nepal Demographic and Health Survey
Further analysis of the 2011 Nepal Demographic and Health Survey
Further analysis of the Nepal Demographic and Health Surveys, 2001-2011
Further analysis of the 2011 Nepal Demographic and Health Survey
Further Analysis of the 2000, 2005, and 2011 Demographic and Health Surveys. DHS Further Analysis Reports No. 81
Further Analysis of the 2000, 2005, and 2011 Demographic and Health Surveys. DHS Further Analysis Reports No. 79
Further Analysis of the 2011 Ethiopia Demographic and Health
Survey. DHS Further Analysis Reports No. 82
Further Analysis of the 2000, 2005, and 2011 Demographic Health Surveys. DHS Further Analysis Reports No. 72
Demographic Health Survey Working Paper 2017 No. 130
DEMOGRAPHIC RESEARCH, VOLUME 36, ARTICLE 37, PAGES 1081-1108; PUBLISHED 5 APRIL 2017; http://www.demographic-research.org/Volumes/Vol36/37/; DOI: 10.4054/DemRes.2017.36.37
Joint data assessment by the Central Statistical Organization and UNDP
The report shows that the National Statistical System of Myanmar has some work ahead of it in terms of preparing for the monitoring of the SDG indicators. Only 44 of the SDG indicators are currently produced and readily avai...lable at the national level. However, the good news is that many (97) of the missing indicators can be computed from existing data sources – often with little effort - and don’t require any additional data collection. The report concludes that Myanmar is in a decent position to start monitoring the SDGs, and should start as soon as possible in putting its existing data to full use for the SDGs.
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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.
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