Based on further analysis of the 2004 Kenya Service Provision Assessment Survey
Further analysis of the 1996, 2001, and 2006 Demographic and Health Surveys Data
2,202,059* South Sudanese refugees in the region as of 31 October 2019 (preand post-Dec 2013 caseload).
65,669* South Sudanese refugee’s arrivals so far in 2019, with 4,389 refugee arrivals in October 2019.
297,135 Refugees in South Sudan and 1.46 million IDPs with 12% inside six UNMISS Prot...ection of Civilians sites.
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Reference Manual for Programme Managers on Accreditation Process
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 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 6th edition of the essential medicine list has been developed based on the 5th edition list of essential medicines, the National Standard Treatment Guidelines and Protocols 2013, list of laboratory commodities and List of Consumables used in public health facilities.
GUIDELINES FOR AWWs/ASHAs/ANMs/PRIs
A Training Course for Service Providers
DHS Methodological Report No. 20
This study used Service Provision Assessment (SPA) and Demographic and Health Survey (DHS) data from Haiti, Malawi, and Tanzania to compare traditionally used additive methods with a data reduction method—principal component analysis (PCA).
We scored ...the quality of health facilities with three approaches (simple additive, weighted additive, and PCA) for two constructs: quality of services, with only facilities-level data, and quality of care, which incorporates observation and client data. We ranked facilities as high, medium, or low quality based on their scores. Our results indicated that the rankings change with the scoring methodology. There was more consistency in the rankings of facilities by the simple additive and PCA methods than the weighted additive and PCA-based rankings. This may be due to the low factor loadings and little variance explained by the first component in the PCA. We aggregated facility scores to their respective DHS clusters (Haiti, Malawi) or regions (Tanzania) and geographically linked them to women interviewed in DHS surveys to test associations between the use of family planning services and the quality environment, as measured with each index.
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