Further Analysis of the 2014 Cambodia Demographic and Health Survey | DHS Further Analysis Reports No. 105
Further Analysis of the 2010 and 2014 Cambodia Demographic and Health Surveys | DHS Further Analysis Reports No. 104
Further Analysis of the 2000, 2005, and 2011 Demographic Health Surveys. DHS Further Analysis Reports No. 72
DHS Analytical Studies No. 60
DHS Working Papers No. 82
DHS Analytical Studies No. 41
DHS Further Analysis Reports No. 103
DHS Analytical Studies No. 55.
DHS Further Analysis Reports No. 97
The Global Campaign Against Epilepsy “Out of the Shadows”
"This document has been developed for outpatient oncology
facilities to serve as a model for a basic infection
control and prevention plan. It contains policies
and procedures tailored to these settings to meet minimal
expectations of patient protections as described
in the CDC Guide to Infecti...on Prevention in Outpatient
Settings."
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World Health Organisation Report on the global Tobacco Epidemic Rwanda Country profile (2017)
DHS Further Analysis Reports No. 111
This study is a theory-driven analysis of the socio-demographic determinants of maternal care seeking in Kenya. Specifically, it examines predisposing, enabling, and need factors potentially associated with use of antenatal care (ANC), health facility delive...ry, and timely postnatal care (PNC).
This study uses data from the 2014 Kenya Demographic and Health Survey (KDHS) conducted among women age 15-49 with a live birth in the five years preceding the survey. It includes data from all 47 counties of Kenya, grouped contiguously into 12 regions. We apply Andersen’s Behavioral Model of Health Services Use to examine socio-demographic predictors of health service use. We estimate logistic regression models for adequate use of ANC (defined as attending at least four ANC visits, starting in the first three months of pregnancy), delivery in a health facility, and PNC within 48 hours of delivery.
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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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