DEMOGRAPHIC AND HEALTH SURVEYS DHS WORKING PAPERS 2015 No. 117
Data from the 2011 Ethiopia Demographic and Health Survey
Data from the 2011 Ethiopia Demographic and Health Survey
Further Analysis of the 2000, 2005, and 2011 Demographic and Health Surveys. DHS Further Analysis Reports No. 83
Further Analysis of the 2000, 2005, and 2011 Demographic and Health Surveys. DHS Further Analysis Reports No. 81
FAST FACTS FROM THE 2014 CAMBODIA DEMOGRAPHIC AND HEALTH SURVEY
Data from the 2011 Ethiopia Demographic and Health Survey
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 and Health Surveys. DHS Further Analysis Reports No. 80
Further analysis of the 2011 Nepal Demographic and Health Survey
2015-16 Demographic and Health Survey and Malaria Indicator Survey
The Fifth Integrated Household Living Survey (EICV5) was conducted from October 2016 to October 2017, and is designed to provide accurate and up-to-date information that are useful to government, analysts and the public as they seek to monitor and evaluate efforts to reduce poverty.
This report pre...sents and discusses key results from the EICV5 in the areas of demographic characteristics, migration, health, education, the characteristics of households and dwellings in Rwanda, economic activity patterns, environmental issues and households' access to credits and savings.
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2006-07 Swaziland Demographic and Health Survey
FAST FACTS FROM THE 2013-14 ZAMBIA DEMOGRAPHIC AND HEALTH SURVEY
Info-graphic on Fast Facts from the 2014-15 Rwanda Demographic and Health Survey.
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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Accessed Online June 2018 | Single-page summary highlighting trends in modern contraceptive prevalence in Rwanda using data from the Demographic & Health Surveys.
Massoda Tonye et al. Malar J (2018) 17:156
https://doi.org/10.1186/s12936-018-2284-7
Background: In 2011, the demographic and health survey (DHS) in Cameroon was combined with the multiple indicator
cluster survey. Malaria parasitological data were collected, but the survey period did not overl...ap with the high
malaria transmission season. A malaria indicator survey (MIS) was also conducted during the same year, within the
malaria peak transmission season. This study compares estimates of the geographical distribution of malaria parasite
risk and of the effects of interventions obtained from the DHS and MIS survey data.
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We combine data on Chinese development projects with data from Demographic and Health Surveys to study the impact of Chinese aid on household welfare in sub-Saharan Africa. We use a novel methodology to test the effect of Chinese aid on three important development outcomes: education, health, and nu...trition. For each outcome, we use difference-in-difference estimations to compare household areas near Chinese project sites to control areas located farther away, before and after receiving Chinese aid. This empirical strategy rules out many confounding factors that can bias measuring the impact of Chinese aid on our outcome variables. First, we find that Chinese projects significantly improve education and child mortality in treatment areas, but do not significantly affect nutrition. Second, social sector projects have a larger effect on outcomes than economic projects. Third, we do not find significant effects for projects that ended more than five years before the post-treatment survey wave. Our results are robust to a host of robustness checks.
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According to the 2016 Nepal Demographic and Health Survey, 66% of Nepali households use mainly solid fuel for cooking on inefficient stoves. Incomplete fuel combustion of solid fuels emits greenhouse gases and harmful smoke, contributing to climate change, forest degradation, ill health and preventa...ble deaths. Further, the physical burden and time necessary to collect wood for fuel is borne primarily by women and children, thus compromising their productive time, such as social activities and education.
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