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Further Analysis of the 2000, 2005, and 2011 Demographic and Health Surveys. DHS Further Analysis Reports No. 79
DHS Working Papers No. 113
As the culminating volume in the DCP3 series, volume 9 will provide an overview of DCP3 findings and methods, a summary of messages and substantive lessons to be taken from DCP3, and a further discussion of cross-cutting and synthesizing topics across the first eight volumes. The introductory chapte
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rs (1-3) in this volume take as their starting point the elements of the Essential Packages presented in the overview chapters of each volume. First, the chapter on intersectoral policy priorities for health includes fiscal and intersectoral policies and assembles a subset of the population policies and applies strict criteria for a low-income setting in order to propose a "highest-priority" essential package. Second, the chapter on packages of care and delivery platforms for universal health coverage (UHC) includes health sector interventions, primarily clinical and public health services, and uses the same approach to propose a highest priority package of interventions and policies that meet similar criteria, provides cost estimates, and describes a pathway to UHC.
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you can find all 14 Technical Booklets on Inclusive Education, produced by UNICEF, in ENGLISH. Other versions can be found on the website https://www.ded4inclusion.com/inclusive-education-resources-free/unicef-inclusive-education-booklets-and-webinars-english-version. Each Technical Booklet (and com
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panion webinar) introduces a sub-theme of particular interest within Inclusive Education, and each was written by an expert, and peer-reviewed widely
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This report outlines the results of a scientific study of the impacts of weather, climate variability, and climate change on health in Mozambique, with a focus on diarrheal disease and malaria.
DHS Further Analysis Reports No. 109 - This report documents trends in key child nutrition indicators in Rwanda. Data from the Demographic and Health Surveys (DHS) in 2005, 2010, and 2014-15 were analyzed, disaggregated by selected equity-related variables, and tested for trends. Over the survey per
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iod, Rwanda had high rates of exclusive breastfeeding, with regional variation. Rates of continued breastfeeding were also high but generally decreased as mother’s education and household wealth increased in all survey years. Complementary feeding practices varied by region, mother’s education, household wealth, urban-rural residence, and sex of the child.
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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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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. more
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. more
The Essential WASH Actions toolkit expands the connection between WASH and nutrition. This resource offers a comprehensive set of essential WASH actions, references training materials for health workers, nutrition managers and community workers to build capacity, and outlines accompanying behaviors
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needed to support the Essential Nutrition Actions.
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Through public-private partnerships, the government of Rwanda can make more efficient use of public resources by targeting and meeting the needs of specific populations and thus help ensure family planning services and products will be available to all Rwandans in the long term. This report aims to
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inform stakeholders working to strengthen family planning through multisectoral partnerships about Rwanda’s family market.
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The Capacity Project worked to strengthen HRIS in several low-resource countries to assist decision-makers and human resources managers in identifying and responding to critical gaps in HRH. The findings and recommendations in this report cover the Capacity Project’s implementation of HRIS in Swaz
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iland, Rwanda and Uganda.
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This toolkit provides practical guidance to governments, funders, civil society organizations and other implementing partners on conducting a gender analysis and using findings to inform HIV prevention, care and treatment programs with key populations. It outlines considerations and steps for conduc
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ting a gender analysis; explores how to engage with stakeholders, including key population members, in a meaningful partnership; shares lessons learned from a comprehensive gender analysis in Kenya and an abridged gender analysis in Cameroon; and provides tools and resources for conducting a gender analysis with key populations.
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