Further analysis of the Nepal Demographic and Health Surveys, 2001-2011
DHS Further Analysis Reports No. 103
DHS Analytical Studies No. 55.
DHS Further Analysis Reports No. 97
Lessons learned in Burkina Faso, Ethiopia & Mozambique 2009 - 2011
Open Access Maced J Med Sci. 2017 Mar 15; 5(1):37-41.
This report summarizes the findings of the 2010 Rwanda Demographic and Health Survey (RDHS). The 2010 Rwanda Demographic and Health Survey (RDHS) was designed to provide data for monitoring the population and health situation in Rwanda. The 2010 RDHS is the fifth Demographic and Health Survey to be ...conducted in Rwanda (DHS in 1992, 2000, and 2005 and Interim DHS in 2007-08). The objective of the survey was to provide up-to-date information on fertility, family planning, childhood mortality, nutrition including anemia testing, maternal and child health, domestic violence, malaria including malaria testing, maternal mortality, awareness and behavior regarding HIV/AIDS and other sexually transmitted infections, and HIV prevalence.
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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 ...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.
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Building on the successes and learnings of the 2012 Nutrition Policy, the new Nutrition Policy of the World Food Programme (WFP) reaffirms the organization’s commitment to addressing malnutrition as a primary channel to reach Zero Hunger. The new policy aims to contribute to the elimination of all... forms of malnutrition—including overweight/obesity—and confirms addressing nutrition in emergencies as a central priority
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