The primary objectives of the 2017 TMIS are to measure the level of ownership and use of mosquito nets; assess coverage of intermittent preventive treatment for pregnant women; identify treatment practices, including the use of specific antimalarial medications to treat malaria among c...hildren age 6-59 months; measure the prevalence of malaria and anemia among children age 6-59 months; and assess knowledge, attitudes, and practices among adults with malaria.
This table provides estimates of key indicators for the country as a whole and for each of the 31 geographic regions in Tanzania. A comprehensive analysis of the 2017 TMIS data will be presented in a final report.
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General fact sheet in booklet form about the 2014-2015 Demographic and Health Survey conducted in Rwanda. The 2014-15 Rwanda Demographic and Health Survey (RDHS) provides data for monitoring the health situation of the population in Rwanda. The 2014-15 RDHS is the 5th Demographic and Health Survey c...onducted in the country. The survey is based on a nationally representative sample. It provides estimates at the national and provincial levels, as well as for urban and rural areas, and for some, at the district level.
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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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The National Institute of statistics of Rwanda (NISR) in collaboration with the worldwide Demographic and Health Surveys Program implemented the 2014-15 Rwanda Demographic and Health Survey (RDHS) to collect data for monitoring progress on health programs and policies in Rwanda. This publication ill...ustrates the profile of Kigali City
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The National Institute of statistics of Rwanda (NISR) in collaboration with the worldwide Demographic and Health Surveys Program implemented the 2014-15 Rwanda Demographic and Health Survey (RDHS) to collect data for monitoring progress on health programs and policies in Rwanda. This publication ill...ustrates the profile of Eastern Province.
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he National Institute of statistics of Rwanda (NISR) in collaboration with the worldwide Demographic and Health Surveys Program implemented the 2014-15 Rwanda Demographic and Health Survey (RDHS) to collect data for monitoring progress on health programs and policies in Rwanda. This publication illu...strates the profile of Northern Province.
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The National Institute of statistics of Rwanda (NISR) in collaboration with the worldwide Demographic and Health Surveys Program implemented the 2014-15 Rwanda Demographic and Health Survey (RDHS) to collect data for monitoring progress on health programs and policies in Rwanda. This publication ill...ustrates the profile of Southern province
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