Cet atlas présente des résultats régionaux de la cinquième phase de l’Enquête Continue (Enquête Continue 2017) qui a été exécutée d’avril à décembre 2017 par l’Agence Nationale de la Statistique et de la Démographie (ANSD), le Ministère de la Santé et de l’Action Sociale (MSAS...) et la Cellule de Lutte contre la Malnutrition. L’Enquête Continue 2017 a été réalisée avec l’appui financier du Gouvernement du Sénégal, de l’Agence des États-Unis pour le Développement International (USAID), de l’UNICEF (United Nations Children Fund), de l’UNFPA (United Nations Population Fund) de Nutrition International, et de la Banque Mondiale. Elle a bénéficié de l’assistance technique de The Demographic and Health Surveys Program (DHS Program) de ICF dont l’objectif est de collecter, d’analyser et de diffuser des données démographiques et de santé.
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The survey highlights changes that have taken place in Bangladesh’s demographic and health situation since the previous BDHS surveys. The survey provides important information for policymakers and program personnel in addressing the monitoring and evaluation needs of the 4th Health, Population and... Nutrition Sector Program (4th HPNSP) of the Ministry of Health Family Welfare (MOHFW).
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The 2019 SLDHS is a national sample survey that provides up-to-date information on demographic and health indicators. The sample was selected using a stratified, two-stage cluster design, with enumeration areas (EAs) as the sampling units for the first stage. The second stage was a complete listing ...of households carried out in each of the 578 selected EAs. The target groups were women age 15-49 and men age 15-59 in
randomly selected households across the country. A representative sample of approximately 13,872 households was selected for the survey. Half of the households (6,936) were selected for biomarker and men’s interview. The men’s survey was conducted in half (50%) of the sample households, and all men age 15-59 in these households were included. In this subsample, one eligible woman in each household was randomly selected to be asked additional questions about domestic violence.
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The LDHS provides an opportunity to inform policy and provide data for planning, implementation, and monitoring and evaluation of national health programs. It is designed to provide up-to-date information on health indicators including fertility levels, sexual activity, fertility preferences, awaren...ess and use of family
planning methods, breastfeeding practices, nutritional status of children, early childhood and maternal mortality, maternal and child health, and awareness and behaviors regarding HIV/AIDS and other sexually transmitted infections. The study also incorporated measurements of HIV, hepatitis B, and hepatitis Cprevalence along with seroprevalence of Ebola virus disease antibodies, the results of which will be included in future addendums. In addition to presenting national estimates, the report provides estimates of key indicators for both rural and urban areas, the country’s 15 counties, and the capital, Monrovia.
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La publication des résultats de l’EDSC-V intervient quelques années seulement après l’échéance en 2015 des Objectifs du millénaire pour le développement (OMD), le lancement de l’Agenda 2065 de l’Union Africaine, de l’Agenda 2030 des Nations Unies pour le Développement Durable, et a...u moment de la finalisation de la
Stratégie nationale de développement de la deuxième génération (2020-2027) dans le cadre de la Vison 2035.Nul doute qu’ils serviront à établir la situation finale ou la situation de référence pour le suivi évaluation des progrès accomplis dans le cadre de ces agendas nationaux et internationaux.
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Nigeria Malaria Indicator Survey (NMIS) - Key Indicators 2021
The 2013 RMIS is a nationally representative, household-based survey that provides data on malaria indicators, which are used to assess the progress of a malaria control program. The primary objective of the 2013 Rwanda Malaria Indicator Survey (2013 RMIS) was to provide up-to date information on th...e prevention of malaria to policymakers, planners, and researchers.
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This report provides an overview of the Key findings of the Rwanda 2014-2015 Demographic and Health Survey (RDHS). The 2014-15 Rwanda Demographic and Health Survey (RDHS) was designed to provide data for monitoring the population and health situation in Rwanda. The 2014-15 RDHS is the fifth Demogra...phic and Health Survey
conducted in Rwanda since 1992. The objective of the survey was to provide reliable estimates of fertility levels, marriage, sexual activity, fertility preferences, family planning methods, breastfeeding practices, nutrition, childhood and maternal mortality, maternal and child health, early childhood development, malaria, domestic violence, and HIV/AIDS and other sexually transmitted infections (STIs) that can be used by program managers and policymakers to evaluate and improve existing programs.
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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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