Demographic Health Survey Working Paper 2017 No. 130
Further Analysis of the 2014 Cambodia Demographic and Health Survey | DHS Further Analysis Reports No. 105
Further Analysis of the 2000, 2005, and 2011 Demographic and Health Surveys. DHS Further Analysis Reports No. 79
Further analysis of the 2011 Nepal Demographic and Health Survey
Further analysis of the 2011 Nepal Demographic and Health Survey
Further Analysis of the 2000, 2005, 2010, and 2014 Cambodia Demographic and Health Surveys | DHS Further Analysis Reports No. 106
Further Analysis of the 2010 and 2014 Cambodia Demographic and Health Surveys | DHS Further Analysis Reports No. 104
Further Analysis of the 2000, 2005, and 2011 Demographic Health Surveys. DHS Further Analysis Reports No. 72
Further analysis of the Nepal Demographic and Health Surveys, 2001-2011
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 Further Analysis Reports No. 88 - This further analysis examines levels, trends, and determinants of neonatal mortality in Rwanda, using data from the 2000, 2005, and 2010 Rwanda Demographic and Health Surveys (RDHS).
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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Ce rapport présente les principaux résultats de la cinquième phase de l’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.
Le Sénégal est le premier pays en Afrique à réaliser une enquête continue par le biais de The Demographic and Health Surveys Program. L’Enquête Continue collecte des données chaque année pour atteindre deux objectifs :
•Répondre aux besoins permanents en données pour planifier, suivre et évaluer les programmes de santé et de population.
•Renforcer les capacités des institutions du Sénégal dans le domaine de la collecte et de l’utilisation des données.
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This study is a theory-driven analysis of the socio-demographic determinants of maternal care seeking in Kenya. Specifically, it examines predisposing, enabling, and need factors potentially associated with use of antenatal care (ANC), health facility delivery, and timely postnatal care (PNC).This s...tudy uses data from the 2014 Kenya Demographic and Health Survey (KDHS) conducted among women age 15-49 with a live birth in the five years preceding the survey. It includes data from all 47 counties of Kenya, grouped contiguously into 12regions.We apply Andersen’s Behavioral Model of Health Services Use to examine socio-demographic predictors of health service use.We estimate logistic regression models for adequate use of ANC (defined as attending at least four ANC visits, starting in the first three months of pregnancy), delivery in a health facility, and PNC within 48 hours of delivery.
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Encuesta Demográfica y de salud familiar. Resumen ejecutivo
République de Guinée . Rapport final
Global cardiovascular disease (CVD) burden is high and rising, especially in low-income and middle-income countries (LMICs). Focussing on 45 LMICs, we aimed to determine (1) the adult population’s median 10-year predicted CVD risk, including its variation within countries by socio-demographic char...acteristics, and (2) the prevalence of self-reported blood pressure (BP) medication use among those with and without an indication for such medication as per World Health Organization (WHO) guidelines.
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