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Trends and determinants of neonatal mortality in Nepal
Paudel, D., A. Thapa, P. R. Shedain, and B. Paudel
Nepal Ministry of Health and Population, New ERA, and ICF International
(2013)
C2
Further analysis of the Nepal Demographic and Health Surveys, 2001-2011
Impact of Male Migration on Contraceptive Use, Unmet Need and Fertility in Nepal
Khanal M. N., Shrestha D.R., Panta P.D., and Mehata S.
Nepal Ministry of Health and Population, New ERA, and ICF International
(2013)
C2
Further analysis of the 2011 Nepal Demographic and Health Survey
Maternal and Child Health in Nepal: The Effects of Caste, Ethnicity, and Regional Identity
Pandey, J. P., M.R. Dhakal, S. Karki, P. Poudel, and M.S. Pradhan
Nepal Ministry of Health and Population, New ERA, and ICF International
(2013)
C2
Further analysis of the 2011 Nepal Demographic and Health Survey
DHS Working Paper No. 133
DHS Further Analysis Reports No. 103
DHS Further Analysis Reports No 102
DHS Further Analysis Reports No. 101
Este relatório apresenta informações sobre o conhecimento, atitutes e comportamentos em relação ao HIV/SIDA, a prevalência da malária e de indicadores básicos de saúde da mulher e da criança em Moçambique, tal como recolhida através do IMASIDA 2015 que foi realizado no âmbito do program
...
a de Inquéritos Demográficos e de Saúde.
more
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.
more
This report presents the findings of the Estimating the Size of Populations through a Household Survey (ESPHS) study that took place in 2011. The study utilized a single household survey to estimate the size of several key populations, including sex workers, men who have sex with men (MSM), injectin
...
g drug users (IDU), and clients of sex workers. These populations include several groups outlined in the National Strategic Plan for HIV and AIDS as most at risk for HIV infection, specifically sex workers and MSM.
more
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
...
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.
more
DHS Further Analysis Reports No. 108 - This report examines levels, trends, and inequalities in maternal health in Rwanda from 2010 to 2014-15 among women age 15-49 with a recent birth. The analysis uses Demographic and Health Survey (DHS) data for 15 key indicators of maternal health: 6 for antenat
...
al care, 3 for delivery, 1 for postnatal care, and 5 for barriers to accessing medical care. Levels and trends in these indicators were analyzed overall and by three background characteristics: women’s education, household wealth quintile, and region.
more
DHS Further Analysis Reports No. 107 - This report, based largely on the 2014-15 national survey in Rwanda, focuses on changes and trends in reproductive behavior since 2010. In the 4-5 years after the 2010 survey, fertility continued its decline to 4.2 births per woman as contraceptive prevalence i
...
ncreased slightly. However, the earlier downward trend in number of children desired appears stalled. This is clearly evident from an increase in the proportions of married women and men who say they want more children. Child mortality has significantly declined and remains strongly related to fertility; while age at marriage has continued to increase. The demographic goals specified in the 1998-99 plan for development, Rwanda Vision 2020, appear on track, but the annual rate of population growth remains high, currently 2.5%, because fertility is high. Furthermore, large numbers of young people are now entering their child-bearing years. Although most trends seem encouraging, especially compared with other countries in sub-Saharan Africa, significant population growth is expected in Rwanda, from 12 to 16 million people by 2030, and to 22 million people by mid-century, even with assumed reductions of fertility.
more
Recent Trends in HIV-Related Knowledge and Behaviors in Rwanda, 2005-2010: Further Analysis of the Demographic and Health Surveys.
Hong, Rathavuth, Jean de Dieu, Jeanine Umutesi Condo, Muhayimpundu Ribakare, and Egidie Murekatete
Calverton, Maryland, USA: ICF International
(2013)
C2
DHS Further Analysis Reports No. 89 - The 2010 Rwanda Demographic and Health Survey shows that 3 percent of Rwandan adults age 15-49 have been infected with HIV. The prevalence was much higher in urban areas, among women, and among adults who had multiple lifetime sexual partners and used a condom a
...
t last sexual intercourse. The
level of and differences in HIV prevalence in Rwanda in 2010 are very similar to those observed in 2005. Using data from the two recent Rwanda Demographic and Health Surveys, implemented in 2005 and
2010, this study examined changes in key HIV-related knowledge, attitudes, and sexual behavior indicators. Significant changes in selected indicators during 2005 and 2010 were determined by Student ttest with p-values less than 0.05.
more
Trends in Neonatal Mortality in Rwanda, 2000-2010
Winter, Rebecca, Thomas Pullum, Anne Langston, Ndicunguye V. Mivumbi, Pierre C. Rutayisire, Dieudonne N. Muhoza, and Solange Hakiba
Calverton, Maryland, USA: ICF International.
(2013)
C2
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).
Measuring the Success of Family Planning Initiatives in Rwanda: A Multivariate Decomposition Analysis.
uhoza, Dieudonné Ndaruhuye, Pierre Claver Rutayisire, and Aline Umubyeyi.
Calverton, Maryland, USA: ICF International
(2013)
C2
DHS Working Papers No. 94 - This study described the family planning initiatives in Rwanda and analyzed the 2005 and 2010 RDHS data to identify factors that contribute to the increase in contraceptive use. The Blinder-Oaxaca technique was used to decompose the contributions of women’s characterist
...
ics and their effects.
more
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.
more
Levels and Inequities
DHS Further Analysis Reports No. 110
This study shows large variations in maternal health indicators across high-priority counties in Kenya. Nairobi exceeds the national average on all maternal health indicators in this study, while other highpriority counties consist ... ently are disadvantaged compared with Kenya as a whole in most maternal health indicators. Kisumu exceeds the national average in use of antenatal care, delivery in a health facility, and postnatal care, but not other indicators. Nakuru has fewer women with fertility risk and fewer women who report that the distance they must travel to reach a health facility is a problem.
This study identifies a number of inequities in maternal health indicators across socio-demographic characteristics in the high-priority counties—most in the distribution of delivery care and least in antenatal care. Inequities are also observed in fertility risk and postnatal care. more
DHS Further Analysis Reports No. 110
This study shows large variations in maternal health indicators across high-priority counties in Kenya. Nairobi exceeds the national average on all maternal health indicators in this study, while other highpriority counties consist ... ently are disadvantaged compared with Kenya as a whole in most maternal health indicators. Kisumu exceeds the national average in use of antenatal care, delivery in a health facility, and postnatal care, but not other indicators. Nakuru has fewer women with fertility risk and fewer women who report that the distance they must travel to reach a health facility is a problem.
This study identifies a number of inequities in maternal health indicators across socio-demographic characteristics in the high-priority counties—most in the distribution of delivery care and least in antenatal care. Inequities are also observed in fertility risk and postnatal care. more
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
