FunCoup network information for gene RDHS in Homo sapiens. DR9C7_HUMAN Short-chain dehydrogenase/reductase family 9C member 7

Individuals included in the analyses of child and maternal care with RDHS.

Rwanda Demographic Health Surveys, part of the USAID Demographic Health Surveys Program, 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 STIs. The information collected is intended to assist policymakers and program managers in evaluating and designing programs and strategies for improving the health of the country’s population. The Demographic and Health Survey has been conducted in Rwanda for the following years: 1992, 2000, 2005, 2010, 2014-15, and 2019-20.

Abbreviations: N: sample size; SD: standard deviation; Unmatched data: full set of data; Matched data: subset of data which excluded outliers in observed variables.

The 2010 Rwanda Demographic and Health Survey (RDHS) is 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. The objective of the survey is to provide up-to-date information on fertility, family planning, childhood mortality, nutrition, maternal and child health, domestic violence, malaria, maternal mortality, awareness and behavior regarding HIV/AIDS, HIV prevalence, malaria prevalence, and anemia prevalence. A nationally representative sample of 13,671 women, age 15–49 from 12,540 surveyed households, and 6,329 men, age 15–59 from half of these households, were interviewed. This represents a response rate of 99 percent for women and 99 percent for men. The sample provides estimates at the national and provincial levels. The main objectives of the 2010 RDHS were to: Collect data at the national level to facilitate calculation of essential demographic rates, especially rates for fertility and infant and child mortality, and to analyze the direct and indirect factors that determine levels and trends in fertility and child mortality Measure the levels of knowledge of contraceptive practices among women Collect data on family health, including immunization practices; prevalence and treatment of diarrhea, acute upper respiratory infections, fever and/or convulsions among children under age 5; antenatal visits; and assistance at delivery Collect data on the prevention and treatment of malaria, in particular the possession and use of bed nets among children under 5 and among women and pregnant women Collect data on nutritional practices of children, including breastfeeding Collect data on the knowledge and attitudes of men and women concerning sexually transmitted infections (STIs) and acquired immune deficiency syndrome (AIDS) and evaluate recent behavioral changes with regard to condom use Collect data for the estimation of adult mortality and maternal mortality at the national level Take anthropometric measurements in half of surveyed households in order to evaluate the nutritional status of children, men, and women Conduct confidential testing for malaria parasitemia using Rapid Diagnostic Testing in half of the surveyed households and anonymous blood smear testing at the National Reference Laboratory Collect dried blood spots (from finger pricks) for anonymous HIV testing at the National Reference Laboratory in half of surveyed households Measure hemoglobin level (by finger prick) for anemia of surveyed respondents in half of surveyed households.

Abstract

The 2019-20 Rwanda Demographic and Health Survey (2019-20 RDHS) follows those implemented in 1992, 2000, 2005, 2010, and 2014-15. A nationally representative sample of 500 clusters and 13,000 households were selected. All women age 15-49 who were usual residents of the selected households or who slept in the households the night before the survey were eligible for the survey.

The primary objective of the 2019-20 RDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the 2019-20 RDHS: • collected data on fertility levels and preferences; contraceptive use; maternal and child health; infant, child, and neonatal mortality levels; maternal mortality; gender; nutrition; awareness about HIV/AIDS; self-reported sexually transmitted infections (STIs); and other health issues relevant to the achievement of the Sustainable Development Goals (SDGs) • obtained information on the availability of, access to, and use of mosquito nets as part of the National Malaria Control Program • gathered information on other health issues such as injections, tobacco use, and health insurance • collected data on women’s empowerment and domestic violence • tested household salt for iodine levels • obtained data on child feeding practices, including breastfeeding, and conducted anthropometric measurements to assess the nutritional status of children under age 5 and women age 15-49 • conducted anemia testing of women age 15-49 and children age 6-59 months • conducted malaria testing of women age 15-49 and children age 6-59 months • conducted HIV testing of women age 15-49 and men age 15-59 • conducted micronutrient testing of women age 15-49 and children age 6-59 months

The information collected through the 2019-20 RDHS is intended to assist policymakers and program managers in evaluating and designing programs and strategies for improving the health of the country’s population.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15 to 59

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, all men age 15-59, and all children aged 0-5 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2019-20 RDHS is the fourth Rwanda Population and Housing Census (RPHC), which was conducted in 2012 by the National Institute of Statistics of Rwanda (NISR). The sampling frame is a complete list of enumeration areas (EAs) covering the whole country provided by the National Institute of Statistics, the implementing agency for the RDHS. An EA is a natural village or part of a village created for the 2012 RPHC; these areas served as the counting units for the census.

The 2019-20 RDHS followed a two-stage sample design and was intended to allow estimates of key indicators at the national level as well as for urban and rural areas, five provinces, and each of Rwanda’s 30 districts for some limited indicators. The first stage involved selecting sample points (clusters) consisting of EAs delineated for the 2012 RPHC. A total of 500 clusters were selected, 112 in urban areas and 388 in rural areas.

The second stage involved systematic sampling of households. A household listing operation was undertaken in all selected EAs from June to August 2019, and households to be included in the survey were randomly selected from these lists. Twenty-six households were selected from each sample point, for a total sample size of 13,000 households. Because of the approximately equal sample sizes in each district, the sample is not self-weighting at the national level, and weighting factors have been added to the data file so that the results will be proportional at the national level.

For further details on sample selection, see Appendix A of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Five questionnaires were used for the 2019-20 RDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, the Biomarker Questionnaires, and the Fieldworker Questionnaire. These questionnaires, based on The DHS Program’s standard Demographic and Health Survey (DHS-7) questionnaires, were adapted to reflect the population and health issues relevant to Rwanda.

Cleaning operations

The processing of the 2019-20 RDHS data began almost as soon as the fieldwork started. As data collection was completed in each cluster, all electronic data files were transferred via the Internet File Streaming System (IFSS) to the NISR central office in City of Kigali. These data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted to any inconsistencies and errors. Secondary editing, carried out in the central office, involved resolving inconsistencies and coding the open-ended questions. The NISR data processor coordinated the exercise at the central office. The biomarker paper questionnaires were compared with electronic data files to check for any inconsistencies in data entry. Data entry and editing were carried out using the CSPro software package. The concurrent processing of the data offered a distinct advantage because it maximized the likelihood of the data being error-free and accurate. Timely generation of field check tables allowed for effective monitoring. The secondary editing of the data was completed in the second week of September 2020.

Response rate

A total of 13,005 households were selected for the sample, of which 12,951 were occupied. All but two occupied households (12,949) were successfully interviewed, yielding a response rate of 100.0%. In the interviewed households, 14,675 women age 15-49 were identified for individual interviews; interviews were completed with 14,634 women, yielding a response rate of 99.7%. In the subsample selected for the male survey, 6,503 households were selected, of which 6,472 were occupied. All but one occupied household (6,471) were successfully interviewed, yielding a response rate of 100.0%. In this subsample, 6,544 men age 15-59 were identified and 6,513 were successfully interviewed, yielding a response rate of 99.5%. In the subsample selected for the micronutrient survey, 3,501 households were selected, of which 3,492 were occupied. All but one of the occupied households (3,491) were successfully interviewed, yielding a response rate of 100.0%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2019-20 Rwanda Demographic and Health Survey (2019-20 RDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2019-20 RDHS is only one of many samples that could have been selected from the same population, using the same design and sample size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected by simple random sampling, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2019-20 RDHS sample was the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed using SAS programs developed by ICF. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Note: A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables

• Household age distribution
• Age distribution of eligible and interviewed women
• Age distribution of eligible and interviewed men
• Completeness of reporting
• Births by calendar years
• Reporting of age at death in days
• Reporting of age at death in months
• Standardization exercise results from anthropometry training
• Height and weight data completeness and quality for children
• Height measurements from random sub-sample of measured children
• Number of enumeration areas

Abbreviations: N: sample size; SD: standard deviation; Unmatched data: full set of data; Matched data: subset of data which excluded outliers in observed variables.

From 2014 to 2015, with the aim of collecting data to monitor progress across Rwanda’s health programs and policies, the Government of Rwanda (GOR) conducted the Rwanda Demographic and Health Survey (RDHS) through the Ministry of Health (MOH) and the National Institute of Statistics of Rwanda (NISR) with the members of the national steering committee to the DHS and the technical assistance of ICF International. The main objectives of the 2014-15 RDHS were to: • Collect data at the national level to calculate essential demographic indicators, especially fertility and infant and child mortality, and analyze the direct and indirect factors that relate to levels and trends in fertility and child mortality • Measure levels of knowledge and use of contraceptive methods among women and men • Collect data on family health, including immunization practices; prevalence and treatment of diarrhea, acute upper respiratory infections, and fever among children under age 5; antenatal care visits; assistance at delivery; and postnatal care • Collect data on knowledge, prevention, and treatment of malaria, in particular the possession and use of treated mosquito nets among household members, especially children under age 5 and pregnant women • Collect data on feeding practices for children, including breastfeeding • Collect data on the knowledge and attitudes of women and men regarding sexually transmitted infections (STIs) and HIV and evaluate recent behavioral changes with respect to condom use • Collect data for estimation of adult mortality and maternal mortality at the national level • Take anthropometric measurements to evaluate the nutritional status of children, men, and women • Assess the prevalence of malaria infection among children under age 5 and pregnant women using rapid diagnostic tests and blood smears • Estimate the prevalence of HIV among children age 0-14 and adults of reproductive age • Estimate the prevalence of anemia among children age 6-59 months and adult women of reproductive age • Collect information on early childhood development • Collect information on domestic violence

Multilevel regression analyses of good iron-rich food consumption in Rwanda (n = 2455).

Socio-demographic and other variables of children with their respective caregivers in Rwanda (n = 2455).

Iron-rich foods consumption among children aged 6–23 months in Rwanda (n = 2455).

We revisit the question of the role health insurance coverage played in increasing use of Skilled Birth Attendants (SBA) at delivery in Rwanda. Previous studies have suggested that enrollment in Mutuelles health insurance increased the odds of using an SBA by up to 163%. We take advantage of latest Rwanda Demographic Health Survey (RDHS 2010) to increase the sample size and extend the time frame of analysis to five years (2005 to 2010). We also adopt stronger matching methods to control for model dependence. We find that although enrollment in Mutuelles insurance increases use of SBAs at delivery, the size of the effect is orders of magnitude lower than previously published (12 to 18 percent versus 78 to 163 percent). We also find that the effect of education on use of SBA is similar in magnitude and direction as that of Mutuelles insurance enrollment. Our findings lead us to conclude that Mutuelles only had a modest effect on increasing use of SBA at delivery and therefore insurance alone may not be the “magic gullet” that solves the problem of non-use of SBA at delivery.

Checking endogeneity of Mutuelles: mean difference of self-reported illness and birth delivery by Mutuelles status (RDHS).

Abstract

Rwanda Interim Demographic and Health Survey (RIDHS) follows the Demographic and Health Surveys (RDHS) that were successfully conducted in 1992, 2000, and 2005, and is part of a broad, worldwide program of socio-demographic and health surveys conducted in developing countries since the mid-1980s. RIDHS collected the indicators on fertility, family planning and maternal and child health which the survey normally provides. In addition, RIDHS integrated a malaria module and tests for the prevalence of malaria and anemia among women and children, thus determining the prevalence of malaria and anemia for women and children at the national level.

The main objectives of the RIDHS were: • At the national level, gather data to determine demographic rates, particularly fertility and infant and child mortality rates, and analyze the direct and indirect factors that determine fertility and child mortality rates and trends. • Evaluate the level of knowledge and use of contraceptives among women and men. • Gather data concerning family health: vaccinations; prevalence and treatment of diarrhea, acute respiratory infections (ARI), and fever in children under the age of five; antenatal care visits; and assistance during childbirth. • Gather data concerning the prevention and treatment of malaria, particularly the possession and use of mosquito nets, and the prevention of malaria in pregnant women. • Gather data concerning child feeding practices, including breastfeeding. • Gather data concerning circumcision among men between the ages of 15 and 59. • Collect blood samples in all of the households surveyed for anemia testing of women age 15-49, pregnant women and children under age five. • Collect blood samples in all of the households surveyed for hemoglobin and malaria diagnostic testing of women age 15 to 49, pregnant women and children under age five.

Geographic coverage

National coverage

Analysis unit

Household Individual Woman age 15-49 Man age 15-59

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the RIDHS is a two-stage stratified area sample. Clusters are the primary sampling units and are constituted from enumeration areas (EA). The EA were defined in the 2002 General Population and Housing Census (RGPH) (SNR, 2005).

These enumeration areas provided the master frame for the drawing of 250 clusters (187 rural and 63 urban), selected with a representative probability proportional to their size. Only 249 of these clusters were surveyed, because one cluster located in a refugee camp had to be eliminated from the sample. A strictly proportional sample allocation would have resulted in a very low number of urban households in certain provinces. It was therefore necessary to slightly oversample urban areas in order to survey a sufficient number of households to produce reliable estimates for urban areas. The second stage involved selecting a sample of households in these enumeration areas. In order to adequately guarantee the accuracy of the indicators, the total number drawn was limited to 30 households per cluster. Because of the nonproportional distribution of the sample among the different strata and the fact that the number of households was set for each cluster, weighting was used to ensure the validity of the sample at both national and provincial levels.

All women age 15-49 years who were either usual residents of the selected household or visitors present in the household on the night before the survey were eligible to be interviewed (7,528 women). In addition, a sample of men age 15-59 who were either usual residents of the selected household or visitors present in the household on the night before the survey were eligible for the survey (7,168 men). Finally, all women age 15-49 and all children under the age of five were eligible for the anemia and malaria diagnostic tests.

The sample for the 2007-08 RIDHS covered the population residing in ordinary households across the country. A national sample of 7,469 households (1,863 in urban areas and 5,606 in rural areas) was selected. The sample was first stratified to provide adequate representation from urban and rural areas as well as all the four provinces and the city of Kigali, the nation’s capital.

Sampling deviation

One cluster located in a refugee camp had to be eliminated from the sample.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the 2007-08 RIDHS: the Household Questionnaire, the Women’s Questionnaire, and the Men’s Questionnaire. The content of these questionnaires was based on model questionnaires developed by the MEASURE DHS project.

Initial technical meetings that were held beginning in September 2007 allowed a wide range of government agencies as well as local and international organizations to contribute to the development of the questionnaires. Based on these discussions, the DHS model questionnaires were modified to reflect the needs of users and relevant issues in population, family planning, anemia, malaria and other health concerns in Rwanda. The questionnaires were then translated from French into Kinyarwanda. These questionnaires were finalized in December 2007 before the training of male and female interviewers.

The Household Questionnaire was used to list all of the usual members and visitors in the selected households. In addition, some basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women and men who were eligible for the individual interview. The Household Questionnaire also collected information on characteristics of the household’s dwelling unit such as the main source of drinking water, type of toilet facilities, materials used for the floor of the house, the main energy source used for cooking and ownership of various durable goods. Finally, the Household Questionnaire was also used to identify women and children eligible for the hemoglobin (anemia) and malaria diagnostic tests.

The Women’s Questionnaire was used to collect information on women of reproductive age (15-49 years) and covered questions on the following topics: • Background characteristics • Marital status • Birth history • Knowledge and use of family planning methods • Fertility preferences • Antenatal and delivery care • Breastfeeding practices • Vaccinations and childhood illnesses

The Men’s Questionnaire was administered to all men age 15-59 years living in the selected households. The Men’s Questionnaire collected information similar to that of the Women’s Questionnaire, with the only difference being that it did not include birth history or questions on maternal and child health or nutrition. In addition, the Men’s Questionnaire also collected information on circumcision.

Cleaning operations

Data entry began on January 7, 2008, three weeks after the beginning of data collection activities in the field. Data were entered by a team of five data processing personnel recruited and trained by staff from ICF Macro. The data entry team was reinforced during this work with an additional staffer. Completed questionnaires were periodically brought in from the field to the National Institute of Statistics in Kigali, where assigned staff checked them and coded the open-ended questions. Next, the questionnaires were sent to the data entry staff. Data were entered using CSPro, a program developed jointly by the United States Census Bureau, the ICF Macro MEASURE DHS program, and Serpro S.A. All questionnaires were entered twice to eliminate as many data entry errors as possible from the files. In addition, a quality control program was used to detect data collection errors for each team. This information was shared with field teams during supervisory visits to improve data quality. The data entry and internal consistency verification phase of the survey was completed on May 14, 2008.

Response rate

The response rate was high for both men (95.4 percent) and women (97.5 percent).

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2007-08 RIDHS to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2007-08 RIDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population

Biochemical properties of SpRDH and other RDHs.

Purification summary of SpRDH from Sphingomonas sp. PAMC 26621.

Prevalence of prenuptial HIV/AIDS testing across women’s characteristics (n = 14,634).

Random effect estimates of contextual factors associated with prenuptial HIV/AIDS testing among reproductive-aged women in Rwanda.

Metal ion analysis.

Background characteristics of pregnant women as per the 2020 Rwanda demographic health survey.