Abstract

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.

Geographic coverage

National. The sample provides estimates at the national and provincial levels.

Analysis unit

Household, adult woman, adult man

Kind of data

Sample survey data

Sampling procedure

The sample for the 2010 RDHS was designed to provide population and health indicator estimates for the country as a whole and for urban and rural areas in particular. Survey estimates are also reported for the provinces (South, West, North, and East) and for the City of Kigali. The results presented in this report show key indicators that correspond to these provinces and the City of Kigali.

A representative sample of 12,792 households was selected for the 2010 RDHS. The sample was selected in two stages. In the first stage, 492 villages (also known as clusters or enumeration areas) were selected with probability proportional to the village size. The village size is the number of households residing in the village. Then, a complete mapping and listing of all households existing in the selected villages was conducted. The resulting lists of households served as the sampling frame for the second stage of sample selection. Households were systematically selected from those lists for participation in the survey.

All women age 15-49 who were either permanent residents of the household or visitors present in the household on the night before the survey were eligible to be interviewed. In addition, in a subsample of half of all households selected for the survey, all men age 15-59 were eligible to be interviewed if they were either permanent residents or visitors present in the household on the night before the survey.

SAMPLING FRAME

The sampling frame used for the 2010 RDHS is the preparatory frame for the Rwanda General Population and Housing Census (RGPH), which will be conducted in 2012. Provided by the National Institute of Statistics of Rwanda (NISR), the sampling frame is a complete list of natural villages covering the entire country. Though it is preferable to work with a frame consisting of enumeration areas (EAs) because the natural villages are too variable in size, an EA frame is not available at the time of sampling design. The sampling frame that was available is the list of 14,837 natural villages, which contains the administrative characteristics for each village and village population. The village population comes from the national ID card project carried out in 2007-08, which may be under estimated compared with the population projection conducted in 2009 by NISR.

Rwanda's administrative units were reformed in 2006, so the country is currently divided into 5 provinces; 30 districts, 417 sectors, and 14,837 villages.The average village size is 610 residents, which is equivalent to 133 households. The sizes of the districts are quite homogeneous, varying from 2.7 percent to 4.4 percent. There is no urban-rural specification in the sampling frame because the urban-rural definition has not been released by the Ministry of Local Administration (MINALOC). It was expected that the urban-rural definition of the sampled villages will be determined during the data collection or in the office once the MINALOC releases the definition.

Mode of data collection

Face-to-face

Research instrument

Three questionnaires were used for the 2010 RDHS: the Household Questionnaire, the Woman’s Questionnaire, and the Man’s Questionnaire. They are based on questionnaires developed by the worldwide Demographic and Health Surveys (DHS) program and on questionnaires used during the 2005 RDHS and 2007-08 RIDHS surveys. To reflect relevant issues in population and health in Rwanda, the questionnaires were adapted during a series of technical meetings with various stakeholders from government ministries and agencies, nongovernmental organizations, and international donors. The questionnaires were translated from English and French into Kinyarwanda.

The Household Questionnaire was used to list all the usual members and visitors in the selected households as well as to identify women and men eligible for individual interviews. Basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of household. For children under 18, survival status of the parents was determined. The Household Questionnaire also collected information on the following: - Dwelling characteristics - Utilization of health services and health expenditures for recent illness and injury - Possession of iodized salt - Possession and utilization of mosquito nets - Height and weight of women and children - Hemoglobin measurement of women and children - Blood collection from women and children for rapid test and laboratory testing of malaria - Blood collection from women and men for laboratory testing for HIV

The Woman’s Questionnaire was used to collect information from all women age 15-49 and was organized by the following sections: - Respondent background characteristics - Reproduction, including a complete birth and death history of respondents’ children and information on abortion - Contraception - Pregnancy and postnatal care - Child’s immunization, health, and nutrition - Marriage and sexual activity - Fertility preferences - Husband’s background and woman’s work - HIV/AIDS and other sexually transmitted infections - Other health issues - Adult mortality - Relationship in the household

The Man’s Questionnaire was administered to all men age 15-59 living in every other household in the RDHS sample. The Man’s Questionnaire collected much of the same information as the Woman’s Questionnaire but was shorter because it did not contain a detailed reproductive history or questions on maternal and child health or nutrition.

An instruction manual was also developed to support standardized data collection. All data collection instruments were pretested in June-July 2010. The observations and experiences gathered from the pretest were used to improve the instruments for the main survey data collection.

Cleaning operations

Data entry began on November 1, 2010, almost one month after the survey was launched in the field. Data were entered by a team of 15 data processing personnel recruited and trained for this task. They were assisted during these operations by 4 data verification and codification officers and 2 receptionists. Completed questionnaires were periodically brought in from the field to the National Institute of Statistics headquarters, where assigned agents checked them and coded the open-ended questions. Next, the questionnaires were sent to the data entry facility and the blood samples (DBS and malaria slides) were sent to the NRL to be screened for HIV. Data were entered using CSPro, a program developed jointly by the United States Census Bureau, the ORC Macro MEASURE DHS+ program, and Serpro S.A. Processing the data concurrently with data collection allowed for regular monitoring of teams’ performance and data quality. Field check tables were regularly generated during data processing to check

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

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

Analysis unit

Woman, Birth, Child, Man, Member

Universe

Women age 15-49, Births, Children age 0-4, Men age 15-59, All persons

Kind of data

Demographic and Household Survey [hh/dhs]

Sampling procedure

MICRODATA SOURCE: National Institute of Statistics of Rwanda, Ministry of Health [Rwanda] and ICF International.

SAMPLE UNIT: Woman SAMPLE SIZE: 13497

SAMPLE UNIT: Birth SAMPLE SIZE: 30058

SAMPLE UNIT: Child SAMPLE SIZE: 7856

SAMPLE UNIT: Man SAMPLE SIZE: 6217

SAMPLE UNIT: Member SAMPLE SIZE: 54905

Mode of data collection

Face-to-face [f2f]

Not specified

Analysis unit

Woman, Birth, Child, Birth, Man, Household Member

Universe

Women age 15-49, Births, Children age 0-4, Men age 15-59, All persons

Kind of data

Demographic and Household Survey [hh/dhs]

Sampling procedure

MICRODATA SOURCE: Office National de la Population [Rwanda] and ORC Macro.

SAMPLE UNIT: Woman SAMPLE SIZE: 10421

SAMPLE UNIT: Birth SAMPLE SIZE: 27602

SAMPLE UNIT: Child SAMPLE SIZE: 7922

SAMPLE UNIT: Man SAMPLE SIZE: 2717

SAMPLE UNIT: Member SAMPLE SIZE: 45247

Mode of data collection

Face-to-face [f2f]

Contains data from the DHS data portal. There is also a dataset containing Rwanda - Subnational Demographic and Health Data on HDX.

The DHS Program Application Programming Interface (API) provides software developers access to aggregated indicator data from The Demographic and Health Surveys (DHS) Program. The API can be used to create various applications to help analyze, visualize, explore and disseminate data on population, health, HIV, and nutrition from more than 90 countries.

Analysis unit

Woman, Birth, Child, Man, Member

Universe

Women age 15-49, Births, Children age 0-4, Men age 15-59, All persons

Kind of data

Demographic and Household Survey [hh/dhs]

Sampling procedure

MICRODATA SOURCE: Institut National de la Statistique, Ministère des Finances et de la Planification Économique [Rwanda] and ORC Macro.

SAMPLE UNIT: Woman SAMPLE SIZE: 11321

SAMPLE UNIT: Birth SAMPLE SIZE: 30072

SAMPLE UNIT: Child SAMPLE SIZE: 8649

SAMPLE UNIT: Man SAMPLE SIZE: 4820

SAMPLE UNIT: Member SAMPLE SIZE: 47851

Mode of data collection

Face-to-face [f2f]

Feed the Future Rwanda Interim Survey in the Zone of Influence: This dataset contains records for all children under 3 years of age (0-35 months) (n=438, vars=31) . This file includes data in Module I. Note that the children's anthropometry indicators and dietary intake indicators were calculated with secondary data, the 2014-2015 Rwanda Demographic and Health Survey.

Contains data from the DHS data portal. There is also a dataset containing Rwanda - National Demographic and Health Data on HDX.

The DHS Program Application Programming Interface (API) provides software developers access to aggregated indicator data from The Demographic and Health Surveys (DHS) Program. The API can be used to create various applications to help analyze, visualize, explore and disseminate data on population, health, HIV, and nutrition from more than 90 countries.

BackgroundTeenage pregnancy is a significant public health issue and is strongly associated with risky sexual behaviors such as early sexual initiation, unprotected sex, and multiple sexual partners. According to the 2014 World Health Organization report, 11% of all births worldwide were to teenagers aged 15–19 years, with more than 95% of these pregnancies occurring in low- and middle-income countries, particularly in sub-Saharan Africa, which bears much of this burden. In Rwanda, the prevalence of teenage pregnancy has risen from 4.1% in 2005 to 7.3% in 2014, indicating a growing concern. However, there is limited and inconsistent evidence on the factors contributing to teenage pregnancy. Hence, our study aimed to investigate the factors associated with teenage pregnancy. This research seeks to provide valuable insights for targeted interventions, which are urgently needed in light of the increasing rates.MethodsWe employed a cross-sectional study design, utilizing data from the 2019/2020 Rwanda Demographic Health Survey of 3,258 eligible participants aged 15–19 years. To identify factors associated with teenage pregnancy, we performed a bivariate logistic regression analysis. The significant variables from the bivariate analysis were then exported into multivariate logistic regression models, with the results presented as odds ratios (ORs) along with 95% confidence intervals (CIs) and a significance threshold set at 5%.ResultsOur findings indicated that teenagers aged 18–19 years were more likely to experience pregnancy compared to those younger than 17 (OR = 4.2; 95% CI: 2.16–8.37). Adolescents who had engaged in sexual activity 95 times or more had a significantly higher likelihood of becoming pregnant than those with less frequent sexual activity (OR = 13.53; 95% CI: 5.21–35.12). Furthermore, adolescents with parents with a secondary education were 80% less likely to become pregnant compared to those with parents with a primary or no education (OR = 0.2; 95% CI: 0.07–0.63).ConclusionOur study revealed that teenage pregnancy is shaped by several individual factors including age and sexual behavior, along with parental education levels. These findings underscore the critical need for targeted sexual education and enhanced family support systems to mitigate teenage pregnancies. Further, longitudinal studies are essential for establishing causality and guiding effective policy development.

The urban indicators data available here are analyzed, compiled and published by UN-Habitat’s Global Urban Observatory which supports governments, local authorities and civil society organizations to develop urban indicators, data and statistics. Urban statistics are collected through household surveys and censuses conducted by national statistics authorities. Global Urban Observatory team analyses and compiles urban indicators statistics from surveys and censuses. Additionally, Local urban observatories collect, compile and analyze urban data for national policy development. Population statistics are produced by the United Nations Department of Economic and Social Affairs, World Urbanization Prospects.

Anemia remains a public health problem in Rwanda, affecting 38% of young children and 17% of reproductive-aged women (Demographic and Health Survey [DHS] 2010). The importance of iron deficiency (ID) as a cause of anemia in Rwanda is not known. We conducted a cluster randomized survey, selecting 408 rural households each in the Northern and Southern Provinces of Rwanda in 2010, to estimate the prevalence of ID and iron deficiency anemia (IDA) among young children and women of reproductive age.

*Statistically significant different PORsPosterior odds ratio (POR) estimates for fixed effects with 95% credible intervals based on Model 5.

UNICEF's country profile for Rwanda, including under-five mortality rates, child health, education and sanitation data.

Abstract

In this project, a national energy survey was conducted in Rwanda in June 2022, which followed up on an inaugural energy survey conducted in 2016. The survey captured the status of access to electricity and clean cooking among Rwandan households, including those of refugees, and also among public institutions. Survey results were analyzed using the multi-tier framework (MTF) for energy access, which measures energy access across six levels (Tier 0 to Tier 5) instead of evaluating it based on a binary definition, having access or not, and explores the multi-dimensional nature of energy access and the diverse technologies and sources that can provide it.

Geographic coverage

Nationwide

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample design and selection

The energy survey included a household survey, a public institution survey, and a survey of refugee camps and adjacent host communities. For each survey, different sampling frames were used to select representative samples.

(1) Household Survey The sampling frame for the household survey was the complete list of residential households from the Rwanda Population and Housing Census Report 2012 (RPHC2012) provided by the National Institute of Statistics. It excluded households located within 5km from the refugee camp, and it comprised information on Rwandan residential households grouped by Province, District, Sector, Cell, and Village.

The sample size for the household survey was designed to obtain estimates with high precision for the main indicators targeted by the survey. Since the Rwanda Demographic and Health Survey (2019/2020) indicated that in Rwanda, 46% of households have access to electricity, the value was used in the calculation of the sample size and considered as the benchmark indicator value. The minimum sample size required for the household survey was calculated using the formula provided in the document Rwanda Energy Survey (2022) Sampling Strategy and Weighting provided under Resources.

Stratification for the household survey was done considering province, urban, and rural area as strata to increase the efficiency of the sample design. An equal sample allocation was applied to allocate households between urban and rural areas and later a square root allocation was applied to allocate the sample size between provinces

Sample selection: In the first stage, 223 villages were selected using probability proportional to size (PPS), and in the second stage, after listing all residential households in the sampled villages, 18 households were selected using systematic random sampling methods (it was anticipated that 50% of electrified households and 50% of non-electrified households be sampled from each sampled village for interview).

(2) Public Institutions Survey For the public institutions survey, given that the sampling frame is not available, all education and health centers located in the area where the household survey took place were interviewed. The sample size for the public institutions survey depended on the number of institutions found in villages selected for the household survey at the time of survey administration.

The sample size for the public institutions’ survey corresponded to all educational institutions and health centers found in the sampled villages. In other words, the final sample size for the public institution survey was determined based on the number of villages selected for the household survey.

(3) Refugee Households and Host Community Surveys The refugee household survey was conducted in the five refugee camps in Rwanda located in different areas. The sampling frame for this survey was the exhaustive list of households in the five refugee camps. Additionally, adjacent host communities, within 5km of the refugee camps, were surveyed.

Sample Size: The sample size for the refugee camp and hosting communities was determined using the same formula used to calculate the minimum sample size for the household survey. The only difference is that since the indicator of interest (P) is unknown (proportion of households connected to the national grid), the prevalence rate was assumed to be 50% to maximize sample size and relative standard error to 3.6%. Therefore, the minimum sample size was 1,700 households. The total population of refugee camps and host communities was 264,316 of which the population of refugee camps represented 10% and the population of hosting communities represents 90%. To design an effective sample size for each category, 40% of the sample was drawn from the refugee camp and 60% from hosting communities. In other words, 700 households were selected for interview in refugee camps and 1,000 households in hosting communities; this implies a relative standard error (RSE) of 5.7% and 4.7% respectively.

Sample Selection: For the refugee household survey, in the first stage, 39 segments from refugee camps were selected using probability proportion to size (PPS), and in the second stage, a systematic random sampling technique was applied to select 18 refugee households from the sampled segment. For host communities survey, 56 villages were selected using probability proportional to size (PPS) and at the second stage, 18 households from each sampled village were selected using systematic random sampling technics.

For more information, see the document Rwanda Energy Survey (2022) Sampling Strategy and Weighting provided under Resources.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The following questionnaires are available for download in PDF format: Rwanda Energy Survey - Community Questionnaire for Impact Evaluation and Tier Analysis Rwanda Energy Survey - Education Facility Questionnaire for Impact Evaluation and Tier Analysis Rwanda Energy Survey - Health Facility Questionnaire for Impact Evaluation and Tier Analysis Rwanda Energy Survey - Household Questionnaire Medium Version

The 2017 Rwanda Malaria Indicator Survey (RMIS) is a nationwide survey with a nationally representative sample of approximately 5,041 households. The survey provides information on key malaria control indictors, such as the proportion of households having at least one bed net and at least one insecticide-treated net (ITN). It looks at the proportion under age 5 who slept under a bed net the previous night, and under an ITN, and tests for the prevalence of malaria among all household members. Among pregnant women, the survey assesses the proportion of pregnant women who slept under a bed net the previous night. The primary objective of the 2017 RMIS project is to provide up-to-date estimates of basic demographic and health indicators related to malaria. Specifically, the 2017 RMIS collected information on household ownership of mosquito nets, care seeking behavior by adults, and treatment of fever in children. All members of sampled households were also tested for malaria infection. Knowledge of malaria was assessed among interviewed women. The information collected through the 2017 RMIS is intended to assist policy makers and program managers in evaluating and designing programs and strategies for improving the health of the country’s population.

Abstract

The EICV-W4 survey (Enquête Intégrale sur les Conditions de Vie des ménages) was conducted over a 12-month cycle from October 2013 to October 2014. Data collection was divided into 10 cycles in order to represent seasonality in the income and consumption data. A main cross-sectional sample survey, a panel survey and a VUP sample survey were conducted simultaneously.

The EICV-W4 provides information on poverty and living conditions in Rwanda and measures changes over time as part of the on-going monitoring of the Poverty Reduction Strategy and other Government policies. The survey data are also very important for national accounts and updating the consumer price index (CPI).

Geographic coverage

National coverage, including rural and urban households and allowing province- and district-level estimation of key indicators

Analysis unit

Households

Universe

All household members (variable s1q15 identifies household membership).

Kind of data

Sample survey data [ssd]

Sampling procedure

The EICV4 cross sectional (CS) sample includes two independent subsets selected using different sampling frames: 1) a new EICV4 sample of households in enumeration areas (EAs) selected using the 2012 Rwanda Population and Housing Census frame and 2) a panel of households selected from 177 EICV3 villages. A new listing of households was conducted in both the panel and new sample clusters in order to update the frame for the CS Survey. The sample households in the new CS sample EAs were selected from the new listing.

1) The new EICV4 sample The main sampling frame for the EICV4 is based on the 2012 Rwanda Census. The primary sampling units (PSUs) are the 2012 census Enumeration Areas (EAs). In the Census, each EA was classified as urban, semi-urban, peri-urban or rural. The urban areas include Kigali-Ville and the district capitals. The semi-urban areas generally correspond to smaller towns that have service facilities and markets. The peri-urban areas currently have the characteristics of rural areas, but they are located on the periphery of urban areas and are designated for future development. For the EICV4 sampling frame, the semi-urban areas were grouped with the urban strata, and the peri-urban areas with the rural strata. This results in a final distribution of 17.2% urban households and 82.8% rural households in the sampling frame. EAs in the 177 EICV3 sample villages selected for the panel study were excluded from the sampling frame, in order to avoid any overlap between the two samples.

The new EICV4 sample of 12,312 households was selected using a stratified two-stage design. At the first stage, sample EAs were selected within each stratum (district) with probability proportional to size (PPS) from the ordered list of EAs in the sampling frame. The EAs are implicitly stratified by urban and rural strata within each district, ordered first by urban, semi-urban, peri-urban and rural areas, and then geographically by sector, cellule, village and EA codes. This first stage sampling procedure provides a proportional allocation of the sample to the urban and rural areas of each district. At the second stage, households in each sample EA are selected from the listing. For the three districts in Kigali Province, 9 households were selected in each sample EA as original households; for the remaining 27 districts, 12 households were selected in each sample EA as original households. In addition, a reserve sample of 3 replacement households were selected for each sample EA in Kigali Province and 4 replacement households for each sample EA in the remaining provinces.

This new EICV4 sample contains 12,312 households, including 12,233 original households and 79 replacement households.

2) Households from 177 EICV3 villages used for panel study The second component of the EICV4 cross sectional sample consists of all the sample households interviewed inside the 177 EICV3 villages selected for the panel study (including any replacements households and panel split households inside the clusters).

Within each of the 177 villages, all households that were interviewed during EICV3 were included in the cross-sectional sample. When an EICV3 sample household moved and a new household occupied the same house in the cluster, it was interviewed for the Cross-Sectional Survey, and assigned a PID (dependency) code of 94. If an EICV3 household was empty or not found, a random replacement household was selected for the EICV4 Cross-Sectional Survey from the new listing of the sample cluster, and assigned a PID code of 95. The sample households with PID codes 94 and 95 are only used for the cross-sectional study, not the panel study.

This second component of the cross-sectional sample includes 2108 households drawn from the 177 EICV3 villages sampled for the panel study. These include 1604 original EICV3 households, 181 dependent household splitting from the original household in the same cluster, along with 243 households living in the dwelling formerly occupied by a panel household and 80 replacement households in the cluster in order to have 9/12 households per cluster.

The reason why we combine the EICV4 data from the new and panel clusters for the CS analysis is to obtain the most accurate CS estimates. In the case of the CS estimates from the combined samples, the additional data from the 177 sample panel clusters will result in a significant reduction in the variance component of the MSE. Although the bias of the CS data from the sample panel clusters may slightly increase the bias component, this bias is very small compared to the corresponding reduction in the variance component. Therefore the CS results from the EICV4 data for the combined new and panel clusters can be considered more accurate than the corresponding results using only the EICV4 data for the new sample clusters.

In total, the final EICV4 cross-sectional sample contains 14,419 households.

3) Assignment of EAs to cycles and sub-cycles Data collection covering a period of 12 month is divided into 10 cycles to represent seasonality in consumption, income, employment and agricultural activity patterns. For rural enumeration, each cycle is further divided into two sub-cycles. For the 177 EICV3 villages, the cycle and sub-cycle were pre-determined. Households were re-interviewed in the same cycle, corresponding to the same time of the year as they were in EICV3. To assign cycles to the new EICV4 sample EAs, random cycle numbers from 1 to 10 were generated to identify the selection sequence. For the 27 districts outside Kigali, sub-cycle numbers of 1 or 2 were assigned systematically with a random start. This process ensured that the final distribution of the sample EAs to cycles and sub-cycles was geographically representative within each district.

Mode of data collection

Face-to-face paper [f2f]

Research instrument

The same questionnaire was used for cross-sectional, panel and VUP samples. Part A of the questionnaire contains modules on household and individual information. Part B is on agriculture and consumption. The questionnaire was developed in English, and translated into Kinyarwanda.

Questionnaire design took into account the requests raised by major data users and stakeholders, as well as consistency with the previous EICV questionnaires. In addition to methodological improvements, some simplifications were made:

-The major changes introduced in this survey were changes to Section 6, the Economic Activity. Further questioning was added on unemployment and underemployment in response to questions from users, and also to comply with international standards. The section was simplified to enable the analysis to be undertaken by local analysts.

-The Section on the VUP participation was expanded to provide more information, better classification of beneficiaries and to provide greater consistency within the questionnaire. The same questionnaire is to be used on the separate VUP sample which runs in parallel with the EICV4

-The health section was reduced to try to cut respondent burden, as health-related information is being collected by Demographic and Health Surveys (DHS).

-The expenditure section was changed in minor ways to provide better information for national accounts (housing investment) and for CPI weights (retail outlets).

Questionnaire was tested in pilot surveys and amended in time prior to the fieldwork starting in October 2013.

The complete questionnaire is provided as external resources.

Cleaning operations

A day before the interview started, the enumerator, accompanied by a controller, did an introduction to household, explaining how often they will come in that household and delivering a letter indicating that the HH has been selected.

During the field work, after each cycle, the data processing team produced tables and reports of inconsistencies, which were checked by the field supervisor. The data entry system also contained consistency checks that alerted the data entry operators. In case of an alert, the questionnaire was sent back to the supervisor of data entry for correction.

Response rate

Out of the 12,312 sample households selected in the new sample clusters for EICV4, only 79 were non-interviews, for a response rate of 99.4% for this sample. All of the 79 non-interviews were replaced. There were only 12 refusals, and there were few cases of houses that were empty or not found, given that the listing was conducted very close to the interviewing period.

Abstract

The 2017 Rwanda Malaria Indicator Survey (RMIS) is a nationwide survey with a nationally representative sample of approximately 5,041 households. The survey provides information on key malaria control indictors, such as the proportion of households having at least one bed net and at least one insecticide-treated net (ITN). It looks at the proportion under age 5 who slept under a bed net the previous night, and under an ITN, and tests for the prevalence of malaria among all household members. Among pregnant women, the survey assesses the proportion of pregnant women who slept under a bed net the previous night.

The primary objective of the 2017 RMIS project is to provide up-to-date estimates of basic demographic and health indicators related to malaria. Specifically, the 2017 RMIS collected information on household ownership of mosquito nets, care seeking behavior by adults, and treatment of fever in children. All members of sampled households were also tested for malaria infection. Knowledge of malaria was assessed among interviewed women. The information collected through the 2017 RMIS is intended to assist policy makers 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
• Woman age 15 to 49

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2017 RMIS followed a two-stage sample design that would allow estimates of key indicators to be determined for the nation as a whole, for urban and rural areas, and for the five provinces. In the first stage, sample points, or clusters, were selected from the sampling frame, which consisted of enumeration areas (EAs) delineated during the 2012 Population and Housing Census. A total of 170 clusters with probability proportional to size were selected from these EAs.

In the second stage, sampling involved systematic selection of households. A household listing operation was undertaken in all selected EAs during the main data collection. Households to be included in the survey were then randomly selected from these lists. Thirty households were selected from each EA, for a total sample size of 5,100 households. Because of the approximately equal sample size for each region, the sample is not selfweighting at the national level. Results shown in this report have been weighted to account for the complex sample design. See Appendix A for additional details on the sampling procedures.

Note: See Appendix A of the final report for additional details on the sampling procedure.

Mode of data collection

Face-to-face [f2f]

Research instrument

Data was primarily collected using three questionnaires: the Household Questionnaire, the Woman’s Questionnaire, and the Biomarker Questionnaire. Core questionnaires available from the RBM-MERG were adapted to reflect the population and health issues relevant to Rwanda.

Cleaning operations

Data entry began on November 1, 2017, 2 weeks after the survey launched in the field. Data were entered by a team of eight data processing personnel recruited and trained for this task. They were assisted during these operations by two staff members who aided in questionnaire reception, data verification, and coding. Completed questionnaires were periodically brought in from the field to the MOPDD headquarters, where assigned agents checked them and coded the open-ended questions. Next, the questionnaires were sent to the data entry facility and the blood samples (blood smear slides) were sent to the lab to be read for the malaria parasites. Data were entered using CSPro, a program developed jointly by the United States Census Bureau, the ORC Macro MEASURE DHS+ program, and Serpro S.A. Processing the data concurrent with data collection allowed for regular monitoring of teams’ performance and data quality. Field check tables were regularly generated during data processing to check various data quality parameters. As a result, feedback was given on a regular basis, encouraging teams to continue quality work and to correct areas in need of improvement. Feedback was individually tailored to each team. Data entry, which included 100% double entry to minimize keying error, was completed on December 31, 2017. Data editing, was completed on January 26, 2018. Data cleaning and finalization was completed on February 9, 2018.

Response rate

A total of 5,096 households selected for the sample, 5,061 were occupied at the time of fieldwork. Among the occupied households, 5,041 were successfully interviewed, yielding a total household response rate of 99.6%. In the interviewed households, 5,088 women were identified as eligible for individual interview, and 5,022 were successfully interviewed, yielding a response rate of 98.7%.

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 Rwanda MIS 2017 (2017 RMIS) 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 2017 RMIS 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 among 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 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 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2017 RMIS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the 2017 RMIS is a SAS program. This program used the Taylor linearization method of variance estimation for survey estimates that are means, proportions, or ratios.

Note: Detailed description of sampling error estimates is presented in APPENDIX B of the final report.

Data appraisal

Data quality tables are produced to review the quality of the data: - Household age distribution - Age distribution of eligible and interviewed women - Completeness of reporting - Births by calendar years - Household composition

Note: The tables are presented in APPENDIX C of the final report.

Sensitivity analysis: DIC, posterior mean variance, its standard error (within brackets), and 95% CI based on Model 5.