Malaria Indicator Survey 2017
Rwanda, 2017
Get MicrodataIdentification
RWA_2017_MIS_v01_M
Malaria Indicator Survey 2017
| Name | Country code |
|---|---|
| Rwanda | RWA |
Malaria Indicator Survey
The 2017 Rwanda Malaria Indicator Survey (RMIS) is the second survey of its kind in Rwanda. It 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).
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.
Sample survey data [ssd]
- Household
- Woman age 15 to 49
Version
The data dictionary was generated from hierarchical data that was downloaded from the DHS website (http://dhsprogram.com).
Scope
The survey covered the following topics:
HOUSEHOLD
• Identification
• Background information on each person listed, such as relationship to head of the household, age, sex, marital status, availability of health insurance, and wealth level
• Characteristics of the household's dwelling unit, such as the source of water, type of toilet facilities, type of fuel used for cooking, number of rooms, materials used for the floor, roof and walls of the house, possessions of livestock (inluding land) and durable goods
• Mosquito nets
WOMEN
• Identification
• Background characteristics (age, residential history, education, literacy, and religion)
• Reproductive history for the last 5 years
• Prevalence and treatment of fever among children under age 5
• Knowledge about malaria (symptoms, causes, and how to prevent)
• Sources of messages about malaria
Biomarker
• Identification
• Malaria testing for children age 6 months - 14 years
• Malaria testing for adults age 15 and above
Coverage
National coverage
Producers and sponsors
| Name | Affiliation |
|---|---|
| Malaria and Other Parasitic Diseases Division of the Rwanda Biomedical Center | Ministry of Health |
| Name | Affiliation | Role |
|---|---|---|
| ICF | The DHS Program | Provided technical assistance through The DHS Program |
| Name | Role |
|---|---|
| Government of Rwanda | Financial support |
| United States President’s Malaria Initiative | Financial support |
| Global Fund | Financial support |
| United States Agency for International Development | Financial support |
Sampling
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.
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%.
A spreadsheet containing all sampling parameters and selection probabilities will be constructed to facilitate the calculation of sampling weights. Household sampling weights and the women’s individual sampling weights are obtained by adjusting the above-calculated weight to compensate for household nonresponse and women’s individual nonresponse, respectively. These weights will be further normalized at the national level to produce unweighted cases equal to weighted cases for both households and individual women at the national level. The normalized weights are valid for estimation of proportions and means at any aggregation levels, but not valid for estimation of totals.
Details of sampling weight calculation is available in Appendix A.4 of the final report.
Survey 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.
Data collection
| Start | End |
|---|---|
| 2017-10 | 2017-12 |
| Name | Affiliation |
|---|---|
| Malaria and Other Parasitic Diseases Division of the Rwanda Biomedical Center | Ministry of Health |
Fifteen teams were organized for field data collection. Each team consisted of one field supervisor, two health professionals to interview and administer treatment, one laboratory technician to conduct biomarker testing, and one driver. The field staff also included national coordinators who collected slides from the field teams and delivered them to the Malaria Laboratory of the National Referral Laboratory.
Field data collection for the 2017 RMIS started on October 23, 2017. For maximum effect, survey coordinators visited all 15 teams at least twice per week. Fieldwork concluded on December 23, 2017.
Data processing
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.
Data appraisal
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 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.
Data Access
| Name | URL | |
|---|---|---|
| The DHS Program | http://www.DHSprogram.com | archive@dhsprogram.com |
Request Dataset Access
The following applies to DHS, MIS, AIS and SPA survey datasets (Surveys, GPS, and HIV).
To request dataset access, you must first be a registered user of the website. You must then create a new research project request. The request must include a project title and a description of the analysis you propose to perform with the data.
The requested data should only be used for the purpose of the research or study. To request the same or different data for another purpose, a new research project request should be submitted. The DHS Program will normally review all data requests within 24 hours (Monday - Friday) and provide notification if access has been granted or additional project information is needed before access can be granted.
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Access to DHS, MIS, AIS and SPA survey datasets (Surveys, HIV, and GPS) is requested and granted by country. This means that when approved, full access is granted to all unrestricted survey datasets for that country. Access to HIV and GIS datasets requires an online acknowledgment of the conditions of use.
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The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Contacts
| Name | Affiliation | URL | |
|---|---|---|---|
| Information about The DHS Program | The DHS Program | reports@DHSprogram.com | http://www.DHSprogram.com |
| General Inquiries | The DHS Program | info@dhsprogram.com | http://www.DHSprogram.com |
| Data and Data Related Resources | The DHS Program | archive@dhsprogram.com | http://www.DHSprogram.com |