{"type":"survey","doc_desc":{"title":"RWA_2013_MIS_v01_M","idno":"DDI_RWA_2013_MIS_v01_M_WB","producers":[{"name":"Development Data Group","abbreviation":"DECDG","affiliation":"The World Bank","role":"Metadata preparation"}],"prod_date":"2014-06-09","version_statement":{"version":"Version 01 (June 2014)"}},"study_desc":{"title_statement":{"idno":"RWA_2013_MIS_v01_M","title":"Malaria Indicator Survey 2013","alt_title":"MIS 2013 \/ RMIS 2013"},"authoring_entity":[{"name":"Malaria and Other Parasitic Diseases Division of the Rwanda Biomedical Center","affiliation":"Ministry of Health"}],"production_statement":{"producers":[{"name":"National Institute of Statistics","affiliation":"Government of Rwanda","role":"Collaborated in the implementation of the survey"},{"name":"ICF International","affiliation":"MEASURE DHS","role":"Provided technical assistance"}],"funding_agencies":[{"name":"Government of Rwanda","abbreviation":"GovRWA","role":"Funding the study"},{"name":"United States Agency for International Development","abbreviation":"USAID","role":"Funding the study"},{"name":"Global Fund to Fight AIDS, Tuberculosis and Malaria","abbreviation":"GFATM","role":"Funding the study"}]},"distribution_statement":{"contact":[{"name":"Malaria and Other Parasitic Diseases Division of the Rwanda Biomedical Center","affiliation":"Ministry of Health","email":"info@rwandamalariaforum.org","uri":"http:\/\/www.rbc.gov.rw"},{"name":"Malaria and Other Parasitic Diseases Division of the Rwanda Biomedical Center","affiliation":"Ministry of Health","email":"info@rwandamoh.gov.rw","uri":"http:\/\/www.rbc.gov.rw"},{"name":"Information about The DHS Program","affiliation":"The DHS Program","email":"reports@DHSprogram.com","uri":"http:\/\/www.DHSprogram.com"},{"name":"General Inquiries","affiliation":"The DHS Program","email":"info@dhsprogram.com","uri":"http:\/\/www.DHSprogram.com"},{"name":"Data and Data Related Resources","affiliation":"The DHS Program","email":"archive@dhsprogram.com","uri":"http:\/\/www.DHSprogram.com"}]},"series_statement":{"series_name":"Demographic and Health Survey, Special [hh\/dhs-sp]","series_info":"The Rwanda Malaria Indicator Survey (2013 RMIS) is the first survey of its kind in Rwanda. It selects a nationally representative sample of 4,770 households from 159 sample clusters. The survey is designed to provide information on the following key malaria control indictors: (1) the proportion of households having at least one mosquito net and at least one insecticide-treated net (ITN); (2) the proportion of children under age 5 who slept under a mosquito net and under an ITN the night before the survey; (3) and the proportion of pregnant women who slept under a mosquito net and under an ITN the night before the survey."},"study_info":{"abstract":"The 2013 Rwanda Malaria Indicator Survey (RMIS) is a nationally representative, household-based survey that provides data on malaria indicators, which are used to assess the progress of a malaria control program. The control program is geared toward meeting Millennium Development Goals.\n\nThe objectives of the 2013 Rwanda Malaria Indicator Survey (RMIS) were to collect data on (1) ownership and utilization of treated mosquito nets and (2) knowledge of symptoms, causes, treatments, and prevention of malaria.\n\nA related objective was to produce survey results in a timely manner and to ensure that the data were disseminated to a wide audience of potential users in government and nongovernmental organizations within and outside of Rwanda. Most survey indicators were produced separately for each of the five provinces.\n\nKey indicators were malaria-specific and general.\nMalaria indicators:\n\u2022 Ownership of insecticide-treated mosquito nets\n\u2022 Usage of insecticide-treated mosquito nets among persons in the household, children under age 5, and pregnant women\n\u2022 Proportion of children under age 5 with recent fever who were treated with timely, appropriate antimalarial drugs\n\u2022 Proportions of mothers who know the symptoms, treatments, and prevention of malaria\n\nGeneral indicators:\n\u2022 Source of household drinking water; type of toilet facility\n\u2022 Household socioeconomic status (wealth quintile)","coll_dates":[{"start":"2013-02-17","end":"2013-04-26","cycle":""}],"nation":[{"name":"Rwanda","abbreviation":"RWA"}],"geog_coverage":"National coverage","analysis_unit":"- Household\n- Woman age 15 to 49","data_kind":"Sample survey data [ssd]","notes":"The survey covered the following topics:\n\nHOUSEHOLD:\n- Household member demographic information\n- Household characteristics\n\nWOMEN:\n- Respondent's background\n- Reproduction\n- Pregnancy and intermittent preventative treatment\n- Fever in children"},"method":{"data_collection":{"data_collectors":[{"name":"Malaria and Other Parasitic Diseases Division of the Rwanda Biomedical Center","abbreviation":"MAL & OPD","affiliation":"Ministry of Health"}],"sampling_procedure":"Sample Design\nThe sample for the 2013 RMIS was designed to provide malaria indicator estimates for the country as a whole and for separate urban and rural areas. Survey estimates are also be reported for the provinces (South, West, North, and East provinces) and Kigali City.\n\nA representative sample of 4,772 households was selected for the 2013 RMIS. The sample was selected in two stages. In the first stage, 159 villages (also known as clusters or enumeration areas) were selected with probability proportional to village size. Village size is determined by the number of households residing in the village. Then, a complete mapping and listing of all households 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.\n\nAll women age 15-49 who were either permanent residents of the households or visitors present in the household on the night before the survey were eligible for interviews.\n\nNote: Detailed description of the sample design is presented in Appendix A of the final report.","coll_mode":"Face-to-face [f2f]","research_instrument":"The 2013 RMIS involved two questionnaires: a Household Questionnaire and a Woman\u2019s Questionnaire for all women age 15-49 in the selected households. Both of these instruments were based on the model Demographic and Health Survey Phase III and the model Roll Back Malaria (RBM) Malaria Indicator Survey (MIS) questionnaires developed by the MEASURE DHS program, as well as on previous surveys conducted in Rwanda, including the 2007-08 Rwanda Interim DHS (RIDHS) and the 2010 Rwanda Demographic and Health Survey (RDHS). The MAL & OPD Division reviewed the draft questionnaires with potential stakeholders, including government health agencies and interested donor groups.\n\nThe Household Questionnaire was used to list all the usual members and visitors in the selected households. Some basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of household. The main purpose of the Household Questionnaire was to identify women eligible for individual interview. Questions on ownership and use of mosquito nets were included in the Household Questionnaire as were questions about proxy indicators for wealth such as ownership of various durable goods, dwelling unit characteristics, and land.\n\nThe Woman\u2019s Questionnaire was used to collect information from women age 15-49 on the following topics:\n\u2022 Background characteristics (age, education, media exposure, employment, religion, and so on)\n\u2022 Reproductive history (number of births, date of last birth, current pregnancy status, and antimalarial treatment for children under age 5 with recent fever)\n\u2022 Knowledge about malaria symptoms, causes, and prevention","coll_situation":"Training for pretesting occurred from January 9 to January 18, 2013. Fifteen women and men were trained to administer the RMIS survey questionnaire and household listing update. Four days of fieldwork were followed by one day of interviewer debriefing and testing. Pretest fieldwork was conducted in 80 households in two rural and two urban villages outside of Kigali City. All pretest participants attended the main training and served as team leaders\/field editors for the main survey.\n\nFor the main data collection, the Mal & OPD Division recruited and trained 50 participants from 21 January to 1 February 2013 on how to use the RMIS survey instruments, including the household listing update, questionnaires, and fieldwork practice. The training consisted of instruction regarding interviewing techniques and field procedures, a detailed review of items on the questionnaires, and mock interviews and role plays. Instruction and practice on updating a household listing was also included in the main training. At the end of the training 48 participants were organized into 12 data collection teams consisting of a team leader\/field editor, and three interviewers. The National Institute of Statistics of Rwanda (NISR) assisted in training for the household listing update.\n\nFieldwork was launched immediately upon the conclusion of field staff training. Fieldwork supervision was conducted by the MAL & OPD Division through regular visits to teams to review their work and monitor data quality. Additional contact between the central office and the teams was maintained through cell phones. Fieldwork was conducted from February 17, 2013, through April 26, 2013. Questionnaires were regularly delivered to MAL & OPD Division headquarters.","weight":"Because of the non-proportional allocation of the sample to the different reporting domains, sampling weights will be required for any analysis using RMIS 2013 data to ensure the sample is representative. Because the RMIS 2013 sample was a two-stage stratified cluster sample, sampling weights were calculated based on sampling probabilities that were calculated separately for each sampling stage and for each cluster.\n\nDetails of sampling weight calculation is available in Appendix A.4 of the final report.","cleaning_operations":"Processing of the 2013 RMIS data began as soon as questionnaires were received from the field. Completed questionnaires were returned from the field to MAL & OPD Division headquarters, where they were entered and edited by data processing personnel who were specially trained for this task. Processing the data concurrently with data collection allowed for regular monitoring of team 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 their high quality work and to correct areas in need of improvement. Feedback was individually tailored to each team. Data entry, which included 100 percent double entry to minimize errors in keying and data editing, was completed on May 10, 2013. Data cleaning and finalization was completed on June 3, 2013."},"analysis_info":{"response_rate":"A total of 4,772 households was selected, of which 4,769 households were identified and occupied at the time of the survey. Among these households, 4,766 completed the Household Questionnaire, yielding a response rate of nearly 100 percent.\n\nIn the 4,766 households surveyed, 5,164 women age 15-49 were identified as being eligible for the individual interview. Interviews were completed with 5,135 of these women, yielding a response rate of 99.4 percent. The response rates were slightly higher in rural areas than in urban areas.","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 2013 Rwanda Malaria Indicator Survey (2013 RMIS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.\n\nSampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2013 RMIS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling error is 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.\n\nA 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.\n\nIf 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 2013 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 2013 RMIS is an SAS program. This program used the Taylor linearization method for variance estimation for survey estimates that are means or proportions.\n\nThe Taylor linearization method treats any percentage or average as a ratio estimate, r = y\/x, where y represents the total sample value for variable y, and x represents the total number of cases in the group or subgroup under consideration.\n\nNote: Detailed description of estimate of sampling error is presented in APPENDIX B of the final report.","data_appraisal":"Data quality tables are produced to review the quality of the data:\n- Household age distribution\n- Age distribution of eligible and interviewed women\n- Completeness of reporting\n\nNote: The tables are presented in APPENDIX C of the final report."}},"data_access":{"dataset_use":{"contact":[{"name":"The DHS Program","affiliation":"","email":"archive@dhsprogram.com","uri":"http:\/\/www.DHSprogram.com"}],"cit_req":"Use of the dataset must be acknowledged using a citation which would include:\n- the Identification of the Primary Investigator\n- the title of the survey (including country, acronym and year of implementation)\n- the survey reference number\n- the source and date of download","conditions":"Request Dataset Access\nThe following applies to DHS, MIS, AIS and SPA survey datasets (Surveys, GPS, and HIV). \nTo 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. \n\nThe 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. \n\nDATASET ACCESS APPROVAL PROCESS\nAccess 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.\n\nRequired Information\nA dataset request must include contact information, a research project title, and a description of the analysis you propose to perform with the data.\n\nRestricted Datasets\nA few datasets are restricted and these are noted. Access to restricted datasets is requested online as with other datasets. An additional consent form is required for some datasets, and the form will be emailed to you upon authorization of your account. For other restricted surveys, permission must be granted by the appropriate implementing organizations, before The DHS Program can grant access. You will be emailed the information for contacting the implementing organizations. A few restricted surveys are authorized directly within The DHS Program, upon receipt of an email request. \n\nWhen The DHS Program receives authorization from the appropriate organizations, the user will be contacted, and the datasets made available by secure FTP. \n\nGPS\/HIV Datasets\/Other Biomarkers\nBecause of the sensitive nature of GPS, HIV and other biomarkers datasets, permission to access these datasets requires that you accept a Terms of Use Statement. After selecting GPS\/HIV\/Other Biomarkers datasets, the user is presented with a consent form which should be signed electronically by entering the password for the user's account.\n\nDataset Terms of Use\nOnce downloaded, the datasets must not be passed on to other researchers without the written consent of The DHS Program. All reports and publications based on the requested data must be sent to The DHS Program Data Archive in a Portable Document Format (pdf) or a printed hard copy. \n\nDownload Datasets\nDatasets are made available for download by survey. You will be presented with a list of surveys for which you have been granted dataset access. After selecting a survey, a list of all available datasets for that survey will be displayed, including all survey, GPS, and HIV data files. However, only data types for which you have been granted access will be accessible. To download, simply click on the files that you wish to download and a \"File Download\" prompt will guide you through the remaining steps.","disclaimer":"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."}}},"schematype":"survey","data_files":[{"file_id":"F6","file_name":"RECH0","description":"HOUSEHOLD \n- Household's basic data","case_count":4772,"var_count":38,"producer":null,"data_checks":null,"version":null,"notes":null,"metadata":null},{"file_id":"F7","file_name":"RECH1","description":"HOUSEHOLD \n- Household Schedule","case_count":20914,"var_count":31,"producer":null,"data_checks":null,"version":null,"notes":null,"metadata":null},{"file_id":"F8","file_name":"RECH2","description":"HOUSEHOLD \n- Household 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