{"type":"survey","doc_desc":{"title":"KEN_2015_MIS_v01_M","idno":"DDI_KEN_2015_MIS_v01_M_WB","producers":[{"name":"Development Data Group","abbreviation":"DECDG","affiliation":"The World Bank","role":"Documentation of the DDI"}],"prod_date":"2016-04-07","version_statement":{"version":"Version 01 (April 2016). Metadata is excerpted from \"Kenya Malaria Indicator Survey 2015\" Report."}},"study_desc":{"title_statement":{"idno":"KEN_2015_MIS_v01_M","title":"Malaria Indicator Survey 2015","alt_title":"MIS 2015 \/ KMIS 2015"},"authoring_entity":[{"name":"National Malaria Control Programme (NMCP)","affiliation":"Ministry of Health, Government of Kenya"},{"name":"Kenya National Bureau of Statistics (KNBS)","affiliation":"Ministry of Health, Government of Kenya"}],"production_statement":{"producers":[{"name":"ICF International","affiliation":"","role":"Provided technical assistance"}],"funding_agencies":[{"name":"Government of Kenya","abbreviation":"GovKEN","role":"Funded the study"},{"name":"United States Agency for International Development","abbreviation":"USAID","role":"Funded the study"},{"name":"United States President\u2019s Malaria Initiative","abbreviation":"PMI","role":"Funded the study"},{"name":"Global Fund","abbreviation":"GF","role":"Funded the study"},{"name":"World Health Organization","abbreviation":"WHO","role":"Funded the study"},{"name":"United Kingdom Department for International Development","abbreviation":"UKAID","role":"Funded the study"},{"name":"United Nations Children\u2019s Fund","abbreviation":"UNICEF","role":"Funded the study"}]},"distribution_statement":{"contact":[{"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 Kenya Malaria Indicator Survey is one of the key performance monitoring tools that are periodically used to provide an in-depth assessment of malaria control over time. This is the third survey undertaken; the previous two were in 2007 and in 2010. As with the previous KMIS surveys, the 2015 survey was designed to follow the Roll Back Malaria Monitoring and Evaluation Working Group guidelines, the Kenya National Malaria Strategy 2009-2018 (revised 2014), and the Kenya Malaria Monitoring and Evaluation Plan 2009-2017."},"study_info":{"abstract":"The 2015 Kenya Malaria Indicator Survey (KMIS) was carried out from July 6 to August 15, 2015, covering a nationallyrepresentative sample of 7,313 households. All women age 15-49 in the selected households were eligible for individual interviews. They were asked questions about prevention of malaria during pregnancy and treatment of childhood fever. In addition, the survey included testing for anaemia and malaria among children age 6 months to 14 years using a finger- or heel-prick blood sample. The results of anaemia and malaria rapid diagnostic testing were available immediately and were provided to the children\u2019s parents or guardians. Thin and thick blood smears were collected in the field and transported to the Kenya Medical Research Institute (KEMRI) Walter Reed Project Malaria Diagnostics Centre of Excellence Laboratory in Kisumu where they were tested for the presence of malaria parasites.\n\nThe main objective of the 2015 KMIS was to measure progress achieved in key malaria indicators since the 2010 KMIS. Specific objectives were the following:\n1. To determine the progress of key malaria interventions as stated in the Kenya Malaria Strategy 2009-2018 (revised 2014)\n2. To assess malaria parasite prevalence among children age 6 months to 14 years\n3. To determine anaemia prevalence among children age 6 months to 14 years","coll_dates":[{"start":"2015-07-06","end":"2015-08-15","cycle":""}],"nation":[{"name":"Kenya","abbreviation":"KEN"}],"geog_coverage":"National coverage","analysis_unit":"- Women age 15-49\n- Children age 0-5","data_kind":"Sample survey data [ssd]","notes":"The 2015 Kenya Malaria Indicator Survey covered the following topics:\n\nHOUSEHOLD\n\u2022 Identification\n\u2022 Usual members and visitors in the selected households\n\u2022 Background information on each person listed, such as relationship to head of the household, age, and sex\n\u2022 Characteristics of the household's dwelling unit, such as the source of water, type of toilet facilities, materials used for the floor, roof and walls of the house, and ownership of various durable goods\n\u2022 Mosquito net roster\n\u2022 Source and uses of mosquito nets\n\nWOMAN\n\u2022 Background characteristics (e.g., age, education, religion)\n\u2022 Reproductive history\n\u2022 Pregnancy and intermittent preventive treatment\n\u2022 Fever in children\n\u2022 Knowledge and attitudes\n\nBIOMARKER\n\u2022 Identification\n\u2022 Hemoglobin measurement and malaria testing for children age 0-14 years"},"method":{"data_collection":{"sampling_procedure":"The 2015 KMIS sample was designed to produce estimates for key indicators for the country as a whole, for urban and rural areas separately, and for each of the malaria epidemiologic zones: highland epidemic; lake endemic; coast endemic; semi-arid, seasonal; and low risk.\n\nThe sampling frame used for the 2015 KMIS was the Fifth National Sample Survey and Evaluation Program (NASSEP V) master sampling frame, which is created and maintained by KNBS for household-based surveys in Kenya. Development of the frame started in 2012. It contains a list of all enumeration areas (EAs) created for the 2009 census and covers the entire country. The frame is split into four equal subsamples, from one of which the 2015 KMIS sample was drawn. Kenya is administratively divided into 47 counties, created in the 2010 Constitution; within the frame, each county is stratified into urban and rural areas and is contained within one or two of the five malaria endemic zones.\n\nThe survey used a two-stage stratified cluster sampling design. In the first stage, 246 clusters (131 rural, 115 urban) were selected with equal probability from the NASSEP V. The second stage involved selection of a uniform sample of 30 households using systematic sampling from each of the selected clusters. Prior to household selection, all the clusters were updated by KNBS. This entailed undertaking a household listing in each of the selected clusters in order to update the list of residential households within it. As part of the listing, KNBS also updated the necessary maps and recorded the geographic coordinates of each cluster. Only selected households were interviewed, and replacement of nonresponding households was not allowed.\n\nFor further details of the sample design, see Appendix A of the final report.","coll_mode":"Face-to-face [f2f]","research_instrument":"Three types of questionnaires were used in the 2015 KMIS: a Household Questionnaire, a Woman\u2019s Questionnaire, and a Biomarker Questionnaire. These questionnaires were developed by the Roll Back Malaria Monitoring and Evaluation Reference Group in collaboration with ICF International. The questionnaires were adapted for use in Kenya by the KMIS Technical Working Group and were translated into Kiswahili. A team from ICF International programmed the questionnaires into the tablet computers for data collection.\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, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women age 15-49 eligible for the individual interview and children age 6 months to 14 years eligible for anaemia and malaria testing. The Household Questionnaire also collected information on characteristics of the household\u2019s dwelling unit, such as the source of water, type of toilet facilities, materials used for the floor, roof, and walls of the house, ownership of various durable goods, and ownership and use of mosquito nets. In addition, this questionnaire was used to capture some information on attitudes about malaria.\n\nThe Woman\u2019s Questionnaire was used to collect information from women age 15-49 on background characteristics, reproductive history for the last 6 years, antenatal care and preventive malaria treatment for the most recent birth, fever prevalence and treatment among children under age 5, and knowledge and attitudes regarding malaria treatment and prevention.\n\nThe Biomarker Questionnaire was used to record haemoglobin measurements and results of malaria rapid diagnostic testing for children age 6 months to 14 years. The questionnaire was filled in by the field teams\u2019 health workers and transcribed into the tablet computer by the household interviewer.","coll_situation":"Training of Field Staff\nThe main survey training took place from June 25 to July 4, 2015 in Nakuru. A total of 133 trainees participated in the training. These included 25 team supervisors, 50 interviewers and 50 health workers (25 clinicians and 25 laboratory technologists) and 8 reserves. The training schedule included sessions on survey background, interviewing techniques, the questionnaire, and testing procedures.\n\nTeam supervisors and the interviewers were trained on the content of the questionnaires, consent procedures, interviewing skills, and collection and transfer of data using the tablets. Health workers were trained on consent procedures, on conducting the anaemia and malaria testing, as well as making the thick and thin blood smears. The clinicians were trained on administering artemether lumefantrine to those who tested positive for malaria, according to the national guidelines, and in how to refer complicated malaria cases.\n\nAs part of the training, there was a day of practice in eight urban and four rural clusters not included in the sample survey. The KNBS County Statistical Office in Nakuru provided support in identifying local cluster boundaries.\n\nFieldwork\nFieldwork took a period of 6 weeks from July 6 to August 15, 2015. Twenty-five teams, each comprised of a supervisor, two interviewers, a clinician, and a laboratory technologist, completed the fieldwork. Each team was allocated clusters in the different counties according to their local language competency. The teams spent an average of 3 days in a cluster.\n\nDetails of the assigned clusters and sampled households were provided to field teams to enable them to properly identify the selected households; in each cluster, field teams were facilitated by the KNBS County Statistical Officer and a village guide. Where eligible respondents were absent from their home, a minimum of two additional callback visits were made on different days to facilitate the participation of the respondents. A courier service provided timely transport of the thick and thin blood smears from the field to the lab. Taking blood samples can be a sensitive issue in some communities. Therefore, sampled clusters received information about the KMIS through informational advertising and mass media.\n\nFieldwork was closely supervised by a team of national coordinators from NMCP and KNBS who visited the teams in the field to ensure that the survey was conducted according to the protocol and to provide solutions to any challenges encountered.","weight":"Because of the nonproportional allocation of the sample to the different counties and the possible differences in response rates, sampling weights are required for any analysis using the 2015 KMIS data. This approach ensures the actual representativeness of the survey results at a national as well as domain level. Since the 2015 KMIS sample is a two-stage stratified cluster sample selected from a master sample, sampling weights were calculated based on sampling probabilities separately for each sampling stage, including the master sample selection probabilities, and for each cluster.\n\nThe design weight is adjusted for household nonresponse and individual nonresponse to get the sampling weights for households and for women, respectively. Nonresponse is adjusted at the sampling stratum level. For the household sampling weight, the household design weight is multiplied by the inverse of the household response rate, by stratum. For the women\u2019s individual sampling weight, the household sampling weight is multiplied by the inverse of the women\u2019s individual response rate, by stratum. After adjusting for nonresponse, the sampling weights are normalized to get the final standard weights that appear in the data files. The normalization process is done to obtain a total number of unweighted cases equal to the total number of weighted cases at the national level, for the total number of households and women. Normalization is done by multiplying the sampling weight by the estimated sampling fraction obtained from the survey for the household weight and the individual woman\u2019s weight. The normalized weights are relative weights, which are valid for estimating means, proportions, ratios, and rates, but not valid for estimating population totals or for pooled data.","cleaning_operations":"The 2015 KMIS used ASUS Transformer T100 tablet computers with data entry programs developed in CSPro by The DHS Program at ICF International. Tablets were Bluetooth-enabled to facilitate the electronic transfer of household assignment among field team members and the transfer of completed questionnaires to team supervisors for transfer to the central office. Code division multiple access wireless technology via Internet File Streaming System (IFSS) developed by The DHS Program was used to transfer encrypted data from the field to the central office in Nairobi. Each tablet was fitted with a micro-SD card for encrypted data back-up.\n\nTo facilitate communication and monitoring, each field worker was assigned a unique identification number. In the central office, data received from the field team supervisors\u2019 tablets were registered and checked against any inconsistencies and outliers. Data editing and cleaning included range checks and structural and internal consistency checks. Any anomalies were communicated to the respective team through their team supervisor. The corrected results were re-sent to the central processing office."},"analysis_info":{"response_rate":"A total of 7,313 households were selected for the study, of which 6,667 were occupied at the time of fieldwork. Of these, 6,481 households were successfully interviewed, yielding an overall household response rate of 97 percent.","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 by either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2015 Kenya Malaria Indicator Survey (KMIS) 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 2015 KMIS 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.\n\nSampling 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 2015 KMIS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in either ISSA or SAS, using programs developed by ICF International. These programs use the Taylor linearization method of variance estimation for survey estimates that are means, proportions, or ratios.\n\nNote: A more detailed description of estimate of sampling error is presented in APPENDIX B of the survey report.","data_appraisal":"Data Quality Tables\n- Household age distribution\n- Age distribution of eligible and interviewed women\n- Completeness of reporting\n\nNote: See detailed data quality tables in APPENDIX C of the report."}},"data_access":{"dataset_availability":{"access_place":"The DHS Program","access_place_uri":"http:\/\/dhsprogram.com\/data\/available-datasets.cfm","original_archive":"The DHS Program\nhttp:\/\/dhsprogram.com\/data\/available-datasets.cfm\nCost: None"},"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":"F1","file_name":"RECH0","description":"HOUSEHOLD \n- Household's basic data","case_count":7313,"var_count":38,"producer":null,"data_checks":null,"version":null,"notes":null,"metadata":null},{"file_id":"F2","file_name":"RECH1","description":"HOUSEHOLD \n- Household Schedule","case_count":25430,"var_count":31,"producer":null,"data_checks":null,"version":null,"notes":null,"metadata":null},{"file_id":"F3","file_name":"RECH2","description":"HOUSEHOLD \n- Household 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