{"type":"survey","doc_desc":{"title":"KEN_2014_DHS_v01_M","idno":"DDI_KEN_2014_DHS_v01_M_WB","producers":[{"name":"Development Data Group","abbreviation":"DECDG","affiliation":"The World Bank","role":"Documentation of the DDI"}],"version_statement":{"version":"Version 01 (January 2016). Metadata is excerpted from \"Kenya Demographic and Health Survey 2014\" Report."}},"study_desc":{"title_statement":{"idno":"KEN_2014_DHS_v01_M","title":"Demographic and Health Survey 2014","alt_title":"DHS 2014 \/ KDHS 2014"},"authoring_entity":[{"name":"Kenya National Bureau of Statistics","affiliation":"Government of Kenya"}],"production_statement":{"producers":[{"name":"Ministry of Health","affiliation":"","role":"Collaborated in the implementation of the study"},{"name":"National AIDS Control Council","affiliation":"","role":"Collaborated in the implementation of the study"},{"name":"National Council for Population and Development","affiliation":"","role":"Collaborated in the implementation of the study"},{"name":"Kenya Medical Research Institute","affiliation":"","role":"Collaborated in the implementation of the study"},{"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 Nations Population Fund","abbreviation":"UNFPA","role":"Funded the study"},{"name":"United Kingdom Department for International Development","abbreviation":"DfID","role":"Funded the study"},{"name":"The World Bank Group","abbreviation":"WBG","role":"Funded the study"},{"name":"Danish International Development Agency","abbreviation":"DANIDA","role":"Funded the study"},{"name":"United Nations Children\u2019s Fund","abbreviation":"UNICEF","role":"Funded the study"},{"name":"German Development Bank","abbreviation":"KfW","role":"Funded the study"},{"name":"Clinton Health Access Initiative","abbreviation":"CHAI","role":"Funded the study"},{"name":"World Food Programme","abbreviation":"WFP","role":"Funded the study"},{"name":"Micronutrient Initiative","abbreviation":"MI","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 (Standard) - DHS VII","series_info":"The 2014 Kenya Demographic and Health Survey is the fifth of its kind and follows similar surveys conducted in 1989, 1993, 1998, 2003, and 2008-09. The 2014 KDHS provides information to help monitor and evaluate population and health status in Kenya. In 2014 KDHS, new indicators not collected in previous KDHS surveys, such as noncommunicable diseases, fistula, and men\u2019s experience of domestic violence, are included. Also, it is the first national survey to provide estimates for demographic and health indicators at the county level. Following adoption of a constitution in Kenya in 2010 and devolution of administrative powers to the counties, the new 2014 KDHS data should be valuable to managers and planners.\n\nThe 2014 KDHS has specifically collected data to estimate fertility, to assess childhood, maternal, and adult mortality, to measure changes in fertility and contraceptive prevalence, to examine basic indicators of maternal and child health, to estimate nutritional status of women and children, to describe patterns of knowledge and behaviour related to the transmission of HIV and other sexually transmitted infections, and to ascertain the extent and pattern of domestic violence and female genital cutting. Unlike the 2003 and 2008-09 KDHS surveys, this survey did not include HIV and AIDS testing. HIV prevalence estimates are available from the 2012 Kenya AIDS Indicator Survey (KAIS), completed prior to the 2014 KDHS."},"study_info":{"abstract":"The 2014 Kenya Demographic and Health Survey (KDHS) was designed to provide information to monitor and evaluate population and health status in Kenya and to be a follow-up to the previous KDHS surveys. In addition, it provides new information on indicators previously not collected in KDHS surveys, such as fistula and men\u2019s experience of domestic violence. The survey also aims to provide estimates for selected demographic and health indicators at the county level.\n\nThe specific objectives of the 2014 KDHS were to:\n\u2022 Estimate fertility and childhood, maternal, and adult mortality\n\u2022 Measure changes in fertility and contraceptive prevalence\n\u2022 Examine basic indicators of maternal and child health\n\u2022 Collect anthropometric measures for children and women\n\u2022 Describe patterns of knowledge and behaviour related to transmission of HIV and other sexually transmitted infections\n\u2022 Ascertain the extent and pattern of domestic violence and female circumcision","coll_dates":[{"start":"2014-05-07","end":"2014-10-20","cycle":""}],"nation":[{"name":"Kenya","abbreviation":"KEN"}],"geog_coverage":"National coverage","analysis_unit":"- Household\n- Individual\n- Children age 0-5\n- Woman age 15-49\n- Man age 15-54","data_kind":"Sample survey data [ssd]","notes":"The 2014 Kenya Demographic and Health 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, sex, marital status, survivorship and residence of bilogical parents, and highest educational attainment\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 (these items are used as proxy indicators of the household's socioeconomic status) \n\u2022 Household food consumption\n\u2022 Weight and height measurement for children age 0-5\n\u2022 Weight and height measurement for women age 15-49\n\nINDIVIDUAL WOMAN\n\u2022 Background characteristics (education, marital status, media exposure, etc.)\n\u2022 Reproductive history\n\u2022 Knowledge and use of family planning methods\n\u2022 Fertility preferences\n\u2022 Antenatal and delivery care\n\u2022 Breastfeeding and infant feeding practices\n\u2022 Vaccinations and childhood illnesses\n\u2022 Marriage and sexual activity\n\u2022 Women\u2019s work and husbands\u2019 background characteristics\n\u2022 Childhood mortality\n\u2022 Awareness and behaviour regarding HIV and other sexually transmitted infections\n\u2022 Adult mortality, including maternal mortality\n\u2022 Domestic violence\n\u2022 Female circumcision\n\u2022 Fistula\n\nINDIVIDUAL MAN\n\u2022 Respondent background characteristics\n\u2022 Reproduction\n\u2022 Contraception\n\u2022 Marriage and sexual activity\n\u2022 Fertility preferences\n\u2022 Employment and gender roles\n\u2022 HIV\/AIDS\n\u2022 Other health issues","study_scope":"The 2014 Kenya Demographic and Health 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, sex, marital status, survivorship and residence of bilogical parents, and highest educational attainment\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 (these items are used as proxy indicators of the household's socioeconomic status) \n\u2022 Household food consumption\n\u2022 Weight and height measurement for children age 0-5\n\u2022 Weight and height measurement for women age 15-49\n\nINDIVIDUAL WOMAN\n\u2022 Background characteristics (education, marital status, media exposure, etc.)\n\u2022 Reproductive history\n\u2022 Knowledge and use of family planning methods\n\u2022 Fertility preferences\n\u2022 Antenatal and delivery care\n\u2022 Breastfeeding and infant feeding practices\n\u2022 Vaccinations and childhood illnesses\n\u2022 Marriage and sexual activity\n\u2022 Women\u2019s work and husbands\u2019 background characteristics\n\u2022 Childhood mortality\n\u2022 Awareness and behaviour regarding HIV and other sexually transmitted infections\n\u2022 Adult mortality, including maternal mortality\n\u2022 Domestic violence\n\u2022 Female circumcision\n\u2022 Fistula\n\nINDIVIDUAL MAN\n\u2022 Respondent background characteristics\n\u2022 Reproduction\n\u2022 Contraception\n\u2022 Marriage and sexual activity\n\u2022 Fertility preferences\n\u2022 Employment and gender roles\n\u2022 HIV\/AIDS\n\u2022 Other health issues"},"method":{"data_collection":{"data_collectors":[{"name":"Kenya National Bureau of Statistics","abbreviation":"KNBS","affiliation":"Government of Kenya"}],"sampling_procedure":"The sample for the 2014 KDHS was drawn from a master sampling frame, the Fifth National Sample Survey and Evaluation Programme (NASSEP V). This is a frame that the KNBS currently operates to conduct household-based surveys throughout Kenya. Development of the frame began in 2012, and it contains a total of 5,360 clusters split into four equal subsamples. These clusters were drawn with a stratified probability proportional to size sampling methodology from 96,251 enumeration areas (EAs) in the 2009 Kenya Population and Housing Census. The 2014 KDHS used two subsamples of the NASSEP V frame that were developed in 2013. Approximately half of the clusters in these two subsamples were updated between November 2013 and September 2014. Kenya is divided into 47 counties that serve as devolved units of administration, created in the new constitution of 2010. During the development of the NASSEP V, each of the 47 counties was stratified into urban and rural strata; since Nairobi county and Mombasa county have only urban areas, the resulting total was 92 sampling strata.\n\nThe 2014 KDHS was designed to produce representative estimates for most of the survey indicators at the national level, for urban and rural areas separately, at the regional (former provincial) level, and for selected indicators at the county level. In order to meet these objectives, the sample was designed to have 40,300 households from 1,612 clusters spread across the country, with 995 clusters in rural areas and 617 in urban areas. Samples were selected independently in each sampling stratum, using a two-stage sample design. In the first stage, the 1,612 EAs were selected with equal probability from the NASSEP V frame. The households from listing operations served as the sampling frame for the second stage of selection, in which 25 households were selected from each cluster.\n\nThe interviewers visited only the preselected households, and no replacement of the preselected households was allowed during data collection. The Household Questionnaire and the Woman's Questionnaire were administered in all households, while the Man's Questionnaire was administered in every second household. Because of the non-proportional allocation to the sampling strata and the fixed sample size per cluster, the survey was not self-weighting. The resulting data have, therefore, been weighted to be representative at the national, regional, and county levels.\n\nFor further details on sample selection, see Appendix A of the final report.","coll_mode":"Face-to-face [f2f]","research_instrument":"The 2014 KDHS used a household questionnaire, a questionnaire for women age 15-49, and a questionnaire for men age 15-54. These instruments were based on the model questionnaires developed for The DHS Program, the questionnaires used in the previous KDHS surveys, and the current information needs of Kenya. During the development of the questionnaires, input was sought from a variety of organisations that are expected to use the resulting data. A two-day workshop involving key stakeholders was held to discuss the questionnaire design.\n\nA total of five questionnaires were used in the 2014 KDHS: (1) a full Household Questionnaire, (2) a short Household Questionnaire, (3) a full Woman\u2019s Questionnaire, (4) a short Woman\u2019s Questionnaire, and (5) a Man\u2019s Questionnaire. The 2014 KDHS sample was divided into halves. In one half, households were administered the full Household Questionnaire, the full Woman\u2019s Questionnaire, and the Man\u2019s Questionnaire. In the other half, households were administered the short Household Questionnaire and the short Woman\u2019s Questionnaire. Selection of these subsamples was done at the household level - within a cluster, one in every two households was selected for the full questionnaires, and the remaining households were selected for the short questionnaires.\n\nThe Household Questionnaire was used to list all of the usual members of the household and visitors who stayed in the household the night before the survey. One of the main purposes of the Household Questionnaire was to identify women and men who were eligible for the individual interview. 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 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 and roof of the house, ownership of various durable goods, and ownership and use of mosquito nets. In addition, this questionnaire was used to record height and weight measurements of women age 15-49 and children under age 5.\n\nThe Woman\u2019s Questionnaires were used to collect information from women age 15-49.\n\nThe Man\u2019s Questionnaire was administered to men age 15-54 living in every second household in the sample. The Man\u2019s Questionnaire collected information similar to that contained in the Woman\u2019s Questionnaire but was shorter because it did not contain questions on maternal and child health, nutrition, adult and maternal mortality, or experience of female circumcision or fistula.","coll_situation":"Main Training of Field Staff\nSeveral categories of personnel were recruited and trained to undertake the 2014 KDHS. These included 48 supervisors, 48 field editors, 144 female interviewers, 48 male interviewers, 28 quality assurance personnel, and 20 reserves.\n\nThe training for these personnel took place from March 24 to April 17, 2014, in Nakuru. Trainees were divided into six classrooms, each managed by three trainers. The training consisted of a detailed, question-by-question explanation of the questionnaires, accompanied by explanations from the interviewer\u2019s manual, demonstration through role-plays, group discussions, and in-class practice interviewing in pairs. Several graded take-home assignments and quizzes were administered, the results of which were used both to enhance understanding of key terms and concepts and to identify candidates for further strengthening or elimination from the field teams. A number of guest speakers were invited to give lectures on specific topics relevant to the KDHS.\n\nAnthropometry training provided all trainees with instruction, demonstration, and practice in length\/height and weight measurements for children and adults. Trainees completed a standardisation exercise measuring children, intended to gauge and improve measurement accuracy and precision. In this exercise, 175 children age 0-59 months and their caregivers were invited to the training site in groups of 50 child-caregiver pairs assigned throughout the day to one of three classrooms. Fifteen nutrition specialists from partnering organisations were trained to support the exercise; they provided a reference measurement for children and monitored the standardisation activity. Each of the 336 trainees served as both measurers and assistants and measured the same 10 children twice. Results were recorded and analysed using Software for Emergency Nutrition Assessment (ENA for SMART); more than 70 percent of trainees\u2019 scores were acceptable or higher. A debriefing session was held the following day to provide feedback and correction to trainees.\n\nThree field practice sessions were held throughout the main training. Trainees were organised into teams with a team leader selected from the pretest trainees. Team leaders assisted with logistics, guided trainees through fieldwork, monitored trainees\u2019 performance, edited trainees\u2019 questionnaires for errors, and debriefed their team on errors\/corrections. The first field practice occurred early in the training and focused only on the Household Questionnaire. The final two days of field practice occurred at the end of training and covered the full KDHS protocol: all questionnaires, salt testing, and anthropometry.\n\nFIELDWORK\nFieldwork for the main survey took place from May 7 to October 20, 2014. Field staff were divided into 48 teams according to counties and languages spoken in the areas where they conducted the interviews. Each team had one supervisor, one field editor, three female interviewers, one male interviewer, a driver, and a vehicle. Data collection was overseen by 18 coordinators who had also served as trainers during the pretest and main training and by a staff of 28 quality assurance personnel. Coordinators were each assigned two to three teams for which they were responsible for observing and monitoring data collection quality, ensuring uniformity in data collection procedures and fidelity to the survey protocol, providing moral support to the field teams, and replenishing field team supplies. Coordinators met in person and via phone with teams throughout the fieldwork, spending a total of 70 days in the field. Quality control staff fulfilled similar responsibilities and spent a total of 60 days in the field.","cleaning_operations":"Completed questionnaires were sent to the KNBS Data Processing Centre in Nairobi. Office editors who received the questionnaires verified cluster and household numbers to ensure that they were consistent with the sampled list. They also ensured that each cluster had 25 households and that all questionnaires for a particular household were packaged together.\n\nData entry began on May 28, 2014, with a four-day training session and continued until November 21, 2014. All data were double entered (100 percent verification) using CSPro software. The data processing team included 42 keyers, three office editors, two secondary editors, four supervisors, and one data manager. Secondary editing, which included further data cleaning and validation, ran simultaneously with data entry and was completed on January 28, 2015, in collaboration with ICF International. The KDHS Key Indicators Report was prepared and launched in April 2015."},"analysis_info":{"response_rate":"A total of 39,679 households were selected for the sample, of which 36,812 were found occupied at the time of the fieldwork. Of these households, 36,430 were successfully interviewed, yielding an overall household response rate of 99 percent. The shortfall of households occupied was primarily due to structures that were found to be vacant or destroyed and households that were absent for an extended period of time.\n\nAs noted, the 2014 KDHS sample was divided into halves, with one half of households receiving the full Household Questionnaire, the full Woman\u2019s Questionnaire, and the Man\u2019s Questionnaire and the other half receiving the short Household Questionnaire and the short Woman\u2019s Questionnaire. The household response rate for the full Household Questionnaire was 99 percent, as was the household response rate for the short Household Questionnaire.\n\nIn the households selected for and interviewed using the full questionnaires, a total of 15,317 women were identified as eligible for the full Woman\u2019s Questionnaire, of whom 14,741 were interviewed, generating a response rate of 96 percent. A total of 14,217 men were identified as eligible in these households, of whom 12,819 were successfully interviewed, generating a response rate of 90 percent.\n\nIn the households selected for and interviewed with the short questionnaires, a total of 16,855 women were identified as eligible for the short Woman\u2019s Questionnaire, of whom 16,338 were interviewed, yielding a response rate of 97 percent.\n\nResponse rates are lower in the urban sample than in the rural sample, more so for men. The principal reason for non-response among both eligible men and eligible women was failure to find them at home despite repeated visits to the household. The lower response rates for men reflect the more frequent and longer absences of men from the household","sampling_error_estimates":"The estimates from a sample survey are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling 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 2014 Kenya Demographic and Health Survey (2014 KDHS) to minimise this type of error, non-sampling 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 2014 KDHS 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.\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 2014 KDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. Sampling errors are computed in either ISSA or SAS, using programs developed by ICF Macro. These programs use the Taylor linearisation method of variance estimation 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.\n\nThe Taylor linearisation 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: 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- Age distribution of eligible and interviewed men\n- Completeness of reporting\n- Births by calendar years\n- Reporting of age at death in days\n- Reporting of age at death in months\n- Nutritional status of children based on the NCHS\/CDC\/WHO International Reference Population\n- Completeness of information on siblings\n- Sibship size and sex ratio of siblings\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."}}},"data_files":[{"file_id":"F49","file_name":"RECH0","description":"HOUSEHOLD \n- Household's basic data","case_count":"39679","var_count":"38","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F50","file_name":"RECH1","description":"HOUSEHOLD \n- Household Schedule","case_count":"153840","var_count":"31","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F51","file_name":"RECH2","description":"HOUSEHOLD \n- Household Characteristics","case_count":"36430","var_count":"92","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F52","file_name":"RECH3","description":"HOUSEHOLD \n- Survey specific Household variables","case_count":"39679","var_count":"91","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F53","file_name":"RECH4","description":"HOUSEHOLD \n- Survey specific Household Schedule variables","case_count":"153840","var_count":"13","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F54","file_name":"RECH5","description":"HOUSEHOLD \n- Women Height\/Weight\/Hemoglobin","case_count":"16419","var_count":"38","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F55","file_name":"RECH6","description":"HOUSEHOLD \n- Children Height\/Weight\/Hemoglobin","case_count":"21896","var_count":"42","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F56","file_name":"RECHMH","description":"HOUSEHOLD \n- Malaria: by Household Member","case_count":"153840","var_count":"26","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F57","file_name":"RECHML","description":"HOUSEHOLD \n- Malaria: by Mosquito Bed Net","case_count":"46792","var_count":"20","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F58","file_name":"REC01","description":"WOMAN\n - Respondent's basic data","case_count":"32172","var_count":"40","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F59","file_name":"REC11","description":"WOMAN\n - Respondent's basic data 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Use","case_count":"31079","var_count":"104","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F64","file_name":"REC41","description":"WOMAN\n -  Maternity","case_count":"20964","var_count":"147","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F65","file_name":"REC42","description":"WOMAN\n - Health and Breastfeeding","case_count":"31079","var_count":"171","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F66","file_name":"REC43","description":"WOMAN\n - Child's Health and Vaccinations","case_count":"20964","var_count":"169","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F67","file_name":"REC44","description":"WOMAN\n - Child's Height and 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