{"type":"survey","doc_desc":{"title":"LBR_2013_DHS_v01_M","idno":"DDI_LBR_2013_DHS_v01_M_WB","producers":[{"name":"Development Data Group","abbreviation":"DECDG","affiliation":"The World Bank","role":"Metadata preparation"}],"prod_date":"2014-09-25","version_statement":{"version":"Version 01 (September 2014). Metadata is excerpted from \"Liberia Demographic and Health Survey 2013\" Report."}},"study_desc":{"title_statement":{"idno":"LBR_2013_DHS_v01_M","title":"Demographic and Health Survey 2013","alt_title":"DHS 2013 \/ LDHS 2013"},"authoring_entity":[{"name":"Liberia Institute of Statistics and Geo-Information Services (LISGIS)","affiliation":"Ministry of Health and Social Welfare (MOHSW)"}],"production_statement":{"producers":[{"name":"ICF International","affiliation":"MEASURE DHS project","role":"Technical assistance"}],"funding_agencies":[{"name":"Government of Liberia","abbreviation":"GovLBR","role":"Funded the study"},{"name":"United States Agency for International Development","abbreviation":"USAID","role":"Funded the study"},{"name":"The Global Fund","abbreviation":"GF","role":"Funded the study"},{"name":"United Nations Children\u2019s Fund","abbreviation":"UNICEF","role":"Funded the study"},{"name":"United Nations Population Fund","abbreviation":"UNFPA","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 VI","series_info":"The National Demographic and Health Survey (NDHS) is part of the worldwide MEASURE Demographic and Health Surveys program, which is designed to collect information on a variety of health-related topics including fertility, family planning, and maternal and child health.\n\nThe 2013 Liberia Demographic and Health Survey (2013 LDHS) constitutes the second post-war, and fourth overall, LDHS in the Republic of Liberia. The first LDHS was conducted in 1986 as part of the worldwide DHS program; Liberia was the second country in the world and the first in Africa to conduct a DHS under this program. Liberia undertook the second LDHS in 1999\/2000 outside the purview of the international DHS program and with no outside technical assistance. Liberia undertook a third LDHS in 2007, this time as part of the MEASURE DHS program."},"study_info":{"abstract":"The 2013 Liberia Demographic and Health Survey (LDHS) is designed to provide data for monitoring the population and health situation in Liberia. The 2013 LDHS is the fourth Demographic and Health Survey conducted in Liberia since 1986. The primary objective of the 2013 LDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the 2013 LDHS collected information on fertility levels, marriage, sexual activity, fertility preferences, family planning methods, breastfeeding practices, nutrition, childhood and maternal mortality, maternal and child health, and HIV\/AIDS and other sexually transmitted infections (STIs). In addition, the 2013 LDHS provides estimates on HIV prevalence among adult Liberians.","coll_dates":[{"start":"2013-03","end":"2013-07","cycle":""}],"nation":[{"name":"Liberia","abbreviation":"LBR"}],"geog_coverage":"National coverage","analysis_unit":"- Household\n- Individual\/ person\n- Children age 0-5 years\n- Woman age 15 to 49 years\n- Man age 15 to 49 years","data_kind":"Sample survey data [ssd]","notes":"The 2013 Liberia Demographic and Health Survey covered the following topics:\n\nHOUSEHOLD\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, 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 Inpatient health expenditures\n\u2022 Outpatient health expenditures\n\u2022 Weight and height measurement for children age 0-5\n\u2022 Weight, height, and HIV testing for women age 15-49\n\u2022 Weight, height, and HIV testing for men age 15-49\n\nINDIVIDUAL WOMAN\n\u2022 Background characteristics (age, education, religion, etc.)\n\u2022 Birth history and child mortality\n\u2022 Knowledge and use of family planning methods\n\u2022 Fertility preferences\n\u2022 Prenatal, delivery, and postnatal care\n\u2022 Breastfeeding and infant feeding practices\n\u2022 Vaccinations and childhood illnesses\n\u2022 Marriage and sexual activity\n\u2022 Women's work and husband's background characteristics\n\u2022 Malaria prevention and treatment\n\u2022 Knowledge, awareness, and behavior regarding AIDS and other sexually transmitted infections (STIs)\n\u2022 Adult mortality, including maternal mortality\n\nINDIVIDUAL MAN\n\u2022 Background characteristics (age, education, religion, etc.)\n\u2022 Knowledge and use of family planning methods\n\u2022 Fertility preferences\n\u2022 Marriage and sexual activity\n\u2022 Employment and gender roles\n\u2022 Knowledge, awareness, and behavior regarding AIDS and other sexually transmitted infections (STIs)","study_scope":"The 2013 Liberia Demographic and Health Survey covered the following topics:\n\nHOUSEHOLD\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, 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 Inpatient health expenditures\n\u2022 Outpatient health expenditures\n\u2022 Weight and height measurement for children age 0-5\n\u2022 Weight, height, and HIV testing for women age 15-49\n\u2022 Weight, height, and HIV testing for men age 15-49\n\nINDIVIDUAL WOMAN\n\u2022 Background characteristics (age, education, religion, etc.)\n\u2022 Birth history and child mortality\n\u2022 Knowledge and use of family planning methods\n\u2022 Fertility preferences\n\u2022 Prenatal, delivery, and postnatal care\n\u2022 Breastfeeding and infant feeding practices\n\u2022 Vaccinations and childhood illnesses\n\u2022 Marriage and sexual activity\n\u2022 Women's work and husband's background characteristics\n\u2022 Malaria prevention and treatment\n\u2022 Knowledge, awareness, and behavior regarding AIDS and other sexually transmitted infections (STIs)\n\u2022 Adult mortality, including maternal mortality\n\nINDIVIDUAL MAN\n\u2022 Background characteristics (age, education, religion, etc.)\n\u2022 Knowledge and use of family planning methods\n\u2022 Fertility preferences\n\u2022 Marriage and sexual activity\n\u2022 Employment and gender roles\n\u2022 Knowledge, awareness, and behavior regarding AIDS and other sexually transmitted infections (STIs)"},"method":{"data_collection":{"data_collectors":[{"name":"Liberia Institute of Statistics and Geo-Information Services","abbreviation":"LISGIS","affiliation":"Ministry of Health and Social Welfare (MOHSW)"}],"sampling_procedure":"Sample Design\nThe sampling frame for the 2013 LDHS was developed by the Liberia Institute of Statistics and Geo-Information Services (LISGIS) after the 2008 National Population and Housing Census (NPHC). The sampling frame is similar to that used for the 2009 and 2011 Liberia Malaria Indicator Surveys (LMIS), except that the classification of localities as urban or rural was updated through the application of standardized definitions. The sampling frame excluded nomadic and institutional populations such as residents of hotels, barracks, and prisons. Notably, the sampling frame for the 2013 LDHS differs markedly from that used for the 2007 LDHS, which was based on the 1984 NPHC. Taken together, these differences may complicate data comparisons between surveys.\n\nThe 2013 LDHS followed a two-stage sample design that allowed estimates of key indicators for the country as a whole, for urban and rural areas separately, for Greater Monrovia and other urban areas separately, and for each of 15 counties. To facilitate estimates of geographical differentials for certain demographic indicators, the 15 counties were collapsed into five regions as follows:\nNorth Western: Bomi, Grand Cape Mount, and Gbarpolu\nSouth Central: Montserrado, Margibi, and Grand Bassa\nSouth Eastern A: River Cess, Sinoe, and Grand Gedeh\nSouth Eastern B: River Gee, Grand Kru, and Maryland\nNorth Central: Bong, Nimba, and Lofa\n\nRegional data were presented in the 2007 LDHS, the 2009 LMIS, and the 2011 LMIS. However, in contrast with these past surveys, the South Central region now includes Monrovia. Thus, data presented for the South Central region in this report is not directly comparable to that presented in the 2007 LDHS, the 2009 LMIS, or the 2011 LMIS.\n\nThe first stage of sample selection involved selecting sample points (clusters) consisting of enumeration areas (EAs) delineated for the 2008 NPHC. Overall, the sample included 322 sample points, 119 in urban areas and 203 in rural areas. To allow for separate estimates of Greater Monrovia and Montserrado as a whole, 44 sample points were selected in Montserrado; 16 to 26 sample points were selected in each of the other 14 counties.\n\nThe second stage of selection involved the systemic sampling of households. A household listing operation was undertaken in all the selected EAs from mid-September to mid-October 2012. From these lists, households to be included in the survey were selected. Approximately 30 households were selected from each sample point for a total sample size of 9,677 households. During the listing, geographic coordinates (latitude and longitude) were taken in the center of the populated area of each EA using global positioning system (GPS) units.\n\nBecause of the approximately equal sample sizes in each region, 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.\n\nAll women age 15-49 who were either permanent residents of the selected households or visitors who stayed in the household the night before the survey were eligible to be interviewed. In half of the households, all men age 15-49 who were either permanent residents of the selected households or visitors who stayed in the household the night before the survey were eligible to be interviewed. In the subsample of households selected for the male survey, blood samples were collected for laboratory testing to detect HIV from eligible women and men who consented; in this same subsample of households, height and weight information was collected from eligible women, men, and children 0-59 months.\n\nFurther details on the sample design and implementation are given in Appendix A of the final report.","coll_mode":"Face-to-face [f2f]","research_instrument":"Three questionnaires were used for the 2013 LDHS: the Household Questionnaire, the Woman\u2019s Questionnaire, and the Man\u2019s Questionnaire. These questionnaires are based on MEASURE DHS standard survey questionnaires and were adapted to reflect the population and health issues relevant to Liberia. Input was solicited from various stakeholders representing government ministries and agencies, nongovernmental organizations, and international donors.\n\nGiven that there are dozens of local languages in Liberia, most of which have no accepted written script and are not taught in the schools, and given that English is widely spoken, it was decided not to attempt to translate the questionnaires into vernaculars. However, many of the questions were broken down into a simpler form of Liberian English that interviewers could use with respondents.\n\nThe Household Questionnaire was used to list all the usual members of and visitors to selected households. Some basic demographic information was collected on the characteristics of each person listed, including his or her age, sex, education, and relationship to the head of the household. For children under age 18, survival status of the parents was determined. The data on age and sex of household members obtained in the Household Questionnaire were used to identify women and men who were eligible for individual interview and HIV testing. The Household Questionnaire also collected information on characteristics of the household\u2019s dwelling unit, such as the source of water, type of toilet facility, materials used for the floor of the house, ownership of various durable goods, ownership and use of mosquito nets, and information on household out-of-pocket health-related expenditures. The Household Questionnaire was also used to record height and weight measurements of children 0-59 months and eligible adults. Also recorded was whether or not eligible adults consented to HIV testing.\n\nThe Woman\u2019s Questionnaire was used to collect information from all eligible women age 15-49.\n\nThe Man\u2019s Questionnaire was administered to all men age 15-49 in the subsample of households selected for the male survey in the 2013 LDHS sample. The Man\u2019s Questionnaire collected much of the same information as the Woman\u2019s Questionnaire, but was shorter because it did not contain a detailed reproductive history or questions on maternal and child health.","coll_situation":"Training of Field Staff\nSix women and nine men participated in a training to pretest the LDHS survey protocol from 20 August to 7 September 2012. Most participants had worked on various LDHS survey activities previously, including the 2007 LDHS, or were employed by LISGIS. Trainers were staff from LISGIS and MEASURE DHS. Ten days of classroom instruction were provided. Additionally, pretest field practice took place over four days in both rural and urban locations. Following field practice, a debriefing session was held with the pretest field staff, and modifications to the questionnaires were made based on lessons drawn from the exercise.\n\nThe recruitment of the LDHS field staff began in October 2012. The positions were advertised via announcements on bulletin boards in LISGIS headquarters and all LISGIS county offices. Minimum requirements of applicants included a high school diploma, fluency in English, and familiarity with one or more local dialects. A total of 3,662 applications were received from all counties. Vetting of all applications was done over a two-week period; 1,339 candidates were short-listed to sit for aptitude testing. Two aptitude tests were arranged. The first occurred in November 2013; those who passed were eligible for a second aptitude test, which was administered in January 2013. One thousand and sixty-four candidates sat for the first test, and 564 candidates sat for the second test. Based on the outcome of the second test combined with prior survey experience and other intangibles, a total of 128 persons (82 females and 46 males) were invited to the main training.\n\nThe field staff main training took place over four weeks (11 February to 8 March 2013). The training was conducted following MEASURE DHS training procedures, which included class presentations, mock interviews, tests, and field practice. Trainers included LISGIS staff who participated in the LDHS pretest; staff from MOHSW, WHO, and Planned Parenthood Association of Liberia; and staff from ICF International.\n\nOut of those persons who were recruited and attended the main training, 65 women and 31 men were selected to carry out field work. Among this group, 16 persons were selected as team supervisors and 16 persons were selected as field editors; all others served as interviewers. Team supervisors and field editors were provided with additional training in methods of field editing, data quality control procedures, and fieldwork coordination.\n\nFieldwork\nData collection was carried out by 16 field teams, each consisting of one team supervisor, one field editor, three female interviewers, one male interviewer, and one driver. On each team, one of the female interviewers and the male interviewer were also tasked with biomarker collection (conducting height and weight measurements and blood collection for HIV testing from eligible respondents). Five senior staff members from LISGIS and a senior staff member from NACP coordinated and supervised the fieldwork activities. Participants in fieldwork monitoring also included a resident advisor, a survey technical specialist, and a senior data processing specialist, all of whom worked directly for the MEASURE DHS project.\n\nData collection took place over a four-month period from 10 March to 19 July 2013. For logistical reasons, including the difficulty in reaching the clusters located in the Southeast during the rainy season, fieldwork was divided into three phases:\n\u2022 Phase I: Maryland, Grand Kru, Sinoe, River Gee, Grand Gedeh\n\u2022 Phase II: Lofa, Bong, Nimba, Grand Bassa, River Cess\n\u2022 Phase III: Margibi, Montserrado, Greater Monrovia, Bomi, Gbarpolu, Grand Cape Mount\n\nAt least three teams were assigned to each county.","weight":"Due to the nonproportional allocation of the sample across domains and urban-rural areas, and the differential response rates, sampling weights must be calculated using all analyses of the LDHS results to ensure that survey results are representative at both the national and domain level. Since the LDHS sample is a two-stage stratified cluster sample, sampling weights are based on sampling probabilities calculated separately for each sampling stage and for each cluster.\n\nThe design weight is adjusted for household non-response and individual non-response to get the sampling weights for households and for women and men, respectively. Non-response 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. For the men\u2019s individual sampling weight, the household sampling weight for the male sub-sample is multiplied by the inverse of the men\u2019s individual response rate, by stratum. After adjusting for non-response, 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, women, and men. Normalization is done by multiplying the sampling weight by the estimated sampling fraction obtained from the survey for the household weight, the individual woman\u2019s weight, and the individual man\u2019s weight. The normalized weights are relative weights that are valid for estimating means, proportions, ratios, and rates, but they are not valid for estimating population totals or pooled data. The sampling weights for HIV testing are calculated in a similar way, but the normalization of the HIV weights is different. The individual HIV testing weights are normalized at the national level for women and men together so that HIV prevalence estimates calculated for women and men together are valid.\n\nFurther details on the sample weight calculation are given in Appendix A.4 in the final report.","cleaning_operations":"All questionnaires were returned to the LISGIS central office in Monrovia for data processing, which consisted of office editing, coding of open-ended questions, data entry, and editing computer-identified errors. The data were processed by a team of 12 data entry clerks, two data editors, one data entry supervisor, and two administrators of questionnaires; the latter checked that the clusters were completed according to the sample selection and that all members of the household eligible for individual interview were identified. Secondary editing was led by an LDHS coordinator. Several LISGIS staff took on the responsibility of receiving the blood samples from the field and checking them before sending them to the Montserrado Regional Blood Bank for storage. Data entry and editing using CSPro software was initiated in April 2013 and completed in late August 2013."},"analysis_info":{"response_rate":"A total of 9,677 households were selected for the sample, of which 9,386 were occupied. Of the occupied households, 9,333 were successfully interviewed, yielding a response rate of 99 percent.\n\nIn the interviewed households, 9,462 eligible women were identified for individual interview; of these, complete interviews were conducted with 9,239 women, yielding a response rate of 98 percent. In the subsample of households selected for the male survey, 4,318 eligible men were identified and 4,118 were successfully interviewed, yielding a response rate of 95 percent. The lower response rate for men was likely due to their more frequent and longer absences from the household.","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 2013 Liberia Demographic and Health Survey 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 LDHS 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 2013 LDHS 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. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.\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\nFurther details on sampling errors calculation are given in Appendix B of the final report.","data_appraisal":"Data quality tables were 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- Births by calendar years\n- Reporting of age at death in days\n- Reporting of age at death in months\n- Completeness of information on siblings\n- Sibship size and sex ratio of siblings\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."}}},"data_files":[{"file_id":"F1","file_name":"RECH0","description":"HOUSEHOLD \n- Household's basic data","case_count":"9677","var_count":"38","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F2","file_name":"RECH1","description":"HOUSEHOLD \n- Household Schedule","case_count":"48219","var_count":"31","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F3","file_name":"RECH2","description":"HOUSEHOLD \n- Household Characteristics","case_count":"9333","var_count":"92","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F4","file_name":"RECH3","description":"HOUSEHOLD \n- Survey specific Household variables","case_count":"9677","var_count":"30","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F5","file_name":"RECH4","description":"HOUSEHOLD \n- Survey specific Household Schedule variables","case_count":"48219","var_count":"12","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F6","file_name":"RECH5","description":"HOUSEHOLD \n- Women Height\/Weight\/Hemoglobin","case_count":"4967","var_count":"39","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F7","file_name":"RECH6","description":"HOUSEHOLD \n- Children Height\/Weight\/Hemoglobin","case_count":"4131","var_count":"43","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F8","file_name":"RECH7","description":"HOUSEHOLD \n- Inpatient Health Expenditures","case_count":"2404","var_count":"17","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F9","file_name":"RECHMA","description":"HOUSEHOLD \n- Men Height\/Weight\/Hemoglobin","case_count":"4586","var_count":"38","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F10","file_name":"RECHMH","description":"HOUSEHOLD \n- Malaria: by Household Member","case_count":"48219","var_count":"25","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F11","file_name":"RECHML","description":"HOUSEHOLD \n- Malaria: by Mosquito Bed Net","case_count":"10954","var_count":"17","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F12","file_name":"REC01","description":"WOMAN\n - Respondent's basic data","case_count":"9462","var_count":"40","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F13","file_name":"REC11","description":"WOMAN\n - Respondent's basic data (continued)","case_count":"9239","var_count":"56","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F14","file_name":"REC21","description":"WOMAN\n - Reproduction and Birth History","case_count":"30804","var_count":"20","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F15","file_name":"REC22","description":"WOMAN\n - Reproduction (continued)","case_count":"9239","var_count":"44","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F16","file_name":"REC31","description":"WOMAN\n - Contraception Table","case_count":"97010","var_count":"10","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F17","file_name":"REC32","description":"WOMAN\n - Contraception Knowledge and Use","case_count":"9239","var_count":"104","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F18","file_name":"REC41","description":"WOMAN\n -  Maternity","case_count":"7606","var_count":"147","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null},{"file_id":"F19","file_name":"REC42","description":"WOMAN\n - Health and 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