{"type":"survey","doc_desc":{"title":"AGO_2011_MIS_v01_M","idno":"DDI_AGO_2011_MIS_v01_M_WB","producers":[{"name":"Development Data Group","abbreviation":"DECDG","affiliation":"The World Bank","role":"Documentation of the DDI"}],"prod_date":"2014-01-23","version_statement":{"version":"Version 01 (January 2014)"}},"study_desc":{"title_statement":{"idno":"AGO_2011_MIS_v01_M","title":"Malaria Indicator Survey 2011","alt_title":"MIS 2011"},"authoring_entity":[{"name":"Consultoria de Servicos e Pesquisas\u2013COSEP, Consultoria, Lda","affiliation":""},{"name":"Consultoria de Gestao e Administracao em Sa\u00fade\u2013Consa\u00fade, Lda","affiliation":""}],"production_statement":{"producers":[{"name":"Ministry of Health","affiliation":"","role":""},{"name":"National Malaria Control Program","affiliation":"","role":""},{"name":"United States Agency for International Development","affiliation":"","role":""},{"name":"ICF International","affiliation":"","role":"Technical assistance"}],"funding_agencies":[{"name":"United States Agency for International Development","abbreviation":"USAID","role":""},{"name":"President\u2019s Malaria Initiative","abbreviation":"PMI","role":""}]},"distribution_statement":{"contact":[{"name":"General Inquiries","affiliation":"","email":"info@measuredhs.com","uri":"www.measuredhs.com"},{"name":"Data and Data Related Resources","affiliation":"","email":"archive@measuredhs.com","uri":"www.measuredhs.com"}]},"series_statement":{"series_name":"Demographic and Health Survey [hh\/dhs]","series_info":"The 2011 Angola Malaria Indicator Survey (2011 AMIS) is the second survey of this type in the country and is conducted as part of the third phase of the MEASURE DHS project."},"version_statement":{"version":"- v01:  Edited, anonymous datasets for public distribution."},"study_info":{"abstract":"The 2011 Malaria Indicator Survey in Angola (2011 AMIS) was conducted by Cosep Consultoria, Consa\u00fade Lda., and the Programa Nacional de Controle da Mal\u00e1ria, with technical assistance from ICF Macro. Fieldwork took place from January 2011 through May 2011. The Angola Malara Indicator Survey (AMIS) is part of the Demographic and Health Surveys (MEASURE DHS) program and the Malaria Indicator Surveys (MIS) programs, implemented by ICF International under contract with USAID Washington. The objectives of the 2011 AMIS are (1) to evaluate behavior related to the prevention and treatment of malaria and (2) to estimate the prevalence of malaria among children under age 5. Additional questions were included to facilitate the estimation of fertility and infant mortality.\n\nFieldwork for the 2011 AMIS took place between January 2011 and May 2011, amidst heavy rains and floods typical of the period of high transmission of malaria. The survey collected data from 8,030 households and 8,589 women age 15-49. The sample was designed to represent populations at the national level, at urban and rural levels, and in four recognized malaria epidemiological regions: Hyperendemic, Mesoendemic Stable, Mesoendemic Unstable, and the Province of Luanda.","coll_dates":[{"start":"2011-01","end":"2011-05","cycle":""}],"nation":[{"name":"Angola","abbreviation":"AGO"}],"geog_coverage":"National","analysis_unit":"- Household,\n- Individual.","universe":"The survey covered all de jure household members (usual residents), all women aged between 15-49 years, all children under age 5 living in the household.","data_kind":"Sample survey data [ssd]","notes":"The scope of the 2011 Angola Malaria Indicator Survey includes:\n- Household: age, sex, relationship to the head of the household, characteristics of the household dwelling, such as the water source; type of toilet facilities; materials used for the roof, floors, and walls; possession of durable goods; and possession and use of mosquito nets.\n- Women: sociodemographic characteristics of the respondent, birth history, prenatal care and intermittent preventive treatment (IPT) of malaria during pregnancy for the most recent birth, treatment of malaria symptoms in children, malaria knowledge."},"method":{"data_collection":{"data_collectors":[{"name":"Consultoria de Servicos e Pesquisas\u2013COSEP, Consultoria, Lda","abbreviation":"","affiliation":""},{"name":"Consultoria de Gestao e Administracao em Sa\u00fade\u2013Consa\u00fade, Lda","abbreviation":"","affiliation":""}],"sampling_procedure":"OBJECTIVES OF THE SAMPLING DESIGN\n(1) The 2011 AMIS survey was designed to determine reliable malaria prevalence estimates among children under age 5 at the various domains of interest (when feasible) and mortality estimates for children under age 5.\n(2) The major domains to be distinguished in the tabulation of key indicators are:\n- Angola at the national level\n- The majority of indicators for each of the four domains defined for Angola and classified as the following regions:\n1) Hyperendemic region, high malaria prevalence\n2) Mesoendemic Stable region, medium malaria prevalence\n3) Mesoendemic Unstable region, medium malaria prevalence, though prevalence is affected by the amount of rain\n4) Luanda province\n- Urban and rural areas of Angola (each as a separate domain)\n- Any contiguous group of provinces with an adequate sample size of at least 1,500 households\n(3) The primary objective of the 2011 AMIS is to provide estimates with acceptable precision for important population indicators associated with each domain, such as:\na. Ownership and use of mosquito bednets.\nb. Practices to treat malaria among children under age 5 and the use of specific antimalarial drugs\nc. Prevalence of malaria and anemia among children age 6-59 months\nd. Knowledge, attitudes, and practices regarding malaria in the general population\n\nSAMPLE FRAME\nAdministratively, Angola is divided into 18 provinces, which can be grouped into eight subregions depending on how they share some common factors.2 In turn, each province is subdivided into municipalities (164 in total), and each municipality is divided into communes (532 in total). Each commune is classified as either urban or rural. In addition to these administrative units, in preparation for the last population census, each urban commune was subdivided into segments named census sections (CSs) that were equivalent to enumeration areas. The National Statistical Institute (INE) had been preparing cartographic materials, including a count of rooms and dwellings, for each CS in the urban areas. This material became an appropriate sampling frame for the 2011 AMIS. However, INE does not have updated cartographic material for the rural areas. To compensate for this lack, INE uses its regional offices to collect a list of villages, with estimated populations in each village, for most of the rural communes,. To develop the sample frame for the 2011 AMIS, the list of CSs was used for the urban communes and the list of villages was used for the rural communes.\n\nSTRATIFICATION\nThe communes were grouped by major region, by rural or urban location, by sub-region, and by province as a way to identify homogeneous sampling units. In addition, within each urban commune, several CSs were grouped, taking advantage of the existing neighborhoods (sub-districts) for stratification of the sample.\n\nSAMPLE SIZE\nThe following table includes different scenarios used to select a sample size in a populationbased survey. In the absence of domains, the numbers are valid for the entire population; however, if analyses are expected for more than one domain, then the numbers should be interpreted as required for each domain.\n\nSAMPLE ALLOCATION\nThe clusters for the implementation of the 2011 AMIS are defined on the basis of census sections (CSs) for urban communes and on the basis of villages for rural communes. The 240 clusters considered for the 2011 AMIS were equally allocated at 60 clusters in each domain. The target for the 2011 AMIS was to select about 8,800 households. Therefore, the sample take is on average 36 selected households per cluster (i.e., 8,800\/240). Clusters are distributed as 96 in the urban areas and 144 in the rural areas.\n\nUnder the final sample allocation, it is expected that each of the four major malaria regions in Angola will provide a minimum of about 2,200 completed women interviews, 2,100 children under age 5, and 2,000 births in the last five years. Neither the distribution of the 240 clusters among major regions nor the distribution of households in the sample is proportional to the estimated population distribution. This is due to the disproportional number of CSs among major regions. As a result, the sample for the 2011 AMIS is not a selfweighted household sample. Therefore, the 2011 AMIS sample is unbalanced for residence areas and regions and will require the design of a final weighting adjustment procedure to provide representative estimates for all the study domains.\n\nSAMPLE SELECTION\nThe sample for the 2011 AMIS was selected using a stratified three-stage cluster design consisting of 240 clusters, with 96 in urban areas and 144 in rural areas. In each urban or rural area in a given region, clusters are selected systematically with probability proportional to size.\n\nThe sampling procedures are fully described in Appendix A of \" Angola Malaria Indicator Survey 2011 - Final Report\" pp.43-48.","coll_mode":"Face-to-face [f2f]","research_instrument":"Two types of questionnaires were used for the 2011 AMIS: a household questionnaire and another questionnaire for women age 15-49 in the households selected for the survey. The questionnaires were developed from the ones used for the 2006-07 malaria indicator survey, which followed the methodology of the Roll Back Malaria and MEASURE DHS programs.\n\nThe Household Questionnaire was used to list all the usual members and visitors who stayed in the selected households the night before the survey. It also identified women eligible for interviewing and children age 6-59 months eligible for anemia and malaria tests.\n\nBasic information collected on the characteristics of each person included age, sex, and relationship to head of household. The Household Questionnaire was also used to collect information on characteristics of the household dwelling, such as the water source; type of toilet facilities; materials used for the roof, floors, and walls; possession of durable goods; and possession and use of mosquito nets.\n\nThe Woman\u2019s Questionnaire, used to collect information for all women age 15-49, covered the following topics:\n- Sociodemographic characteristics of the respondent\n- Birth history\n- Prenatal care and intermittent preventive treatment (IPT) of malaria during pregnancy for the most recent birth\n- Treatment of malaria symptoms in children\n- Malaria knowledge\n\nThe survey protocol was submitted to and approved by the National Ethical Review Committee of the National Malaria Control Program and by the Institutional Review Board (IRB) of ICF Macro.","coll_situation":"Pretest\nAfter adapting the questionnaires to ensure they took into account the local situation, interviewers and health technicians were hired to implement the pretest as well as the main training on the use of questionnaires and procedures in the field. The pretest took place in December 2010.\n\nFieldwork\nFieldwork started in January 2011, after training. Twelve teams were designated to carry out the fieldwork. Each team consisted of three interviewers, one supervisor, one editor, and one health technician. Fieldwork started in Luanda first, and other regions were visited afterward. Although fieldwork was initially planned to last three months, it was delayed by accessibility problems generated by heavy rains between January 2011 and April 2011, and lasted through May 2011.","act_min":"There is one supervisor for each of the 12 data collection teams in the field.","cleaning_operations":"Data entry started two weeks after the beginning of fieldwork. Twelve data entry operators were used, six in the morning and six in the afternoon. They were supervised by the data processing manager, the questionnaire organizer, and the questionnaire editor. Control tables with data on interviewer and team performance were assessed periodically, especially during the first two weeks of fieldwork. The tables helped identify mistakes some teams made at the beginning of fieldwork; these mistakes resulted in extra supervisory field visits. Once the data entry was finalized, a consultant verified completeness of the questionnaires and consistency betwen data entry and the initial results."},"analysis_info":{"response_rate":"A total of 8,806 households were selected, of which 8,493 were occupied. The total number of households interviewed was 8,030, yielding a household response rate of 95 percent.\n\nA total of 8,746 eligible women were identified in these households, and interviews were completed for 8,589 women, yielding a response rate of 98 percent. Household response rates were 97 percent in urban areas and 93 percent in rural areas, and response rates for eligible women were 97 percent in urban areas and 99 percent in rural areas.","sampling_error_estimates":"The sample of respondents selected in the 2011 AMIS is only one of many samples that could have been selected from the same population, using the same sample design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. The variability that would be observed between all possible samples constitutes the sampling error. Although the degree of variability is not known exactly, it can be estimated from the sample actually selected.\n\nA sampling error is usually measured in terms of the standard error (SE). The standard error for a mean, percentage, difference, or any other statistic calculated from the data in the sample can be defined as the square root of the variance, which is a measure of the variation in all possible samples. For example, for any given statistic calculated from the sample, 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 households had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors and the limits for the confidence intervals. However, as has been mentioned, the 2011 AMIS sample is the result of a complex, multi-stage stratified design, and, consequently, it was necessary to use more complex formulas that take the effects of stratification and clustering into consideration. It was possible to calculate the sampling errors for the 2011 AMIS using a computer program known as Module for Sampling Errors, included in the computer package ISSA (Integrated System for Survey Analysis). This program processes percentages or medians as a ratio estimate r = y\/x where both the numerator y and the denominator x are random variables.\n\nSampling errors for the 2011 AMIS are calculated for selected variables considered to be of primary interest. The results are presented in this appendix for the country as a whole, for urban and rural areas, and for each endemic region. For each variable, the tables present the value of the statistic (R), its standard error (SE), the number of unweighted (N) and weighted (WN) cases, the design effect (DEFT), the relative standard error (SE\/R), and the 95 percent confidence limits, i.e., the values R+2SE and R-2SE. The DEFT is considered undefined when the standard error for a simple random sample is zero (when the estimate is close to 0 or 1).\n\nThe confidence interval (for example, the one calculated for the variable \u201chouseholds with at least one ITN\u201d) can be interpreted the following way: the overall proportion from the national sample is 0.345 and the standard error is 0.014. To obtain the 95 percent confidence limits, one adds and subtracts twice the standard error to the sample estimate, i.e., 0.345 \u00b1 2 \u00d7 0.014. There is a high probability (95 percent) that the true average proportion of households with at least one ITN lies between 0.318 and 0.373.\n\nFor the total sample, the value of the design effect (DEFT), averaged over all variables, is 1.95. This means that, due to multi-stage clustering of the sample, the average standard error is increased by a factor of 1.95 over the value observed for a corresponding simple random sample. The actual precision differs from the precision expected during the design of the sample due to several factors: the final size of the sample versus the sample selected; the actual size of DEFT versus the expected; and the actual value of the estimate versus the expected estimate. In addition, the actual precision is different from the expected precision, separately, for each indicator.\n\nThe sampling errors are fully described in Appendix B of \" Angola Malaria Indicator Survey 2011 - Final Report\" pp.49-55.","data_appraisal":"A series of data quality tables are available to review the quality of the data and include the following:\n\n- Age distribution of the household population\n- Age distribution of eligible and interviewed women\n- Completeness of reporting\n- Births by calendar year\n- Reporting of age at death in days\n- Reporting of age at death in months\n \nThe results of each of these data quality tables are shown in Appendix C of \"Angola Malaria Indicator Survey 2011 - Final Report\" pp.57-60."}},"data_access":{"dataset_use":{"contact":[{"name":"MEASURE DHS","affiliation":"ICF International","email":"archive@measuredhs.com","uri":"www.measuredhs.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 acronym and year of implementation)\n- the survey reference number\n- the source and date of download\n\nExample:\n\nCosep Consultoria, Consa\u00fade Lda., the Programa Nacional de Controle da Mal\u00e1ria, and ICF International. Angola Malaria Indicator Survey 2011. Ref. AGO_2011_MIS_v01_M. Dataset downloaded from www.measuredhs.com on [date]","conditions":"DISSEMINATION POLICY\n\nMEASURE DHS believes that widespread access to survey data by responsible researchers has enormous advantages for the countries concerned and the international community in general. Therefore, MEASURE DHS policy is to release survey data to researchers after the main survey report is published, generally within 12 months after the end of fieldwork. with few limitations these data have been made available for wide use. \n\nDISTRIBUTION OF DATASETS\n\nMEASURE DHS is authorized to distribute, at no cost, unrestricted survey data files for legitimate academic research, with the condition that we receive a description of any research project that will be using the data. \n\nRegistration is required for access to data. \n\nDatasets are available for download to all registered users, free of charge. To download datasets, you must first register online and request the country(ies) and datasets that you are interested in. When submitting a dataset request, users must include a brief description of how the data will be used.\n\nDATASETS TERMS OF USE\n\nDatasets are made available with the following conditions:\n- Survey data files are distributed by MEASURE DHS for academic research\/statistical analysis. Researchers need to provide a description of any research\/analysis that will be using the data, before access is granted to the datasets. \n- Once downloaded, the datasets must not be passed on to other researchers without the written consent of MEASURE DHS. \n- All reports and publications based on the requested data must be sent to the MEASURE DHS Data Archive as a Portable Format Document (pdf) or a hard copy, for us to forward to the country(ies) whose data have been used.\n\nMore information on the access policy and terms of use is available at www.measuredhs.com","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 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