{"doc_desc":{"title":"DaYTA Project","idno":"DDI-KEN-NGA-DRC-DaYTA-2023-v1.0","producers":[{"name":"African Population and Health Reseach Center","abbreviation":"APHRC","affiliation":"","role":"Documentation of the DDI"}],"prod_date":"2024-10-13","version_statement":{"version":"Version 1.0 (October 2024)"}},"study_desc":{"title_statement":{"idno":"DDI-KEN-NGA-DRC-DaYTA-2023-v1.0","title":"Primary Research for the Data on Youth and Tobacco in Africa (DaYTA) program","sub_title":"DaYTA","alt_title":"DaYTA"},"authoring_entity":[{"name":"Shukri Mohamed, PharmD, PhD","affiliation":"African Population and Health Research Center"}],"oth_id":[{"name":"Anne Kendagor","affiliation":"Ministry of Health, Division of Drug and Subtance Abuse Control Program, Kenya","email":"","role":"Technical Advisory"},{"name":"Elias Nyaga","affiliation":"Kenya National Bureau of Statistics","email":"","role":"Technical and Research Advisory"},{"name":"Jackline Chepkorir","affiliation":"Kenya National Bureau of Statistics","email":"","role":"Technical and Research Advisory"},{"name":"Jim Kirimi","affiliation":"Kenya National Bureau of Statistics","email":"","role":"Technical and Research Advisory"},{"name":"Edwin Metto","affiliation":"Kenya National Bureau of Statistics","email":"","role":"Technical and Research Advisory"},{"name":"James Kavai","affiliation":"African Population and Health Research Center","email":"","role":"Data Documentation Specialist"},{"name":"Bonface Butichi Ingumba","affiliation":"African Population and Health Research Center","email":"","role":"Data Governance Officer"},{"name":"Dr. Andrew Toro","affiliation":"Ministry of Health, Division of Drug and Subtance Abuse Control Program, Kenya","email":"","role":"Technical Advisory"},{"name":"Pauline Achieng","affiliation":"Ministry of Health, Division of Drug and Subtance Abuse Control Program, Kenya","email":"","role":"Technical Advisory"},{"name":"Samuel Kinyua","affiliation":"Ministry of Health, Division of Drug and Subtance Abuse Control Program, Kenya","email":"","role":"Technical Advisory"},{"name":"Dr Christine Wambugu","affiliation":"Ministry of Health, Division of Adolescent and School Health","email":"","role":"Research Advisory Committee"},{"name":"Dr. Lydia Mucheru","affiliation":"Kenya Institute of Curriculum Development","email":"","role":"Research Advisory Committee"},{"name":"Rosemary Chepkoech","affiliation":"Kenya National Bureau of Statistics","email":"","role":"Research Advisory Committee"},{"name":"Elvina Majiwa","affiliation":"Youth In Power Africa Rise (YIPAR)","email":"","role":"Research Advisory Committee"},{"name":"Fabian Oriri","affiliation":"International Institute of Legal Affairs (IILA)","email":"","role":"Research Advisory Committee"},{"name":"Terefe Gelibo Agerfa","affiliation":"Development Gateway","email":"","role":"Research Team"},{"name":"Retselisitsoe Pokothoane","affiliation":"Development Gateway","email":"","role":"Research Team"},{"name":"Josiane Djiofack Tsague","affiliation":"Development Gateway","email":"","role":"Research Team"},{"name":"Noreen Dadirai Mdege","affiliation":"Development Gateway","email":"","role":"Research Team"},{"name":"Rachel Kitonyo-Devotsu","affiliation":"Development Gateway","email":"","role":"Programs Team"},{"name":"Winnie Awuor","affiliation":"Development Gateway","email":"","role":"Programs Team"},{"name":"Mohammed Maikudi","affiliation":"Development Gateway","email":"","role":"Programs Team"},{"name":"Lauren Eby","affiliation":"Development Gateway","email":"","role":"Program Manager"},{"name":"Patrick Shamba","affiliation":"Development Gateway","email":"","role":"Country Lead, DRC"},{"name":"Ahmad Muhammad Ozi","affiliation":"Federal Ministry of Health and Social Welfare","email":"","role":"Technical Advisory"},{"name":"Malau Mangai Toma","affiliation":"Federal Ministry of Health and Social Welfare","email":"","role":"Technical and Research Advisory"},{"name":"Emmanuel Agbons Abraham","affiliation":"Federal Ministry of Health and Social Welfare","email":"","role":"Technical and Research Advisory"},{"name":"Kehinde Akinkoye","affiliation":"Federal Ministry of Health and Social Welfare","email":"","role":"Technical Advisory"},{"name":"Philip Osung","affiliation":"National Population Commission","email":"","role":"Technical Advisory"},{"name":"Emmanuel Agada","affiliation":"National Population Commission","email":"","role":"Technical Advisory"},{"name":"Desmond Onyene","affiliation":"National Population Commission","email":"","role":"Technical Advisory"},{"name":"Billy Shinguu","affiliation":"National Population Commission","email":"","role":"Technical Advisory"},{"name":"Chibuike Nwokorie","affiliation":"National Tobacco Control Alliance","email":"","role":"Technical and Research Advisory"},{"name":"Philip Jakpor","affiliation":"Renevlyne Development Innitiative","email":"","role":"Technical and Research Advisory"},{"name":"Esther Aghotor","affiliation":"Gatefield","email":"","role":"Technical and Research Advisory"},{"name":"Adedeji Adeniran","affiliation":"Center for the Study of the Ecomies of Africa","email":"","role":"Technical and Research Advisory"},{"name":"Uche Okezie","affiliation":"APIN Public Health","email":"","role":"Research Team"},{"name":"Joshua Fakorede","affiliation":"APIN Public Health","email":"","role":"Research Team"},{"name":"Akinsewa Akiode","affiliation":"Research Communication Services","email":"","role":"Research Team"},{"name":"Thompson Ademola","affiliation":"Research Communication Services","email":"","role":"Research Team"},{"name":"Olatunbosun Abolarin","affiliation":"APIN Public Health","email":"","role":"Research Team"},{"name":"Christelle Tchoup\u00e9 Makougoum, PhD","affiliation":"Research Initiatives for Social Development ","email":"","role":" Co_PI and Lead Statistician"},{"name":"Munguakonkwa Mirindi Didier,","affiliation":"Research Initiatives for Social Development ","email":"","role":"Field Coordinator and Research Analyst"},{"name":"Kandate Musema Emmanuel","affiliation":"Research Initiatives for Social Development ","email":"","role":"Research Coordinator"},{"name":"Prof. David Kayembe","affiliation":"INSP, Ministry of Health, DRC","email":"","role":"Technical Advisory"},{"name":"Jean Chris Mampuya","affiliation":"INS Kananga, DRC","email":"","role":"Technical Advisory"},{"name":"Patrice Milambo","affiliation":"PNLCT, Ministry of Health, DRC","email":"","role":"Technical Advisory"},{"name":"Prof. Banza Nkulu","affiliation":"Academic Advisor, University of Lubumbashi","email":"","role":"Technical Advisory"},{"name":"Prof. Ndelo Phanzu","affiliation":"5. Academic Advisor, University of Kinshasa","email":"","role":"Technical Advisory"}],"production_statement":{"producers":[{"name":"Damazo Kadengye, PhD","affiliation":"African Population and Health Research Center","role":"Co-Investigator"},{"name":"Samuel Iddi, PhD","affiliation":"African Population and Health Research Center","role":"Co-Investigator"},{"name":"Boscow Okumu, PhD","affiliation":"African Population and Health Research Center","role":"Co-Investigator"},{"name":"Nelson Mbaya","affiliation":"African Population and Health Research Center","role":"Co-Investigator"},{"name":"Lyagamula Kisia, MPH","affiliation":"African Population and Health Research Center","role":"Co-Investigator"},{"name":"James Kavai","affiliation":"African Population and Health Research Center","role":"Research officer"},{"name":"Franklin Koech","affiliation":"African Population and Health Research Center","role":"Research officer"},{"name":"Grace Kyule","affiliation":"African Population and Health Research Center","role":"Data Manager"},{"name":"Uche Okezie","affiliation":"APIN Public Health","role":"Co-Investigator"},{"name":"Joshua Fakorede","affiliation":"APIN Public Health","role":"Research Officer"},{"name":"Akinsewa Akiode","affiliation":"Research Communication Services","role":"Co-Investigator"},{"name":"Thompson Ademola","affiliation":"Research Communication Services","role":"Research Officer"},{"name":"Olatunbosun Abolarin","affiliation":"APIN Public Health","role":"Co-Investigator"},{"name":"KANDATE MUSEMA Emmanuel","affiliation":"Research Initiatives for Social Development","role":"Field Coordinator and Research Analyst"},{"name":"Christelle Tchoup\u00e9 Makougoum, PhD","affiliation":"Research Initiatives for Social Development","role":"Co_PI and Lead Statistician"},{"name":"MUNGUAKONKWA MIRINDI Didier","affiliation":"Research Initiatives for Social Development","role":"Research Coordinator, Statistician"}],"copyright":"Copyright \u00a9 APHRC, 2024","funding_agencies":[{"name":"Development Gateway: An IREX Venture (DG), with support from Gates Foundation","abbreviation":"DG","role":"Funder"}]},"series_statement":{"series_name":"Sample Frame, Households [sf\/hh]","series_info":"The study was a household-based cross-sectional nation-level survey among adolescents aged 10- to 17 years in  Kenya, Nigeria and Democratic republic of Congo. The primary goal was to collect country-level data  on tobacco and nicotine use among adolescents aged 10 to 17 years to address key evidence gaps and enhance existing knowledge."},"version_statement":{"version_date":"2024-10-13","version_notes":"N\/A"},"study_info":{"abstract":"Tobacco usage rates are on the rise in low- and middle-income countries (LMIC). Adolescents are especially vulnerable to taking up tobacco use at a young age in some African countries because the tobacco industry aggressively markets to them. Most of the available evidence captures data from 13- to 15-year-olds even though evidence from Sub Saharan Africa (SSA) shows that the age of smoking initiation among young people ranges from as young as 7 years old to about 16 years old. The lack of data on adolescent tobacco use in African countries limits policymakers\u2019 ability to make data-informed decisions on tobacco control policies. The problem that the study aims to address is the  lack of quality and timely primary data on adolescent tobacco use which significantly inhibits the country\u2019s ability to appropriately target efforts, engage county governments to action, and increase global attention and funding for adolescent health.","coll_dates":[{"start":"2024-04-02","end":"2024-06-24","cycle":""}],"nation":[{"name":"KENYA, NIGERIA and Democratic Republic of Congo","abbreviation":"KEN, NGA,DRC"}],"geog_coverage":"Nation-wide household survey (Kenya, Nigeria and DRC)","analysis_unit":"The study was a household-based with the household head and adolescents to be interviewed.\n\nIndividual\nHousehold","universe":"The survey covered household head (either male or female) and adolescents aged (10-17 years old)","notes":"HOUSEHOLD:Usual Residents, Relationship to Head of household, Sex, Residence, Disability status, age, marital status, eligibility, Survivorshiip of biological parents, household characteristics-Source of drinking water, kind of toilet facility used, type of fuel used, main material of the floor, main material of the roof, main material of the exterior walls.\n\nINDIVIDUAL: Socio-demographic Characteristics Individuals-Gender, age, ever attended school, Grade, Pocket money, ethnic background, Engagement in work. Functional Difficulties-Vision, hearing, mobility, cognition (remembering), self care, communication. Manufactured\/factory made cigarettes, Roll your own cigarettes, Shisha, waterpipe or hookah, Other smoked tobacco products, smokeless tobacco, electronic cigarettes, Heated tobacco products, nicotine pouches, cessation of tobacco use, second hand smoke.","study_scope":"HOUSEHOLD:Usual Residents, Relationship to Head of household, Sex, Residence, Disability status, age, marital status, eligibility, Survivorshiip of biological parents, household characteristics-Source of drinking water, kind of toilet facility used, type of fuel used, main material of the floor, main material of the roof, main material of the exterior walls.\n\nINDIVIDUAL: Socio-demographic Characteristics Individuals-Gender, age, ever attended school, Grade, Pocket money, ethnic background, Engagement in work. Functional Difficulties-Vision, hearing, mobility, cognition (remembering), self care, communication. Manufactured\/factory made cigarettes, Roll your own cigarettes, Shisha, waterpipe or hookah, Other smoked tobacco products, smokeless tobacco, electronic cigarettes, Heated tobacco products, nicotine pouches, cessation of tobacco use, second hand smoke."},"method":{"data_collection":{"sampling_procedure":"Kenya\n\nSample size: The sample size for this survey was calculated using the United Nations (UN) formula (see Appendix 2) for estimating sample sizes in prevalence studies for household surveys (UN, 2008). In the computation of the sample, a 95% confidence level was applied, along with a default design effect of 2.0 to account for multistage sampling. A 10% non-response rate was factored into the calculations, consistent with other studies in Kenya (KNBS, 2015). An estimate of 16.2% was used for the expected prevalence of tobacco use among \nadolescents (Nazir et al., 2019). The adolescent population proportion was estimated at 20.45% and the average household size estimated at 3.9, based on the 2019 Kenya Population and Housing Census (KNBS, 2019). Using these parameters, the calculation resulted in a nationally representative sample of 6,061 adolescents in Kenya, which is sufficient for analysis and national-level inferences. However, to adjust for the 10% non-response rate, a targeted sample size of 6,734 was computed\n\nSampling procedure:The survey utilized a three-stage stratified cluster sample design.The first stage involved the selection of 16 counties from Kenya's 47 counties. Prior to sampling, the \ncounties were stratified by grouping them into the eight former provinces. Thereafter, a representative and proportionate sample was selected from each province. The number of sampled counties was computed using Taro Yamane's simplified formula for proportions (Tepping, 1968). Nairobi county was included by default because it is a capital city, a region, and a county. The remaining 15 counties were randomly selected based on a computer-generated sequence using R statistical software.The second stage involved random selection of EAs within the 16 sampled counties, which was done with probability proportional to the size of the EA. Prior to EA sample selection, the EA sampling frame was first stratified by residence (rural and urban) and 224 EAs were selected: 81 in urban areas and 143 in rural areas. To generate a household sampling frame and identify households with eligible adolescents, the survey team conducted a household listing operation within the selected EAs. The operation involved visiting each EA to list all eligible households and their addresses.In the third stage, 30 households were randomly selected from each EA. In each selected household, only one adolescent aged 10 to 17 years was interviewed. These interviewees were randomly sampled if multiple adolescents were present in the household.\n\n\nNigeria\n\nSample size: Nigeria: The sample size for this study was estimated using the UN formula for estimating sample sizes in prevalence studies (UN, 2008), with a 95% confidence level. A sample design effect of 2.5 (default value) was applied since sampling was to be conducted at different administrative levels, such as geopolitical zones, states, and EAs. A non-response rate of 20% was factored into the calculations. While non-response rates for adult populations and previous adolescent studies in Nigeria are typically around 10% (NPC & ICF, 2019), a higher rate was considered due to the assumption that the target population may be mobile. The global prevalence of tobacco use among adolescents, reported as 19.4% (Itanyi et al.,2018) was used as the estimated prevalence due to a lack of recent national estimates. The adolescent population proportion was estimated at 17.9%, and the average household size was set at 4.7, based on national statistics from the 2018 Nigeria Demographic and Health Survey (NDHS) (NPC and ICF, 2019). Using these parameters, the calculation resulted in a nationally representative\nsample of 6,358 adolescents in Nigeria, which is sufficient for analysis and national-level inferences. However, to adjust the 20% non-response rate, a targeted sample size of 7,948 was envisaged.\n\nSampling Procedure: The survey employed a multi-stage stratified cluster sampling design to produce a nationally representative sample of adolescents, covering both urban and rural areas. The first sampling stage involved randomly selecting 13 study states (12 states and the FCT, Abuja) from the national sampling frame of 36 states as provided by the NPC. The states were stratified by grouping them into their respective geopolitical zones, and then a representative and proportionate sample from each zone was randomly selected using a computer-generated sequence. The number of sampled states was calculated using Taro Yamane's simplified formula for proportions. The FCT was included by default due to its status as the capital. In the second stage, 265 EAs were selected using probability proportional to the size of the sampled states. Before selecting the EAs, the sampling frame was stratified by residence\n(urban\/rural). Among the selected EAs, 105 were in urban areas and 160 in rural areas. Prior to field work, the survey team carried out a household listing operation in all selected EAs to\nobtain an updated list of eligible households in the selected EAs, which served as the sampling frame at the third stage of sample selection. In the third stage, 30 households per EA were randomly selected to reduce clustering effects. In each selected household, one adolescent aged 10 to 17 years was randomly selected to be interviewed (where multiple adolescents were available). If a selected adolescent was unavailable, interviewers made up to three return visits to complete the interview. If the adolescents remained unavailable after the third visit, the survey was closed, and no replacements were made.\n\n\nDRC\n\nSample size  :The sample size for the study was determined using the United Nations (2008) formula for estimating prevalence at a 95% confidence level. To account for the multistage sampling design, a design effect of 1.5 was applied, and a 10% non-response rate was incorporated based on evidence from previous studies conducted in the Democratic Republic of the Congo (DRC). Key parameters included an estimated adolescent population proportion of 23%, an average household size of 5.25, and an assumed adolescent tobacco use prevalence of 25%, informed by earlier surveys in Kinshasa and Lubumbashi. Using these assumptions, the minimum required sample size was calculated to be 4,323 adolescents. After adjusting for non-response and applying rounding at different sampling stages, the final target sample size was set at 4,892 adolescents.\n\nSampling procedures: A multi-stage stratified sampling design was employed to ensure national representativeness. Given the country's size and logistical constraints, it was not feasible to include all 26 provinces. Therefore, 16 provinces were selected using Taro Yamane's formula. To preserve representativeness, the provinces were first grouped into six historical strata Katanga, Kasa\u00ef, L\u00e9opoldville, \u00c9quateur, Orientale, and Kivu based on shared socio-cultural and historical characteristics. Provinces were then randomly selected within each stratum in proportion to the number of provinces in that stratum, ensuring broad geographical coverage. The allocation of households to be surveyed was also proportional to the population size of each stratum.\nThe sampling process proceeded through several stages. First, provinces were randomly selected within each stratum, and the number of households to be surveyed was evenly distributed across selected provinces. Second, within each selected province, three health zones (HZs) were randomly chosen, consisting of two rural and one urban HZ, resulting in a total of 48 HZs (16 urban and 32 rural). Third, within each HZ, three health areas (HAs) were selected (two rural and one urban) yielding 144 HAs (48 urban and 96 rural). Fourth, within each HA, one enumeration area (EA), defined as a village in rural settings or an avenue in urban settings, was selected, resulting in 144 EAs. At each stage, the number of households to be surveyed was distributed equally across the selected units.\nFinally, a comprehensive household listing was conducted in all selected enumeration areas to identify households with adolescents aged 10 to 17 years. From this sampling frame, households were selected using simple random sampling. In each selected household that consented to participate, one eligible adolescent was randomly chosen and interviewed.","sampling_deviation":"N\/A","coll_mode":"Face-to-face [f2f]","research_instrument":"The DaYTA standardized questionnaire was developed through intensive review of literature, including other standardized survey questionnaires that are used internationally. Examples include the following:\nCDC National Youth Tobacco Survey (NYTS)\nThe Global Youth Tobacco Survey (GYTS)\nGlobal Adult Tobacco Survey (GATS)\nASH Smokefree Great Britain Youth survey (ASH-Y)\nInternational Tobacco Control (ICT)-Youth Surveys\nWHO Tobacco Questions for Surveys of Youth (TQS-Youth)\nThe reviews were complemented by consultations with country stakeholders and field testing to ensure that the questionnaires were appropriate and relevant to policy decisions in and across-countries \nBoth household and individual-level data will be collected as follows:\nHousehold data: The household questionnaire will be administered to the consenting head of household or acting head of household. The questionnaire will collect information on demographics and socio-economic status as presented below:\nModule 1: Household roster - demographic data of household members (de facto residents who stay in the household)\nModule 2: Household characteristics - socio-economic data.\nIndividual-level data from participating adolescents: Information to be collected through core modules will include the following:\nModule 1: Socio-demographic characteristics such as age, sex, school year (if in school), average weekly spending money; Functional difficulties i.e. vision, mobility, cognition remembering, self-care and communication. \nModule 2 - 7: Tobacco use for both smoked tobacco [manufactured\/factory-made cigarettes, roll-your-own (RYO)\/hand rolled cigarettes, shisha\/waterpipe\/hookah and emerging tobacco products such as heated tobacco products), and other tobacco products e.g. cigars, cheroots, cigarillos] and smokeless tobacco [chewing tobacco such as tobacco leaf, tobacco leaf and lime; Kuber, applying tobacco such as, tobacco toothpaste-dentobac etc.; tobacco tooth powder-lal, etc.; snuff)], including type, quantity, frequency, dependency, age of initiation, where they smoke, and with whom; Use of novel products such as electronic nicotine\/ non-nicotine delivery systems; Access to tobacco and novel products (e.g., how they access, where and for how much); Multi-level (e.g., individual-, household- and environment-level) factors associated with tobacco use among adolescents,19-22 such as in-school\/ out-of-school, parents\/guardians\/other family members\u2019 tobacco use histories, exposure to second-hand tobacco smoke within the home, or tobacco use amongst close friends, exposure to tobacco advertising, promotion or sponsorship, and exposure to anti-tobacco messages.\nModule 8: Knowledge, Attitudes, Perceptions, intentions about using tobacco and its consequences\nInformation to be collected through optional modules will include the following:\nUse of nicotine pouches\nCessation of tobacco use\nExposure to tobacco smoke in indoor and outdoor public places","act_min":"Field interviewers conducted data collection activities under the supervision of experienced researchers. Prior to the commencement of data collection, the research team consulted key stakeholders about the logistics of reaching out to eligible participants within the selected EAs. Based on the advice of the key stakeholders, the research team prepared a schedule to assign field interviewers to interviews based on the availability of participants. Since data was collected electronically, questionnaires were designed to prevent inconsistency in data collection including logic skips to provide clean data. In addition to this, supervisors conducted spot-check interviews on at least 5% of the sample to verify accuracy of data collected. Supervisors\/ team leaders reviewed all data captured on the tablets, looking for any errors, such as incorrectly filled forms, missing data and inconsistencies. This helped to verify that data collectors were following all the procedures outlined in the training and ensure that interviews were being conducted to the highest standards. Moreover, all field interviewers  reviewed each questionnaire before leaving the households to be sure that every question has been asked and that responses recorded are clear and reasonable. Survey records with errors were returned to the fieldworker\u2019s tablet for verification before the final records were transmitted to the online database. Regular data validation and verification checks were run on 100% of the data collected using a syntax script to ensure data completeness, correctness, and consistency. During the data collection period, supervisors consulted regularly with the central coordination team on achievements and constraints of the operation. These consultations facilitated any necessary adjustments to the data collection process.\n\nKenya\n\nThe 118 field interviewers were initially grouped into 16 teams (one team for each county). The teams were then sub-divided into 26 smaller teams of three to five interviewers per team. The exact number of field interviewers per EA varied based on the number of EAs to be covered within a county. Each of the 26 team leaders supervised a team of between 2 to 6 field interviewers. The team leaders were further supervised by three researchers who oversaw the day-to-day coordination of field work activities.\n\nNigeria \n\nOverall, the field teams consisted of 392 field staff, which included field interviewers, supervisors, and quality assurance officers from across the 13 states. A staggered approach was used wherein the field teams completed data collection within the sampled EAs in one state before moving to those in the next state. However, since there were two larger teams, data collection was able to take place in two states at a time.\n\nDRC\n\nThe research team developed a field management procedure to guide the supervision of the data collection process, ensuring consistency, efficiency, and adherence to ethical standards. A supervisor was assigned to each team to oversee field operations, provide technical support, and address any logistical challenges encountered during data collection","weight":"Since the survey was a multi-stage survey with a complex sampling design, it was essential to properly account for all sample design features. Initial sampling weights were assigned to sample units. These initial weights were computed by taking the inverse of the selection probability at each stage of the survey design and multiplying the inverse of the selection probabilities at each stage. Calculations in this computation stage included probabilities of selection of counties\/States, selection of EAs, selection of households, and selection of eligible adolescents. Next, adjustments to the initial weights were made to account for non-response by dividing the weights of respondents by the response rates. This step made non-response adjustments for \nhouseholds and adolescents. In the final stage, a calibration adjustment factor was calculated by dividing the population totals with the corresponding sample estimates for each stratum (to adjust the weights to conform to the projected population census data distributed by stratum and age). The strata variable was formed by region\/geo-political zones, residence type (urban\/rural), and age group. The final weights were computed by multiplying the initial weights, the non-response adjustment factor, and the calibration or post-stratification factor for each sampled unit. These weights were used when conducting the analysis to ensure that the results accurately represented the target population.","cleaning_operations":"Data was collected using tablets with the tool programmed in Survey CTO and transmitted to online secure APHRC and RISD servers for storage. The internet was used to upload the data via internet bundles loaded onto the tablets. Backup of the data remained on the tablets until the end of field activities. Data transmitted to the central servers was password protected to allow access to only authorised users. All identifiers (name, identity numbers, phone numbers and places of residence) collected during data collection or for recruitment procedures were removed from analytical datasets before any data was shared or used in analysis, and replaced with unique identifiers. The raw data was cleaned and transformed as needed for the statistical analysis. This process involved checking for missing values, outliers, and any data inconsistencies. With the clean data, detailed reports with completed tables on different variables as well a more condensed summary of results report were produced.","method_notes":"n\/a"},"analysis_info":{"response_rate":"Kenya - 96%\nNigeria - 94%\nDRC- 96%","sampling_error_estimates":"N\/A"}},"data_access":{"dataset_use":{"contact":[{"name":"African Population and Health Research Center","affiliation":"","email":"datarequest@aphrc.org","uri":""}],"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":"Data Access and Sharing Conditions \n\n\nAll non-APHRC staff seeking to use data generated at the Center must obtain written approval to use the data from the Director of Research. This form is developed to assess applications for data use and facilitate responsible sharing of data with external partners\/collaborators\/researchers. By entering into this agreement, the undersigned agrees to use these data only for the purpose for which they were obtained and to abide by the conditions outlined below:\n\n1. Data Ownership: The data remain the property of APHRC; any unauthorized reproduction and sharing of the data is strictly prohibited. The user will, therefore, not release nor permit others to use or release the data to any other person without the written authorization from the Center.\n\n2. Purpose: The provided data must be used for the purpose specified in the Data Request Form; any other use not specified in the form must receive additional or separate authorization.\n\n3. Respondent Identifiers: The Center is committed to protecting the identity of the respondents who provide information in its research. All analytical data sets (both qualitative and quantitative) released by the Data Unit MUST are stripped of respondent identifiers to protect the identity of the respondents. By accepting to use APHRC data, the user is pledging that he\/she will not, under any circumstance, regenerate the identifiers or permit others to use the data to learn the identity of any individual, household or community included in any data set.\n\n4. Confidentiality pledge: The user will not use nor permit others to use the data to report any information in the data sets that could identify, directly or by inference, individuals or households.\n\n5. Reporting of errors or inconsistencies: The user will promptly notify the Head of the Statistics and Survey Unit any errors discovered in the data as soon as the errors are discovered.\n\n6. Publications resulting from APHRC data: The Center requires external collaborators to work with APHRC staff on all publications resulting from its data. In order to facilitate this, lead authors should send a detailed concept note of the paper (including the background, rationale, data, analytical methods, and preliminary findings) to the Principle Investigator (or Theme Leader) for the project (with a copy to the Director of Research), who will circulate the abstract to concerned researchers for possible expression of interest in participating in the publication as co-authors. Any exception to the involvement of APHRC staff should be approved by the Director of Research, APHRC.\n\n7. Security: The user will take responsibility for the security of the data by ensuring that the data are used and stored in a secure environment where access is password protected. This will ensure that non-authorized people should not have access to the data.\n\n8. Loss of privilege to use data: In the event that APHRC determines that the data user is in violation of the conditions for using the data, or if the user wishes to cancel this agreement, the user will destroy the data files provided to him\/her. APHRC retains the right to revoke this agreement or informs publishers to withhold publication of any work based wholly or in part on its data if the conditions for using the data are violated.\n\n9. Acknowledgement: Any work\/reports from this data must acknowledge APHRC as the source of these data. For example, the suggested acknowledgement for NUHDSS data is:\n\n\"This research uses livelihoods data collected under the longitudinal Nairobi Urban Health and Demographic Surveillance System (NUHDSS) since 2006. The NUHDSS is carried out by the African Population and Health Research Center in two slums settlements (Korogocho and Viwandani) in Nairobi City.\"\n\nAdditionally all funders, the study communities that provided the data, and staff who collected and analyzed or processed the data should be acknowledged.\n\n10. Deposit of Reports\/Papers: The user should submit electronic and paper copies of all publications generated using APHRC data to the Policy Engagement and Communications Department, with copies to the Director of Research.\n\n11. Change of contact details: The user will promptly inform the Director of Research of any change in your personal details as contained on this data request form.\n\n12. Data will be made publicly accessible on November 30, 2025. Access to this dataset is contingent upon an agreement not to publish any findings before the year 2026.","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."}}}}