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Primary Research for the Data on Youth and Tobacco in Africa (DaYTA) program, DaYTA

KENYA and NIGERIA, 2024
Health and Well-Being (HaW)
Shukri Mohamed, PharmD, PhD
Last modified October 02, 2025 Page views 6604 Documentation in PDF Metadata DDI/XML JSON

Identification

IDNO
DDI-KEN-NGA-PHRC-DaYTA-2023-v1.0
Title
Primary Research for the Data on Youth and Tobacco in Africa (DaYTA) program, DaYTA
Subtitle
DaYTA
Country
Name Country code
KENYA and NIGERIA KEN, NGA
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’ 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’s ability to appropriately target efforts, engage county governments to action, and increase global attention and funding for adolescent health.

Version

Version Date
2024-10-13
Version Notes
N/A

Coverage

Geographic Coverage
Nation-wide household survey (Kenya and Nigeria)
Unit of Analysis
The study was a household-based with the household head and adolescents to be interviewed.

Individual
Household
Universe
The survey covered household head (either male or female) and adolescents aged (10-17 years old)

Producers and sponsors

Authoring entity/Primary investigators
Agency Name Affiliation
Shukri Mohamed, PharmD, PhD African Population and Health Research Center
Producers
Name Affiliation Role
Damazo Kadengye, PhD African Population and Health Research Center Co-Investigator
Samuel Iddi, PhD African Population and Health Research Center Co-Investigator
Boscow Okumu, PhD African Population and Health Research Center Co-Investigator
Nelson Mbaya, BSC, MSc African Population and Health Research Center Co-Investigator
Lyagamula Kisia, MPH African Population and Health Research Center Co-Investigator
James Kavai African Population and Health Research Center Research officer
Franklin Koech African Population and Health Research Center Research officer
Grace Kyule African Population and Health Research Center Data Manager
Uche Okezie APIN Public Health Co-Investigator
Joshua Fakorede APIN Public Health Research Officer
Akinsewa Akiode Research Communication Services Co-Investigator
Thompson Ademola Research Communication Services Research Officer
Olatunbosun Abolarin APIN Public Health Co-Investigator
Funding Agency/Sponsor
Name Abbreviation Role
Development Gateway: An IREX Venture (DG), with support from Gates Foundation DG Funder
Other Identifications/Acknowledgments
Name Affiliation Role
Anne Kendagor Ministry of Health, Division of Drug and Subtance Abuse Control Program, Kenya Technical Advisory
Elias Nyaga Kenya National Bureau of Statistics Technical and Research Advisory
Jackline Chepkorir Kenya National Bureau of Statistics Technical and Research Advisory
Jim Kirimi Kenya National Bureau of Statistics Technical and Research Advisory
Edwin Metto Kenya National Bureau of Statistics Technical and Research Advisory
James Kavai African Population and Health Research Center Data Documentation Specialist
Bonface Butichi Ingumba African Population and Health Research Center Data Governance Officer
Dr. Andrew Toro Ministry of Health, Division of Drug and Subtance Abuse Control Program, Kenya Technical Advisory
Pauline Achieng Ministry of Health, Division of Drug and Subtance Abuse Control Program, Kenya Technical Advisory
Samuel Kinyua Ministry of Health, Division of Drug and Subtance Abuse Control Program, Kenya Technical Advisory
Dr Christine Wambugu Ministry of Health, Division of Adolescent and School Health Research Advisory Committee
Dr. Lydia Mucheru Kenya Institute of Curriculum Development Research Advisory Committee
Rosemary Chepkoech Kenya National Bureau of Statistics Research Advisory Committee
Elvina Majiwa Youth In Power Africa Rise (YIPAR) Research Advisory Committee
Fabian Oriri International Institute of Legal Affairs (IILA) Research Advisory Committee
Terefe Gelibo Agerfa Development Gateway Research Team
Retselisitsoe Pokothoane Development Gateway Research Team
Josiane Djiofack Tsague Development Gateway Research Team
Noreen Dadirai Mdege Development Gateway Research Team
Rachel Kitonyo-Devotsu Development Gateway Programs Team
Winnie Awuor Development Gateway Programs Team
Mohammed Maikudi Development Gateway Programs Team
Ahmad Muhammad Ozi Federal Ministry of Health and Social Welfare Technical Advisory
Malau Mangai Toma Federal Ministry of Health and Social Welfare Technical and Research Advisory
Emmanuel Agbons Abraham Federal Ministry of Health and Social Welfare Technical and Research Advisory
Kehinde Akinkoye Federal Ministry of Health and Social Welfare Technical Advisory
Philip Osung National Population Commission Technical Advisory
Emmanuel Agada National Population Commission Technical Advisory
Desmond Onyene National Population Commission Technical Advisory
Billy Shinguu National Population Commission Technical Advisory
Chibuike Nwokorie National Tobacco Control Alliance Technical and Research Advisory
Philip Jakpor Renevlyne Development Innitiative Technical and Research Advisory
Esther Aghotor Gatefield Technical and Research Advisory
Adedeji Adeniran Center for the Study of the Ecomies of Africa Technical and Research Advisory
Uche Okezie APIN Public Health Research Team
Joshua Fakorede APIN Public Health Research Team
Akinsewa Akiode Research Communication Services Research Team
Thompson Ademola Research Communication Services Research Team
Olatunbosun Abolarin APIN Public Health Research Team

Sampling

Sampling Procedure
Kenya

Sample 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
adolescents (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

Sampling 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
counties 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.


Nigeria

Sample 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
sample 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.

Sampling 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
(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
obtain 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.
Deviations from the Sample Design
N/A
Response Rate
Kenya - 96%
Nigeria - 98%
Weighting
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
households 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.

Data Collection

Dates of Data Collection (YYYY/MM/DD)
Start date End date
2024-04-02 2024-06-24
Mode of data collection
Face-to-face [f2f]
Supervision
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’s 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.

Kenya

The 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.

Nigeria

Overall, 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.
Type of 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:
CDC National Youth Tobacco Survey (NYTS)
The Global Youth Tobacco Survey (GYTS)
Global Adult Tobacco Survey (GATS)
ASH Smokefree Great Britain Youth survey (ASH-Y)
International Tobacco Control (ICT)-Youth Surveys
WHO Tobacco Questions for Surveys of Youth (TQS-Youth)
The 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
Both household and individual-level data will be collected as follows:
Household 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:
Module 1: Household roster - demographic data of household members (de facto residents who stay in the household)
Module 2: Household characteristics - socio-economic data.
Individual-level data from participating adolescents: Information to be collected through core modules will include the following:
Module 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.
Module 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’ 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.
Module 8: Knowledge, Attitudes, Perceptions, intentions about using tobacco and its consequences
Information to be collected through optional modules will include the following:
Use of nicotine pouches
Cessation of tobacco use
Exposure to tobacco smoke in indoor and outdoor public places

Data Processing

Cleaning Operations
Data was collected using tablets with the tool programmed in Survey CTO and transmitted to online secure APHRC 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.
Other Processing
n/a

Data Appraisal

Estimates of Sampling Error
N/A

Data access

Contact
Name Email
African Population and Health Research Center datarequest@aphrc.org
Conditions
Data Access and Sharing Conditions


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"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."

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Use of the dataset must be acknowledged using a citation which would include:
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- the title of the survey (including country, acronym and year of implementation)
- the survey reference number
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Metadata production

Document ID
DDI-KEN-NGA-PHRC-DaYTA-2023-v1.0
Producers
Name Abbreviation Role
African Population and Health Reseach Center APHRC Documentation of the DDI
Date of Production
2024-10-13
Document version
Version 1.0 (October 2024)