The total population of South Africa amounted to 63.02 million people in 2024. Following a continuous upward trend, the total population has risen by 33.94 million people since 1980. Between 2024 and 2030, the total population will rise by 5.86 million people, continuing its consistent upward trajectory.This indicator describes the total population in the country at hand. This total population of the country consists of all persons falling within the scope of the census.

In the first quarter of 2020, the number of Black South Africans of working age reached approximately 31.4 million, marking a year-on-year change of 1.9 percent compared to the first quarter of 2019. The number of coloreds of working age reached roughly 3.5 million in the first quarter of 2020.

The total labor force in sub-Saharan Africa was estimated at *** million people in 2022. By 2023, the number is expected to reach *** million. According to the source estimates, within the period observed, the total of both the employed and unemployed individuals in the region increased annually.

Context

Africa - Population and Internet users statistics

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

Source: https://data.humdata.org/dataset/africa-population-and-internet-users-statistics Last updated at https://data.humdata.org/organization/openafrica : 2019-09-11

Context The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on earth, which far exceeds the world population of 7.2 billion in 2015. Our own estimate based on UN data shows the world's population surpassing 7.7 billion.

China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, the country of India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.

The following 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.

Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.

In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added each year.

This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growing more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Content In this Dataset, we have Historical Population data for every Country/Territory in the world by different parameters like Area Size of the Country/Territory, Name of the Continent, Name of the Capital, Density, Population Growth Rate, Ranking based on Population, World Population Percentage, etc.

In 2021, Egypt was the North African country with the highest share of people aged **** years, making up **** percent of the total population. On the contrary, Tunisia presented the lowest percentage of young people in this age group, which constituted **** percent of the total population.

School age population, pre-primary education, male (number) in South Africa was reported at 2370530 Persons in 2019, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Africa - Population of the official age for pre-primary education, male - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

School age population, post-secondary non-tertiary education, male (number) in South Africa was reported at 976471 Persons in 2019, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Africa - Population of the official age for post-secondary non-tertiary education, male - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

School age population, post-secondary non-tertiary education, both sexes (number) in South Africa was reported at 1940953 Persons in 2019, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Africa - Population of the official age for post-secondary non-tertiary education, both sexes - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (StatsSA). It collects data on the labour market activities of individuals aged 15 years or older who live in South Africa. Since 2008, StatsSA have produced an annual dataset based on the QLFS data, "Labour Market Dynamics in South Africa". The dataset is constructed using data from all all four QLFS datasets in the year. The dataset also includes a number of variables (including income) that are not available in any of the QLFS datasets from 2010.

Geographic coverage

The survey had national coverage.

Analysis unit

Individuals

Universe

The QLFS sample covers the non-institutional population except for those in workers' hostels. However, persons living in private dwelling units within institutions are enumerated. For example, within a school compound, one would enumerate the schoolmaster's house and teachers' accommodation because these are private dwellings. Students living in a dormitory on the school compound would, however, be excluded.

Kind of data

Sample survey data

Sampling procedure

Each year the LMDSA is created by combining the QLFS waves for that year and then including some additional variables. The QLFS master frame for this LMDSA was based on the 2011 population census by Stas SA. The sampling is stratified by province, district, and geographic type (urban, traditional, farm). There are 3324 PSUs drawn each year, using probability proportional to size (PPS) sampling. In the second stage Dwelling Units (DUs) are systematically selected from PSUs. The 3324 PSU are split into four groups for the year, and at each quarter the DUs from the given group are replaced by substitute DUs from the same PSU or the next PSU on the list (in the same group). It should be noted that the sampling unit is the dwelling, and the unit of observation is the household. Therefore, if a household moves out of a dwelling after being in the sample for, two quarters and a new household moves in, the new household will be enumerated for two more quarters until the DU is rotated out. If no household moves into the sampled dwelling, the dwelling will be classified as vacant (or unoccupied).

Mode of data collection

Face-to-face

Data appraisal

The statistical release notes that missing values were "generally imputed" for item non-response but provides no detail on how Statistics SA did so.

As of 2019, most rural inhabitants in Africa resided close to small and mid-sized towns. The nearest city to almost ** percent of the rural population had between 10,000 and ****** inhabitants. Smaller shares of rural households, on the other hand, lived closer to larger urban areas. As of the same year, roughly half of the rural residents lived within ** kilometers from a city.

Europe And Africa Waterborne Coatings Market was valued at USD 71.88 Billion in 2024 and is projected to reach USD 104.90 Billion in 2032, growing at a CAGR of 4.29% from 2026 to 2032.

Europe And Africa Waterborne Coatings Market Overview

In Europe and Africa, population density has been increasing gradually. In 2019, population density across Europe was 7.47 Million whereas the population density was 7.46 Million in 2018. Similarly, in Africa, the population density was 13.4 Million in 2019 whereas the population density was 13.08 Million in 2018. As a result, the number of Middle-Class Population is also increasing in both continents. So, the need for dwelling places and office places are increasing day by day.

School age population, post-secondary non-tertiary education, female (number) in South Africa was reported at 964482 Persons in 2019, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Africa - Population of the official age for post-secondary non-tertiary education, female - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (Stats SA). It collects data on the labour market activities of individuals aged 15 years or older who live in South Africa.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The QLFS sample covers the non-institutional population of South Africa with one exception. The only institutional subpopulation included in the QLFS sample are individuals in worker's hostels. Persons living in private dwelling units within institutions are also enumerated. For example, within a school compound, one would enumerate the schoolmaster's house and teachers' accommodation because these are private dwellings. Students living in a dormitory on the school compound would, however, be excluded.

For more see the statistical release.

Kind of data

Sample survey data

Sampling procedure

The Quarterly Labour Force Survey (QLFS) uses the Master Sample frame that has been developed as a general-purpose household survey frame. The 2013 Master Sample is based on information collected during the 2011 Census. There are 3 324 primary sampling units in the Master Sample, with an expected sample of approximately 33 000 dwelling units. The sampling procedure for the QLFS is based on a stratified two-stage design with probability proportional to size (PPS) sampling of PSUs in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage.

The Master Sample is designed to be representative at the provincial level and within provinces at metro/non-metro levels. The sample is divided equally into four subgroups or panels called rotation groups. For each quarter of the QLFS, a quarter of the sampled dwellings are rotated out of the sample and replaced by new dwellings from the same PSU or the next PSU on the list.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

In general, imputation is used for item non-response (i.e. blanks within the questionnaire) and edit failures (i.e. invalid or inconsistent responses).

As of 2022, South Africa's population increased and counted approximately 60.6 million inhabitants in total, of which the majority (roughly 49.1 million) were Black Africans. Individuals with an Indian or Asian background formed the smallest population group, counting approximately 1.56 million people overall. Looking at the population from a regional perspective, Gauteng (includes Johannesburg) is the smallest province of South Africa, though highly urbanized with a population of nearly 16 million people.

Increase in number of households

The total number of households increased annually between 2002 and 2022. Between this period, the number of households in South Africa grew by approximately 65 percent. Furthermore, households comprising two to three members were more common in urban areas (39.2 percent) than they were in rural areas (30.6 percent). Households with six or more people, on the other hand, amounted to 19.3 percent in rural areas, being roughly twice as common as those in urban areas.

Main sources of income

The majority of the households in South Africa had salaries or grants as a main source of income in 2019. Roughly 10.7 million drew their income from regular wages, whereas 7.9 million households received social grants paid by the government for citizens in need of state support.

There were 4 021 000 Messenger users in South Africa in October 2019, which accounted for 6.5% of its entire population. The slight majority of them were men - 50.3%. People aged 25 to 34 were the largest user group (1 410 000). The highest difference between men and women occurs within people aged 25 to 34, where men lead by 740 000.

Abstract

The GHS is an annual household survey which measures the living circumstances of South African households. The GHS collects data on education, health, and social development, housing, access to services and facilities, food security, and agriculture.

Geographic coverage

The General Household Survey has national coverage.

Analysis unit

Households and individuals

Universe

The survey covers all de jure household members (usual residents) of households in the nine provinces of South Africa, and residents in workers' hostels. The survey does not cover collective living quarters such as student hostels, old age homes, hospitals, prisons, and military barracks.

Kind of data

Sample survey data [ssd]

Sampling procedure

From 2015 the General Household Survey (GHS) uses a Master Sample (MS) frame developed in 2013 as a general-purpose sampling frame to be used for all Stats SA household-based surveys. This MS has design requirements that are reasonably compatible with the GHS. The 2013 Master Sample is based on information collected during the 2011 Census conducted by Stats SA. In preparation for Census 2011, the country was divided into 103 576 enumeration areas (EAs). The census EAs, together with the auxiliary information for the EAs, were used as the frame units or building blocks for the formation of primary sampling units (PSUs) for the Master Sample, since they covered the entire country, and had other information that is crucial for stratification and creation of PSUs. There are 3 324 primary sampling units (PSUs) in the Master Sample, with an expected sample of approximately 33 000 dwelling units (DUs). The number of PSUs in the current Master Sample (3 324) reflect an 8,0% increase in the size of the Master Sample compared to the previous (2008) Master Sample (which had 3 080 PSUs). The larger Master Sample of PSUs was selected to improve the precision (smaller coefficients of variation, known as CVs) of the GHS estimates. The Master Sample is designed to be representative at provincial level and within provinces at metro/non-metro levels. Within the metros, the sample is further distributed by geographical type. The three geography types are Urban, Tribal and Farms. This implies, for example, that within a metropolitan area, the sample is representative of the different geography types that may exist within that metro.

The sample for the GHS is based on a stratified two-stage design with probability proportional to size (PPS) sampling of PSUs in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage.After allocating the sample to the provinces, the sample was further stratified by geography (primary stratification), and by population attributes using Census 2011 data (secondary stratification).

Mode of data collection

Computer Assisted Personal Interview

Research instrument

Data was collected with a household questionnaire and a questionnaire administered to a household member to elicit information on household members.

Data appraisal

Since 2019, the questionnaire for the GHS series changed and the variables were also renamed. For correspondence between old names (GHS pre-2019) and new name (GHS post-2019), see the document ghs-2019-variables-renamed.

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.

Geographic coverage

Nation-wide household survey (Kenya and Nigeria)

Analysis unit

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)

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.

Sampling deviation

N/A

Mode of data collection

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: 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

Abstract

The Genomic and environmental risk factors for cardiometabolic disease in Africans (AWI-Gen) project was a collaborative study between the University of the Witwatersrand (Wits) and the INDEPTH Network funded under the Human Heredity and Health in Africa (H3Africa) initiative. The H3Africa was a ground-breaking initiative to build institutional and individual capacity to undertake genetic and genomic studies in the African region. This collaboration, involved five INDEPTH sites i.e. 1) Navrongo - Ghana; 2) Nanoro - Burkina Faso; 3&4) Agincourt and Digkale - South Africa; and 5) Nairobi - Kenya) plus the Soweto-based birth-to-twenty cohort. AWI-Gen phase I was a population based cross-sectional study with a research platform of over 12,045 participants aged 40-60 years from Burkina Faso, Ghana, Kenya and South Africa. It aimed to understand the interplay between genetic, epigenetic and environmental risk factors for obesity and related cardiometabolic diseases (CMD) in sub-Saharan Africa and it generated epi-demographic, environmental, health history, behavioral, anthropometric, physiological and genetic data across a range of rapidly transitioning African settings. This provided a unique resource to examine genetic associations and gene-environment interactions that will contribute to Afrocentric risk prediction models and African-appropriate Mendelian Randomization instruments, and exploit their potential to improve personal and population health - while strengthening regional research capacity. We plan to continue this work in AWIGEN-phase II among the same participants recruited in AWIGen-I offering an opportunity to examine data in a longitudinal manner. The AWI-Gen phase II project aims to establish the genomic contribution to CMD and risk at a time when multiple interacting transitions, in the presence of high background HIV or malaria prevalence, are driving a rapid escalation in CMD across the African continent. The project capitalizes on the unique strengths of existing longitudinal cohorts and well-established health and demographic surveillance systems(HDSS) run by the partner institutions. The six study sites represent geographic and social variability of African populations which are also at different stages of the demographic and epidemiological transitions. The work in Kenya will be undertaken in the Nairobi Urban Health and Demographic Surveillance System (NUHDSS) run by African Population and Health Research Center (APHRC) following participants who were recruited in AWIGEN-Phase I. AWI-Gen II consisted of five main aims: i) AIM-1 (Sub-study 1): Genetic associations studies to elucidate functional pathways involved in determining body composition and risk for CMD by detecting pivotal genomic and environmental contributors; ii) AIM 2 (Sub-study 2): Genomics and bioinformatics-impact of genomic diversity on disease risk and precision public health; iii) AIM 3 (Sub-study 3): Examine changes over the menopausal transition in body composition and CMD risk; iv) AIM 4 (Sub-study 4): Examine gut microbiome in older adults and its relationship to obesity, diabetes and glucose tolerance and ageing; and v) AIM 5 (Sub-study 5): Explore respiratory disease in context of multi-morbidity. In this application, we sought ethical approval for the Kenya study only. The other partners sought approval from their appropriate ethics review authorities in their countries. The study budget was $248,613 and was funded by National Institute of Health (NIH)-USA under H3Africa. Data collection was undertaken for approximately 12 months but sample processing, data analysis, manuscript writing, capacity building and policy engagement was continued up to three years after field work (up to 2022).

Geographic coverage

County coverage (Informal settlements of Korogocho and Viwandani in Nairobi)

Analysis unit

Individual Household

Universe

The survey covered individual participants aged 45-65 years.

Sampling procedure

a) Study design: A prospective cohort study to examine genetic associations and gene-environment interactions with measures of change in CMD and risk derived over 5 years (AWI-Gen I survey was in 2014/2015, and survey for phenotypic characteristics (under AWI-Gen II) among the same individuals will was repeated in 2019/2020). This was an extend baseline (AWI-Gen I) to provide longitudinal data (AWI-Gen II). b) Study site (geographical) The study in Kenya was conducted in Nairobi, specifically in Korogocho and Viwandani urban informal settlements which are covered by the NUHDSS. c) Study populations Sub-study 1 & 5: Adult (40-60 years at baseline) residents of Korogocho and Viwandani informal settlements registered in the NUHDSS. Sample size A sample size of 2000 per site (12000 in total) was used in AWIGEN-I based on power calculations and effect sizes. The power calculations show that we have power to detect realistic effect sizes, based on studies in other populations. Figure 2 illustrates the relationship between power and effect size for two different phenotypes, illustrating that the detectable effect size is realistic. Power analysis for a sample size of 12000 individuals based on proposed candidate gene study for BMI (shown on the left) and for DXA (total body fat) (shown on the right). Given a sample size of 12000 in the AWI-Gen study, this graph shows effect size (x) which could be detected at a given power (y) for different minor allele frequencies (ranging from 0.05-045). For example, with a minor allele frequency of 0.25, we will have 80% power to detect an effect size (Beta) of 0.20 per allele change in BMI, and an effect size of 0.25 per allele change in body fat percentage. For AWIGEN 2, we will follow the same participants. We anticipate a retention of 70% from the 2000 participants recruited in phase 1. Thus, our sample size for AWIGEN-11 was approximately 1400 participants for the Kenyan site to for sub-studies 1 and 2. For Sub-studies 3 & 4 we will randomly sample 250 individuals for each sub-study which is a large sample by most microbiome project standards. For Sub-study 5 we will include all participants selected in Sub-study 1

Sampling deviation

N/A

Mode of data collection

Other [oth]

Research instrument

The questionnaire for AWIGen 2 was a structured questionnaire developed by the University of Witwatersrand. The questionnaire was translated from English to Swahili. The individual questionnaire was administered to an adult (40-60 years old), which collected various information of the individual including, age, gender, BMI, Visceral fat levels, T2 diabetes status, blood pressure, socio-economic status, lifestyle (diet, tobacco, alcohol, exercise etc.) and HIV infection status. In addition, for participants in microbiome study we will ask information on antibiotics use. We will repeat the anthropometric measurements including height, weight, waist and hip circumference and ultrasound measurements of visceral and subcutaneous fat, and cIMT.

Cleaning operations

Data was edited on REDCap during data entry and also secondary editing was performed once the files were submitted to the server.

Response rate

59%

Sampling error estimates

N/A

Tuberculosis (TB) is among diseases of global health importance with Sub Saharan Africa (SSA) accounting for 25% of the global TB burden. TB prevalence among miners in SSA is estimated at 3,000–7,000/100,000, which is about 3 to 10-times higher than in the general population. The study’s objective was to determine the prevalence of TB and associated risk factors among mining communities in Mererani, northern Tanzania. This was a cross-sectional study conducted from April 2019 to November 2021 involving current Small Scale Miners (SSM) and the General Community (GC). A total of 660 participants, 330 SSM and 330 GC were evaluated for the presence of TB. Data were analysed using Statistical Package for the Social Sciences (SPSS) database (IBM SPSS Statistics Version 27.0.0.0). Binary logistic regression (Generalized Linear Mixed Model) was used to determine the association between TB and independent predictors. Prevalence of TB was 7%, about 24-times higher than the national prevalence of 0.295%. Participants from the general community had higher prevalence of TB 7.9% than SSM (6.1%). Both for SSM and the GC, TB was found to be associated with: lower education level (aOR = 3.62, 95%CI = 1.16–11.28), previous lung disease (aOR = 4.30, 95%CI = 1.48–12.53) and having symptoms of TB (aOR = 3.24, 95%CI = 1.38–7.64). Specifically for the SSM, TB was found to be associated with Human Immunodeficiency Virus (HIV) infection (aOR = 8.28, 95%CI = 1.21–56.66).Though significant progress has been attained in the control of the TB epidemic in Tanzania, still hot spots with significantly high burden of TB exists, including miners. More importantly, populations surrounding the mining areas, are equally affected, and needs more engagement in the control of TB so as to realize the Global End TB targets of 2035.