As of 2021, Akan was the largest ethnic group in Ghana, accounting for 45.7 percent of the country's population. Simultaneously, Akan, as a language, was the most widely spoken in Ghana. Mole-Dagbani and Ewe covered 18.5 percent and 12.8 percent of the groups of ethnicity, respectively. Other ethnic groups include Ga-Dangme, Gurma, Guan, and Grusi.

BackgroundAnaemia in pregnancy (AIP) is a public health concern due to its devastating effects on women and their unborn babies, resulting in increased maternal and neonatal deaths in developing countries. Despite several Ghanaian health policies to combat AIP, AIP is still on the rise. It becomes imperative to identify geographic-specific factors for developing appropriate interventions for the management of AIP. However, Kassena Nankana West District (KNWD) in the Upper East Region of Ghana lacks a study on anaemia risk factors, therefore, this study estimated the prevalence and risk factors for anaemia among pregnant women in the district.MethodsA cross-sectional study was conducted from February to March 2023 in the KNWD. Approximately 376 pregnant women in their third trimester were randomly selected from 10 health facilities by utilizing the antenatal register as the sampling frame. Anthropometric, obstetric, sociodemographic, and health facility resource characteristics were collected using structured questionnaires and from antenatal records. Mixed-effect logistic regression was used to identify independent factors of anaemia at 95% confidence interval.ResultsPrevalence of AIP was 53.9% (95%CI:48.5%–58.8%). Mild, moderate, and severe anaemia prevalence was 16.9%, 35.3%, and 1.7% respectively. Malaria infection during pregnancy (aOR = 1.64; 95%CI:1.03–2.62) and accessing health facilities without trained laboratory personnel (aOR = 5.49; 95%CI:1.67–18.00) were associated with increased odds of AIP. Belonging to the major ethnic group (aOR = 0.52; 95%CI:0.28–0.85), accessing health facilities without laboratory services (aOR = 0.14; 95%CI:0.04–0.47), and accessing health facilities without sulphadoxine-pyrimethamine drugs (aOR = 0.22; 95%CI:0.06–0.86) in KNWD were also associated with decreased odds of AIP.ConclusionKNWD has a severe burden of AIP. Maternal and health facility-related factors were associated with AIP in the district. These factors are preventable. Therefore, the provision of functional laboratory services with dedicated technical personnel, regular supply of sulphadoxine-pyrimethamine drugs to the health facilities, and enhanced community education on malaria prevention are recommended for anaemia control in the district.

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.

Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

Antenatal and obstetric characteristics of respondents (n = 376).

Abstract

The Kintampo north and south districts (previously simply referred to as the Kintampo district) are two of the 19 districts currently in the Brong Ahafo Region of Ghana. The Kintampo HDSS area (constituting of Kintampo North Municipality and Kintampo south district), has a surface area of 7,162 square kilometers. It is bounded to the north by the Black Volta, west by the Wenchi and Tain districts, in the East by the Atebubu District and to the south by Techiman and south-east by the Nkoranza north and south districts respectively.

The main indigenous ethnic groups are of the Bono, and the Mo origin. There is however a large permanent immigrant population from the northern Regions of Ghana (Dagarbas, Dagombas and Konkombas) who are mostly farmers. A few Dangbes and Ewes who are mainly fishermen are settled along the banks of the Black Volta. Settlements are mainly concentrated along the main trunk road linking the district capitals (Kintampo/Jema) to northern Region. There are 24 public health facilities made up of 15 Community-based Health Planning and Services (CHPS) compounds, 7 health centre and 2 hospitals. The hospitals are located at the district capitals. In the private sector, there are 3 private clinics and 3 private maternity homes In April 2010, The Kintampo HDSS established a satellite HDSS (Ahafo Mining Area Health and Demographic Surveillance System, AMAHDSS) in Tano North and Asutifi Districts of the Newmont Ghana Gold mining concession area. It is to monitor population and health dynamics in a mining area. It is the first HDSS in a mining area.

Geographic coverage

The Kintampo north and south districts (previously simply referred to as the Kintampo district) are two of the 19 districts currently in the Brong Ahafo Region of Ghana. The Kintampo HDSS area (constituting of Kintampo North Municipality and Kintampo south district), has a surface area of 7,162 square kilometers. It is bounded to the north by the Black Volta, west by the Wenchi and Tain districts, in the East by the Atebubu District and to the south by Techiman and south-east by the Nkoranza north and south districts respectively.

Analysis unit

Individual

Universe

The survey covered all resident population

Kind of data

Event history data

Frequency of data collection

Round 1 to Round 12: 2 times a year Round 13 to Round 22: 3 times a year

Sampling procedure

This dataset is not based on a sample, but contains information from the complete demographic surveillance area.

Sampling deviation

Not Applicable

Mode of data collection

Proxy Respondent [proxy]

Response rate

Response rate is 99% on an average over the years in all rounds

Sampling error estimates

Not Applicable

Data appraisal

CentreId MetricTable QMetric Illegal Legal Total Metric RunDate GH021 MicroDataCleaned Starts 254483 2017-05-17 12:26
GH021 MicroDataCleaned Transitions 0 700506 700506 0 2017-05-17 12:26
GH021 MicroDataCleaned Ends 254483 2017-05-17 12:26
GH021 MicroDataCleaned SexValues 32 700474 700506 0 2017-05-17 12:27
GH021 MicroDataCleaned DoBValues 32 700474 700506 0 2017-05-17 12:27

Growing Infrastructure and Prosperity of Ghana The derivation of the name Ghana signifies "Strong Warrior King" and was the title concurred to the rulers of the middle age "Ghana" Empire in West Africa — in no way related to the present Ghana, for the realm was further north, in current Republic of Mali, Senegal and southern Mauritania, as well as in the district of Guinea. Ghana is a multi-ethnic country with a different populace, semantic and strict groups; while the Akan are the biggest ethnic gathering, they comprise just a majority. Most of Ghanaians are Christian (71.3%), with near a fifth being Muslim and a 10th rehearsing conventional beliefs or detailing no religion. Ghana is a unitary established vote based system drove a both by a president head of state and head of government. Beginning around 1993, it has kept one of the freest and most stable legislatures on the landmass, and performs generally well in measurements of medical care, financial development, and human turn of events. Ghana thus appreciates critical impact in West Africa, and is profoundly coordinated in foreign relations, being an individual from the Non-Aligned Movement, the African Union, the Economic Community of West African States (ECOWAS), the Group of 24 and the Commonwealth of Nations

The 2021 population and housing census in Ghana revealed that Pentecostal/Charismatic Christians were the largest religious group in Ghana, reaching a share of **** percent. This translated into over *** million of the country's population, an increase compared to the 2010 census year. The Islamic region followed with a nation-wide coverage of nearly ** percent. Moreover, only *** percent of the country's population had no religion, which was a decrease from the *** percent in the previous census year.

Abstract

The Ghana Child Labour Survey (GCLS) field data collection took place in January-February 2001, after two months of preparatory activities that included a pretest of instruments and methodology.

Socio-Demographic Characteristics of Household Population

The 9889 households interviewed contained 47,955 persons, with a sex ratio of 96.7. About one-fifth of the population is made up of household heads, while children constitute about a half (49.7%); children aged 5-17, in comparison, make up 35.5 percent of the population. The rural areas make up 60.3 percent of the population. Information collected on school attendance shows that nearly the same proportion of the sample population had never attended school (30.8%), as were those currently in school (34.4%) or had attended school in the past (34.8%). Marked disparities existed in school attendance at the regional level, with over 60 percent of the sample population in the three northern regions having never gone to school.

The economically active persons constituted 57.5 percent of the sample, the majority of whom were in agriculture/forestry/fishing (51.1%), followed by sales workers (16.9%). The pattern applied to all regions, except Greater Accra where sales workers predominated. Majority of the economically active population were self-employed, own account workers (54.7%), followed by unpaid family workers (29.8%). Over 90 percent of population worked in the informal sector.

Households in the country derive much of their income from self-employment in agricultural activities (49.1%); self-employment in non-agricultural activities accounts for 28.0 percent, while regular wage employment makes up 14.0 percent. With the exception of Greater Accra, agriculture is the major source of income for households in all the regions.

Socio-Demographic Characteristics of Children aged 5-17

The number of children aged 5-17 is estimated by the survey to be about 6.4 million (6,361,111). Children aged 5-9 years constitute 41.8 percent (2,657,258); the 10-14 age group is 39.5 percent (2,515,463) while the 15-17 age group is 18.7 percent (1,188,390). Males constitute 52.9 percent of the 5-17 age group; indeed, there are more boys than girls in each of the three age groups. Most of the children live in rural areas (62.3%).

Ashanti Region has the largest share (15.5%) of the children, followed by Northern (14.0%) and Greater Accra (11.7%). Variations in regional distribution of children (5-17) from the 2000 census are attributable mainly to differences in the average household sizes for the various regions. The predominant ethnic groups of the children are Akans (44.3%) and Mole-Dagbani (18.7%).

Over three quarters (76.5%) of the children are attending school, while 17.6 percent have never attended school. With the exception of the three northern regions, more than 80 percent of the children in all the other regions are attending school. Nearly half (46.5%) of the children in the Northern Region have never attended school. Slightly higher proportion of males in all regions are attending school, compared with females.

The three major reasons for children never attending school are affordability (44.2%), distance from school (18.4%) and lack of interest in schooling (17.1%). These reasons apply to both males and females.

The highest level of schooling attained by majority of the children is primary (56.1%), which is what is expected of the age group. The survey shows that only 2.0 percent of the children are receiving training, with males being in fitting/mechanics and carpentry and females in dress making, catering/bakery and hairdressing. About 20 percent of the children are neither schooling nor receiving any training.

Background information on parents indicates that neither death nor divorce/ separation of parents are significant factors for child labour. Virtually all the children (99.7%) reported that both parents were working. Majority of the parents were self-employed.

Activities of Children

Economic Activity

Information collected indicates that 2,474,545 children were engaged in usual economic activity, which is about 2 in every 5 children aged 5-17 years. Half of the rural children and about one fifth of the urban children were in economic activity. About 40 percent of working children (39.8%) worked for more than 6 months. More than a half of the children in Greater Accra, Central and Eastern regions worked for more than 6 months out of the year.

Estimates indicate that 1,590,765 children were attending school while working, which is 64.3 percent of children engaged in usual economic activity.

With respect to current economic activity, 31.3 percent (or 1,984,107) of the children aged 5-17 years were estimated to engage in economic activity during the 7 days preceding the interview; the proportion increased with age. A higher proportion of children in rural areas (39.7%) are more likely to engage in economic activity than urban children (17.6%).

About two-thirds of the children (68.7%) did no work; 80.5 percent of these were full-time students. Over 90 percent of children in urban areas did no work because they were attending school, compared to 71.7 percent in rural areas.

Nature and Conditions of Work

About 57 percent (1,128,072) of the working children were engaged in agriculture/forestry/fishing, while 21 percent worked as hawkers and street vendors, selling iced water, food and other items. Eleven percent engaged in general labourer work, such as washing of cars, fetching firewood and water, pushing trucks (males), and carrying goods as porters (mainly females). It is estimated that 1,338,794 of the working children were part-time workers. About a third were in full-time and permanent employment.

A significant proportion (88.0%) of the working children were unpaid family workers, and apprentices, while 5.9 percent were own-account workers (or self-employed). About 70 percent (68.7%) of the children worked between two and five hours a day.

Over a third of the children (36.7%) were paid daily, while 28.5 percent were on piece rate. Over 80 percent received payment themselves.

Most working children (60%) were satisfied with their jobs. Those who were not satisfied reported that their work was too tiring or wages and earnings were too low.

Non-economic activity

About 90 percent of the children engage in housekeeping activities on a regular basis. There are slight rural (92.0%) and urban (86%) and regional variations. On average, 73 percent of the children spend less than 3 hours a day on household chores. The older the child, the more time he/she spends on household chores. Only about one percent of the children spend more than 7 hours a day on household chores. Gender of the head of household does not affect children's involvement in household chores. Only about 5 percent of the children were reported by parents to have been idle, with the reason that either the child was too young to work or sick.

Health and Safety

According to parents, 29.4 percent of the children had suffered injuries, compared to 22.7 percent reported by the children themselves. More than half of the injuries occurred at home and were mostly cuts and wounds. About a quarter of the children who were injured at the work place worked in agriculture. The injuries, in a great number of cases (40.0%), were not serious and did not require any medical treatment, while 38.6 percent were treated and discharged.

Parents Perception and Preferences

According to parents of 93 percent of the children, child work is basically to contribute to the economic welfare of households; either to supplement household income (58.8%) or help in household enterprises (34.2%). Parents of 44 percent of the children reported that household living standards would fall and household enterprises could not operate in 21 percent of the cases, if the children did not work. About 30 percent of children did not need to work as household welfare would not be affected.

If parents had the choice they would prefer their children to be either schooling or in training and to complete their education. Most of the children themselves (70.3%) also preferred to go to school or complete their education before starting work. Parents' and children's preferences were thus different from what the children were actually doing. This suggests that some policy measure could help enroll and keep more children in the classroom as expected of their age group.

STREET CHILDREN SURVEY

Socio-Demographic Characteristics

Areas throughout the country, identified as sleeping places of street children, were purposely selected for the survey. A total of 2,314 street children were interviewed, out of whom 52.4 percent were females. The 15-17 age group constituted 50.1 percent of the total number. The highest proportion (56.6%) of the females was in the 10-14 age group, while that of the males (50.1%) was in the 15-17 age group. Greater Accra Region had the highest proportion (49.7%) of the street children, followed by Ashanti with 26.5 percent. Street children as a phenomenon, is virtually absent in the Upper West Region.

The street children were predominantly of Mole-Dagbani (40.2%) and Akan (32.2%) ethnic origins. Akans formed the greater proportion (53.4%) of male street children, while Mole-Dagbon made up 63.1 percent of the females. Only about 2 percent of the street children were married, with almost all of them being females.

School Attendance

A sizeable proportion of the street children (45.7%) had never attended school; only 11.2 percent (258) were attending school at the time of the survey. Of the 995 children who had attended school in the past, only 15.5 percent completed school. The rest had dropped out of school for one reason or the other, the major reason being the problem of affordability (60.9%). More than half

We aim to provide a powerful reference set for genome-wide association studies (GWAS) in African populations. Our pilot study to sequence 100 individuals each from Fula, Jola, Mandinka and Wollof from the Gambia to low coverage has been completed - this first part of the main effort will make available low coverage WGS data for 400 individuals from multiple ethnic groups in Burkina Faso, Cameroon, Ghana and Tanzania. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

Islam is the major religion in many African countries, especially in the north of the continent. In Comoros, Libya, Western Sahara, at least 99 percent of the population was Muslim as of 202. These were the highest percentages on the continent. However, also in many other African nations, the majority of the population was Muslim. In Egypt, for instance, Islam was the religion of 79 percent of the people. Islam and other religions in Africa Africa accounts for an important share of the world’s Muslim population. As of 2019, 16 percent of the Muslims worldwide lived in Sub-Saharan Africa, while 20 percent of them lived in the Middle East and North Africa (MENA) region. Together with Christianity, Islam is the most common religious affiliation in Africa, followed by several traditional African religions. Although to a smaller extent, numerous other religions are practiced on the continent: these include Judaism, the Baha’i Faith, Hinduism, and Buddhism. Number of Muslims worldwide Islam is one of the most widespread religions in the world. There are approximately 1.9 billion Muslims globally, with the largest Muslim communities living in the Asia-Pacific region. Specifically, Indonesia hosts the highest number of Muslims worldwide, amounting to over 200 million, followed by India, Pakistan, and Bangladesh. Islam is also present in Europe and America. The largest Islamic communities in Europe are in France (5.72 million), Germany (4.95 million), and the United Kingdom (4.13 million). In the United States, there is an estimated number of around 3.45 million Muslims.

Abstract

The Afrobarometer is a comparative series of public attitude surveys that assess African citizen's attitudes to democracy and governance, markets, and civil society, among other topics. The surveys have been undertaken at periodic intervals since 1999. The Afrobarometer's coverage has increased over time. Round 1 (1999-2001) initially covered 7 countries and was later extended to 12 countries. Round 2 (2002-2004) surveyed citizens in 16 countries. Round 3 (2005-2006) 18 countries, Round 4 (2008) 20 countries, Round 5 (2011-2013) 34 countries, Round 6 (2014-2015) 36 countries, and Round 7 (2016-2018) 34 countries. The survey covered 34 countries in Round 8 (2019-2021).

Geographic coverage

National coverage

Analysis unit

Individual

Universe

Citizens of Zambia who are 18 years and older

Kind of data

Sample survey data [ssd]

Sampling procedure

Afrobarometer uses national probability samples designed to meet the following criteria. Samples are designed to generate a sample that is a representative cross-section of all citizens of voting age in a given country. The goal is to give every adult citizen an equal and known chance of being selected for an interview. They achieve this by:

• using random selection methods at every stage of sampling; • sampling at all stages with probability proportionate to population size wherever possible to ensure that larger (i.e., more populated) geographic units have a proportionally greater probability of being chosen into the sample.

The sampling universe normally includes all citizens age 18 and older. As a standard practice, we exclude people living in institutionalized settings, such as students in dormitories, patients in hospitals, and persons in prisons or nursing homes. Occasionally, we must also exclude people living in areas determined to be inaccessible due to conflict or insecurity. Any such exclusion is noted in the technical information report (TIR) that accompanies each data set.

Sample size and design Samples usually include either 1,200 or 2,400 cases. A randomly selected sample of n=1200 cases allows inferences to national adult populations with a margin of sampling error of no more than +/-2.8% with a confidence level of 95 percent. With a sample size of n=2400, the margin of error decreases to +/-2.0% at 95 percent confidence level.

The sample design is a clustered, stratified, multi-stage, area probability sample. Specifically, we first stratify the sample according to the main sub-national unit of government (state, province, region, etc.) and by urban or rural location.

Area stratification reduces the likelihood that distinctive ethnic or language groups are left out of the sample. Afrobarometer occasionally purposely oversamples certain populations that are politically significant within a country to ensure that the size of the sub-sample is large enough to be analysed. Any oversamples is noted in the TIR.

Sample stages Samples are drawn in either four or five stages:

Stage 1: In rural areas only, the first stage is to draw secondary sampling units (SSUs). SSUs are not used in urban areas, and in some countries they are not used in rural areas. See the TIR that accompanies each data set for specific details on the sample in any given country. Stage 2: We randomly select primary sampling units (PSU). Stage 3: We then randomly select sampling start points. Stage 4: Interviewers then randomly select households. Stage 5: Within the household, the interviewer randomly selects an individual respondent. Each interviewer alternates in each household between interviewing a man and interviewing a woman to ensure gender balance in the sample.

To keep the costs and logistics of fieldwork within manageable limits, eight interviews are clustered within each selected PSU.

Zambia - Sample size: 1,200 - Sampling Frame: 2020 population projections based on the 2016 Bureau of Statistics Population Census - Sample design: Nationally representative, random, clustered, stratified, multi-stage area probability sample - Stratification: District and urban/peri-urban/rural location - Stages: PSUs (from strata), start points, households, respondents - PSU selection: Probability Proportionate to Population Size (PPPS) - Cluster size: 8 households per PSU - Household selection: Randomly selected start points, followed by walk pattern using 5/10 interval - Respondent selection: Gender quota filled by alternating interviews between men and women; respondents of appropriate gender listed, after which computer randomly selects individual

Mode of data collection

Face-to-face [f2f]

Research instrument

The Round 8 questionnaire has been developed by the Questionnaire Committee after reviewing the findings and feedback obtained in previous Rounds, and securing input on preferred new topics from a host of donors, analysts, and users of the data.

The questionnaire consists of three parts: 1. Part 1 captures the steps for selecting households and respondents, and includes the introduction to the respondent and (pp.1-4). This section should be filled in by the Fieldworker. 2. Part 2 covers the core attitudinal and demographic questions that are asked by the Fieldworker and answered by the Respondent (Q1 – Q100). 3. Part 3 includes contextual questions about the setting and atmosphere of the interview, and collects information on the Fieldworker. This section is completed by the Fieldworker (Q101 – Q123).

Response rate

Outcome rates: - Contact rate: 93% - Cooperation rate: 74% - Refusal rate: 9% - Response rate: 69%

Sampling error estimates

The sample size yields country-level results with a margin of error of +/-3 percentage points at a 95% confidence level.

We aim to provide a powerful reference set for genome-wide association studies (GWAS) in African populations. Our pilot study to sequence 100 individuals each from Fula, Jola, Mandinka and Wollof from the Gambia to low coverage has been completed - this first part of the main effort will make available low coverage WGS data for 400 individuals from multiple ethnic groups in Burkina Faso, Cameroon, Ghana and Tanzania. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

Christianity is the major religion in numerous African countries. As of 2024, around 96 percent of the population of Zambia was Christian, representing the highest percentage on the continent. Seychelles and Rwanda followed with roughly 95 percent and 94 percent of the population being Christian, respectively. While these countries present the highest percentages, Christianity was also prevalent in many other African nations. For instance, in South Africa, Christianity was the religion of nearly 85 percent of the people, while the share corresponded to 71 percent in Ghana. Religious variations across Africa Christianity and Islam are the most practiced religions in Africa. Christian adherents are prevalent below the Sahara, while North Africa is predominantly Muslim. In 2020, Christians accounted for around 60 percent of the Sub-Saharan African population, followed by Muslims with a share of roughly 30 percent. In absolute terms, there were approximately 650 million Christians in the region, a number forecast to increase to over one billion by 2050. In contrast, Islam is most prevalent in North Africa, being the religion of over 90 percent of the population in Algeria, Morocco, Tunisia, and Libya. Christianity in the world As opposed to other religions, Christianity is widely spread across continents worldwide. In fact, Sub-Saharan Africa, Latin America and the Caribbean, and Europe each account for around 25 percent of the global Christian population. By comparison, Asia-Pacific and North America make up 13 percent and 12 percent of Christians worldwide, respectively. In several regions, Christians also suffer persecution on religious grounds. Somalia and Libya presented the most critical situation in Africa in 2021, reporting the strongest suppression of Christians worldwide just after North Korea and Afghanistan.