As of 2043, Nairobi was the most populated city in Kenya, with more than 2.7 million people living in the capital. The city is also the only one in the country with a population exceeding one million. For instance, Mombasa, the second most populated, has nearly 800 thousand inhabitants. As of 2020, Kenya's population was estimated at over 53.7 million people.

Historical dataset of population level and growth rate for the Nairobi, Kenya metro area from 1950 to 2025.

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. Kenya data available from WorldPop here. Data and Resources TIFF Kenya - Population density (2015) DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid...

While the East African region, including Kenya, is one of first regions believed to have modern humans inhabit it, population growth in the region remained slow to non-existent throughout the 19th century; in the past hundred years, however, Kenya’s population has seen an exponential increase in size, going from 2.65 million in 1920, to an estimated 53.77 million in 2020.

Along with this population growth, Kenya has seen rapid urbanization and industrialization, particularly in recent decades. The metropolitan area of Kenya’s capital, Nairobi, with an estimated population of 9.35 million in 2020, now contains on its own over three and a half times the population of the entire country just a century earlier.

This statistic shows the total population of Kenya from 2013 to 2023 by gender. In 2023, Kenya's female population amounted to approximately 27.82 million, while the male population amounted to approximately 27.52 million inhabitants.

Analysis unit

Persons and households Nairobi oversample. Weighted by district and age.

UNITS IDENTIFIED: - Dwellings: no - Vacant Units: - Households: yes - Individuals: yes - Group quarters: no

UNIT DESCRIPTIONS: - Dwellings: no - Households: Yes - Group quarters:

Universe

All persons who were in Kenya at midnight on Census Night.

Kind of data

Population and Housing Census [hh/popcen]

Sampling procedure

MICRODATA SOURCE: Statistics Division Ministry of Finance and Planning

SAMPLE SIZE (person records): 659310.

SAMPLE DESIGN: Unknown sample design includes oversample of Nairobi. Data are weighted by age and district of residence.

Mode of data collection

Face-to-face [f2f]

Research instrument

Single enumeration form that requested information on individuals.

Abstract

The places we live affect our health status and the choices and opportunities we have (or do not have) to lead fulfilling lives. Over the past ten years, the African Population & Health Research Centre (APHRC) has led pioneering work in highlighting some of the major health and livelihood challenges associated with rapid urbanization in sub-Saharan Africa (SSA). In 2002, the Centre established the first longitudinal platform in urban Africa in the city of Nairobi in Kenya. The platform known as the Nairobi Urban Health and Demographic Surveillance System collects data on two informal settlements - Korogocho and Viwandani - in Nairobi City every four months on issues ranging from household dynamics to fertility and mortality, migration and livelihood as well as on causes of death, using a verbal autopsy technique. The dataset provided here contains key demographic and health indicators extracted from the longitudinal database. Researchers interested in accessing the micro-data can look at our data access policy and contact us.

Geographic coverage

The Demographic Surveillance Area (combining Viwandani and Korogocho slum settlements) covers a land area of about 0.97 km2, with the two informal settlements located about 7 km from each other. Korogocho is located 12 km from the Nairobi city center; in Kasarani division (now Kasarani district), while Viwandani is about 7 km from Nairobi city center in Makadara division (now Madaraka district). The DSA covers about seven villages each in Korogocho and Viwandani.

Analysis unit

Individual

Universe

Between 1st January and 31st December,2015 the Nairobi HDSS covered 86,304 individualis living in 30,219 households distributed across two informal settlements(Korogocho and Viwandani) were observed. All persons who sleep in the household prior to the day of the survey are included in the survey, while non-resident household members are excluded from the survey.

The present universe started out through an initial census carried out on 1st August,2002 of the population living in the two Informal settlements (Korogocho and Viwandani). Regular visits have since then been made (3 times a year) to update information on births, deaths and migration that have occurred in the households observed at the initial census. New members join the population through a birth to a registered member, or an in-migration, while existing members leave through a death or out-migration. The DSS adopts the concept of an open cohort that allows new members to join and regular members to leave and return to the system.

Kind of data

Event history data

Frequency of data collection

Three rounds in a year

Sampling procedure

This dataset is related to the whole demographic surveillance area population. The number of respondents has varied over the last 13 years (2002-2015), with variations being observed at both household level and at Individual level. As at 31st December 2015, 66,848 were being observed under the Nairobi HDSS living in 25,812 households distributed across two informal settlements(Korogocho and Viwandani). The variable IndividualId uniquely identifies every respondent observed while the variable LocationId uniquely identifies the room in which the individual was living at any point in time. To identify individuals who were living together at any one point in time (a household) the data can be split on location and observation dates.

Sampling deviation

None

Mode of data collection

Proxy Respondent [proxy]

Research instrument

Questionnaires are printed and administered in Swahili, the country's national language.

The questionnaires for the Nairobi HDSS were structured questionnaires based on the INDEPTH Model Questionnaire and were translated into Swahili with some modifications and additions.After an initial review the questionnaires were translated back into English by an independent translator with no prior knowledge of the survey. The back translation from the Swahili version was independently reviewed and compared to the English original. Differences in translation were reviewed and resolved in collaboration with the original translators. The English and Swahili questionnaires were both piloted as part of the survey pretest.

At baseline, a household questionnaire was administered in each household, which collected various information on household members including sex, age, relationship, and orphanhood status. In later rounds questionnaires to track the migration of the population observed at baseline, and additonal questionnaires to capture demographic and health events happening to the population have been introduced.

Cleaning operations

Data editing took place at a number of stages throughout the processing, including: a) Office editing and coding b) During data entry c) Structure checking and completeness d) Secondary editing e) Structural checking of STATA data files

Where changes were made by the program, a cold deck imputation is preferred; where incorrect values were imputed using existing data from another dataset. If cold deck imputation was found to be insufficient, hot deck imputation was used, In this case, a missing value was imputed from a randomly selected similar record in the same dataset.

Some corrections are made automatically by the program(80%) and the rest by visual control of the questionnaires (20%).

1. 100% forms filled in by FRAs are rechecked for completeness, ensured that all the necessary event forms are filled in.
2. Spot checks are done on field over data collection by FRAs for reliability of data.
3. FRS instructs revisits wherever required.
4. Forms are checked on sample basis
5. Checks if all the necessary event forms are filled in.
6. Forms with inconsistencies identified at the time of entry are sent back to the field.
7. Creating and managing data entry checks for picking up inconsistencies
8. Monitoring field work: balancing work target and quality.
9. Dealing with data inconsistencies at data level and giving feedbacks to field staff.
10. Conducting training and refresher training wherever required.
11. Data cleaning

Response rate

Over the years the response rate at household level has varied between 95% and 97% with response rate at Individual Level varying between 92% and 95%. Challenges to acheiving a 100% response rate have included: - high population mobility within the study area - high population attrition - respondent fatigue - security in some areas

Sampling error estimates

Not applicable for surveillance data

Data appraisal

CentreId MetricTable QMetric Illegal Legal Total Metric RunDate KE031 MicroDataCleaned Starts 219285 2017-05-16 18:25
KE031 MicroDataCleaned Transitions 825036 825036 0 2017-05-16 18:25
KE031 MicroDataCleaned Ends 219285 2017-05-16 18:25
KE031 MicroDataCleaned SexValues 825036 2017-05-16 18:25
KE031 MicroDataCleaned DoBValues 42 824994 825036 0 2017-05-16 18:25

Nairobi is the most populated county in Kenya. The area formed by the country's capital and its surroundings has a population of over 4.3 million inhabitants. Of the 47 counties in Kenya, 18 have a population of more than one million people.

The African Cities Population Database (ACPD) has been produced by the Birkbeck College of the University of London in 1990 at the request of the United Nations Environment Programme (UNEP) in Nairobi, Kenya. The database contains head counts for 479 cities in Africa which either have a population of over 20,000 or are capitals of their nation state. Listed are the geographical location of the cities and their population sizes. The material is primarily derived from a 1988 report of the Economic Commission for Africa (ECA) and several issues of the United Nations Demographic Yearbook (1973-81). Severe problems were found with several countries such as Togo, Ghana and South Africa. For South Africa, the data were derived from the United Nations Demographic Yearbook 1987.

WCPD is an Arc/Info point coverage. It has no projection, as the cities are located on the basis of their latitude and longitude. Coordinates were assigned on the basis of gazetteers or African maps. Each record in the data base contains details of the city name, country name, latitude and longitude of the city, and its population at a defined time. The Arc/Info attribute table contains the following fields:

AREA Arc/Info item PERIMETER Arc/Info item ACPD# Arc/Info item ACPD-ID Arc/Info item ID-NUM Unique number for each city CITY City name COUNTRY Country name CITY-POP Population of city proper YEAR Latest available year of collection

ACPD comes as an Arc/Info EXPORT file originally called "ACPD.E00" and contains 67 Kb of data. The file has a record length of 80 and a block size of 8000 (blocking factor = 100). The file can be read from tape using Arc/Info's TAPEREAD command or any other generic copy utility. If distributed on a diskette it can be read using the ordinary DOS 'COPY' command. The file has to be converted to Arc/Info internal format using its IMPORT command.

References to the WCPD data set can be found in:

• SERLL News, Issue No. 1, January 1991, Birkbeck College, London, UK.
• D. Rhind. "Cartographically-related research at Birkbeck College 1987-91" in: The Cartographic Journal, Vol. 28, June 1991, pp. 63-66.

The source of the WCPD data set as held by GRID is Birkbeck College, University of London, Department of Geography, London, UK.

These are datasets generated from multi-state model (MSM) project on understanding demographic events and migration patterns in two urban slums of Nairobi City in Kenya at the African Population and Health Research Center (APHRC). The project focuses on using MSM techniques to analyze residence demographic events in Nairobi urban slums, with an emphasis on key events such as:

• Births
• Deaths
• Migration (in-migration and out-migration)
• Changes in residence status (exit and entry)

The primary aim of these datasets is to allow those who want to understand and model the demographic transitions in Nairobi's informal settlements, identifying factors that influence residence changes over time.

Abstract

The World Bank in collaboration with the Joint Data Center on Forced Displacement, Kenya National Bureau of Statistics (KNBS) and the United Nations High Commissioner for Refugees (UNHCR) conducted a cross-sectional survey on refugee and host populations living in Nairobi. The survey was based on the Kenya Continuous Household Survey (KCHS) and targets both host populations and refugees living in Nairobi. Through a participatory training format, enumerators learned how to collect quality data specific for refugees as well as nationals. Daily data quality monitoring dashboards were produced during the data collection periods to provide feedback to the field team and correct possible errors. The data was collected with CAPI technique through the World Bank developed Survey Solutions software; this ensured high standards of data storage, protection and pre-processing.

The sample is representative of refugees and other residents living in Nairobi. The refugee sample was drawn from UNHCR’s database of refugees and asylum seekers (proGres) using implicit stratification by sub-county and country of origin. The host community sampling frame was drawn using a two-stage cluster design. In the first stage, eligible enumeration areas (EAs) based on the 2019 Population and Housing Census were selected. In the second stage 12 households were sampled from each EA. The survey differentiates between two types of host communities: ‘core’ host communities were drawn from EAs located within the three areas with the largest number of refugee families: Kasarani, Eastleigh North and Kayole. At least 10 percent of the Nairobi refugee families reside in each of these areas. ‘Wider’ host communities cover the rest of the Nairobi population and were drawn from EAs which do not cover the three areas in which many refugees live.

For a subset of households, a women empowerment module was administered by a trained female enumerator to one randomly selected woman in each household aged 15 to 49.

The data set contains two files. hh.dta contains household level information. The ‘hhid’ variable uniquely identifies all households. hhm.dta contains data at the level of the individual for all household members. Each household member is uniquely identified by the variable ‘hhm_id’.

This cross-sectional survey was conducted between May 22 to July 27, 2021. It comprises a sample of 4,853 households in total, 2,420 of which are refugees and 2,433 are hosts.

Geographic coverage

Nairobi county, Kenya

Analysis unit

Household, Individual

Sampling procedure

The survey has two primary samples contained in the ‘sample’ variable: the refugee sample and the host community sample. The refugee sample used the UNHCR database of refugees and asylum seekers in Kenya (proGres) as the sampling frame. ProGres holds information on all registered refugees and asylum seekers in Kenya including their contact information and data on nationality and approximate location of living. We considered only refugees living in Nairobi and implicitly stratified by nationality and location. In total, the sample comprises 2,420 refugee families.

The host community sample differentiates between two types of communities. We consider ‘core’ host communities as residents who live in Eastleigh North, Kayole or Kasarani – at least 10 percent of the Nairobi refugee families reside in each of these areas. Nationals living outside these areas are considered part of the ‘wider’ host community in Nairobi. The samples for both host communities were drawn using a 2-stage cluster design. In the first stage, eligible enumeration areas (EA) were drawn from the list of EAs covering Nairobi taken from the 2019 Population and Housing Census. In the second stage a listing of all host community households was established through a household census within all selected EAs, ensuring that refugee households were excluded to prevent overlap with the refugee sampling frame. 12 households and 6 replacements were drawn per EA. Our total sample consists of 2,433 host community households, 1,221 core hosts and 1,212 wider hosts.

The three sub-samples – refugees, core hosts, and wider hosts – are reflected in the ‘strata’ variable. The EAs which form the primary sampling units for the two host samples are anonymized and included in the ‘psu’ variable. Please note that the ‘psu’ variable clusters refugees under one numeric code (888).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The Questionnaire is provided as external resources in pdf format. Questionnaires were produced through the World Bank developed Survey Solutions software. The survey was implemented in English,Swahili and Somali.

Cairo, in Egypt, ranked as the most populated city in Africa as of 2025, with an estimated population of over 23 million inhabitants living in Greater Cairo. Kinshasa, in Congo, and Lagos, in Nigeria, followed with some 17.8 million and 17.2 million, respectively. Among the 15 largest cities in the continent, another one, Kano, was located in Nigeria, the most populous country in Africa. Population density trends in Africa As of 2023, Africa exhibited a population density of 50.1 individuals per square kilometer. Since 2000, the population density across the continent has been experiencing a consistent annual increment. Projections indicated that the average population residing within each square kilometer would rise to approximately 58.5 by the year 2030. Moreover, Mauritius stood out as the African nation with the most elevated population density, exceeding 627 individuals per square kilometre. Mauritius possesses one of the most compact territories on the continent, a factor that significantly influences its high population density. Urbanization dynamics in Africa The urbanization rate in Africa was anticipated to reach close to 45.5 percent in 2024. Urbanization across the continent has consistently risen since 2000, with urban areas accommodating only around a third of the total population then. This trajectory is projected to continue its rise in the years ahead. Nevertheless, the distribution between rural and urban populations shows remarkable diversity throughout the continent. In 2024, Gabon and Libya stood out as Africa’s most urbanized nations, each surpassing 80 percent urbanization. As of the same year, Africa's population was estimated to expand by 2.27 percent compared to the preceding year. Since 2000, the population growth rate across the continent has consistently exceeded 2.3 percent, reaching its pinnacle at 2.63 percent in 2013. Although the growth rate has experienced a deceleration, Africa's population will persistently grow significantly in the forthcoming years.

Abstract

In 2016, UNHCR became aware of a group of stateless persons living in or near Nairobi, Kenya. Most of them were Shona, descendants of missionaries who arrived from Zimbabwe and Zambia in the 1960s and remained in Kenya. The total number of Shona living in Kenya is estimated to be between 3,000 and 3,500 people. On their first arrival, the Shona were issued certificates of registration, but a change in the Registration of Persons Act of 1978 did not make provision for people of non-Kenyan descent, consequently denying the Shona citizenship. Zimbabwe and Zambia did not consider them nationals either, rendering them stateless. Besides the Shona, there are other groups of stateless persons of different origins and ethnicities, with the total number of stateless persons in Kenya estimated at 18,500. UNHCR and the Government of Kenya are taking steps to address statelessness in the country, among them is the registration of selected groups for nationalization. In April 2019, the Government of Kenya pledged to recognize qualifying members of the Shona community as Kenyan citizens. However, the lack of detailed information on the stateless population in Kenya hinders advocacy for the regularization of their nationality status. Together with the Kenyan Government through the Department of Immigration Services (DIS) and the Kenya National Bureau of Statistics (KNBS), UNHCR Kenya conducted registration and socioeconomic survey for the Shona community from May to July 2019. While the primary objective of the registration was to document migration, residence and family history with the aim of preparing their registration as citizens, this survey was conducted to provide a baseline on the socio-economic situation of the stateless Shona population for comparison with non-stateless populations of Kenya.

Geographic coverage

Githurai, Nairobi, Kiambaa and Kinoo

Analysis unit

Household and individual

Universe

All Shona living in Nairobi and Kiambu counties, Kenya

Kind of data

Census/enumeration data [cen]

Sampling procedure

The objective of the socio-economic survey was to cover the entire Shona population living in areas of the Nairobi and Kiambu counties. This included Shona living in Githurai, Kiambaa, Kinoo and other urban areas in and around Nairobi. Data collection for the socioeconomic survey took place concurrently with a registration verification. The registration verification was to collect information on the Shona's migration history, residence in Kenya and legal documentation to prepare their registration as citizens. The registration activity including questions on basic demographics also covered some enumeration areas outside the ones of the socio-economic survey, such as institutional households in Hurlingham belonging to a religious order who maintain significantly different living conditions than the average population. The total number of households for which socio-economic data was collected for is 350 with 1,692 individuals living in them. A listing of Shona households using key informant lists and respondent-driven referral to identify further households was conducted by KNBS and UNHCR before the start of enumeration for the registration verification and socio-economic survey.

Sampling deviation

None

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The following sections are included: household roster, education, employment, household characteristics, consumption and expenditure.

Cleaning operations

The dataset presented here has undergone light checking, cleaning and restructuring (data may still contain errors) as well as anonymization (includes removal of direct identifiers and sensitive variables, recoding and local suppression).

Response rate

Overall reponse rate was 99 percent, mainly due to refusal to participate.

Kenya had over ** million households according to the last census done in 2019. The majority, some *** million, lived in urban areas, while *** million dwelled in rural zones. Nairobi City was the county with more households, approximately *** million.

Abstract

This was a prospective population based study comparing education outcomes and education services among slum and non-slum settlements in Nairobi. The study was being conducted in two slum settlements of Korogocho and Viwandani, and two non-slum settlements of Jericho and Harambee. Korogocho is situated within Korogocho administrative location, Viwandani in Viwandani administrative location, and Jericho and Harambee in Makadara administrative location. The study identified households who had children aged between 5 and 19 years old and living within the boundaries of the study sites. The households were followed untl 2010. New households fitting the inclusion criteria were enrolled each year, while the upper age limit increased by a single year for each additional year. By 2010, the study wa following about 16400 individuals aged bewteen 5 and 24 years. The study targetted also schools where the idenfied pupils attended. Several questionnaires were administered and included schooling history to capture schooling information for the current schooling years and 5 years backwards. Afterwards, an update questionnaire was introduced to capture prospective schooling information. The second questionnaire captured information from the parents on their perceptions towards free primary education and support for their children schooling. In addition, individuals who were 12 years and above responded to a behvaior questionnaires. In the schools, a school characteristics questionniare was administred.

The objectives of the ERP I were:

· What is the impact of free primary education on school enrolment patterns and dropout rates among urban slum and non-slum children?

· What factors are associated with school participation (enrolment, attendance, repetition, performance and dropout) among urban slum and non-slum children?

· What are the (causal) linkages between school participation and the onset and extent of indulgence in risky behaviors in children?

Geographic coverage

Two slums of Nairobi (Korogocho and Viwandani) and two non-slums of Nairobi (Harambee and Jericho)

Analysis unit

1. HOUSEHOLDS

2. INDIVIDUALS WITHIN THE AGE OF STUDY. AVERAGE OF 2.7 INDIVIDIDUALS PER HOUSEHOLD

3. SCHOOLS

Universe

The data covers individuals aged 5 and 19 years in 2005 who were by 2010 aged between 5 and 24 years. It also covered primary schools within Nairobi, where majority of the pupils were reported to be enrolled.

Sampling procedure

Selection of study sites

Using the Kenya 1999 housing and population census, and the 1997 Welfare Monitoring Survey III collected by the Central Bureau of Statistics (Government of Kenya 2000), all the 49 locations of Nairobi were ranked into five groups according to the percentage of the population below the poverty line. NUHDSS slum locations of both Viwandani and Korogocho were in the poorest percentile (ranked 48th and 44th, respectively). Those in the richest quintile were excluded because most children in the wealthy communities go to formal private schools which are scattered all over the city. The majority of the locations in the 4th quintile have a mixture of formal and informal settlement features. In order to have a formal residential area in the middle income category where most children are likely to go to public schools, three locations were explored in the second quintile (i.e. the second richest set of locations). During discussion of the project's design, participants, who were mainly Kenyans with comprehensive knowledge of the areas, recommended carrying out the study in Bahati, as opposed to Umoja or Kariokor, the other locations in the second quintile. APHRC researchers visited the three communities to assess their suitability as a comparison site for the project. Bahati (Harambee and Jericho) was chosen because it is relatively stable, is mostly inhabited by middle-income parents with school-going children who mostly go to public schools in the area. In Bahati, 26% of the population lived below the poverty line while in Korogocho and Viwandani, the corresponding percentages were 60% and 76%, respectively. Having Bahati as the comparison area was therefore to enable the study to assess factors affecting schooling among some relatively poor households that did not reside in slum settlements.

Sampling of households

All households included in the NUHDSS database and with individuals aged between 5 and 19 years in 2005 were included in the study. thereafter they were followed until 2010. In between those who entered into the system or reach the aged of 5 years were also included and followed prospectively.

Sampling of schools

Schools were the pupils under serveillance were reported to be enrolled formed part of the sampling frame for schools. The inclusion criteria for the schools survey was that the school should be located within Nairobi and that it should have a minimum of five pupils in oyr household survey enrolled in it.

Mode of data collection

Face-to-face [f2f]; FGD

Research instrument

The Questionnaires

The questionnaires hereafter are referred to as modules. There are several modules since the beginning of the education project:

1) Household module

2) Primary school module

3) Parent guardian module

4) Education child update (schooling history) module

5) Child school status update questionnaire

6) Education child update module

7) Primary school questionnaire

8) Child Behavior Questionnaire

9) Supplementary primary school module

The household module

The household module served as a starting point of the interview. It identified the respondent's household. The module was administered to the owner of the household or any other adult who was credible and who usually lives in the household. It served to identify individual households and its occupants and thus served as a basis for the other modules to be administered. It contains a complete list of the household members and some basic information on age, sex, parental survivorship, education, and labor force participation.

For each of the household, information on water source and trash disposal methods, type of toilet facility used by the household, materials for the house (roof, floor, and walls), fuel for lighting and cooking, and ownership of assets was collected.

The Primary schools module:

This module serves to generate indicators on schooling participation. The module is meant for headmasters or teachers knowledgeable enough to provide information on the school. It comprises of the following sections:

Background

This serves to identify the name of the school, the date and time of the interview and the location of the school.

Particulars of respondent

This section of the module collected information on the respondent and establishes the respondent's full names, position held by respondent in the school and how long the respondent has been working in the school.

School background

This section sets to establish whether the school is registered, if registered under which ministry the school is registered(ministry of culture or ministry of education), its registration number, the type of curriculum followed by the school and the management of the school. The understanding is that the name of the school being used maybe different from the one under which the school is registered. The information is important especially if we are to link the school to the Ministry of Education or Ministry of Culture records. This information will most probably be obtained from the school records (if they exist).

School facilities

This section sets to collect information on the school facilities, such as textbooks provided by the school to the pupils in each grade they include Mathematics, Science ,Kiswahili and English, a library, science lab for pupils use ,a playground for outdoor sports pupils use and inventory of all school's equipment e.g. desks. For purposes of this project, a library is considered to be a room which has reference books where pupils can go to read.

This section also offers information on the school ownership of a toilet facility for use by the pupils and whether there are separate toilets provided for boys and girls. It also offers information on the school's water source and the availability of electricity in the school.

In addition the module in this section probes the respondent on whether the inspector of schools has visited the school in the current schooling year and requests for the date and year of visit. The inspector of school is from the City Council education department or from the Ministry of Education.

Enrollment for the current school year

Enrollment refers to children who are current registered for specific grades/classes in the school.

The objective of this section is to provide information on the number of boys and girls in each of the streams in the school in the current school year. It also sets to establish whether there were any pupils who were turned away during enrollment in the current year and the approximate number of pupils who were turned away from enrolling in the school.

Expenditure on schooling

The module here asks questions on the school fee structure, it seeks to establish whether the pupils are required to pay fees and how much (Kenya shillings) they pay for the following: tuition, construction fund, Parents Teachers Association (PTA), extra classes, examination fees, school meals and other items.

This was required to be filled for all grades in the school and whether paid annually, termly, monthly and weekly.

It also provides information on whether the pupils are required to wear uniform in order to be allowed in class and the source of purchase of the

A multi-modal population dataset for three cities in Sub-Saharan Africa, namely Dakar (Senegal), Nairobi (Kenya) and Dar es Salaam (Tanzania). The dataset contains patches of rasterized building footprints (0.5 m), Sentinel-2 MSI data (10 m) and population labels at the patch level (100 m).

Abstract

The 2022 Kenya Demographic and Health Survey (2022 KDHS) is the seventh DHS survey implemented in Kenya. The Kenya National Bureau of Statistics (KNBS) in collaboration with the Ministry of Health (MoH) and other stakeholders implemented the survey. Survey planning began in late 2020 with data collection taking place from February 17 to July 19, 2022. ICF provided technical assistance through The DHS Program, which is funded by the United States Agency for International Development (USAID) and offers financial support and technical assistance for population and health surveys in countries worldwide. Other agencies and organizations that facilitated the successful implementation of the survey through technical or financial support were the Bill & Melinda Gates Foundation, the World Bank, the United Nations Children's Fund (UNICEF), the United Nations Population Fund (UNFPA), Nutrition International, the World Food Programme (WFP), the United Nations Entity for Gender Equality and the Empowerment of Women (UN Women), the World Health Organization (WHO), the Clinton Health Access Initiative, and the Joint United Nations Programme on HIV/AIDS (UNAIDS).

SURVEY OBJECTIVES The primary objective of the 2022 KDHS is to provide up-to-date estimates of demographic, health, and nutrition indicators to guide the planning, implementation, monitoring, and evaluation of population and health-related programs at the national and county levels. The specific objectives of the 2022 KDHS are to: Estimate fertility levels and contraceptive prevalence Estimate childhood mortality Provide basic indicators of maternal and child health Estimate the Early Childhood Development Index (ECDI) Collect anthropometric measures for children, women, and men Collect information on children's nutrition Collect information on women's dietary diversity Obtain information on knowledge and behavior related to transmission of HIV and other sexually transmitted infections (STIs) Obtain information on noncommunicable diseases and other health issues Ascertain the extent and patterns of domestic violence and female genital mutilation/cutting

Geographic coverage

National coverage

Analysis unit

Household, individuals, county and national level

Universe

The survey covered sampled households

Sampling procedure

The sample for the 2022 KDHS was drawn from the Kenya Household Master Sample Frame (K-HMSF). This is the frame that KNBS currently operates to conduct household-based sample surveys in Kenya. In 2019, Kenya conducted a Population and Housing Census, and a total of 129,067 enumeration areas (EAs) were developed. Of these EAs, 10,000 were selected with probability proportional to size to create the K-HMSF. The 10,000 EAs were randomized into four equal subsamples. The survey sample was drawn from one of the four subsamples. The EAs were developed into clusters through a process of household listing and geo-referencing. To design the frame, each of the 47 counties in Kenya was stratified into rural and urban strata, resulting in 92 strata since Nairobi City and Mombasa counties are purely urban.

The 2022 KDHS was designed to provide estimates at the national level, for rural and urban areas, and, for some indicators, at the county level. Given this, the sample was designed to have 42,300 households, with 25 households selected per cluster, resulting into 1,692 clusters spread across the country with 1,026 clusters in rural areas and 666 in urban areas.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Eight questionnaires were used for the 2022 KDHS: 1. A full Household Questionnaire 2. A short Household Questionnaire 3. A full Woman's Questionnaire 4. A short Woman's Questionnaire 5. A Man's Questionnaire 6. A full Biomarker Questionnaire 7. A short Biomarker Questionnaire 8. A Fieldworker Questionnaire.

The Household Questionnaire collected information on: o Background characteristics of each person in the household (for example, name, sex, age, education, relationship to the household head, survival of parents among children under age 18) o Disability o Assets, land ownership, and housing characteristics o Sanitation, water, and other environmental health issues o Health expenditures o Accident and injury o COVID-19 (prevalence, vaccination, and related deaths) o Household food consumption

The Woman's Questionnaire was used to collect information from women age 15-49 on the following topics: o Socioeconomic and demographic characteristics o Reproduction o Family planning o Maternal health care and breastfeeding o Vaccination and health of children o Children's nutrition o Woman's dietary diversity o Early childhood development o Marriage and sexual activity o Fertility preferences o Husbands' background characteristics and women's employment activity o HIV/AIDS, other sexually transmitted infections (STIs), and tuberculosis (TB) o Other health issues o Early Childhood Development Index 2030 o Chronic diseases o Female genital mutilation/cutting o Domestic violence

The Man's Questionnaire was administered to men age 15-54 living in the households selected for long Household Questionnaires. The questionnaire collected information on: o Socioeconomic and demographic characteristics o Reproduction o Family planning o Marriage and sexual activity o Fertility preferences o Employment and gender roles o HIV/AIDS, other STIs, and TB o Other health issues o Chronic diseases o Female genital mutilation/cutting o Domestic violence

The Biomarker Questionnaire collected information on anthropometry (weight and height). The long Biomarker Questionnaire collected anthropometry measurements for children age 0-59 months, women age 15-49, and men age 15-54, while the short questionnaire collected weight and height measurements only for children age 0-59 months.

The Fieldworker Questionnaire was used to collect basic background information on the people who collected data in the field. This included team supervisors, interviewers, and biomarker technicians.

All questionnaires except the Fieldworker Questionnaire were translated into the Swahili language to make it easier for interviewers to ask questions in a language that respondents could understand.

Cleaning operations

Data were downloaded from the central servers and checked against the inventory of expected returns to account for all data collected in the field. SyncCloud was also used to generate field check tables to monitor progress and flag any errors, which were communicated back to the field teams for correction.

Secondary editing was done by members of the central office team, who resolved any errors that were not corrected by field teams during data collection. A CSPro batch editing tool was used for cleaning and tabulation during data analysis.

Response rate

A total of 42,022 households were selected for the sample, of which 38,731 (92%) were found to be occupied. Among the occupied households, 37,911 were successfully interviewed, yielding a response rate of 98%. The response rates for urban and rural households were 96% and 99%, respectively. In the interviewed households, 33,879 women age 15-49 were identified as eligible for individual interviews. Interviews were completed with 32,156 women, yielding a response rate of 95%. The response rates among women selected for the full and short questionnaires were the similar (95%). In the households selected for the male survey, 16,552 men age 15-54 were identified as eligible for individual interviews and 14,453 were successfully interviewed, yielding a response rate of 87%.

This statistic shows the biggest cities in Kenya as of 2019. In 2019, approximately *** million people lived in Nairobi, making it the biggest city in Kenya.

Abstract

Background: The burden of cardiovascular disease is rising in sub-Saharan Africa with hypertension being the main

risk factor. However, context-specific evidence on effective interventions for primary prevention of cardiovascular diseases in resource-poor settings is limited. This study aims to evaluate the feasibility and cost-effectiveness of one such intervention-the “Sustainable model for cardiovascular health by adjusting lifestyle and treatment with economic perspective in settings of urban poverty”.

Methods/Design: Design: A prospective quasi-experimental community-based intervention study.

Setting: Two slum settlements (Korogocho and Viwandani) in Nairobi, Kenya.

Study population: Adults aged 35 years and above in the two communities.

Intervention: The intervention community (Korogocho) will be exposed to an intervention package for primary prevention of cardiovascular disease that comprises awareness campaigns, household screening for cardiovascular diseases risk factors, and referral and treatment of people with high cardiovascular diseases risk at a primary health clinic. The control community (Viwandani) will continue accessing the usual standard of care for primary prevention of cardiovascular diseases in Kenya.

Data: Demographic and socioeconomic data; anthropometric and clinical measurements including blood pressure. Population-based data will be collected at the baseline and endline-12 months after implementing the intervention. These data will be collected from a random sample of 1,610 adults aged 35 years and above in the intervention and control sites at both baseline and endline. Additionally, operational (including cost) and clinic-based data will be collected on an ongoing basis.

Main outcomes:

(1) A positive difference in the change in the proportion of the intervention versus control study populations that are at moderate or high risk of cardiovascular disease;

(2) a difference in the change in mean systolic blood pressure in the intervention versus control study populations;

(3) the net cost of the complete intervention package per disability-adjusted life year gained.

Analysis: Primary outcomes comparing pre- and post-, and operational data will be analyzed descriptively and “impact” of the intervention will be calculated using double-difference methods. We will also conduct a cost-effectiveness analysis of the intervention using World Health Organization guidelines

Geographic coverage

Korogocho and Viwandani informal settlements in Nairobi

Analysis unit

Individuals

Universe

Adults 35 years and above in Korogocho and Viwandani who have given informed consent

Sampling procedure

In order to detect a 5% reduction at endline in the proportion of adults aged 35 years and above who are at moderate or high risk of CVD in the intervention population versus no change in the control population (assuming both populations have similar start prevalence at 25%), we need 2,927 respondents in both intervention and control sites, using an alpha of 0.05 and power (1-beta) of 0.90. Taking into account a non-response rate of 10%, we will approach 3,220 individuals per cross-sectional study-that is, 1,610 per site at baseline and endline surveys, respectively. The sampling frame will be based on the most recently updated NUHDSS database. This database contains details of about 72,000 individuals including names, locations, gender, dates of birth and residential status in both slums. In the control site, we will use computer randomization (STATA® statistical software) to select the 1,610 individuals aged 35 years and older per site for each cross-sectional survey. In the intervention site, the same computer randomization process will be followed. However, unlike the control site, the 1,610 individuals to be included in the cross-sectional survey analysis will be collected retrospectively. In other words, the intervention package will be delivered to all adults aged 35 years or older in the intervention site-that is, 6,780 individuals according to the DSS database (as at 15 June 2012). At the clinic level, we calculated that in order to detect a 10 mmHg reduction in blood pressure (at 20 mmHg standard deviation, alpha of 0.05 and 1-ß on 0.9), about 44 participants are needed. However, it is projected that approximately 1,350 participants (out of 6780) will be referred from the door-to-door visit. This number is derived from a 20% prevalence of hypertension among adults aged 35 years and older in the study area.

We estimate that roughly half of these 1,350 participants, being 675, will continue visiting the clinic for treatment. Hence, this number of people is more than

sufficient for the analysis of our main primary outcome at the clinic level.

Sampling deviation

No deviation.

Mode of data collection

Face-to-face [f2f]

Research instrument

POPULATION:

1. Identification Information And Consent

2. Demographics 3 History Of Chronic Conditions

3. Exposure To Health Promotion And Behavior Change

4. Risk Factors And Preventive Behavior 6 Perceived Personal Risk

5. Anthropometrics And Biomarkers 8 Interviewer Assessment

CLINIC:

1. Identification Information And Consent

2. Clinic History

3. Knowledge Of Prevention / Evaluation Intervention

4. Anthropometrics And Biomarkers

Response rate

Population baseline response rate in Korogocho (intervention) was 56.7% and 40.3% in Viwandani (control)

Population endline response rate was 50.2% in Korogocho (intervention) , 77.0% in Viwandani screened at baseline (first control) and 53.6% in Viwandani not screened (second control)

This dataset contains a digital urban scenario, named Tomorrowville, that is developed as a testbed for multi-hazard risk assessments and to evaluate the performance of urbanisation scenarios. Tomorrowville was created to represent a global-south urban setting by means of its socio-economic and physical aspects. It covers an area of 500ha located south of Kathmandu (Nepal). The dataset consists of 5 different data types: - Buildings: Data representing the building footprints for today and 50 years from now including specific attributes to be used within multi-hazard risk assessments. - Land uses: Data representing the land use information for today and 50 years from now. - Vulnerability: Tabular files that contain vulnerability functions for buildings under earthquake and flood hazards. - Household: Data that contains social attributes of the Tomorrowville, such as the level of education, age, gender and working status of the individuals and their states in the households. - Hazards: Data representing the hazards (earthquake (eq), floods (fl) and debris flows (df) that may impact the case study areas of Tomorrowville. Observational data of the built environment and socio-economical properties of Kathmandu and Nairobi were used in addition to synthetic social data to create the initial scenario. This is a synthetic social dataset, meaning it was derived from existing population projections and distributions for the testbed but does not reflect the reality on the ground. It is synthetically created using specific algorithms in a GIS environment to represent a Global South social context. For the building data, Open Street Map (OSM) database is used as a basis. The data is scraped from OSM and modified to represent an urban context for Tomorrowville. The attributes are also modified to be able to use in a multi-hazard risk computation. A taxonomy string is generated for each building that represents an acronym for its building code level, number of storeys, occupation type and structural system. The hazards that were existing in the selected spatial extent were earthquake, flood, and debris flow. Hazard data represents an intensity measure for the relevant hazard type (ground acceleration for earthquake, flow velocity for the flood and debris flow hazards). The following hazard input data are included: - For the flood simulations, the discharge and rainfall time series are generated based on moderate to peak daily data based on recorded data from the Department of Hydrology and Meteorology, Nepal. - Earthquake hazard sources are generated and simulated by Jenkins et al. (2023). - For the debris-flow and flood simulations tri-stereo Pleiades satellite imagery is used to produce a 2m resolution Digital Elevation Model. The work to create this dataset is supported by NERC as part of the GCRF Urban Disaster Risk Hub (NE/S009000/1) Full details about this dataset can be found at https://doi.org/10.5285/8b5834a5-ae8a-4f24-836c-16fab961aeb3