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

This dataset shows the Mombasa population pyramid by Age group as reported by the Kenya National Bureau of statistics during the 2009 National census

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.

Mombasa Population 2022
This dataset falls under the category Traffic Generating Parameters Population.
It contains the following data: Mombasa Population 2022
This dataset was scouted on 2022-02-13 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://worldpopulationreview.com/world-cities/mombasa-population

Population 2020 Mombasa, Ken
This dataset falls under the category Traffic Generating Parameters Population.
It contains the following data: This resource contains the 'Estimated total number of people per grid-cell 2020 ' cropped at Mombasa, Kenya scale for COVID-19 Contagion Risk Hotspots Estimation.
This dataset was scouted on 2022-02-13 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://contagionhotspots.org/nl/dataset/mms/resource/4757d7ec-831d-49df-be94-f88f8ef70092See URL for data access and license information.

Mombasa Pop Pyramid Age Groups - 2009

Mombasa Census 2019
This dataset falls under the category Traffic Generating Parameters Population.
It contains the following data: HURUmap Kenya gives infomediaries like journalists and civic activists an easy plug & play toolkit for finding and embedding interactive data visualisations into their storytelling.
This dataset was scouted on 2022-02-13 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://kenya.hurumap.org/profiles/county-1-mombasa/

Kenya had a Muslim population of roughly 5.6 million people, according to the last country census conducted in 2019. Nearly 50 percent of individuals adhering to Islam lived in the Northern-East counties of Mandera (856.5 thousand people), Garissa (815.8 thousand people), and Wajir (767.3 thousand people). Overall, around 10 percent of Kenya's population identified as Muslim.

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%.

Abstract

The survey of the Pemba was an attempt to reach all households in Kenya with links to Pemba in Tanzania. It was conducted in the two counties of Kilifi and Kwale on the coast, north and south of Mombasa, respectively. According to information from village elders familiar with the Pemba community in Kenya, most of the Pemba population resides in these two counties. While there are some Pemba residents in Lamu, the security situation prevented data collection there. Further, a few Pemba are believed to live in the city of Mombasa and elsewhere in the country. But due to lack of further information, no data were collected in Mombasa or elsewhere.

The objectives of the full survey, conducted in August 2016, were: 1. To establish the number and characteristics of the Pemba living in Kenya, including their arrival period in Kenya, nationality and their problems; 2. To make recommendations for the issuance of the documentation that is required for those who apply for citizenshiop by registration

Geographic coverage

Kwale and Kilifi counties, Kenya.

Analysis unit

Households, individuals

Universe

The total number of households with links to Pemba in Tanzania, in Kilifi and Kwale counties.

Kind of data

Census/enumeration data [cen]

Sampling procedure

A household mapping exercise was conducted in Kilifi and Kwale to identify Pemba households and to make it easier to locate them on the ground. The mapping was done from 4 to 12 August 2016 by a team from UNHCR Kenya office and KNBS.

The mapping in each village commenced with a visit to the chief's office, who put the team in touch with the village chair. The team explained the purpose of its visit to the village chair and began the mapping exercise. The importance of involving the chiefs and village chairpersons is that they are well connected, recognised and trusted by residents in their communities. The same procedure is followed by KNBS when they are mapping for sample surveys and censuses.

The team established physical boundaries of the area to be mapped, located the boundaries on the map and then identified and listed the Pemba households within the enumeration boundary. A Pemba household, in this context, is one identified by the informants as having at least one person with origins or links to Pemba. The links may include a person's spouse, parents or grandparents, who migrated to Kenya from Pemba or where a person has migrated from Pemba to Kenya.

The mapping team was followed by the village chair to the Pemba households, where the UNHCR and Haki Centre staff listed number of persons in each, while the KNBS staff marked the location of the household on the map. The entrances of identified Pemba households were marked in chalk with the letters HCR and a number starting at 001 to make it easier to find the houses during the enumeration. Since it seems to be generally well known where the Pemba live it was not considered stigmatising to mark their doors. During the feedback forums with the Pemba after the survey, there was no mention of stigmatization due to marking the door with chalk.

The maps were from the 2009 national housing and population census, purchased from KNBS. The team made lists with information about the location, number and size of each household. The mapping team visited 17 villages in Kilifi and Kwale (see Table 1 in Section 2.7). All villages visited were identified before the mapping exercise by key informants as locations being home to the Pemba of Kenya. The key informants were Pemba elders in different sub-counties previously identified for providing background information on the Pemba arrival and history in Kenya. In each sub-country, the chief, the assistant chief or the village chair also accompanied the team. In Kwale, 358 households were identified with 2,220 persons, and in Kilifi, 86 households with 558 persons.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was developed before the pilot survey and revised during and after the pilot survey, based on the experience gained. The pilot survey was used to test the questions and to check for inconsistences and misinterpretations due to unclear concepts and definitions. The testing process also revealed some important themes that had been left out.

The structure of the questionnaire was altered, including the order of the questions and the introductory pages, to facilitate administration of the questionnaire.

Finally, the questionnaire was translated into Swahili. Both the English and Swahili versions were used in the survey, even though the English version was preferred by almost all interviewers. The two versions of the questionnaire are attached in Annex 4 and 5. Enumerators used the English questionnaire to frame the questions in the local and less academic version of Swahili.

Cleaning operations

The data were imported into a Statistics Analysis Software (SAS) file and validated. Several errors were identified during the validation process, both on how the data had been recorded by the interviewers in the field and how the data had been entered by the clerks. There were particularly many errors in the entry of the variable “Relation to the household head” (Q.2). There were also many errors in the entry of the age of the household head, which was mostly due to errors in recording the right codes. A substantial amount of time was spent cleaning the data after the data had been entered, which included consulting many paper questionnaires. The quality of the survey data was significantly improved after the data entry revision. The data were analysed using both SAS software and Excel spreadsheets.

Response rate

The rate of non-response was low. Of the 452 households visited, visits to only 23 households can be categorised as non-response. A lot of effort was made to revisit non-responding households, using interviewers living nearby. Out of the 23 non-responsive households, 12 were not at home or there was no adult at home. There were 2 interrupted interviews, 7 refusals and 2 with no links to Pemba. In one household the respondent was not mentally stable enough to be interviewed, according to the enumerator.

Abstract

The 1993 Kenya Demographic and Health Survey (KDHS) was a nationally representative survey of 7,540 women age 15-49 and 2,336 men age 20-54. The KDHS was designed to provide information on levels and trends of fertility, infant and child mortality, family planning knowledge and use, maternal and child health, and knowledge of AIDS. In addition, the male survey obtained data on men's knowledge and attitudes towards family planning and awareness of AIDS. The data are intended for use by programme managers and policymakers to evaluate and improve family planning and matemal and child health programmes. Fieldwork for the KDHS took place from mid-February until mid-August 1993. All areas of Kenya were covered by the survey, except for seven northem districts which together contain less than four percent of the country's population.

The KDHS was conducted by the National Council for Population and Development (NCPD) and the Central Bureau of Statistics of the Government of Kenya. Macro International Inc. provided financial and technical assistance to the project through the intemational Demographic and Health Surveys (DHS) contract with the U.S. Agency for International Development.

OBJECTIVES

The KDHS is intended to serve as a source of population and health data for policymakers and the research community. It was designed as a follow-on to the 1989 KDHS, a national-level survey of similar size that was implemented by the same organisations. In general, the objectives of KDHS are to: - assess the overall demographic situation in Kenya, - assist in the evaluation of the population and health programmes in Kenya, - advance survey methodology, and - assist the NCPD to strengthen and improve its technical skills to conduct demographic and health surveys.

The KDHS was specifically designed to: - provide data on the family planning and fertility behaviour of the Kenyan population to enable the NCPD to evaluate and enhance the National Family Planning Programme, - measure changes in fertility and contraceptive prevalence and at the same time study the factors which affect these changes, such as marriage patterns, urban/rural residence, availability of contraception, breastfeeding habits and other socioeconomic factors, and - examine the basic indicators of maternal and child health in Kenya.

KEY FINDINGS

The 1993 KDHS reinforces evidence of a major decline in fertility which was first revealed by the findings of the 1989 KDHS. Fertility continues to decline and family planning use has increased. However, the disparity between knowledge and use of family planning remains quite wide. There are indications that infant and under five child mortality rates are increasing, which in part might be attributed to the increase in AIDS prevalence.

Geographic coverage

The 1993 KDHS sample is national in scope, with the exclusion of all three districts in North Eastern Province and four other northern districts (Samburu and Turkana in Rift Valley Province and Isiolo and 4 Marsabit in Eastern Province). Together the excluded areas account for less than 4 percent of Kenya's population.

Analysis unit

• Household
• Women age 15-49
• Men age 20-54
• Children under five

Universe

The population covered by the 1993 KDHS is defined as the universe of all women age 15-49 in Kenya and all husband age 20-54 living in the household.

Kind of data

Sample survey data

Sampling procedure

The sample for the 1993 KDHS was national in scope, with the exclusion of all three districts in Northeastern Province and four other northern districts (Isiolo and Marsabit from Eastern Province and Samburu and Turkana from Rift Valley Province). Together the excluded areas account for less than four percent of Kenya's population. The KDHS sample points were selected from a national master sample maintained by the Central Bureau of Statistics, the third National Sample Survey and Evaluation Programme (NASSEP-3), which is an improved version of NASSEP2 used in the 1989 survey. This master sample follows a two-stage design, stratified by urban-rural residence, and within the rural stratum, by individual district. In the first stage, 1989 census enumeration areas (EAs) were selected with probability proportional to size. The selected EAs were segmented into the expected number of standard-sized clusters to form NASSEP clusters. The entire master sample consists of 1,048 rural and 325 urban ~ sample points ("clusters"). A total of 536 clusters---92 urban and 444 rural--were selected for coverage in the KDHS. Of these, 520 were successfully covered. Sixteen clusters were inaccessible for various reasons.

As in the 1989 KDHS, selected districts were oversampled in the 1993 survey in order to produce more reliable estimates for certain variables at the district level. Fifteen districts were thus targetted in the 1993 KDHS: Bungoma, Kakamega, Kericho, Kilifi, Kisii, Machakos, Meru, Murang'a, Nakuru, Nandi, Nyeri, Siaya, South Nyanza, Taita-Taveta, and Uasin Gishu; in addition, Nairobi and Mombasa were also targetted. Although six of these districts were subdivided shortly before the sample design was finalised) the previous boundaries of these districts were used for the KDHS in order to maintain comparability with the 1989 survey. About 400 rural households were selected in each of these 15 districts, just over 1000 rural households in other districts, and about 18130 households in urban areas, for a total of almost 9,000 households. Due to this oversampling, the KDHS sample is not self-weighting at the national level.

After the selection of the KDHS sample points, fieldstaff from the Central Bureau of Statistics conducted a household listing operation in January and early February 1993, immediately prior to the launching of the fieldwork. A systematic sample of households was then selected from these lists, with an average "take" of 20 households in the urban clusters and 16 households in rural clusters, for a total of 8,864 households selected. Every other household was identified as selected for the male survey, meaning that, in addition to interviewing all women age 15-49, interviewers were to also interview all men age 20-54. It was expected that the sample would yield interviews with approximately 8,000 women age 15-49 and 2,500 men age 20-54.

Mode of data collection

Face-to-face

Research instrument

Four types of questionnaires were used for the KDHS: a Household Questionnaire, a Woman's Questionnaire, a Man's Questionnaire and a Services Availability Questionnaire. The contents of these questionnaires were based on the DHS Model B Questionnaire, which is designed for use in countries with low levels of contraceptive use. Additions and modifications to the model questionnaires were made during a series of meetings organised around specific topics or sections of the questionnaires (e.g., fertility, family planning). The NCPD invited staff from a variety of organisations to attend these meetings, including the Population Studies Research Institute and other departments of the University of Nairobi, the Woman's Bureau, and various units of the Ministry of Health. The questionnaires were developed in English and then translated into and printed in Kiswahili and eight of the most widely spoken local languages in Kenya (Kalenjin, Kamba, Kikuyu, Kisii, Luhya, Luo, Meru, and Mijikenda).

a) The Household Questionnaire was used to list all the usual members and visitors of selected households. Some basic information was collected on the characteristics of each person listed, including his/her age, sex, education, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women and men who were eligible for individual interview. In addition, information was collected about the dwelling itself, such as the source of water, type of toilet facilities, materials used to construct the house, and ownership of various consumer goods.

b) The Woman's Questionnaire was used to collect information from women aged 15-49. These women were asked questions on the following topics: Background characteristics (age, education, religion, etc.), Reproductive history, Knowledge and use of family planning methods, Antenatal and delivery care, Breastfeeding and weaning practices, Vaccinations and health of children under age five, Marriage, Fertility preferences, Husband's background and respondent's work, Awareness of AIDS. In addition, interviewing teams measured the height and weight of children under age five (identified through the birth histories) and their mothers.

c) Information from a subsample of men aged 20-54 was collected using a Man's Questionnaire. Men were asked about their background characteristics, knowledge and use of family planning methods, marriage, fertility preferences, and awareness of AIDS.

d) The Services Availability Questionnaire was used to collect information on the health and family planning services obtained within the cluster areas. One service availability questionnaire was to be completed in each cluster.

Cleaning operations

All questionnaires for the KDHS were returned to the NCPD headquarters for data processing. The processing operation consisted of office editing, coding of open-ended questions, data entry, and editing errors found by the computer programs. One NCPD officer, one data processing supervisor, one questionnaire administrator, two office editors, and initially four data entry operators were responsible for the data processing operation. Due to attrition and the need to speed up data processing, another four data entry operators were later hired

Abstract

Kenya hosts over half a million refugees, who, along with their hosts in urban and camp areas, face difficult living conditions and limited socioeconomic opportunities. Most refugees in Kenya live in camps located in the impoverished counties of Turkana (40 percent) and Garissa (44 percent), while 16 percent inhabit urban areas—mainly in Nairobi but also in Mombasa and Nakuru. Refugees in Kenya are not systematically included in national surveys, creating a lack of comparable socioeconomic data on camp-based and urban refugees, and their hosts. As the third of a series of surveys focusing on closing this gap, this Socioeconomic Survey of Urban Refugees's aim is to understand the socioeconomic needs of urban refugees in Kenya, especially in the face of ongoing conflicts, environmental hazards, and others shocks, as well as the recent government announcement to close Kenya’s refugee camps, which highlights the potential move of refugees from camps into urban settings. The SESs are representative of urban refugees and camp-based refugees in Turkana County. For the Kalobeyei 2018 and Urban 2020–21 SESs, households were randomly selected from the UNHCR registration database (proGres), while a complete list of dwellings, obtained from UNHCR’s dwelling mapping exercise, was used to draw the sample for the Kakuma 2019 SES. The Kalobeyei SES and Kakuma SES were done via Computer-Assisted Personal Interviews (CAPI). Due to COVID-19 social distancing measures, the Urban SES was collected via Computer Assisted Telephone Interviewing (CATI). The Kalobeyei SES covers 6,004 households; the Kakuma SES covers 2,127 households; and the Urban SES covers 2,438 households in Nairobi, Nakuru, and Mombasa. Questionnaires are aligned with national household survey instruments, while additional modules are added to explore refugee-specific dynamics. The SES includes modules on demographics, household characteristics, assets, employment, education, consumption, and expenditure, which are aligned with the Kenya Integrated Household Budget Survey (KIHBS) 2015–16 and the recent Kenya Continuous Household Survey (KCHS) 2019. Additional modules on access to services, vulnerabilities, social cohesion, mechanisms for coping with lack of food, displacement trajectories, and durable solutions are administered to capture refugee-specific challenges.

Geographic coverage

Nairobi, Mombasa, Nakuru

Analysis unit

Households and individuals

Universe

All refugees registered with UNHCR via ProGres, verified via the Verification Exercise conducted in 2021

Kind of data

Sample survey data [ssd]

Sampling procedure

– The survey was conducted using the UNHCR proGres data as the sampling frame. Due to the COVID-19 lockdown, the survey data was collected via telephone. Hence, the survey is representative of households with active phone numbers registered by UNHCR in urban Kenya – Nairobi, Mombasa and Nakuru. A sample size of 2,500 was needed to ensure a margin of error of less than 5 percent at a confidence level of 95 percent for groups represented by at least 50 percent of the population. The sample for the urban SES is designed to estimate socioeconomic indicators, such as food insecurity, for groups whose share represents at least 50 percent of the population. Considering the total urban refugee population as of August 2020 and the proportions of main countries of origin, as well as a 10 percent nonresponse rate, the target sample size is 2,500 households in total, with 1,250 in Nairobi, 700 in Nakuru, and 550 in Mombasa. A total of 2,438 households were reached: 1,300 in Nairobi, 409 in Nakuru, and 729 in Mombasa. The units in ProGres list are UNHCR proGres families, which are different from households as defined in standard household surveys. Upon registration, UNHCR groups individuals into ‘proGres’ families which do not necessarily meet the criteria to be considered a household. A proGres family is usually comprised by no more than one household. In turn, a household can be integrated by one or more proGres families. Households were selected as the unit of observation to ensure comparability with national household surveys. Households are a set of related or unrelated people (either sharing the same dwelling or not) who pool ration cards and regularly cook and eat together. As proGres families were sampled, the identification of households was done by an introductory section that confirms that each member of the selected proGres family is a member of the household and whether there are other members in the households that belong to other ProGres families. Thus, the introductory section documents the number of proGres families present in the household under observation. Before selecting the survey strata, the team attempted to better understand the type of bias observed by focusing on refugees with access to phones. From the proGres data, phone penetration in urban areas is high (Nairobi and Mombasa: 93 percent, Nakuru: 95 percent). To understand the type of bias observed by focusing on refugees with access to phone, we looked at socio-economic outcomes for proGres family refugees with access to a phone number and those without

Mode of data collection

Computer Assisted Telephone Interview [cati]

Abstract

The Mombasa Informal Settlement Survey 2009 is a representative sample survey drawn using the informal settlement classification of 1999 Census Enumeration Areas (EAs) as the sample frame. The classification of 1999 Census EAs was carried out in major cities of Kenya by the Kenya National Bureau of Statistics (KNBS) under a project funded by United Nations Environment Program (UNEP) in 2003. The 45 EAs were sampled using the probability proportional to size sampling methodology, and information from a total of 1,080 households were collected using structured questionnaires. The Mombasa informal settlement survey is one of the largest household sample surveys ever conducted exclusively for the informal settlements in Mombasa district.

The survey used a two-stage design. In the first stage, EAs were selected and in the second stage households were selected circular systematically using a random start from the list of households. The data was collected by three teams comprising of six members each (one supervisor, one editor, one measurer and three investigators).

The objective of the Mombasa Informal Settlement Survey 2009 is to provide estimates relating to the wellbeing of children and women living in the informal settlements of Mombasa, to create baseline information and to enable policymakers, planners, researchers, and program managers to take actions based on credible evidence. In Mombasa Informal Settlement Survey 2009, information on specific areas such as reproductive health, child mortality, child health, nutrition, child protection, childhood development, water and sanitation, hand washing practices, education, and HIV/AIDS and orphans were collected. The results indicate that the conditions of people living in the informal settlements are very poor and need immediate attention.

Geographic coverage

Mombasa district

Analysis unit

• individuals,
• households.

Universe

The survey covered all de jure household members (usual residents), all women aged between 15-49 years, all children under 5 living in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary objective of the sample design for the Mombasa Informal Settlement Survey, Kenya (MICS4) was to produce statistically reliable estimates of development indicators related to children and women living in the informal settlements of Mombasa. A two-stage cluster sampling approach was used for the selection of the survey sample.

The target sample size for the Mombasa Informal Settlement Survey was calculated as 1,080 households. For the calculation of the sample size, the key indicator used was proportion of institutional deliveries.

The resulting number of households from this exercise was 1,074 households which is the sample size needed, however, it was decided to cover 1,080 households. The average cluster size was determined as 24 households, based on a number of considerations, including the budget available, and the time that would be needed per team to complete one cluster. This implies a total of 45 clusters for the Mombasa informal settlement survey.

The sampling procedures are more fully described in "Kenya Mombasa Informal Settlements Multiple Indicator Cluster Survey 2009 - Report" pp.95-96.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the Generic MICS were structured questionnaires based on the MICS4 model questionnaire with some modifications and additions. Household questionnaires were administered to a knowledgeable adult living in the household. The household questionnaire includes Household Listing, Education, Water and Sanitation, Indoor Residual Spraying, Insecticide Treated Mosquito Nets (ITN), Children Orphaned & Made Vulnerable By HIV/AIDS, Child Labour, Child Discipline, Disability, Handwashing Facility, and Salt Iodization.

In addition to a household questionnaire, the Questionnaire for Individual Women was administered to all women aged 15-49 years living in the households. The women's questionnaire includes Child Mortality, Birth history, Tetanus Toxoid, Maternal and Newborn Health, Marriage/Union, Contraception, Attitude towards Domestic Violence, Female Genital Mutilation/Cutting, Sexual Behaviour and HIV/AIDS.

The Questionnaire for Children Under-Five was administered to mothers or caretakers of children under 5 years of age living in the households. The children's questionnaire includes Birth Registration and Early Learning, Childhood Development, Vitamin A, Breastfeeding, Care of Illness, Malaria, Immunization, and Anthropometry.

Cleaning operations

Data were entered using the CSPro software. In order to ensure quality control, all questionnaires were double entered and internal consistency checks were performed, and the whole process was monitored initially by the MICS Global data processing specialist, followed by KNBS data processing expert. Procedures and standard programs developed under the global MICS project and adapted to the modified questionnaire were used throughout. Data entry began simultaneously with data collection in February 2009 and was completed at the end of March 2009. Data were analysed using the Statistical Package for Social Sciences (SPSS) software program, and the model syntax and tabulation plans developed by UNICEF were customized for this purpose.

Response rate

Of the 1,080 households selected for the sample, 1,076 were found occupied. Of these, 1,016 were successfully interviewed yielding a household response rate of 94.4 percent. In the interviewed households, 878 women (age 15-49) were identified and information collected from 821 women in these households, yielding a response rate of 93.5 percent. In addition, 464 children under age five were listed in the household questionnaire, and information on 454 children were obtained, which corresponds to a response rate of 97.8 percent. Overall response rates of 88.3 and 92.4 are calculated for the women's and under-5's interviews respectively.

Sampling error estimates

Sampling errors are a measure of the variability between all possible samples. The extent of variability is not known exactly, but can be estimated statistically from the survey results.

The following sampling error measures are presented in this appendix for each of the selected indicators: - Standard error (se): Sampling errors are usually measured in terms of standard errors for particular indicators (means, proportions etc). Standard error is the square root of the variance. The Taylor linearization method is used for the estimation of standard errors. - Coefficient of variation (se/r) is the ratio of the standard error to the value of the indicator. - Design effect (deff) is the ratio of the actual variance of an indicator, under the sampling method used in the survey, to the variance calculated under the assumption of simple random sampling. The square root of the design effect (deft) is used to show the efficiency of the sample design. A deft value of 1.0 indicates that the sample design is as efficient as a simple random sample, while a deft value above 1.0 indicates the increase in the standard error due to the use of a more complex sample design. - Confidence limits are calculated to show the interval within which the true value for the population can be reasonably assumed to fall. For any given statistic calculated from the survey, the value of that statistics will fall within a range of plus or minus two times the standard error (p + 2.se or p - 2.se) of the statistic in 95 percent of all possible samples of identical size and design.

For the calculation of sampling errors from the survey data, SPSS Version 17 Complex Samples module has been used. The results are shown in the tables that follow. In addition to the sampling error measures described above, the tables also include weighted and un-weighted counts of denominators for each indicator.

Sampling errors are calculated for indicators of primary interest. Three of the selected indicators are based on households, 10 are based on household members, 14 are based on women, and 14 are based on children under 5. All indicators presented here are in the form of proportions.

Data appraisal

A series of data quality tables are available to review the quality of the data and include the following:

• Age distribution of household population
• Age distribution of eligible and interviewed women
• Age distribution of eligible and interviewed under-5s
• Age distribution of under-five children
• Heaping on ages and periods
• Completeness of reporting
• Presence of mother in the household and the person interviewed for the under-5 questionnaire
• School attendance by single age
• Sex ratio at birth among children ever born and living
• Distribution of women by time since last birth

The results of each of these data quality tables are shown in appendix D in document "Kenya Mombasa Informal Settlements Multiple Indicator Cluster Survey 2009 - Report" pp.102-109.

Self-reported drug use based on various demographics sub-groups among commercial sex workers visiting a drop in centre in Mombasa, Kenya.

Abstract

To support the urban poor during the COVID-19 crisis, the UK government provided a monthly Cash Transfer (CT) of 4,000 Kenyan Shillings (KSH) (or £27) to approximately 52,000 vulnerable people living in informal settlements in Nairobi and Mombasa over a period of three months. The COVID-19 CT was implemented by a consortium led by GiveDirectly, and the monthly stipend was paid using mobile money transfers, with the first transfers taking place from October 2020. The CT was designed to support beneficiaries to buy food or meet other high-priority needs-such as purchasing water, paying for medical care, or making rent payments as well as to reduce the use of negative coping strategies (e.g., selling assets, borrowing money).

OPM was contracted to conduct the monitoring and evaluation of the COVID-19 CT. The main objective of this evaluation was to determine whether, and to what extent, the emergency COVID-19 CT had a positive effect on its target population in informal urban settlements in Kenya. The evaluation also provided an assessment of the implementation parameters and mechanisms adopted as part of the design and delivery of the COVID-19 CT.

To fulfil these aims, the evaluation was structured around two separate components-an impact evaluation and a process review-and drew on multiple research methods through a mixed methods research framework. The objective of the quantitative impact evaluation was to assess whether the COVID-19 CT has had an impact on its beneficiaries, and to quantify the scale of any effect detected. This estimation of impact was based on a longitudinal non-experimental design, focusing on a panel of beneficiaries interviewed at three points in time (baseline - prior to the intervention, midline, and endline - post-intervention) over the course of the implementation period. All quantitative data collection took place remotely using Computer-Assisted Telephone Interview (CATI) software.

Geographic coverage

Nairobi and Mombasa in Kenya

Analysis unit

Individuals Households

Universe

The study population consists of individuals included in the lists of enrolled beneficiaries covered by Give Directly for the COVID-19 CT.

Sampling procedure

The evaluation team implemented a stratified one-stage probability sampling strategy for the selection of survey respondents from the individuals included in the lists covered by Give Directly for the COVID-19 CT. The goal was to select at baseline a sample of 1,000 eligible individuals who would receive the COVID-19 CT, which would then be interviewed by the evaluation team at baseline, midline, and endline.

The sampling strategy considered the following process:

1) The sample was drawn once the COVID-19 CT beneficiaries were considered as enrolled into the intervention. After discussions with Give Directly, it was decided that an individual was considered a future COVID-19 CT recipient when he/she had responded to the short SMS-based survey delivered by Give Directly.

2) The sample was drawn in two separate batches. The first batch of recipients comprised 6,838 vulnerable individuals from informal settlements in Nairobi, while the second batch contained 1,596 vulnerable individuals from Mombasa. We sampled the same number of beneficiaries from the first and second batches (500 individuals from each batch).

3) Explicit stratification was first applied based on the geographical location of the COVID-19 CT recipient. This entailed that we sample 500 individuals from Nairobi from the first batch, and 500 from Mombasa from the second batch. This allowed us to disaggregate our quantitative findings between Nairobi and Mombasa, and produce informative descriptive and regression analyses for each of the two cities included in the intervention.

4) Implicit stratification was then applied based on the following categorical variables: i) local partner from which the eligible beneficiary was selected, and ii) gender of the COVID-19 CT recipient. The goal of this stratification process was to enhance the representativeness of our sample in terms of these variables, so that our evaluation sample resembled as much as possible the distribution of these characteristics in the target population (i.e. the list of beneficiaries of the COVID-19 CT used as sampling frame for our sample).

5) We did not cluster our survey respondents. Apart from spill-over effect issues, which were not a concern due to the lack of a counterfactual in our methodological approach, this is normally a logistical necessity for in-person surveys. This was not an issue either, given the remote nature of the data collection process.

6) Extensive replacement lists were created to maximise efficiency during survey implementation without sacrificing representativeness of the sample. A detailed replacement protocol was elaborated, which took into account the stratification process described above.
Given the longitudinal nature of the evaluation, the same baseline respondents were tracked and re-interviewed at midline and endline so as to create a panel of survey respondents.The final baseline quantitative survey sample achievement is shown below, including the distribution by county

Sample achievement Baseline Survey Nairobi 500 Mombasa 500 Total 1,000

Midline Survey Nairobi 483 Mombasa 489 Total 972

Endline Survey Nairobi 463 Mombasa 478 Total 941

Mode of data collection

Computer Assisted Telephone Interviewing (CATI)

Kind of data

Sample survey data [ssd]

Sampling procedure

The household survey sample was drawn from the population residing in the five cities/urban centers. The most recent Population and Housing Census (2009) was used to identify clusters from which a representative sample of households for each city/urban center was drawn. A total of 13,140 households were selected for interviewing, ensuring that the sample was sufficient to allow analysis of the findings by each of the five intervention sites. Nairobi was intentionally oversampled (4,260 vs. 2,220 households) due its significantly larger size. With the exception of Machakos and Kakamega, the sample in each urban area was apportioned equally between formal and informal localities. A two-stage cluster sampling design was used for each urban area. Stage one involved selecting a random sample of clusters in each urban area. In Nairobi, 71 clusters were randomly selected in each of the formal and informal areas (domains), for a total of 142. In Mombasa and Kisumu, 37 clusters were randomly drawn from each domain, for a total 74 per urban area. In Machakos and Kakamega, 74 clusters were randomly selected per urban area. In the second stage, a random sample of 30 households was selected within each selected cluster. Interviews with women took place in all households selected. In Nairobi, Mombasa and Kisumu, half of the households (15) in each of the selected clusters were also selected to interview men. For each household selected, an interview with the head of the household (or his or her representative) was conducted. Each interview assessed household assets and environmental circumstances such as sanitation and housing materials, obtained a listing of usual residents of the household and asked about demographic characteristics of the head of the household. All women aged 15-49 years who were either usual residents or visitors present in the sampled households on the night prior to the survey were eligible for a detailed interview. In addition, in half of the sampled households in Nairobi, Mombasa and Kisumu, all men aged 15-59 years were asked to participate in a detailed interview. Interviews took place in a location where the respondent could be assured some level of privacy and were conducted by a same-sex interviewer using a paper-and-pencil questionnaire following the receipt of informed consent. Respondents were asked about demographic characteristics, for information on current and past FP method use, fertility desires and intentions, health-seeking behaviors for themselves and their children, how they pay for health care services, exposure to FP messages, and migration patterns, using a structured questionnaire. At mid-term and end line, the objective will be to find the same female respondents, so contact information was requested to permit extensive tracking procedures at each follow -up round of data collection. Repeated cross-sections of men (not necessarily the same ones) will be interviewed at mid and end line.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used to collect baseline information-one for each of the households, one for women and one for men. In Machakos and Kakamega, only women were interviewed. Questionnaires were based on the questionnaires used by the Demographic and Health Survey program in Kenya but were modified and expanded by all in-country partners to reflect MLE and Tupange objectives. Questionnaires were translated from English into Kiswahili, Luhya, Kamba and Dholuo-the four most commonly spoken languages in the five cities. Final revisions were made to the questionnaires following extensive pre-testing and training of field staff. The household questionnaire was administered prior to the women's and men's questionnaires to facilitate the identification of eligible household members. The methodology and questionnaires were tested in Kisumu and Nairobi August 5-8, 2010, in clusters outside the planned intervention areas to minimize chances of contamination. Survey instruments were finalized based on feedback from and lessons learned during the pre-test.

Socio- demographic characteristics of commercial sex workers visiting a drop in centre in Mombasa, Kenya.

Nairobi has been the Kenyan county most affected by the coronavirus (COVID-19) pandemic. As of March 31, 2022, the capital registered most of the confirmed COVID-19 cases in the country, around 129 thousand. The amount corresponded to nearly 40 percent of the total cases in Kenya. In Kiambu, within the Nairobi Metropolitan Region, 19,778 infected people were registered, whereas Mombasa, Kenya's oldest and second largest city, had 17,794 cases. As of March 2021, Kenya started the vaccination campaign against the coronavirus with doses received through the COVAX initiative.

Kenya's economy rebounds amid vaccination campaign

The coronavirus outbreak had a significant negative impact on Kenya's economy. In the second quarter of 2020, the quarterly country’s GDP decreased by 5.5 percent, the first contraction in recent years. Around one year later, in the third quarter of 2021, Kenya already registered an improved economic performance, with the quarterly GDP growth rate measured at 9.9 percent. The educational sector pushed the result, with an expansion of 65 percent. Mining and quarrying, and accommodation and food services followed, each with a 25 percent growth rate.

Signs of recovery in the tourism sector

Extensively known for its rich nature and wildlife, Kenya felt dramatically the impacts of the COVID-19 pandemic in the tourism industry. The sector's contribution to the country’s GDP roughly halved in 2020, compared to 2019. By the end of 2021, however, signals of recovery were already spotted. The monthly number of arrivals in both Jomo Kenyatta and Moi international airports in December that year corresponded to roughly 70 percent of that registered in December 2019. Additionally, as of March 2022, the bed occupancy rate in Kenyan hotels amounted to 57 percent, against 23 percent in March 2021.

Kenya is a country in Eastern Africa. At 580,367 square kilometres (224,081 sq mi), Kenya is the world's 48th largest country by area. With a population of more than 47.6 million in the 2019 census, Kenya is the 29th most populous country. Kenya's capital and largest city is Nairobi, while its oldest city and first capital is the coastal city of Mombasa. Kisumu City is the third-largest city and also an inland port on Lake Victoria. Other important urban centres include Nakuru and Eldoret. As of 2020, Kenya is the third-largest economy in sub-Saharan Africa after Nigeria and South Africa. Kenya is bordered by South Sudan to the northwest, Ethiopia to the north, Somalia to the east, Uganda to the west, Tanzania to the south, and the Indian Ocean to the southeast.

Source: Objaverse 1.0 / Sketchfab

BACKGROUND HIV continues to be a public health concern in Kenya. Out of an estimated 40 million people the national HIV prevalence among adults aged 15-64 years was 7% as of 2007 representing an estimated 1.4 million people living with HIV (Republic of Kenya 2009). According to the Kenya AIDS Indicator Survey (KAIS) and Demographic Health Surveys (DHS), significant differences in HIV prevalence occur throughout the population and geographic locations. Women are at a greater risk (8.4%) than men (5.4%), and there is a greater than 15-fold variation in HIV prevalence among provinces, ranging from 1% in North Eastern province to 15.3% in Nyanza province (KNBS and ICF Macro 2010; National AIDS Control Council [NACC] 2009a; Republic of Kenya 2009). Despite wider provision of HIV testing services, and a high level of knowledge about HIV testing (95% of adults and 88% of youth know where to get an HIV test) (KDHS 2008-9), only 72% of adult Kenyans report having ever tested for HIV and received their results (KAIS, 2012). STUDY RATIONALE Oral HIV self-testing presents an opportunity to increase testing coverage by addressing barriers to HIV testing in VCT and facility based testing (i.e. limited access to VCT centers and hours of testing, HIV-related stigma and discrimination). Population Services International, Kenya (PSI) was awarded by the International Initiative for Impact Evaluation (3ie) and the National AIDS and STI Control Programme (NASCOP) to conduct a formative study on potential HIV oral self-test kit users, and messaging for HIV oral self-test kits. The purpose of this study was to identify willingness to use oral self-test kits, and factors associated with the potential adoption and use of oral HIV self-testing kits to inform future HIV self-testing pilot progr ams. The objectives are to determine likely users of oral HIV self-test kits, incentives to use a self-test kit, and identify key messages and approaches that could increase demand for the kits. The evidence from this study will inform the design and implementation of oral HIV self-testing pilot projects throughout Kenya; and national policy regarding the availability, cost/subsidies, quality control and procurement. METHODOLOGY This mixed-method study was conducted between November and December 2013 in two provinces/counties of Siaya (rural) and Mombasa (urban). The study population included 782 sexually active men and women aged 18 to 49 from the general population (GP); 100 men who have sex with men (MSM), and 100 female sex workers (FSW).