This statistic shows the age structure in Kenya from 2013 to 2023. In 2023, about 37.22 percent of Kenya's total population were aged 0 to 14 years.

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

Kenya KE: Probability of Dying at Age 15-19 Years: per 1000 data was reported at 6.300 Ratio in 2019. This records a decrease from the previous number of 6.600 Ratio for 2018. Kenya KE: Probability of Dying at Age 15-19 Years: per 1000 data is updated yearly, averaging 9.500 Ratio from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 10.900 Ratio in 1999 and a record low of 6.300 Ratio in 2019. Kenya KE: Probability of Dying at Age 15-19 Years: per 1000 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Health Statistics. Probability of dying between age 15-19 years of age expressed per 1,000 adolescents age 15, if subject to age-specific mortality rates of the specified year.; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

Abstract

The 2008-09 Kenya Demographic and Health Survey (KDHS) is a population and health survey that Kenya conducts every five years. It was designed to provide data to monitor the population and health situation in Kenya and also to be used as a follow-up to the previous KDHS surveys in 1989, 1993, 1998, and 2003.

From the current survey, information was collected on fertility levels; marriage; sexual activity; fertility preferences; awareness and use of family planning methods; breastfeeding practices; nutritional status of women and young children; childhood and maternal mortality; maternal and child health; and awareness and behaviour regarding HIV/AIDS and other sexually transmitted infections. The 2008-09 KDHS is the second survey to collect data on malaria and the use of mosquito nets, domestic violence, and HIV testing of adults.

The specific objectives of the 2008-09 KDHS were to: - Provide data, at the national and provincial levels, that allow the derivation of demographic rates, particularly fertility and childhood mortality rates, to be used to evaluate the achievements of the current national population policy for sustainable development - Measure changes in fertility and contraceptive prevalence use and study the factors that affect these changes, such as marriage patterns, desire for children, availability of contraception, breastfeeding habits, and other important social and economic factors - Examine the basic indicators of maternal and child health in Kenya, including nutritional status, use of antenatal and maternity services, treatment of recent episodes of childhood illness, use of immunisation services, use of mosquito nets, and treatment of children and pregnant women for malaria - Describe the patterns of knowledge and behaviour related to the transmission of HIV/AIDS and other sexually transmitted infections - Estimate adult and maternal mortality ratios at the national level - Ascertain the extent and pattern of domestic violence and female genital cutting in the country - Estimate the prevalence of HIV infection at the national and provincial levels and by urban-rural residence, and use the data to corroborate the rates from the sentinel surveillance system

The 2008-09 KDHS information provides data to assist policymakers and programme implementers as they monitor and evaluate existing programmes and design new strategies for demographic, social, and health policies in Kenya. The data will be useful in many ways, including the monitoring of the country’s achievement of the Millennium Development Goals.

As in 2003, the 2008-09 KDHS survey was designed to cover the entire country, including the arid and semi-arid districts, and especially those areas in the northern part of the country that were not covered in the earlier KDHS surveys. The survey collected information on demographic and health issues from a sample of women at the reproductive age of 15-49 and from a sample of men age 15-54 years in a one-in-two subsample of households.

Geographic coverage

National

Analysis unit

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

Kind of data

Sample survey data

Sampling procedure

The survey is household-based, and therefore the sample was drawn from the population residing in households in the country. A representative sample of 10,000 households was drawn for the 2008-09 KDHS. This sample was constructed to allow for separate estimates for key indicators for each of the eight provinces in Kenya, as well as for urban and rural areas separately. Compared with the other provinces, fewer households and clusters were surveyed in North Eastern province because of its sparse population. A deliberate attempt was made to oversample urban areas to get enough cases for analysis. As a result of these differing sample proportions, the KDHS sample is not self-weighting at the national level; consequently, all tables except those concerning response rates are based on weighted data.

The KNBS maintains master sampling frames for household-based surveys. The current one is the fourth National Sample Survey and Evaluation Programme (NASSEP IV), which was developed on the platform of a two-stage sample design. The 2008-09 KDHS adopted the same design, and the first stage involved selecting data collection points ('clusters') from the national master sample frame. A total of 400 clusters-133 urban and 267 rural-were selected from the master frame. The second stage of selection involved the systematic sampling of households from an updated list of households. The Bureau developed the NASSEP frame in 2002 from a list of enumeration areas covered in the 1999 population and housing census. A number of clusters were updated for various surveys to provide a more accurate selection of households. Included were some of the 2008-09 KDHS clusters that were updated prior to selection of households for the data collection.

All women age 15-49 years who were either usual residents or visitors present in sampled households on the night before the survey were eligible to be interviewed in the survey. In addition, in every second household selected for the survey, all men age 15-54 years were also eligible to be interviewed. All women and men living in the households selected for the Men's Questionnaire and eligible for the individual interview were asked to voluntarily give a few drops of blood for HIV testing.

Note: See detailed description of the sample design in Appendix A of the survey final report.

Mode of data collection

Face-to-face

Research instrument

Three questionnaires were used to collect the survey data: the Household, Women’s, and Men’s Questionnaires. The contents of these questionnaires were based on model questionnaires developed by the MEASURE DHS programme that underwent only slight adjustments to reflect relevant issues in Kenya. Adjustment was done through a consultative process with all the relevant technical institutions, government agencies, and local and international organisations. The three questionnaires were then translated from English into Kiswahili and 10 other local languages (Kalenjin, Kamba, Kikuyu, Kisii, Luhya, Luo, Maasai, Meru, Mijikenda, and Somali). The questionnaires were further refined after the pretest and training of the field staff.

In each of the sampled households, the Household Questionnaire was the first to be administered and was used to list all the usual members and visitors. Basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women age 15-49 and men age 15-54 who were eligible for the individual interviews. The questionnaire also collected information on characteristics of the household’s dwelling unit, such as the source of water, type of toilet facilities, materials used for the floor, walls, and roof of the house, ownership of various durable goods, ownership of agricultural land, ownership of domestic animals, and ownership and use of mosquito nets. In addition, this questionnaire was used to capture information on height and weight measurements of women age 15-49 years and children age five years and below, and, in households eligible for collection of blood samples, to record the respondents’ consent to voluntarily give blood samples. A detailed description of HIV testing procedures is given in Section 1.10 below.

The Women’s Questionnaire was used to capture information from all women age 15-49 years and covered the following topics: - Respondent’s background characteristics (e.g., education, residential history, media exposure) - Reproductive history - Knowledge and use of family planning methods - Antenatal, delivery, and postnatal care - Breastfeeding - Immunisation, nutrition, and childhood illnesses - Fertility preferences - Husband’s background characteristics and woman’s work - Marriage and sexual activity - Infant and child feeding practices - Childhood mortality - Awareness and behaviour about HIV/AIDS and other sexually transmitted diseases - Knowledge of tuberculosis - Health insurance - Adult and maternal mortality - Domestic violence - Female genital cutting

The set of questions on domestic violence sought to obtain information on women’s experience of violence. The questions were administered to one woman per household. In households with more eligible women, special procedures (use of a ‘Kish grid’) were followed to ensure that the woman interviewed about domestic violence was randomly selected.

The Men’s Questionnaire was administered to all men age 15-54 years living in every second household in the sample. The Men’s Questionnaire collected information similar to that collected in the Women’s Questionnaire, but it was shorter because it did not contain questions on reproductive history, maternal and child health, nutrition, maternal mortality, and domestic violence.

Two pilot projects were conducted in 12 districts for the KDHS, the first from July 1-7, 2008, and the second from October 13-17, 2008, to test the questionnaires, which were written in English and then translated into eleven other languages. The pilot was repeated because the first pilot did not include the HIV blood testing component. Twelve teams (one for each language) were formed, each with one female interviewer, one male interviewer, and one health worker. A total of 260 households were covered in the pilots. The lessons learnt from the pilot surveys were used to finalise the survey instruments and set up strong, logistical arrangements to ensure the success of the survey.

Response

Kenya KE: Age Dependency Ratio: % of Working-Age Population data was reported at 75.910 % in 2017. This records a decrease from the previous number of 77.045 % for 2016. Kenya KE: Age Dependency Ratio: % of Working-Age Population data is updated yearly, averaging 105.254 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 113.066 % in 1977 and a record low of 75.910 % in 2017. Kenya KE: Age Dependency Ratio: % of Working-Age Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Population and Urbanization Statistics. Age dependency ratio is the ratio of dependents--people younger than 15 or older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population.; ; World Bank staff estimates based on age distributions of United Nations Population Division's World Population Prospects: 2017 Revision.; Weighted average; Relevance to gender indicator: this indicator implies the dependency burden that the working-age population bears in relation to children and the elderly. Many times single or widowed women who are the sole caregiver of a household have a high dependency ratio.

Abstract

The 2014 Kenya Demographic and Health Survey (KDHS) provides information to help monitor and evaluate population and health status in Kenya. The survey, which follows up KDHS surveys conducted in 1989, 1993, 1998, 2003, and 2008-09, is of special importance for several reasons. New indicators not collected in previous KDHS surveys, such as noncommunicable diseases, fistula, and men's experience of domestic violence, are included. Also, it is the first national survey to provide estimates for demographic and health indicators at the county level. Following adoption of a constitution in Kenya in 2010 and devolution of administrative powers to the counties, the new 2014 KDHS data should be valuable to managers and planners. The 2014 KDHS has specifically collected data to estimate fertility, to assess childhood, maternal, and adult mortality, to measure changes in fertility and contraceptive prevalence, to examine basic indicators of maternal and child health, to estimate nutritional status of women and children, to describe patterns of knowledge and behaviour related to the transmission of HIV and other sexually transmitted infections, and to ascertain the extent and pattern of domestic violence and female genital cutting. Unlike the 2003 and 2008-09 KDHS surveys, this survey did not include HIV and AIDS testing. HIV prevalence estimates are available from the 2012 Kenya AIDS Indicator Survey (KAIS), completed prior to the 2014 KDHS. Results from the 2014 KDHS show a continued decline in the total fertility rate (TFR). Fertility decreased from 4.9 births per woman in 2003 to 4.6 in 2008-09 and further to 3.9 in 2014, a one-child decline over the past 10 years and the lowest TFR ever recorded in Kenya. This is corroborated by the marked increase in the contraceptive prevalence rate (CPR) from 46 percent in 2008-09 to 58 percent in the current survey. The decline in fertility accompanies a marked decline in infant and child mortality. All early childhood mortality rates have declined between the 2003 and 2014 KDHS surveys. Total under-5 mortality declined from 115 deaths per 1,000 live births in the 2003 KDHS to 52 deaths per 1,000 live births in the 2014 KDHS. The maternal mortality ratio is 362 maternal deaths per 100,000 live births for the seven-year period preceding the survey; however, this is not statistically different from the ratios reported in the 2003 and 2008-09 KDHS surveys and does not indicate any decline over time. The proportion of mothers who reported receiving antenatal care from a skilled health provider increased from 88 percent to 96 percent between 2003 and 2014. The percentage of births attended by a skilled provider and the percentage of births occurring in health facilities each increased by about 20 percentage points between 2003 and 2014. The percentage of children age 12-23 months who have received all basic vaccines increased slightly from the 77 percent observed in the 2008-09 KDHS to 79 percent in 2014. Six in ten households (59 percent) own at least one insecticide-treated net, and 48 percent of Kenyans have access to one. In malaria endemic areas, 39 percent of women received the recommended dosage of intermittent preventive treatment for malaria during pregnancy. Awareness of AIDS is universal in Kenya; however, only 56 percent of women and 66 percent of men have comprehensive knowledge about HIV and AIDS prevention and transmission. The 2014 KDHS was conducted as a joint effort by many organisations. The Kenya National Bureau of Statistics (KNBS) served as the implementing agency by providing guidance in the overall survey planning, development of survey tools, training of personnel, data collection, processing, analysis, and dissemination of the results. The Bureau would like to acknowledge and appreciate the institutions and agencies for roles they played that resulted in the success of this exercise: Ministry of Health (MOH), National AIDS Control Council (NACC), National Council for Population and Development (NCPD), Kenya Medical Research Institute (KEMRI), Ministry of Labour, Social Security and Services, United States Agency for International Development (USAID/Kenya), ICF International, United Nations Fund for Population Activities (UNFPA), the United Kingdom Department for International Development (DfID), World Bank, Danish International Development Agency (DANIDA), United Nations Children's Fund (UNICEF), German Development Bank (KfW), World Food Programme (WFP), Clinton Health Access Initiative (CHAI), Micronutrient Initiative (MI), US Centers for Disease Control and Prevention (CDC), Japan International Cooperation Agency (JICA), Joint United Nations Programme on HIV/AIDS (UNAIDS), and the World Health Organization (WHO). The management of such a huge undertaking was made possible through the help of a signed memorandum of understanding (MoU) by all the partners and the creation of active Steering and Technical Committees.

Geographic coverage

County, Urban, Rural and National

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2014 KDHS was drawn from a master sampling frame, the Fifth National Sample Survey and Evaluation Programme (NASSEP V). This is a frame that the KNBS currently operates to conduct household-based surveys throughout Kenya. Development of the frame began in 2012, and it contains a total of 5,360 clusters split into four equal subsamples. These clusters were drawn with a stratified probability proportional to size sampling methodology from 96,251 enumeration areas (EAs) in the 2009 Kenya Population and Housing Census. The 2014 KDHS used two subsamples of the NASSEP V frame that were developed in 2013. Approximately half of the clusters in these two subsamples were updated between November 2013 and September 2014. Kenya is divided into 47 counties that serve as devolved units of administration, created in the new constitution of 2010. During the development of the NASSEP V, each of the 47 counties was stratified into urban and rural strata; since Nairobi county and Mombasa county have only urban areas, the resulting total was 92 sampling strata. The 2014 KDHS was designed to produce representative estimates for most of the survey indicators at the national level, for urban and rural areas separately, at the regional (former provincial1) level, and for selected indicators at the county level. In order to meet these objectives, the sample was designed to have 40,300 households from 1,612 clusters spread across the country, with 995 clusters in rural areas and 617 in urban areas. Samples were selected independently in each sampling stratum, using a two-stage sample design. In the first stage, the 1,612 EAs were selected with equal probability from the NASSEP V frame. The households from listing operations served as the sampling frame for the second stage of selection, in which 25 households were selected from each cluster. The interviewers visited only the preselected households, and no replacement of the preselected households was allowed during data collection. The Household Questionnaire and the Woman's Questionnaire were administered in all households, while the Man's Questionnaire was administered in every second household. Because of the non-proportional allocation to the sampling strata and the fixed sample size per cluster, the survey was not self-weighting. The resulting data have, therefore, been weighted to be representative at the national, regional, and county levels.

Sampling deviation

Not available

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2014 KDHS used a household questionnaire, a questionnaire for women age 15-49, and a questionnaire for men age 15-54. These instruments were based on the model questionnaires developed for The DHS Program, the questionnaires used in the previous KDHS surveys, and the current information needs of Kenya. During the development of the questionnaires, input was sought from a variety of organisations that are expected to use the resulting data. A two-day workshop involving key stakeholders was held to discuss the questionnaire design. Producing county-level estimates requires collecting data from a large number of households within each county, resulting in a considerable increase in the sample size from 9,936 households in the 2008-09 KDHS to 40,300 households in 2014. A survey of this magnitude introduces concerns related to data quality and overall management. To address these concerns, reduce the length of fieldwork, and limit interviewer and respondent fatigue, a decision was made to not implement the full questionnaire in every household and, in so doing, to collect only priority indicators at the county level. Stakeholders generated a list of these priority indicators. Short household and woman's questionnaires were then designed based on the full questionnaires; the short questionnaires contain the subset of questions from the full questionnaires required to measure the priority indicators at the county level. Thus, a total of five questionnaires were used in the 2014 KDHS: (1) a full Household Questionnaire, (2) a short Household Questionnaire, (3) a full Woman's Questionnaire, (4) a short Woman's Questionnaire, and (5) a Man's Questionnaire. The 2014 KDHS sample was divided into halves. In one half, households were administered the full Household Questionnaire, the full Woman's Questionnaire, and the Man's Questionnaire. In the other half, households were administered the short Household Questionnaire and the short Woman's Questionnaire. Selection of these subsamples was done at the household level-within a cluster, one in every two

Kenya KE: Number of Deaths Ages 15-19 Years data was reported at 7,457.000 Person in 2019. This records a decrease from the previous number of 7,526.000 Person for 2018. Kenya KE: Number of Deaths Ages 15-19 Years data is updated yearly, averaging 7,626.500 Person from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 8,569.000 Person in 2004 and a record low of 4,709.000 Person in 1990. Kenya KE: Number of Deaths Ages 15-19 Years data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Health Statistics. Number of deaths of adolescents ages 15-19 years; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Sum; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

Abstract

Introduction;

Rapid urbanization in Kenya has resulted in growth of slums in urban centres, characterised by poverty, inadequate social services and poor health outcomes. The government's initiatives to improve access to quality health care for mothers and children are largely limited to public health facilities which are few and/or inaccessible in underserved areas like the slums. The 'Partnership for Maternal, Newborn and Child Health' (PAMANECH) project is being implemented in two Nairobi slums, Viwandani and Korogocho to assess the impact of strengthening public-private partnerships for the delivery of health care on the health of mothers, newborns and young children in two informal settlements in Kenya.

Methods and analysis;

A quasi-experimental study. Our approach is to support both private and public health providers and the community to enhance access to, and demand for quality health care services. Key activities include; infrastructural upgrade of selected Private Not-For-Profit health facilities operating in the two slums, building capacity for both health care providers and the Health Management Teams in Nairobi, facilitating provision of supportive supervision by the local health authorities and forming networks of Community Health Volunteers (CHVs) to create demand for the health services. To assess the impact of the intervention, the study is utilising multiple data sources using a combination of qualitative and quantitative methods. A baseline survey was conducted in 2013 and an end line survey will be conducted at least one year after full implementation of the intervention. Systematic monitoring and documentation of the intervention is on-going to strengthen the case for causal inference.

Ethics and dissemination.

Ethical approval for the study was obtained from the Kenya Medical Research Institute. Key messages from the results will be packaged and widely disseminated through workshops, conference presentations, reports, factsheets and academic publications to facilitate uptake by policy makers.

Geographic coverage

Two Nairobi urban slums Korogocho and Viwandani

Analysis unit

Women of reproductive age and children under-five years

Universe

The direct beneficiaries of the project are women of reproductive age and children under the age of five years in the two informal settlements who make up 20% and 14% of the population, respectively. In addition, five health facilities are being upgraded and the health care providers in the selected PNFP and other public and private health facilities benefitting from training and skills upgrade. CHVs, the sCHMTs of the two sub-Counties where the study sites are located as well as the Nairobi County Health Management leadership are other direct beneficiaries. Residents of areas outside the NUDHSS as well as residents of the two slums who are male and/or older than 5 years but less than 15 years and/or older than 50 years are the indirect beneficiaries.

Sampling procedure

Sampling procedure was for primary units. Random numbers were generated to select women of reproductive age (12 to 49 years) and children under 5 years from the most up-to-date Nairobi Urban Health and Demographic Surveillance System (NUHDSS) database. The sampling frame was restricted to those households that have individuals within the two study populations.

Sampling deviation

None

Mode of data collection

Face-to-face [f2f]

Research instrument

The women questionnaire was administered to women aged 12 - 49. The questionnaire included;

· Respondents background characteristics

· Care seeking behavior -Family planning services and knowledge, Ante Natal Care, Delivery, Post- Natal Care,

· Referral patterns

· Quality of health care facilities and interaction with Community Health Workers

· Child morbidity and Mortality

· Breastfeeding - Early initiation, breastfeeding knowledge, attitude and practice

· HIV and AIDs, and other STIs

The child questionnaire was for children under 5 years age and it was administered to parents or guardians of the children. This questionnaire included;

· Background characteristics of respondent,

· Care seeking behavior including vaccination,

· Child morbidity and mortality.

Both questionnaires were administered in Kiswahili.

Cleaning operations

Data were colleted using Netbooks with inbuilt consistency checks and was synced to the central server at the office for back up. Project data analyst performed post-data collection consistency checks and labelling of variables.

Response rate

Total number of women interviewed were 849

Total number of interviews for children under 5 years were 975

Response rate: 100%

Abstract

The Turkana County Multiple Indicator Cluster Survey (MICS5) was conducted in collaboration with the Population Studies and Research Institute (PSRI) of the University of Nairobi, the Kenya National Bureau of Statistics (KNBS) and the United Nations Children's Fund (UNICEF).The Kenya National Bureau of Statistics implemented (MICS5) in 2013-2014 in the three counties of Bungoma, Kakamega and Turkana as part of Global MICS round five.

MICS is an international household survey program that was developed by UNICEF in the 1990s as an international household survey program to support countries in the collection of internationally comparable data on a wide range of indicators on the situation of children and women. MICS provides up-to-date information on the situation of children and women and measures key indicators that allow countries to monitor progress towards the Millennium Development Goals (MDGs) and other internationally agreed upon commitments. In Kenya, this information is important to guide the planning and implementation of new development plans targeting the new administrative county levels of governance. Technical and financial support were provided by the United Nations Children's Fund.

The results of this survey provided requisite baseline information that can be used to facilitate evidence-based planning, budgeting and programming by policymakers and stakeholders at the county levels. The survey will go a long way in encouraging increased demand for use of statistics by policy makers at devolved levels and will ensure that resources at both county and national levels are used most effectively through well-planned projects/programs that will benefit especially the women and children of the three counties. The MICS5 results were critical in gauging milestones achieved in the field of education, nutrition, child development, health for women and children in the three counties and in evaluating the various health based policies that the government has formulated over the years towards achieving the national welfare objectives.

The 2013-14 MICS5 data was critical in informing the future planning for the three counties, especially in view of the new constitutional dispensation and Vision 2030. It was anticipated that MICS5 would supplement the data collected during the 2014 Kenya Demographic and Health Survey (KDHS). In addition the information collected would inform strategic communication for social and behavior change interventions by government and partners including UNICEF. Furthermore the data contributed to the improvement of data and monitoring systems in the three counties. The primary objectives of the Turkana County survey are: 1. To provide up-to-date information for assessing the situation of children and women in Turkana County. 2. To generate data for the critical assessment of the progress made in various areas, and to put additional efforts in those areas that require more attention. 3. To furnish data needed for monitoring progress toward goals established in the Millennium Declaration, and other internationally agreed upon goals, as a basis for future action. 4. To collect disaggregated data for the identification of disparities, to allow for evidence based policy-making aimed at social inclusion of the most vulnerable. 5. To contribute to the generation of baseline data for the post-2015 agenda. 6. To validate data from other sources and the results of focused interventions. 7. To contribute to the improvement of data and monitoring systems in Kenya and to strengthen technical expertise in the design, implementation, and analysis of such systems.

Geographic coverage

National

Analysis unit

• Individuals
• Households

Universe

The survey covered all de jure household members (usual residents), all women aged between 15-49 years and 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 Turkana County MICS was to produce statistically reliable estimates of indicators, at county level. The urban and rural areas in Turkana County were the sampling strata. A multi-stage, stratified cluster sampling approach was used for the selection of the survey sample. MICS5 utilized the recently created fifth National Sample Survey and Evaluation Programe (NASSEP V) frame which is a household based master sampling frame developed and maintained by KNBS. The frame was implemented using a multi-tiered structure, in which a set of 4 sub-samples (C1, C2, C3, C4) were developed. It is based on the list of enumeration areas (EAs) from the 2009 Kenya Population and Housing Census. The frame is stratified according to County and further into rural and urban. Each of the sub-samples is representative at county level and at national (i.e. Urban/rural) level and contains 1,340 clusters.

The Primary Sampling Units (PSUs) for the survey were clusters drawn from the NASSEP V sampling frame, so the first component of the probabilities and weights are based on that master sample. Within each stratum the PSUs for the MICS were selected independently from one of the subsamples of the master sample using Equal Probability Selection Method (EPSEM). A total of 58 clusters were selected from the master sample in this way.

Out of the 58 clusters selected for Turkana County, it was established that 30 had been listed more than six months prior to the start of the survey. These listing for these clusters was updated prior to selection of households. For this purpose, listing teams visited each cluster, and listed all occupied households. For the remaining 28 sample clusters a more recent listing was available, so it was used for selecting the sample households.

Mode of data collection

Face-to-face [f2f]

Research instrument

A set of three questionnaires was used in the survey: 1. A household questionnaire which was administered to the household head or any other responsible member of the household. 2. A questionnaire for individual women administered in each household to all women age 15-49 years. 3. An under-5 questionnaire administered to mothers (or caretakers) for all children under-5 years living in the household

Cleaning operations

Data were entered into the computers using the Census and Surveys Processing System (CSPro) software package, Version 5.0. Data entry was done by a trained team of 14 data entry operators, one archivist/system administrator and one data entry supervisor. For quality assurance purposes, all questionnaires were double-entered and internal consistency checks performed.

Procedures and standard programs developed under the global MICS program and adapted to the Turkana County MICS questionnaire were used throughout. Data processing began simultaneously with data collection in November 2013 and was completed in February 2014. Data were analyzed using the Statistical Package for Social Sciences (SPSS) software, Version 21. Model syntax and tabulation plans developed by UNICEF were customized and used for this purpose.

Response rate

Information was collected from a total of 1,277 households representing 93 percent response rate. The composition of these households was 6,594 household members comprising 3,274 males and 3,321 females. The mean household size was 5.2 persons. About 49 percent of the sampled households population is below 15 years, 48 percent are age 15-64 years and three percent are age 65 years and above.

Due to data quality issues, data relating to mortality and anthropometric measures were not analyzed and reported. Anthropometric data suffered from digit preference for both weight and height, while for mortality, deaths especially among under-5 years old were under reported. KDHS 2014 had similar shortcomings.

Sampling error estimates

The sample of respondents selected in the Turkana County MICS is only one of the samples that could have been selected from the same population, using the same design and size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between the estimates from all possible samples. The extent of variability is not known exactly, but can be estimated statistically from the survey data. The following sampling error measures are presented in this appendix for each of the selected indicators: - Standard error (se): Standard error is the square root of the variance of the estimate. For survey indicators those are means, proportions or ratios, the Taylor series linearization method is used for the estimation of standard errors. For more complex statistics, such as fertility and mortality rates, the Jackknife repeated replication method is used for standard error estimation. - Coefficient of variation (se/r) is the ratio of the standard error to the value (r) of the indicator, and is a measure of the relative sampling error. - 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 based on the same sample size. The square root of the design effect (deft) is used to show the efficiency of the sample design in relation to the precision. A deft value of 1.0 indicates that the sample design of the survey is as efficient as a simple random sample for a particular indicator, while a deft value above 1.0 indicates an increase in the standard error due to the use of a more complex sample design. - Confidence limits are calculated to

Abstract

The Kenya Demographic and Health Survey (KDHS) was conducted between December 1988 and May 1989 to collect data regarding fertility, family planning and maternal and child health. The survey covered 7,150 women aged 15-49 and a subsample of 1,116 husbands of these women, selected from a sample covering 95 percent of the population. The purpose of the survey was to provide planners and policymakers with data useful in making informed programme decisions.

OBJECTIVES

On March 1, 1988, 'on behalf of the Government of Kenya, the National Council for Population and Development (NCPD) signed an agreement with the Institute for Resource Development (IRD) to carry out the Kenya Demographic and Health Survey (KDHS).

The KDHS is intended to serve as a source of population and health data for policymakers and for the research community. In general, the objectives of the 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 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 Kcnyan 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.

SUMMARY OF FINDINGS

The survey data can also be used to evaluate Kenya's efforts to reduce fertility and the picture that emerges shows significant strides have been made toward this goal. KDHS data provide the first evidence of a major decline in fertility. If young women continue to have children at current rates, they will have an average of 6.7 births in their lifetime. This is down considerably from the average of 7.5 births for women now at the end of their childbearing years. The fertility rate in 1984 was estimated at 7.7 births per woman.

A major cause of the decline in fertility is increased use of family pIanning. Twenty-seven percent of married women in Kenya are currcntly using a contraceptive method, compared to 17 percent in 1984. Although periodic abstinence continues to he the most common method (8 percent), of interest to programme planners is the fact that two-thirds of marricd women using contraception have chosen a modern method--either the pill (5 percent) or female sterilisation (5 percent). Contraccptive use varies by province, with those closest to Nairobi having the highest levels. Further evidence of the success in promoting family planning is the fact that more than 90 percent of married women know at least one modern method of contraception (and where to obtain it), and 45 percent have used a contraceptive method at some time in their life.

The survey indicates a high level of knowledge, use and approval of family planning by husbands of interviewed women. Ninety-three percent of husbands know a modern method of family planning. Sixty-five percent of husbands have used a method at some time and almost 49 percent are currently using a method, half of which are modern methods. Husbands in Kenya are strongly supportive of family planning. Ninety-one percent of those surveyed approve of family planning use by couples, compared to 88 percent of married women.

If couples are able to realise their childbearing preferences, fertility may continue to decline in the future. One half of married women say that they want no more children; another 26 percent want to wait at least two years before having another child. Husbands report similar views on limiting births--one-half say they want no more children. The desire to limit childbearing appears to be greater in Kenya than in other subSaharan countries. In Botswana and Zimbabwe, for example, only 33 percent of married women want no more children. Another indicator of possible future decline in fertility in Kenya is the decrease in ideal family size. According to the KDHS, the mean ideal family size declined from 5.8 in 1984 to 4.4 in 1989.

The KDHS indicates that in the area of health, government programmes have been effective in providing health services for womcn and children. Eight in ten births benefit from ante-natal care from a doctor, nurse, or midwife and one-half of births are assisted at delivery by a doctor, nurse, or midwife. At least 44 percent of children 12-23 months of age are fully immunised against the major childhood diseases, Almost all children benefit from an extended period of breastfeeding. The average duration of breastfeeding is 19 months and the practice does not appear to be waning among either younger women or urban women. Another encouraging piece of information is the high level of ORT (oral rehydration therapy) use for treating childhood diarrhoea. Among children under five reported to have had an episode of diarrhoea in the two weeks before the survey, half were treated with a homemade solution and almost one-quarter were given a solution prepared from commercially prepared packets.

The survey indicates several areas where there is room for improvement. Although young women are marrying later, many are still having births at young ages. More than 20 percent of teen-age girls have had at least one child and 7 percent were pregnant at the time of the survey. There is also evidence of an unmet need for family planning services. Of the births occurring in the 12 months before the survey, over half were either mistimed or unwanted; one fifth occurred less than 24 months after a previous birth.

Geographic coverage

The 1989 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 not specified

Universe

The population covered by the 1989 KDHS is defined as the universe of all women age 15-49 in Kenya and all husband living in the household.

Kind of data

Sample survey data

Sampling procedure

The sample for the KDHS is based on the National Sample Survey and Ewduation Programme (NASSEP) master sample maintained by the CBS. The KDHS sample is national in coverage, with the exclusion of North Eastern Province and four northern districts which together account for only about five percent of Kenya's population. The KDHS sample was designed to produce completed interviews with 7,500 women aged 15-49 and with a subsample of 1,000 husbands of these women.

The NASSEP master sample is a two-stage design, stratified by urban-rural residence, and within the rural stratum, by individual district. In the first stage, 1979 census enumeration areas (EAs) were selected with probability proportional to size. The selected EAs were segmented into the expected number of standard-sized clusters, one of which was selected at random to form the NASSEP cluster. The selected clusters were then mapped and listed by CBS field staff. In rural areas, household listings made betwecn 1984 and 1985 were used to select the KDHS households, while KDHS pretest staff were used to relist households in the selected urban clusters.

Despite the emphasis on obtaining district-level data for phoning purposes, it was decided that reliable estimates could not be produced from the KDHS for all 32 districts in NASSEP, unless the sample were expanded to an unmanageable size. However, it was felt that reliable estimates of certain variables could be produced lbr the rural areas in the 13 districts that have been initially targeted by the NCPD: Kilifi, Machakos, Meru, Nyeri, Murang'a, Kirinyaga, Kericho, Uasin Gishu, South Nyanza, Kisii, Siaya, Kakamega, and Bungoma. Thus, all 24 rural clusters in the NASSEP were selected for inclusion in the KDHS sample in these 13 districts. About 450 rural households were selected in each of these districts, just over 1000 rural households in other districts, and about 3000 households in urban areas, for a total of almost 10,000 households. Sample weights were used to compensate for the unequal probability of selection between strata, and weighted figures are used throughout the remainder of this report.

Mode of data collection

Face-to-face

Research instrument

The KDHS utilised three questionnaires: a household questionnaire, a woman's questionnaire, and a husband's questionnaire. The first two were based on the DHS Programme's Model "B" Questionnaire that was designed for low contraceptive prevalence countries, while the husband's questionnaire was based on similar questionnaires used in the DHS surveys in Ghana and Burundi. A two-day seminar was held in Nyeri in November 1987 to develop the questionnaire design. Participants included representatives from the Central Bureau of Statistics (CBS), the Population Studies Research Institute at the University of Nairobi, the Community Health Department of Kenyatta Hospital, and USAID. The decision to include a survey of husbands was based on the recommendation of the seminar participants. The questionnaires were subsequently translated into eight local languages (Kalenjin, Kamba, Kikuyu, Kisii, Luhya, Luo, Meru and Mijikenda), in addition to Kiswahili.

Cleaning operations

Data

As of 2023, the total population of Africa was over 1.48 billion. The number of inhabitants on the continent increased annually from 2000 onwards. In comparison, the total population was around 831 million in 2000. According to forecasts, Africa will experience impressive population growth in the coming years and would nearly reach the Asian population by 2100. Over 200 million people in Nigeria Nigeria is the most populous country in Africa. In 2023, the country’s population exceeded 223 million people. Ethiopia followed with a population of around 127 million, while Egypt ranked third, accounting for approximately 113 million individuals. Other leading African countries in terms of population were the Democratic Republic of the Congo, Tanzania, South Africa, and Kenya. Additionally, Niger, the Democratic Republic of Congo, and Chad recorded the highest population growth rate on the continent in 2023, with the number of residents rising by over 3.08 percent compared to the previous year. On the other hand, the populations of Tunisia and Eswatini registered a growth rate below 0.85 percent, while for Mauritius and Seychelles, it was negative. Drivers for population growth Several factors have driven Africa’s population growth. For instance, the annual number of births on the continent has risen constantly over the years, jumping from nearly 32 million in 2000 to almost 46 million in 2023. Moreover, despite the constant decline in the number of births per woman, the continent’s fertility rate has remained considerably above the global average. Each woman in Africa had an average of over four children throughout her reproductive years as of 2021, compared to a world rate of around two births per woman. At the same time, improved health and living conditions contributed to decreasing mortality rate and increasing life expectancy in recent years, driving population growth.

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 1998/99 Integrated Labour Force Survey (ILFS) was the first of its kind to integrate three related surveys (labour force, informal sector and child labour modular surveys) into a single cost-effective survey. It was conducted over the whole country on the household-based NASSEP III sample frame, and covered 11,049 households giving a response rate of 86.2 per cent. As such, the survey collected a wide range of representative information that can be used in the design, implementation, monitoring and evaluation of various policies and programmes. In particular, it provides indicators such as school enrolments rates, housing conditions, access to amenities and facilities, income and expenditures, unemployment rates, and income and expenditure levels which should provide invaluable inputs into the monitoring and evaluation of the economic reforms and poverty reduction programmes that are being implemented by the Government.

The key objectives of the survey were to update data on the labour force, determine the size and output of the informal sector, and estimate the extent of child labour. A rich data bank has been created as a by-product of data processing exercise, which can be used to carry out further analysis of the information collected by the survey.

In designing and implementing the survey, CBS worked closely with other stakeholders through the Inter-Ministerial Steering Committee (IMSC) that was formed to provide overall guidance on the implementation of the survey. The committee was composed of representatives from Ministry of Labour and Human Resource Development, Ministry of Education Science and Technology, and the Macro Planning and Human Resources and Social Services departments in the Ministry of Finance and Planning. A Technical Working Group (TWG) was formed as the survey's secretariat that undertook day-to-day activities on the implementation of the survey.

The Surveyed Population

Age-sex Structure The age-sex pyramid of the surveyed population depicts a youthful population, with those aged below 15 years absorbing 42.3 per cent of the population, leading to a dependency ration of 85.3 per cent. The sex ratio was 0.997 for the whole population and 1.06 at birth (age 0-4). The average household size was 4.2 persons (3.3 persons in urban areas and 4.7 persons in rural areas).

Marital status and migration patterns An estimated 42.7 per cent of the population aged over 12 years had never married. Of those ever married, 51.3 per cent were in current marriage, 3.5 per cent widowed and 3.6 per cent separated or divorced. There was evidence of early marriages where 5.0 percent of the population aged 13-17 reported they were currently married.

Education and Literacy There were 3.6 million children in primary and 0.9 million children in secondary schools, giving gross enrolment ratios of 89.1 percent and 30.7 percent respectively. Student sex ratio, or ratio of males for females, in primary schools was 1.08, while that for secondary schools was 1.20. About 16.4 percent of the Kenyan population aged over 5 years and over reported to have had no formal education at all. Those with primary education constituted 59.0 per cent of the referenced population while 19.7 percent had attained secondary education. Only 1.1 per cent had attained university education.

Housing and amenities About 31.0 per cent of the households had a permanent dwelling unit. Majority of the rural households reported that they owned both the dwelling units they lived in and the land on which it was built, while almost all the urban residents lived in rented dwelling units. About 12.5 per cent of households, mainly in the rural areas, reported they had no toilet facilities. The commonest type of waste disposal was pit latrine, but flush toilet was prevalent in urban areas. Most of the rural households travelled long distances to fetch water, while 80.4 percent of the urban households had water within 50 meters. Firewood was the commonest type of cooking fuel in rural areas, while paraffin (53.3 per cent) and charcoal (22.6 per cent) were the main types of cooking fuels in urban areas. About 77.2 per cent of responding households were using paraffin to light their houses, with 90.5 per cent in rural areas. Urban areas mainly relied on paraffin (50.7 per cent) and electricity (41.8 per cent) as the chief sources of lighting.

Migration Patterns The overall out-migration rate was 13.2 percent, with rural areas losing a large portion of its population to urban areas. Among the eight provinces, Nairobi, Western and Central experienced significant out-migration of over 15.0 percent. Overall, urban areas were net gainers in population flows within the country.

Household expenditure Overall mean monthly expenditure per household amounted to Kshs 6,343. Monthly mean expenditures for rural households were estimated at Kshs 4,101, while the urban equivalent was Kshs 10,826. There were expenditure differentials between male- and female-headed households, where mean monthly expenditures for female-headed households in rural areas was Kshs 2,986, quite below he monthly expenditure of Kshs 4,620 for male-headed households. Similarly, mean expenditure for male-headed households in urban areas was almost twice that of female-headed households.

The Labour Force Participation

Economic activity The results show that there were 15.9 million persons aged 15-64 (the working population) of which 77.4 per cent reported to be economically active. Most of the active population was youth between 24-34 years of age. About 14.6 percent of the economically active were unemployed. Some 3.6 million persons reported to be economically inactive, representing 22.6 per cent of the population aged 15-64 years. Majority of the inactive population was full time students (47.3 per cent). Only 2.0 per cent of the inactive population reported they were out of the labour force because they were retired.

Participation Rates The overall labour force participation rate for the population aged 15 - 64 years stood at 73.6 per cent. Urban areas had higher labour force participation rate of 86.4 per cent compared to rural areas with a rate of 73.8 per cent. Males had a slightly higher participation rate of 74.7 per cent compared to that of females at 72.6 per cent. The results show that participation rates increase along the age spectrum to about 95.2 for the age group 40 - 44 before levelling to 80. 1 per cent for the age cohort 60 - 64. Also, participation rates tend to rise with the level of formal education, rising from 83.7 per cent for those with no education to over 98.8 per cent for those who have completed post-graduate education.

Employment The number of employed persons aged 15-64 years stood at 10.5 million persons, giving employment rate of 85.4 per cent. The overall employment sex ratio was 1.08, but females dominated rural based small-scale farming and pastoralist activities, with a sex ratio of 0.67. Rural area absorbed 70.1 per cent of the employed persons. The working population was largely made up of unpaid family workers (39.6 per cent), mostly working in the rural areas and paid employees, largely concentrated in urban areas (33.4 per cent). Self-employed persons constituted 23.8 per cent of the employed. Of the three sectors of the economy, small-scale farming and pastoralist activities engaged 42.1 per cent of workers. Informal sector and formal or modern sector absorbed 31.6 per cent and 26.3 per cent of the total workforce.

Occupations and industry Most of the employed persons reported to be skilled agricultural and fishery workers (37.3 per cent), largely self-employed based in rural areas. Professionals were mainly in paid employment, and accounted for only 1.2 per cent of the employed persons. The agricultural activities absorbed 63.1 per cent of the employed persons. The other major employers were the service industries with community, social and personal services accounting for 6.1 per cent of the employed. The least popular industries were private households with employed persons, and electricity and water supply. The number of females employed in activities traditionally dominated by males such as construction, mining and quarrying was notably low. However, females were concentrated in agricultural activities, trades, and educational services.

Hours of work Most workers reported 40 working hours per week with a significant proportion of the urban population working above the average hours. Urban workers generally reported to have worked for longer hours than workers in rural areas. Gender analysis shows that females worked for fewer hours than males, particularly in the rural areas. However, females who worked in urban areas (in private households as housemaids) were working quite above 40 hours in a week.

Wage levels Average earnings amounted to KShs 7,766 per month, with the main source of employee's remuneration being basic salary, which formed 81.3 per cent of the overall earnings per person. Earnings in urban were almost double the average earnings in rural areas. There were significant disparities in earnings by gender as females were earnings wages quite below their male counter parts in both rural and urban areas.

Unemployment There were 1.8 million unemployed persons aged 15-64 years, giving an overall unemployment rate of 14.6 per cent. The urban unemployment rate had risen from -- per cent in 1989 to 25.1 per cent by 1999. Like wise, unemployment in the rural areas was high at 9.4 per cent, but less acute then in urban areas. Most of the unemployed were youth and females. Most of the unemployed persons (94.2 per cent) were looking for paid employment during the one-week reference period. It is also worth noting the shift from subsistence farming, as more jobs searchers were ready to start self-employment (mainly found in mostly in the expanding informal sector) than farming activities

Abstract

The Bungoma County Multiple Indicator Cluster Survey (MICS5) was conducted in collaboration with the Population Studies and Research Institute (PSRI) of the University of Nairobi, the Kenya National Bureau of Statistics (KNBS) and the United Nations Children's Fund (UNICEF).The Kenya National Bureau of Statistics implemented (MICS5) in 2013-2014 in the three counties of Bungoma, Kakamega and Turkana as part of Global MICS round five.

The global MICS program was developed by UNICEF in the 1990s as an international household survey program to support countries in the collection of internationally comparable data on a wide range of indicators on the situation of children and women. MICS surveys measure key indicators that allow countries to generate data for use in policies and programs and to monitor progress towards the Millennium Development Goals (MDGs) and other internationally agreed upon commitments. Technical and financial support were provided by the United Nations Children's Fund.

The results of this survey provided requisite baseline information that can be used to facilitate evidence-based planning, budgeting and programming by policymakers and stakeholders at the county levels. The survey will go a long way in encouraging increased demand for use of statistics by policy makers at devolved levels and will ensure that resources at both county and national levels are used most effectively through well-planned projects/programs that will benefit especially the women and children of the three counties. The MICS5 results were critical in gauging milestones achieved in the field of education, nutrition, child development, health for women and children in the three counties and in evaluating the various health based policies that the government has formulated over the years towards achieving the national welfare objectives.

The 2013-14 MICS5 data was critical in informing the future planning for the three counties, especially in view of the new constitutional dispensation and Vision 2030. It was anticipated that MICS5 would supplement the data collected during the 2014 Kenya Demographic and Health Survey (KDHS). In addition the information collected would inform strategic communication for social and behavior change interventions by government and partners including UNICEF. Furthermore the data contributed to the improvement of data and monitoring systems in the three counties. The primary objectives of the Bungoma County survey are: 1. To provide up-to-date information for assessing the situation of children and women in Bungoma County. 2. To generate data for the critical assessment of the progress made in various areas, and to put additional efforts in those areas that require more attention. 3. To furnish data needed for monitoring progress toward goals established in the Millennium Declaration, and other internationally agreed upon goals, as a basis for future action. 4. To collect disaggregated data for the identification of disparities, to allow for evidence based policy-making aimed at social inclusion of the most vulnerable. 5. To contribute to the generation of baseline data for the post-2015 agenda. 6. To validate data from other sources and the results of focused interventions. 7. To contribute to the improvement of data and monitoring systems in Kenya and to strengthen technical expertise in the design, implementation, and analysis of such systems.

Geographic coverage

National

Analysis unit

• Individuals
• Households

Universe

The survey covered all de jure household members (usual residents), all women aged between 15-49 years and 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 Bungoma County MICS was to produce statistically reliable estimates of indicators, at county level. The urban and rural areas in Bungoma County were the sampling strata. A multi-stage, stratified cluster sampling approach was used for the selection of the survey sample. MICS5 utilized the recently created fifth National Sample Survey and Evaluation Program (NASSEP V) frame which is a household based master sampling frame developed and maintained by KNBS. The frame was implemented using a multi-tiered structure, in which a set of 4 sub-samples (C1, C2, C3, C4) were developed. It is based on the list of enumeration areas (EAs) from the 2009 Kenya Population and Housing Census. The frame is stratified according to County and further into rural and urban. Each of the sub-samples is representative at county level and at national (i.e. Urban/rural) level and contains 1,340 clusters.

The Primary Sampling Units (PSUs) for the survey were clusters drawn from the NASSEP V sampling frame, so the first component of the probabilities and weights are based on that master sample. Within each stratum the PSUs for the MICS were selected independently from one of the subsamples of the master sample using Equal Probability Selection Method (EPSEM). A total of 50 clusters were selected from the master sample in this way.

Out of the 50 sample clusters selected for Bungoma County, it was established that 30 had been listed more than six months prior to the start of the survey. These listing for these clusters was updated prior to selection of households. For this purpose, listing teams visited each cluster, and listed all occupied households. For the remaining 20 sample clusters a more recent listing was available, so it was used for selecting the sample households.

Mode of data collection

Face-to-face [f2f]

Research instrument

A set of three questionnaires was used in the survey: 1. A household questionnaire which was administered to the household head or any other responsible member of the household. 2. A questionnaire for individual women administered in each household to all women age 15-49 years. 3. An under-5 questionnaire administered to mothers (or caretakers) for all children under-5 years living in the household.

Cleaning operations

Data were entered into the computers using the Census and Surveys Processing System (CSPro) software package, Version 5.0. Data entry was done by a trained team of 14 data entry operators, one archivist/system administrator and one data entry supervisor. For quality assurance purposes, all questionnaires were double-entered and internal consistency checks performed.

Procedures and standard programs developed under the global MICS program and adapted to the Bungoma County MICS questionnaire were used throughout. Data processing began simultaneously with data collection in November 2013 and was completed in February 2014. Data were analyzed using the Statistical Package for Social Sciences (SPSS) software, Version 21. Model syntax and tabulation plans developed by UNICEF were customized and used for this purpose.

Response rate

Information was collected from a total of 1,246 households representing 95 percent response rate. The composition of these households was 5,983 household members comprising 2,797 males and 3,186 females. The mean household size was 4.8 persons. About 48 percent of the sampled households' population is below 15 years, 48 percent are between age 15-64 years and four percent are age 65 years and above.

Due to data quality issues, data relating to mortality and anthropometric measures were not analyzed and reported. Anthropometric data suffered digit preference for both weight and height, while for mortality, deaths especially among children under-five years were under reported. KDHS 2014 had similar shortcomings.

Sampling error estimates

The sample of respondents selected in the Bungoma County MICS is only one of the samples that could have been selected from the same population, using the same design and size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between the estimates from all possible samples. The extent of variability is not known exactly, but can be estimated statistically from the survey data. The following sampling error measures are presented in this appendix for each of the selected indicators: - Standard error (se): Standard error is the square root of the variance of the estimate. For survey indicators those are means, proportions or ratios, the Taylor series linearization method is used for the estimation of standard errors. For more complex statistics, such as fertility and mortality rates, the Jackknife repeated replication method is used for standard error estimation. - Coefficient of variation (se/r) is the ratio of the standard error to the value (r) of the indicator, and is a measure of the relative sampling error. - 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 based on the same sample size. The square root of the design effect (deft) is used to show the efficiency of the sample design in relation to the precision. A deft value of 1.0 indicates that the sample design of the survey is as efficient as a simple random sample for a particular indicator, while a deft value above 1.0 indicates an 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, with a specified level of confidence. For any given statistic calculated from the survey, the value

Abstract

The Kakamega County Multiple Indicator Cluster Survey (MICS) was carried out in collaboration with the Population Studies and Research Institute (PSRI) of the University of Nairobi, the Kenya National Bureau of Statistics (KNBS) and the United Nations Children's Fund (UNICEF) as part of the global MICS program. Technical and financial support were provided by the United Nations Children's Fund.

The global MICS program was developed by UNICEF in the 1990s as an international household survey program to support countries in the collection of internationally comparable data on a wide range of indicators on the situation of children and women. MICS surveys measure key indicators that allow countries to generate data for use in policies and program, and to monitor progress towards the Millennium Development Goals (MDGs) and other internationally agreed upon commitments.

The results of this survey provided requisite baseline information that can be used to facilitate evidence-based planning, budgeting and programming by policymakers and stakeholders at the county levels. The survey will go a long way in encouraging increased demand for use of statistics by policy makers at devolved levels and will ensure that resources at both county and national levels are used most effectively through well-planned projects/programs that will benefit especially the women and children of the three counties.The MICS5 results were critical in gauging milestones achieved in the field of education, nutrition, child development, health for women and children in the three counties and in evaluating the various health based policies that the government has formulated over the years towards achieving the national welfare objectives.

The 2013-14 MICS5 data was critical in informing the future planning for the three counties, especially in view of the new constitutional dispensation and Vision 2030. It was anticipated that MICS5 would supplement the data collected during the 2014 Kenya Demographic and Health Survey (KDHS). In addition the information collected would inform strategic communication for social and behavior change interventions by government and partners including UNICEF. Furthermore the data contributed to the improvement of data and monitoring systems in the three counties. The primary objectives of the Kakamega County survey are: 1. To provide up-to-date information for assessing the situation of children and women in Kakamega County. 2. To generate data for the critical assessment of the progress made in various areas, and to put additional efforts in those areas that require more attention. 3. To furnish data needed for monitoring progress toward goals established in the Millennium Declaration, and other internationally agreed upon goals, as a basis for future action. 4. To collect disaggregated data for the identification of disparities, to allow for evidence based policy-making aimed at social inclusion of the most vulnerable. 5. To contribute to the generation of baseline data for the post-2015 agenda. 6. To validate data from other sources and the results of focused interventions. 7. To contribute to the improvement of data and monitoring systems in Kenya and to strengthen technical expertise in the design, implementation, and analysis of such systems.

Geographic coverage

National

Analysis unit

• Individuals
• Households

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the Kakamega County MICS, 2013-14 was designed to provide estimates for a large number of indicators on the situation of children and women at the county level. The urban and rural areas within the county were the main sampling strata. The sample was selected in two stages: cluster and household. The survey utilized the fifth National Sample Survey and Evaluation Program (NASSEP V) household-based master sampling frame which is created and maintained by the Kenya National Bureau of Statistics (KNBS). The primary sampling unit for the frame is a cluster, which constitutes one or more EAs, with an average of 100 households.

For the NASSEP V master sample the EAs were selected within each stratum using systematic sampling with probabilities proportion to size (PPS). For the MICS, within each stratum a specified number of census enumeration areas was selected from the master sample using an equal probability selection method (EPSEM). After a household listing was carried out in the selected clusters, a systematic sample of 30 households was drawn in each sampled cluster. In total, 50 clusters were selected for the survey in Kakamega County. The sample was stratified by urban and rural areas, and was not self-weighting. All selected clusters were visited during fieldwork. For reporting county level results, sample weights are used. A more detailed description of the sample design is provided in Appendix C.

Mode of data collection

Face-to-face [f2f]

Research instrument

A set of three questionnaires was used in the survey: 1. A household questionnaire which was administered to the household head or any other responsible member of the household. 2. A questionnaire for individual women administered in each household to all women age 15-49 years. 3. An under-5 questionnaire administered to mothers (or caretakers) for all children under-5 years living in the household.

Cleaning operations

Data were entered into the computers using the Census and Surveys Processing System (CSPro) software package, Version 5.0. Data entry was done by a trained team of 14 data entry operators, one archivist/system administrator and one data entry supervisor. For quality assurance purposes, all questionnaires were double-entered and internal consistency checks performed.

Procedures and standard programs developed under the global MICS program and adapted to the Kakamega County MICS questionnaire were used throughout. Data processing began simultaneously with data collection in November 2013 and was completed in February 2014. Data were analyzed using the Statistical Package for Social Sciences (SPSS) software, Version 21. Model syntax and tabulation plans developed by UNICEF were customized and used for this purpose.

Response rate

The Kenya MICS 2013 was based on a representative sample of 1,221 households representing a 92 percent response rate. The composition of these households was 5,666 household members comprising 2,752 males and 2,914 females. The mean household size was 4.6 persons. About 46 percent of the sampled households' population is below 15 years, 50 percent are age 15-64 years, and four percent are age 65 years and above.

Due to data quality issues, data relating to mortality and anthropometric measures were not analyzed and reported. Anthropometric data suffered from digit preference for both weight and height, while for mortality, deaths especially among under-5 years old were under reported. KDHS 2014 had similar shortcomings.

Sampling error estimates

The sample of respondents selected in the Kakamega Multiple Indicator Cluster Survey is only one of the samples that could have been selected from the same population, using the same design and size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between the estimates from all possible samples. The extent of variability is not known exactly, but can be estimated statistically from the survey data. The following sampling error measures are presented in this appendix for each of the selected indicators: - Standard error (se): Standard error is the square root of the variance of the estimate. For survey indicators that are means, proportions or ratios, the Taylor series linearization method is used for the estimation of standard errors. For more complex statistics, such as fertility and mortality rates, the Jackknife repeated replication method is used for standard error estimation. - Coefficient of variation (se/r) is the ratio of the standard error to the value (r) of the indicator, and is a measure of the relative sampling error. - 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 based on the same sample size. The square root of the design effect (deft) is used to show the efficiency of the sample design in relation to the precision. A deft value of 1.0 indicates that the sample design of the survey is as efficient as a simple random sample for a particular indicator, while a deft value above 1.0 indicates an 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, with a specified level of confidence. For any given statistic calculated from the survey, the value of that statistic will fall within a range of plus or minus two times the standard error (r + 2.se or r - 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 MICS data, programs developed in CSPro Version 5.0, SPSS Version 21 Complex Samples module and CMRJack116 have been used. The results are shown in the tables that follow. In addition to the sampling error measures described above,

Kenya KE: Contraceptive Prevalence: Any Methods: % of Women Aged 15-49 data was reported at 61.600 % in 2016. This records a decrease from the previous number of 66.300 % for 2015. Kenya KE: Contraceptive Prevalence: Any Methods: % of Women Aged 15-49 data is updated yearly, averaging 39.150 % from Dec 1978 (Median) to 2016, with 10 observations. The data reached an all-time high of 66.300 % in 2015 and a record low of 7.000 % in 1978. Kenya KE: Contraceptive Prevalence: Any Methods: % of Women Aged 15-49 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank: Health Statistics. Contraceptive prevalence rate is the percentage of women who are practicing, or whose sexual partners are practicing, any form of contraception. It is usually measured for women ages 15-49 who are married or in union.; ; UNICEF's State of the World's Children and Childinfo, United Nations Population Division's World Contraceptive Use, household surveys including Demographic and Health Surveys and Multiple Indicator Cluster Surveys.; Weighted average; Contraceptive prevalence amongst women of reproductive age is an indicator of women's empowerment and is related to maternal health, HIV/AIDS, and gender equality.

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.

In 2025, *** percent of Kenya’s population live below **** U.S. dollars per day. This meant that over 8.9 million Kenyans were in extreme poverty, most of whom were in rural areas. Over *** million Kenyans in rural communities lived on less than **** U.S. dollars daily, an amount *** times higher than that recorded in urban regions. Nevertheless, the poverty incidence has declined compared to 2020. That year, businesses closed, unemployment increased, and food prices soared due to the coronavirus (COVID-19) pandemic. Consequently, the country witnessed higher levels of impoverishment, although improvements were already visible in 2021. Overall, the poverty rate in Kenya is expected to decline to ** percent by 2025. Poverty triggers food insecurity Reducing poverty in Kenya puts the country on the way to enhancing food security. As of November 2021, *** million Kenyans lacked sufficient food for consumption. That corresponded to **** percent of the country's population. Also, in 2021, over one-quarter of Kenyan children under five years suffered from chronic malnutrition, a growth failure resulting from a lack of adequate nutrients over a long period. Another *** percent of the children were affected by acute malnutrition, which concerns a rapid deterioration in the nutritional status over a short period. A country where prosperity and poverty walk side by side The poverty incidence in Kenya contrasts with the country's economic development. In 2021, Kenya ranked among the ten highest GDPs in Africa, at almost *** billion U.S. dollars. Moreover, its gross national income per capita has increased to ***** U.S. dollars over the last 10 years, a growth of above**** percent. Generally, while poverty decreased in the country during the same period, Kenya still seems to be far from reaching the United Nation's Sustainable Development Goals (SDGs) to eliminate extreme poverty by 2030.

Abstract

The 2015/16 Kenya Integrated Household Budget Survey (KIHBS) was conducted over a 12-month period to obtain up-to-date data on a range of socioeconomic indicators used to monitor the implementation of development initiatives. The Survey collected data on household characteristics, housing conditions, education, general health characteristics, nutrition, household income and credit, household transfers, information communication technology, domestic tourism, shocks to household welfare and access to justice. The findings are presented at national, county, rural and urban domains.

Household Characteristics The findings of the 2015/16 KIHBS basic characteristics of the population show that the sex ratio is 97.5. About 70 per cent of households were headed by males and the reported average household size was 4 members. The age dependency ratio declined to 81.6 per cent in 2015/16 KIHBS as compared to 84.0 per cent recorded in 2005/06 KIHBS. Majority (54.4%) of the population aged 18 years and above are in monogamous unions. At the national level, 8.4 per cent of children were orphans.

Housing Conditions and amenities Information regarding housing conditions and ownership, access to water, energy, sanitation and waste disposal was collected in the 2015/16 KIHBS. Bungalow was the most common dwelling type of housing occupied by 55.4 per cent of the households. About 60 per cent of households reported that they owned the dwellings that they resided in. The findings show that 72.6 per cent of households use improved drinking water sources. The statistics show that six out ten households had access to improved human waste disposal methods. Overall, 41.4 per cent of households were connected to electricity from the main grid.

Education Findings on education are presented for; pre-primary, primary, secondary, middle level college and university levels; and informal education, Madrassa/Duksi. Nationally, 89.4 per cent of the population aged three years and above had ever attended school. The overall Gross Attendance (GAR) for pre-primary, primary and secondary levels was 94.4 per cent, 107.2 per cent and 66.2 per cent, respectively. The population aged 3 years and above that did not have any educational qualification was 49.7 per cent. Most of the population aged 3 years and above that had not attended school cited not being allowed to attend by parent(s) as the reason for non-attendance. The proportion of the population aged 15-24 years that was literate, based on respondents' self -assessment, was 88.3 per cent.

General Health Characteristics General health characteristics discussed in the report comprise: morbidity by sex, health seeking behaviour, utilization of health care services and facilities, disability and engagement in economic activities and health insurance coverage. Information on child survival such as place of delivery, assistance during delivery, immunization and incidences of diarrhoea is also presented. The results show that two out of ten individuals reported a sickness or injury over the four weeks preceding the survey. Majority of the individuals (55.5 %) with a sickness or injury visited a health worker at a health facility for diagnosis. Disabilities were reported by 2.8 per cent of the population. Slightly more than a third of persons with disabilities reported having difficulty in engaging in economic activities. moderately stunted. A higher proportion (32.4%) of children in the rural areas were moderately stunted compared to those in urban areas (24.5%). Overall, 13.0 per cent of children were moderately wasted while 6.7 per cent were moderately underweight. The statistics further indicate that 98.8 per cent of children aged 0-59 months were ever breast fed. The mean length of breastfeeding nationally stood at 16.8 months. Porridge was the most common type of first supplement given to majority (35.9%) of children aged 0-23 months. The survey findings show that eight out of ten children participated in community-based nutritional programmes.

Household Income and Credit Household income is the aggregate earnings of all household members. It includes all forms of income arising from employment, household enterprises, agricultural produce, rent, pension and financial investment. The discussion in this report focuses on income from rent, pension, financial investment and other related incomes. Information is also provided on access and sources of credit. At national level, 7.2 per cent of households reported having received income from rent, pension, financial investment and other related incomes within the 12 months preceding the survey. A third of the households sought credit and over 90 per cent successfully acquired credit.

Household Transfers Transfers constitute income, in cash or in kind, that the household receives without working for it and it augments household income by improving its welfare. Three out of ten households reported having received cash transfers within the 12 months preceding the survey period. The average amount received per household from cash transfers was KSh. 27,097. Majority of households received cash transfers through a family member. Money transfer agents were the preferred mode of transmitting money for most beneficiaries of transfers received from outside Kenya. Over half of the households gave out transfers in kind.

Information and Communication Technology The 2015/16 KIHBS collected information on ICT equipment use and ownership. Findings show that three in every four individuals aged 18 years and above owned a mobile phone with an average number of 1.3 SIM cards per person. The most commonly used ICT equipment is the radio and mobile phone, reported by 79.3 per cent and 68.5 per cent of individuals aged 3 years and above, respectively. The highest proportion (50.3%) of those that did not own a mobile phone cited its high cost as the reason. Urban areas had the highest proportion of population with ownership of a mobile phone. Nairobi City County had the highest proportion of population with a mobile phone while Turkana County had the lowest. The population aged 3 years and above that reported using internet over the last three months preceding the survey was 16.6 per cent. Three in every ten households had internet connectivity and use of internet in mobility was reported as the most common place of use of internet. The internet was used mainly for social networking. No need to use the internet was the most predominant reason for not using the internet reported by 30.1 per cent of those who did not use it.

Domestic Tourism Domestic tourism comprises activities of residents travelling to and staying at least over a night in places outside their usual environment within the country, for not more than 12 months, for leisure, business or other purposes. At national level, 13.4 per cent of individuals reported that they travelled within Kenya in the 3 months preceding the survey. Visiting friends and relatives was reported by the highest proportion (71.1%) of individuals taking trips. Majority of those who took a trip (66.4%) reported that they sponsored themselves. Transport costs accounted for the largest share (38.4%) of expenditure on domestic tourism. Majority of those who did not take a trip reported high cost as a reason.

Shocks to Household Welfare A shock is an event that may trigger a decline in the well-being of an individual, a community, a region, or even a nation. The report presents information on shocks which occurred during the five-year period preceding the survey and had a negative impact on households' economic status or welfare. Three in every five households reported having experienced at least one shock within the five years preceding the survey. A large rise infood prices was reported by the highest proportion (30.1 per cent) of households as a first severe shock. Most households reported that they spent their savings to cope with the shock(s).

Justice The survey sought information from household members on their experiences regarding grievances/disputes, resolution mechanisms, status of grievance/dispute resolution and costs incurred. Majority of households (26.2%) experienced grievances related to succession and inheritance. Approximately seven out of ten households that experienced grievances reported that they were resolved by parties from whom they sought interventions. Lawyers on average received the highest amount of money (KSh 59,849) paid to a primary organization for grievance resolution through a formal channel. Courts accounted for the highest informal costs averaging KSh 6,260 in grievance resolution.

Geographic coverage

The survey covers all the Counties in Kenya based on the following levels National, Urban, Rural and County

Analysis unit

Households Indviduals within Households and Community

Kind of data

Sample survey data [ssd]

Sampling procedure

Design and Sample Selection The second Kenya Integrated Household Budget Survey 2015/16 will be the eighth household budget survey to be conducted in Kenya following those conducted in 1981/82, 1983/84, 1992, 1994, 1997 and 2005/06. The KIHBS 2015/16 is a multi-indicator survey in nature with the main objective of updating the household consumption patterns in all the Counties.

KIHBS 2015/16 is designed to provide estimates for various indicators at the County-level. A total of 50 study domains are envisaged. These are; all the forty-seven (47) counties (Each as a separate domain), urban and rural (each as a separate domain at National level), and lastly the National-level aggregate.

Sampling frame The sampling frame used for KIHBS 2015/16 is the fifth National Sample Survey and Evaluation Program (NASSEP V) master frame developed from the Population and Housing Census (KPHC) conducted in

Abstract

The UNHCR Standardized Expanded Nutrition Surveys (SENS) provide regular nutrition data that plays a key role in delivering effective and timely interventions to ensure good nutritional outcomes among populations affected by forced displacement. UNHCR conducted an annual SENS nutrition surveys in Kakuma refugee camp and Kalobeyei Refugee Settlement. At the time of the survey, the camp was hosting 186,515 refugees originating from 20 countries, comprised of 53.3% (99,320) males and 46.7% (87,195) females. These represented 148,295 from Kakuma and 38,220 from Kalobeyei and originating from 20 nationalities. The number of children under 5 years of age is currently estimated to be 20,468 from Kakuma and 7,576 from Kalobeyei or 15% of the total population. Women of reproductive age were 32,373 from Kakuma and 7,643 from Kalobeyei. According to the United Nations High Commission for Refugees (UNHCR) HIS database (Nov 2018), the main countries of origin are currently South Sudan, 57.8 %, and Somalia, 33.6 %, with the remaining percent originating from various countries in the region including Democratic republic of Congo (6.5%), Ethiopia (5.6%), and Burundi (5.4%) among others. Data collection started on the 26th November and ended on December 8th, 2018. The overall aim of this survey was to assess the general nutrition and health status of refugee population and formulate workable recommendations for appropriate nutritional and public health interventions.

Geographic coverage

Kakuma Refugee Camp and Kalobeyei Refugee Settlement in Turkana County, Kenya

Analysis unit

Households Children 0-23 months Children 6-59 months Women 15-49 years

Universe

Children 0-59 months Women 15-49 years Refugee households

Kind of data

Sample survey data [ssd]

Sampling procedure

A two-stage cluster survey with probability proportion to size sampling was employed in this survey. Standardized Monitoring and Assessment of Relief and Transitions (SMART) methodology to collect and analyse data on child anthropometry and UNHCR's Standardised Expanded Nutrition Survey (SENS) Guidelines for Refugee Populations was used to guide data collection for other indicators. The same households sampled by SMART were used in all indicators. Anaemia sample was drawn from the SMART sample size, as recommended by the UNHCR Standardised Expanded Nutrition Survey (SENS) Guidelines.

For each of the indicators used, households and individuals were sampled as follows: Household-level indicators: - WASH: every household - Food Security: every other household - Mosquito net: every other household

Individual-level indicators: - Children 0-59 months: all eligible children in all households were assessed (based on the above calculations) - Women 15-49: all eligible women in every other household were assessed.

The sample size for children, 6-59 months, was calculated using ENA for SMART software (9th, July 2015) according to UNHCR SENS guidelines (version 2 (2013). The calculation was based on the expected prevalence of global acute malnutrition (GAM) in children, 6-59 months. A precision of 3.5; a design effect (DEFF) of 1.5 for Kakuma and 1 for Kalobeyei; an average household size of 6.6 in Kakuma and 5.2 in Kalobeyei; and percentage of children under the age of five was estimated at 19.5% in Kakuma and 14.9% in Kalobeyei, using the UNHCR ProGres data, November 2019.

A two-stage cluster survey was conducted using the Standardized Monitoring and Assessment of Relief and Transitions (SMART) methodology to collect and analyse data on child anthropometry. Information on other indicators was collected and analysed using UNHCR's Standardised Expanded Nutrition Survey (SENS) Guidelines for Refugee Populations (Version 2 2013) (see www.sens.unhcr.org). ENA for SMART selects the clusters (blocks), once done a team was sent to a block to label the households numerically with indelible pens. Population density varies across the blocks at Kakuma. If a block contained 100 households or less, all households in the block were marked. If a Block contained more than 100 households then the team walked around the block to identify a path that divided the block into approximately two halves. One portion of the block was selected randomly. In the selected segment of the block the team proceeded to number all households from the first to the last. If there was more than one household in a particular compound, this was indicated at the entrance of the compound (e.g., 2019 SENS HH1). The numbering and labelling were done two days prior to commencement of the survey. If there was more than one household in a particular compound, this was indicated at the entrance of the compound (e.g., HH1-HH6). The numbering and labelling were done three days prior to commencement of the survey.

Mode of data collection

Face-to-face [f2f]

Research instrument

1) Children 6-59 months (SENS Modules 1-2): Anthropometric status, oedema, enrolment in selective feeding programmes and blanket feeding programmes (CSB++), immunisation (measles), vitamin A supplementation in last six months, de-worming, morbidity from diarrhea in past two weeks, hemoglobin assessment.

2) Children 0-23 months (SENS Module 3): Questions on infant and young children feeding practices.

3) Women 15-49 years (SENS Module 2): Pregnancy status, coverage of iron-folic acid pills and post-natal vitamin A supplementation, MUAC measurements for pregnant and lactating women (PLW), and hemoglobin assessment for non-pregnant women.

4) Food Security (SENS Module 4): Access and use of the general food ration (GFR), coping mechanisms when the GFR ran out ahead of time and household food dietary diversity using the food consumption score.