Abstract

The 2022 Kenya Demographic and Health Survey (2022 KDHS) was implemented by the Kenya National Bureau of Statistics (KNBS) in collaboration with the Ministry of Health (MoH) and other stakeholders. The survey is the 7th KDHS implemented in the country.

The primary objective of the 2022 KDHS is to provide up-to-date estimates of basic sociodemographic, nutrition and health indicators. Specifically, the 2022 KDHS collected information on: • Fertility levels and contraceptive prevalence • Childhood mortality • Maternal and child health • Early Childhood Development Index (ECDI) • Anthropometric measures for children, women, and men • Children’s nutrition • Woman’s dietary diversity • Knowledge and behaviour related to the transmission of HIV and other sexually transmitted diseases • Noncommunicable diseases and other health issues • Extent and pattern of gender-based violence • Female genital mutilation.

The information collected in the 2022 KDHS will assist policymakers and programme managers in monitoring, evaluating, and designing programmes and strategies for improving the health of Kenya’s population. The 2022 KDHS also provides indicators relevant to monitoring the Sustainable Development Goals (SDGs) for Kenya, as well as indicators relevant for monitoring national and subnational development agendas such as the Kenya Vision 2030, Medium Term Plans (MTPs), and County Integrated Development Plans (CIDPs).

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-54

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, men ageed 15-54, and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

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 uses to conduct household-based sample surveys in Kenya. The frame is based on the 2019 Kenya Population and Housing Census (KPHC) data, in which 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 randomised into four equal subsamples. A survey can utilise a subsample or a combination of subsamples based on the sample size requirements. The 2022 KDHS sample was drawn from subsample one of the K-HMSF. The EAs were developed into clusters through a process of household listing and geo-referencing. The Constitution of Kenya 2010 established a devolved system of government in which Kenya is divided into 47 counties. To design the frame, each of the 47 counties in Kenya was stratified into rural and urban strata, which resulted 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 separately, and, for some indicators, at the county level. The sample size was computed at 42,300 households, with 25 households selected per cluster, which resulted in 1,692 clusters spread across the country, 1,026 clusters in rural areas, and 666 in urban areas. The sample was allocated to the different sampling strata using power allocation to enable comparability of county estimates.

The 2022 KDHS employed a two-stage stratified sample design where in the first stage, 1,692 clusters were selected from the K-HMSF using the Equal Probability Selection Method (EPSEM). The clusters were selected independently in each sampling stratum. Household listing was carried out in all the selected clusters, and the resulting list of households served as a sampling frame for the second stage of selection, where 25 households were selected from each cluster. However, after the household listing procedure, it was found that some clusters had fewer than 25 households; therefore, all households from these clusters were selected into the sample. This resulted in 42,022 households being sampled for the 2022 KDHS. Interviews were conducted only in the pre-selected households and clusters; no replacement of the preselected units was allowed during the survey data collection stages.

For further details on sample design, see APPENDIX A of the survey report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four questionnaires were used in the 2022 KDHS: Household Questionnaire, Woman’s Questionnaire, Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to Kenya. In addition, a self-administered Fieldworker Questionnaire was used to collect information about the survey’s fieldworkers.

Cleaning operations

CAPI was used during data collection. The devices used for CAPI were Android-based computer tablets programmed with a mobile version of CSPro. The CSPro software was developed jointly by the U.S. Census Bureau, Serpro S.A., and The DHS Program. Programming of questionnaires into the Android application was done by ICF, while configuration of tablets was completed by KNBS in collaboration with ICF. All fieldwork personnel were assigned usernames, and devices were password protected to ensure the integrity of the data.

Work was assigned by supervisors and shared via Bluetooth® to interviewers’ tablets. After completion, assigned work was shared with supervisors, who conducted initial data consistency checks and edits and then submitted data to the central servers hosted at KNBS via SyncCloud. 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 identify any errors, which were communicated back to the field teams for correction.

Secondary editing was done by members of the KNBS and ICF 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 survey, 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. Of these, 32,156 women were interviewed, yielding a response rate of 95%. The response rates among women selected for the full and short questionnaires were similar (95%). In the households selected for the men’s 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%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) non-sampling errors, and (2) sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2022 Kenya Demographic and Health Survey (2022 KDHS) to minimise this type of error, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2022 KDHS is only one of many samples that could have been selected from the same population, using the same design and identical 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 all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2022 KDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the 2022 KDHS is a SAS program. This program used the Taylor linearisation method for variance estimation for survey estimates that are means, proportions or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data

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

The primary data collection element of this project related to observational based fieldwork at four universities in Kenya and South Africa undertaken by Louise Bezuidenhout (hereafter ‘LB’) as the award researcher. The award team selected fieldsites through a series of strategic decisions. First, it was decided that all fieldsites would be in Africa, as this continent is largely missing from discussions about Open Science. Second, two countries were selected – one in southern (South Africa) and one in eastern Africa (Kenya) – based on the existence of the robust national research programs in these countries compared to elsewhere on the continent. As country background, Kenya has 22 public universities, many of whom conduct research. It also has a robust history of international research collaboration – a prime example being the long-standing KEMRI-Wellcome Trust partnership. While the government encourages research, financial support for it remains limited and the focus of national universities is primarily on undergraduate teaching. South Africa has 25 public universities, all of whom conduct research. As a country, South Africa has a long history of academic research, one which continues to be actively supported by the government.

Third, in order to speak to conditions of research in Africa, we sought examples of vibrant, “homegrown” research. While some of the researchers at the sites visited collaborated with others in Europe and North America, by design none of the fieldsites were formally affiliated to large internationally funded research consortia or networks. Fourth, within these two countries four departments or research groups in academic institutions were selected for inclusion based on their common discipline (chemistry/biochemistry) and research interests (medicinal chemistry). These decisions were to ensure that the differences in data sharing practices and perceptions between disciplines noted in previous studies would be minimized.

Within Kenya, site 1 (KY1) and Site 2 (KY2) were both chemistry departments of well-established universities. Both departments had over 15 full time faculty members, however faculty to student ratios were high and the teaching loads considerable. KY1 had a large number of MSc and PhD candidates, the majority of whom were full-time and a number of whom had financial assistance. In contrast, KY2 had a very high number of MSc students, the majority of whom were self-funded and part-time (and thus conducted their laboratory work during holidays). In both departments space in laboratories was at a premium and students shared space and equipment. Neither department had any postdoctoral researchers.

Within South Africa, site 1 (SA1) was a research group within the large chemistry department of a well-established and comparatively well-resourced university with a tradition of research. Site 2 (SA2) was the chemistry/biochemistry department of a university that had previously been designated a university for marginalized population groups under the Apartheid system. Both sites were the recipients of numerous national and international grants. SA2 had one postdoctoral researcher at the time, while SA1 had none.

Empirical data was gathered using a combination of qualitative methods including embedded laboratory observations and semi-structured interviews. Each site visit took between three and six weeks, during which time LB participated in departmental activities, interviewed faculty and postgraduate students, and observed social and physical working environments in the departments and laboratories. Data collection was undertaken over a period of five months between November 2014 and March 2015, with 56 semi-structured interviews in total conducted with faculty and graduate students. Follow-on visits to each site were made in late 2015 by LB and Brian Rappert to solicit feedback on our analysis.