This dataset is the result of a phone survey set up to measure the impact of COVID-19 on rural people in Kenya. As most governments have urged the population to stay at home to slow down the transmission of the disease, the impact of COVID-19 can affect women and men in different ways: as an income shock (directly or indirectly); as a health and caring shock; as a shock of mobility (affecting access to water, food, firewood, schooling); and as a risk of increased domestic conflict and violence. To capture these various effects on household welfare, this phone survey was conducted with (around) 600 individuals randomly drawn from an existing list of phone numbers collected from previous household surveys with an equal proportion of women and men. The same individuals were also interviewed during other rounds to generate a longitudinal panel allowing to analyze the impact of COVID-19 through time.

Kenya KE: Population: as % of Total: Female: Aged 15-64 data was reported at 57.169 % in 2017. This records an increase from the previous number of 56.815 % for 2016. Kenya KE: Population: as % of Total: Female: Aged 15-64 data is updated yearly, averaging 48.674 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 57.169 % in 2017 and a record low of 47.013 % in 1976. Kenya KE: Population: as % of Total: Female: Aged 15-64 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. Female population between the ages 15 to 64 as a percentage of the total female population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.; ; World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2017 Revision.; Weighted average; Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services.

Time series data for the statistic A woman can apply for a passport in the same way as a man (1=yes; 0=no) and country Kenya. Indicator Definition:The indicator measures whether there are differences in passport application procedures between men and women.

This research contributes to a growing yet understudied area of elite feminism and elite geography through its summation of black women experiences in Kenya’s Oil and Gas sector. Using African Feminism as a backdrop to the application of Feminist Political Ecology, this research problematises concepts of care, time, difference and equality. This is in an attempt to bring coherence to and ‘Africanise’ the experiences of black women in elite, masculine spaces such as extractive industries. It is based upon empirical study done over 5 months in Kenya’s capital Nairobi where data was collected through interviews and workshops where elite women got to share their experiences of working in the industry. Some men were also shared their experiences working with elite women.The lines of question involved discussions over how they perceived gender in the industry to infrastructure, race issues as well as sexism and equality. From these responses, inferences were made which pointed towards an existential system and infrastructure that was not only foreign/ western but was also designed to limit the inclusion and growth of black women in highly technical elitist positions and leadership. By contextualising black women’s experiences, this research therefore challenges the retrogressive discourses that define, shape and influence the way elite black African women are engaged in extractive processes reconciling existing notions of what a woman and a woman’s body should be or a worker in extractives should look like.
This research presents African feminism as an alternative to/ and moving beyond western feminist ideas to include black African women’s experiences and point of view. This brings about interesting discussions on its intersectionality with feminist ideas such as leaky bodies and glass cliff and how African women’s experiences decent from western feminist ideas and the broader black feminist discussions. I argue that these experiences are intertwined to their environment and black African women’s multi-faceted identity as mothers, leaders, workers and wives makes their lived experiences unique and different from western feminism. However, in this difference, inequalities and exclusionary tendencies thrives and persists (re)creating infrastructures of marginalisation in an environment that is already white, male and patriarchal. It is approved through the University of Sheffield Ethics process- Reference Number 016348. The title of the final PhD thesis has been updated on the consent form to reflect the changes made to the thesis title based on examiners comments.

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

This data supports the study titled "“Men Don’t Feel Comfortable with Successful Female Leaders”: Exploring Participatory Exclusion in Community-based Fisheries Management, South Coast of Kenya " published in Maritime Studies. This study draws on literature on participatory exclusion, intersectionality and lived experiences, to examine gender-inclusiveness in community-based fishery management through a case study on the South Coast of Kenya. For this study, the authors combined qualitative data collection methods, including participant observation, semi-structured interviews (SSI) (n=18), focus group discussions (FGD) (n=6) and relief maps (n= 32), with secondary data on Beach Management Unit (BMU) governance.More specifically, this data set provides the information derived from primary (i.e., participant observation, SSI, FGD) and secondary data that was used to calculate the gender ratios of the main BMU governance bodies (i.e., general assembly, executive committee, and board) for community 1 (C1), community 2 (C2) comprising settlements 1 (S1) et 2 (S2) , and community 3 (C3). Cases where the two-thirds gender principle was not respected (i.e., less than 33% of women represented in a given elective body) are also indicated. In addition, this data set was used to assess women’s and men’s participation levels in community-based fisheries management for each of the studied communities.

The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities.

The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities. The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.

The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population. The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'.

This paper analyses a study done by the authors of this paper on changes on gender role as a factor in gender participation and empowerment in the oil mining industry; a case of Lokichar in Turkana County. The paper centres on three null hypothesis tested. The studies target group was the active labour force aged between 15 to 64 years. Those who retired from work were also targeted. The data was collected from a sample of three hundred (300) respondents selected through systematic random and purposive sampling methods. Focus Group Discussions (FGD) and in-depth interviews were conducted to supplement the questionnaires given to the sampled respondents. Chi-square was used to test the hypotheses. Major findings indicate that there is a relationship between equal hiring and equal opportunity for men and women to work in mining activities; there is relationship between involvement in oil mining activities and change in livelihood and there is no relationship between involvement in oil mining activities and equal opportunity for men and women to work in mining activities. Key recommendation include gender mainstreaming in legal frameworks, policies, Bills and programs. Methods Two levels of analysis were adopted: Univariate and Bivariate. Tabulation and charts were presented to show a comparison between the various categories. At univariate level one variable at a time was analysed to give out characteristic of the variable under study (Babbie, 201:250). At Bivariate level, two variables are analysed (denoted as X, Y) to assess the empirical relationship between them (Singleton et al, 1988:397). Cross tabulation was done and Chi Square test conducted. Chi Square test is common for non- parametric populations.

Kenya KE: Mortality Rate: Under-5: Male: per 1000 Live Births data was reported at 53.200 Ratio in 2016. This records a decrease from the previous number of 55.100 Ratio for 2015. Kenya KE: Mortality Rate: Under-5: Male: per 1000 Live Births data is updated yearly, averaging 66.600 Ratio from Dec 1990 (Median) to 2016, with 5 observations. The data reached an all-time high of 106.400 Ratio in 2000 and a record low of 53.200 Ratio in 2016. Kenya KE: Mortality Rate: Under-5: Male: per 1000 Live Births 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. Under-five mortality rate, male is the probability per 1,000 that a newborn male baby will die before reaching age five, if subject to male 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; Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.

Abstract

The Kenya National Micronutrients Survey (NMS) 2011 was the first NMS to be carried by the Kenya National Bureau of Statistics. The purpose of this survey is to ensure the quality of HIV testing and the interpretation of results, both in the laboratory and in the community. Fort HIV testing, it is extremely important that "the correct results go to the right client". The identity of clients and the labelling of test devices should therefore be preserved properly.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Universe

The survey covered household members (usual residents), womens questinnaire( aged 15-49 years) resident in the household, children( aged 0-6-49months), School age children (aged 5-14 years) resident in the household and Men questionnire (aged 15-54 year).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size estimation The sample size required for each stratum was based on the estimated prevalence for each nutritional indicator, the desired precision for each indicator, an assumed design effect of 2.0, and a non-response of 10% (including refusals) at the household level and 10% at the individual levels for children 6-59 months of age and non-pregnant women. An additional non-response rate of 10% (for a total 30% non-response rate) was assumed for the men and SAC 5-14 years old.

Sampling design In 2010, Kenya ratified a new constitution which established 47 county governments. This change has highlighted the need for national surveys to collect information beyond the provincial level, and move towards collection of county-level estimates. However, obtaining county-level estimates with adequate precision were not considered feasible in KNMS due to limitations in sample size and resources. Therefore KNMS consisted of the three domains as defined earlier. The sampling frame for the 2010 KMNS was based on the National Sample Survey and Evaluation Programme (NASSEP IV) master sampling frame maintained by the Kenya National Bureau of Statistics (KNBS). Administratively, Kenya is divided into 8 provinces. In turn, each province is The Kenya National Micronutrient Survey 2011 subdivided into districts, each district into divisions, each division into locations and each location into sub-locations. In addition to these administrative units, during the last 1999 population census, each sub-location was subdivided into census Enumeration Areas (EAs) i.e. small geographic units with clearly defined boundaries. As defined in the 1999 census, Kenya has eight provinces, 69 districts, and approximately 62,000 EAs. The list of EAs is grouped by administrative units and includes information on the number of households and population. This information was used in 2002 to design a master sample with about 1,800 selected EAs. The cartographic material for each EA in the master sample was updated in the field. The resulting master sampling frame was NASSEP IV which is still currently used by KNBS. The NASSEP IV master frame is a two-stage stratified cluster sample format. The first stage is a selection of Primary Sampling Units (PSUs), which are the EAs using probability proportional to measure of size (PPMOS) method. The second stage involves the selection of households for various surveys. EAs are selected with a basis of one Measure of Size (MOS) defined as the ultimate cluster with an average of 100 households and constitute one (or more) EAs. Although consideration was given to development of a new master frame for KNMS, time and other resource constraints dictated that the sample frame of this survey was NASSEP IV. The KNMS sample was selected using a stratified two-stage cluster design consisting of 296 clusters, 123 in the urban and 173 in the rural areas. From each cluster a total of 10 households were selected using systematic simple random sampling. For the KNMS survey, an urban area was defined as "an area with an increased density of human-created structures in comparison to the areas surrounding it and has a population of 2,000 people and above". Using this definition, urban areas included Cities, Municipalities, Town Councils, Urban Councils and all District Headquarters. A rural area was defined as an isolated large area of an open country in reference to open fields with peoples whose main economic activity was farming. Every attempt was made to conduct interviews in the 10 selected households, and one additional visit was made to ascertain this compliance in cases of absence of household members to minimize potential bias. Non responding households were not replaced.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey covers household members questionnaire (usual residents), women questinnaire ( aged 15-49 years), preschool children questionnarie( aged 6-59 months), school age children questionnaire (aged 5-14 years) and men questionnire (aged 15-54 year). The hosehold member questionnaire includes: Identification, Interviewer Visits, Socio demographic characteristics, Socio-economic characteristics, Food fortification, Wheat flour fortification, Salt fortification, Sugar fortification, Oils/fats fortification, Interviewer's observations. The women questionnarie includes: Identification, Interviewer Visits, Micronutrient Supplementation and Pica Questions, WRA Health questions. The school age children questionnaire includes: Identification, Interviewer Visits, Micronutrient Supplementation and Pica Questions, Child Health questions, Dietary Diversity Score Questions, Infant Feeding Practice Questions children 6-35 months, Interviewer Observations, The preschool children questionnarie includes: Identification, Interviewer Visits, Micronutrient Supplementation and Pica Questions, Child Health questions, Interviewer Observations. The men questionnarie includes: Identification, Interviewer Visits, Health questions, Interviewer Observations.

Cleaning operations

The field questionnaires baring household characteristics, individual population characteristics, and anthropometrics measurements were double entered into a computer database designed using MS-Access application. Regular file back-up was done using flash disks and external hard disk to avoid any loss or tampering. Data comparison was done using Epi-info version 7.0. Data cleaning and validation was performed to achieve clean datasets. The datasets were exported into a Statistical Package format (IBM® SPSS® Statistics version 20.0). The laboratory results were entered in excel format and later exported into a Statistical Package format (IBM® SPSS®Statistics version 20.0). Data merging exercise was systematically conducted using the four datasets i.e. household characteristics, individual population characteristics, anthropometrics measurements, and laboratory results. Each of the five populations namely; Pre-school children (PSC), School aged children (SAC), Pregnant women (PW), Non-pregnant women (NPW), and Men were separately merged. Data merging was conducted as follows: STEP1: The 'laboratory results' file was first merged to the 'anthropometrics' file using 'LABLE NUMBER' as the unique identifier. STEP2: The merged 'laboratory + anthropometrics' file was merged to individual population characteristics file using a merging variable constructed by concatenating 'CLUSTER NUMBER + HOUSEHOLD NUMBER + LINE NUMBER' as the unique identifier. STEP3: The merged 'laboratory + anthropometrics + individual population characteristics' file was merged to the 'household characteristics' file using a merging variable constructed by concatenating 'CLUSTER NUMBER + HOUSEHOLD NUMBER + LINE NUMBER' as the unique identifier. Five master-files were backed-up for safe keeping and a copy was shared with the statisticians for analysis. All the questionnaires and laboratory forms were filed and stored in lockable drawers for confidentiality.

The validated data was exported to SPSS Version 20 for analysis.

Kenya KE: Intentional Homicides: Male: per 100,000 Male data was reported at 7.155 Ratio in 2016. This records an increase from the previous number of 7.027 Ratio for 2015. Kenya KE: Intentional Homicides: Male: per 100,000 Male data is updated yearly, averaging 7.155 Ratio from Dec 2014 (Median) to 2016, with 3 observations. The data reached an all-time high of 7.793 Ratio in 2014 and a record low of 7.027 Ratio in 2015. Kenya KE: Intentional Homicides: Male: per 100,000 Male 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. Intentional homicides, male are estimates of unlawful male homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded.; ; UN Office on Drugs and Crime's International Homicide Statistics database.; ;

Abstract

The 2014 Kenya Demographic and Health Survey (KDHS) was designed to provide information to monitor and evaluate the population and health situations in Kenya and to be a follow-up to the previous KDHS surveys. In addition, it provides information on indicators previously not collected in KDHS surveys, such as fistula and men’s experience of domestic violence. Finally, the 2014 KDHS is the first such survey to provide estimates for selected demographic and health indicators at the county level.

The specific objectives of the 2014 KDHS were to: • Estimate fertility and childhood, maternal, and adult mortality; • Measure changes in fertility and contraceptive prevalence; • Examine basic indicators of maternal and child health; • Collect anthropometric measures for children and women; • Describe patterns of knowledge and behaviour related to transmission of HIV and other sexually transmitted infections; and • Ascertain the extent and pattern of domestic violence and female genital cutting.

Geographic coverage

National coverage

Analysis unit

Households Woman Men Children under 5

Universe

the survey covered all household members or visitor, al Woman 15-49 years, Man 15-54 years, children under 5 years

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.

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 households was selected for the full questionnaires, and the remaining households were selected for the short questionnaires.

It is important to note that the priority data collected in the short questionnaires were collected from all households and from all women since the short questionnaires were subsets of the full questionnaires. Therefore, data collected in both the full and the short questionnaires can produce estimates of indicators at the national, rural/urban, regional, and county levels. Data collected only in the full questionnaires (i.e., in one-half of households) can produce estimates at the national, rural/urban, and regional levels only. Data collected only in the full questionnaires are not recommended for estimation at the county level. A list of topics included in the full and short questionnaires is presented in Appendix E. In this report, county-level data are tabulated for nearly all of the indicators for which they are available; county-level tables are not presented for indicators with insufficient cases for evaluation (less than 50 unweighted cases) within each county. In the case of indicators not collected at the county level, the tables include data at the regional level only.

The Household Questionnaire was used to list all of the usual members of the household and visitors who stayed in the household the night before the survey. One of the main purposes of the Household Questionnaire was to identify women and men who were eligible for the individual interview. Some basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of the household. The Household 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 and roof of the house, ownership of various durable goods, and ownership and use of mosquito nets. In addition, this questionnaire was used to record height and weight measurements of women age 15-49 and children under age 5.

The Woman’s Questionnaires were used to collect information from women age 15-49. The full questionnaire covered the following topics (see Appendix E for a side-by-side comparison of topics included in the full and short questionnaires): • Background characteristics (education, marital status, media exposure, etc.) • Reproductive history • Knowledge and use of family planning methods • Fertility preferences • Antenatal and delivery care • Breastfeeding and infant feeding practices • Vaccinations and childhood illnesses • Marriage and sexual activity • Women’s work and husbands’ background characteristics • Childhood mortality • Awareness and behaviour regarding HIV and other sexually transmitted infections • Adult mortality, including maternal mortality • Domestic violence • Female circumcision • Fistula

The Man’s Questionnaire was administered to men age 15-54 living in every second household in the sample. The Man’s Questionnaire collected information similar to that contained in the Woman’s Questionnaire but was shorter because it did not contain questions on maternal and child health, nutrition, adult and maternal mortality, or experience of female circumcision or fistula.

Both the Woman’s and the Man’s Questionnaires also included a series of questions to obtain information on respondents’ experience of domestic violence. The domestic violence questions were administered in the subsample of households that received the full Household Questionnaire, the full Woman’s Questionnaire, and the Man’s Questionnaire. Additionally, the violence questions were administered to only one eligible individual, a woman or a man, per household. In

Abstract

The Genomic and environmental risk factor for cardiometabolic disease in Africans (AWI-Gen) project is a collaborative study between the University of the Witwatersrand (Wits) and the INDEPTH Network funded under the Human Heredity and Health in Africa (H3Africa) initiative. The H3Africa is a ground-breaking initiative to build institutional and individual capacity to undertake genetic and genomic studies in the African region. This collaboration, involves five INDEPTH sites i.e. 1) Navrongo - Ghana; 2) Nanoro - Burkina Faso; 3&4) Agincourt and Digkale - South Africa; and 5) Nairobi - Kenya) plus the Soweto-based birth-to-twenty cohort. The work in Kenya will be undertaken in the Nairobi Urban Health and Demographic Surveillance Site (NUHDSS) run by APHRC. The AWI-Gen project aims to understand the interplay between genetic, epigenetic and environmental risk factors for obesity and related cardiometabolic diseases (CMD) in sub-Saharan Africa. The project capitalizes on the unique strengths of existing longitudinal cohorts and well-established health and demographic surveillance systems (HDSS) run by the partner institutions. The six study sites represent geographic and social variability of African populations which are also at different stages of the demographic and epidemiological transitions. The study has two components: i) to understand the African population genetic structure and ii) to determine the association between genetic factors and obesity in determining cardiometabolic risk and the effect of environmental factors on this relationship.

In this application, we seek ethical approval for the Kenya study only. The other partners will seek approval from the appropriate ethics review authorities in their countries.

Geographic coverage

The study was conducted in Viwandani and Korogocho informal settlements in Nairobi.

Analysis unit

A survey with community-level questionnaire with the following units of analysis: individuals, households, and communities.

Universe

Component a): Adults (18+years) residents of Korogocho and Viwandani informal settlements in family trios (father, mother and adult offspring). In selecting participants, emphasis will be placed on ethnic groupings that have not been included in previous and ongoing global genome studies (Kang et al., 2010, Patterson et al., 2006). To ensure that the project contributes new knowledge on the genetic diversity of African populations we will select participants whose self-reported ethnicity is either Luo or Kiisi or Luhya since the genomic structure of Kikuyu, Masaai and Kalenjin ethnicities has already been documented as part of completed and ongoing studies.

Component b): Adults (40-60 years) residents of Korogocho and Viwandani informal settlements registered in the NUHDSS.

Sampling procedure

Study participants were drawn from the most recently updated NUHDSS database managed by the African Population and Health Research Center (APHRC). The NUHDSS follows about 74,000 individuals living in approximately 24,000 households in Korogocho and Viwandani.

Objective 1 The strategy for sampling for the population genome structure study is to collect 30 trios (mother, father and one child - all over 18 years of age) from each of the sites, and an additional 10 unrelated individuals to give a total sample of 100. Using the NUHDSS database, a sampling frame of participants who are related i.e. one adult child, father and mother based on information on household structure was drawn. Potential participant trios were then randomly selected and approached to participate. To minimise cases where biological paternity was in doubt, there was selection of the second or third adult child as the index participant in each trio and then both parents. First it was confirmed with the mother whether the other stated parent is the biological father. If either of the stated parents are not the biological parents, the trio was dropped and replaced. Given the possibly high rate of non-participation for various reasons (refusal to participate, non-biological relationship with either parent in the trios) there was randomly selection of as many as 60 trios aiming for a final sample size of 30. In addition to the 30 participating trios and 10 unrelated individuals, there was oversampling of about 10 more trios to compensate for any cases where the child turns out to be unrelated to the father/mother on genetic testing. Phenotype data was not collected on these participants.

Inclusion criteria · Households with (trios) families consisting of mother, father and adult biological child in the NUHDSS area. · Registered and resident in the most up-to-date NUHDSS database. · All members should be over 18 years of age at sampling. · Participants whose self-reported ethnicity is either Luo or Kiisi or Luhya

Exclusion criteria · All visiting non-resident families in the NUHDSS frame. · Household trios that do not have biological relations. · Incapacitated adults who are unable to provide informed consent. · Anyone under the age of 18.

Objectives 2-4 For the genetic association study; stratified sampling (based on sex) was applied to adults aged 40-60 years to ensure we had equal numbers of men and women. The sampling frame was for the current residents in the NUHDSS aged 40-60 years on the day of sampling. The aim was to collect phenotype and genotype data from 2000 participants in each of the study sites - hence we selected 1000 men and 1000 women in the site.

Inclusion criteria · Adults (40-60 years) in Korogocho and Viwandani slums. · Registered and resident in the most up-to-date NUHDSS database.

Exclusion criteria · All visiting non-resident men and women in the NUHDSS frame. · Incapacitated adults who are unable to provide informed consent.

Sample size A sample size of 2000 per site (12000 in total) was based on power calculations and effect sizes. The power calculations show that we have power to detect realistic effect sizes, based on studies in other populations. Figure 2 illustrates the relationship between power and effect size for two different phenotypes, illustrating that the detectable effect size is realistic. Power analysis for a sample size of 12000 individuals based on proposed candidate gene study for BMI (shown on the left) and for DXA (total body fat) (shown on the right). Given a sample size of 12000 in the AWI-Gen study, this graph shows effect size (x) which could be detected at a given power (y) for different minor allele frequencies (ranging from 0.05-045). For example, with a minor allele frequency of 0.25, we will have 80% power to detect an effect size (Beta) of 0.20 per allele change in BMI, and an effect size of 0.25 per allele change in body fat percentage. The Kenya study will thus contribute 2000 individuals (1000 males and 1000 females). In order to have sufficient power for the genetic association studies, data from the Kenyan sample will be pooled with data from the other five sites.

Mode of data collection

Face-to-face [f2f]

Research instrument

AWI_Gen Trait_Ass_Questionnaire: General information, Demographic information, Family composition, Pregnancy, Marital status, Education, Employment, Household attributes, Tobacco use, Alcohol use, General health, Infection history( Malaria, Tb), Cardio metabolic risk factors ( Diabetes, Stroke, Hypertension, Angina, Heart attack, Congestive heart failure, High cholesterol), Thyroid disease, Kidney disease, Physical activity ( Occupation-related physical activity (paid or unpaid work, Travel-related physical activity, Non-work related and leisure time physical activity, Sitting/resting activity), Sleep

Cleaning operations

a) Data management, storage and sharing Individuals were assigned barcodes that was placed on all the samples collected, questionnaires, ultrasound images and sample aliquots. Data was captured on paper and entered in a secure online data capture system (RedCap®). Data transmitted to Wits for pooled analyses did not have any personal identifiers other than the barcodes.

The following key principles guided the data management, storage and sharing. The long term storage of the DNA samples from this project was done in two locations, one in the country of origin (in this case Kenya) and one in the laboratory that was co-ordinating the genetic and epigenetic analyses. Since data collected from the Kenya site was analysed with those from five other sites, there was need for harmonization and standardization in the collection and analysis of samples for phenotyping and DNA extraction. The same applies for genomic and genetic analyses. For these reasons, the samples from this study and all those from the five other sites was shipped to (a yet-to-be determined) central place for DNA extraction and analysis. The choice of the central analysis laboratory depended on capacity to conduct the required analyses, quality standards and certifications, and cost. All sites retained samples in storage that can be used for future analyses and genetic studies.

Second, as part of the data sharing and access policy of the whole H3Africa consortium, all participating sites (not just those in the AWI-Gen project) were required to avail their data and a DNA sample in a publicly accessible repository after an appropriate period of time during which the data generators will exploit the data. These repositories included the H3A Bio-repository, funded through the same H3Africa initiative and the European Genome Phenome Archive (EGA). A draft H3Africa Data Access and Release Policy is provided in Appendix 5. Underpinning the H3Africa data access and sharing policy is the need to maximise the returns

BackgroundDiseases of the posterior segment of the eye, including age-related macular degeneration (AMD), have recently been recognised as the leading or second leading cause of blindness in several African countries. However, prevalence of AMD alone has not been assessed. We hypothesized that AMD is an important cause of visual impairment among elderly people in Nakuru, Kenya, and therefore sought to assess the prevalence and predictors of AMD in a diverse adult Kenyan population. Methods and FindingsIn a population-based cross-sectional survey in the Nakuru District of Kenya, 100 clusters of 50 people 50 y of age or older were selected by probability-proportional-to-size sampling between 26 January 2007 and 11 November 2008. Households within clusters were selected through compact segment sampling. All participants underwent a standardised interview and comprehensive eye examination, including dilated slit lamp examination by an ophthalmologist and digital retinal photography. Images were graded for the presence and severity of AMD lesions following a modified version of the International Classification and Grading System for Age-Related Maculopathy. Comparison was made between slit lamp biomicroscopy (SLB) and photographic grading. Of 4,381 participants, fundus photographs were gradable for 3,304 persons (75.4%), and SLB was completed for 4,312 (98%). Early and late AMD prevalence were 11.2% and 1.2%, respectively, among participants graded on images. Prevalence of AMD by SLB was 6.7% and 0.7% for early and late AMD, respectively. SLB underdiagnosed AMD relative to photographic grading by a factor of 1.7. After controlling for age, women had a higher prevalence of early AMD than men (odds ratio 1.5; 95% CI, 1.2–1.9). Overall prevalence rose significantly with each decade of age. We estimate that, in Kenya, 283,900 to 362,800 people 50 y and older have early AMD and 25,200 to 50,500 have late AMD, based on population estimates in 2007. ConclusionsAMD is an important cause of visual impairment and blindness in Kenya. Greater availability of low vision services and ophthalmologist training in diagnosis and treatment of AMD would be appropriate next steps. Please see later in the article for the Editors' Summary

BackgroundIn most parts of the world, men access health services less frequently than women, and this trend is unrelated to differences in need for services. While male involvement in healthcare as partners or fathers has been extensively studied, less is known about the health-seeking behavior of men as clients themselves. This interventional research study aimed to determine how the introduction of male-friendly clinics impacted male care-seeking behavior and to describe the reasons for accessing services among men in rural Kenya.Methods and findingsWe questioned men to assess utilization and perceptions of existing health clinics, then designed and evaluated a “male clinics” intervention where dedicated male health workers were hired for one year to offer routine, free services exclusively to men within existing healthcare facilities. Results were compared between data from Male Clinics in specific health facilities, the same facilities concurrently, nearby control facilities concurrently, and intervention facilities historically.Costs of services, distance to facilities, and quality of care were the main barriers to healthcare access reported. The number of total visits was significantly higher than control groups (p

Abstract

The Global Adult Tobacco Survey (GATS), a component of Global Tobacco Surveillance System (GTSS), is a global standard for systematically monitoring adult tobacco use and tracking key tobacco control indicators. GATS is designed to produce national and sub-national estimates among adults across countries. The target population includes all non-institutionalized men and women 15 years of age or older who consider the country to be their usual place of residence. All members of the target population will be sampled from the household that is their usual place of residence. GATS is intended to enhance the capacity of countries to design, implement and evaluate tobacco control interventions.

Geographic coverage

National coverage

Analysis unit

Individual

Universe

Non-institutionalized men and women 15 years of age or older

Kind of data

Sample survey data [ssd]

Sampling procedure

The report is not ready to provide the sampling procedure

Sampling deviation

The report is not ready

Mode of data collection

Face-to-face [f2f]

Research instrument

The GATS contains Household Questionnaire and Individual Questionnaire. These questionnaires are administered using an electronic data collection device.

Using the IPAQ to Manage Household Assignments and Input Data. Screening and Respondent Selection: The Household Questionnaire Administering the Individual Questionnaire Transmitting Data Using SD CARDs

Section A. Background Characteristics Section B. Tobacco Smoking Section C. Smokeless Tobacco Section D1. Cessation - Tobacco Smoking Section D2. Cessation - Smokeless Tobacco Section E. Secondhand Smoke Section F. Economics - Manufactured Cigarettes Section G. Media Section H. Knowledge, Attitudes & Perceptions

Cleaning operations

Various checks were done, After exiting the Individual Questionnaire, the iPAQ will automatically code the questionnaire as complete (code 400) and display the Select Case screen. If you were not able to complete the Individual Questionnaire, the iPAQ will prompt you to update the Record of Calls with the appropriate result code. (Note that the program will not allow you to update the Record of Calls for an Individual Questionnaire unless the Household Questionnaire is completed and set as 200.)

Response rate

The report is not ready to provide the table of response rates

Sampling error estimates

Since the report is not ready, no sampling error. The Bureau creates and maintains a National Sampling Survey and Evaluation Programme (NASSEP) household master sampling frame, which provides the framework for designing household surveys to generate different forms of household based data. It has an elaborate infrastructure for data collection, which includes Statistics Offices in all the 47 Counties with trained personne

Data appraisal

Export Data to the SD Card To export data to the SD card, you will need to perform the following steps: 1. Check to be sure that the SD card is set to the unlock position. When the plastic tab is in the up position, the SD card is unlocked, and data can be exported onto the card. 2. Insert the SD card into the card expansion slot at the top of the iPAQ. Make sure the card is inserted fully so that the top of the card is flush with the iPAQ. 3. At the Today screen, tap Start, then tap CMS from the dropdown menu. 4. Confirm that the system clock settings are correct, and enter your password to launch the GATS Case Management System program.

Interview and result code data must be sent back to the Central Office so that project management staff can monitor data collection and protect against data loss.

Exhibit 8-5. Individual Questionnaire Result Codes

Abstract

The 2007 Kenya AIDS Indicator Survey (KAIS) is Kenya's first survey of its type and provides comprehensive information on HIV and other sexually transmitted infections (STIs). These data provide the information needed for advocacy and for planning appropriate interventions for HIV prevention, treatment and care. The 2007 KAIS builds upon previous national-level HIV estimates from the first population-based survey with HIV testing, the 2003 Kenya Demographic and Health Survey (KDHS); this allows us to compare prevalence estimates and important behavioural indicators between 2003 and 2007.

Geographic coverage

The survey covered all the districts in Kenya. The data representativeness are at the following levels: national, urban/rural, provincial, district.

Analysis unit

Person aged 15-64

Universe

All women and men aged 15-64 years in selected households who were either usual residents or visitors present the night before the survey were eligible to participate in the individual interview and blood draw, provided they gave informed consent. For minors aged 15-17 years, parental consent and minor assent were both required for participation. Participants could consent to the interview and blood draw or to the interview alone. The inclusion criteria may have captured non-Kenyans living as usual residents or visitors in a sampled household. Military personnel and the institutionalized population (e.g. imprisoned) are typically not captured in household-based surveys, but may have been included in the 2007 KAIS if at home during the survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

Administratively, Kenya is divided into eight provinces. Each province is divided into districts, each district into divisions, each division into locations, each location into sub-locations, and each sublocation into villages. For the 1999 Population and Household Census, the Kenya National Bureau of Statistics (KNBS) delineated sub-locations into small units called Enumeration Areas (EAs) that constituted a village, a part of a village, or a combination of villages. The primary sampling unit for Kenya's master sampling frame, and for the 2007 KAIS, is a cluster, which is constituted as one or more EAs, with an average of 100 households per cluster. The master sampling frame for the 2007 KAIS was the National Sample Survey and Evaluation Programme IV (NASSEP IV) created and maintained by KNBS. The NASSEP IV frame was developed in 2002 based on the 1999 Census. The frame has 1800 clusters, comprised of 1,260 rural and 540 urban clusters. Of these, 294 (23%) rural and 121 (22%) urban clusters were selected for KAIS.

The 2007 KAIS was conducted among a representative sample of households selected from all eight provinces in the country, covering both rural and urban areas. A household was defined as a person or group of people related or unrelated to each other who live together in the same dwelling unit or compound (a group of dwelling units), share similar cooking arrangements, and identify the same person as the head of household. The household questionnaire was administered to consenting heads of sampled, occupied households. All women and men aged 15-64 years in selected households who were either usual residents or visitors present the night before the survey were eligible to participate in the individual interview and blood draw, provided they gave informed consent. For minors aged 15-17 years, parental consent and minor assent were both required for participation. Participants could consent to the interview and blood draw or to the interview alone. The inclusion criteria may have captured non-Kenyans living as usual residents or visitors in a sampled household. Military personnel and the institutionalized population (e.g. imprisoned) are typically not captured in household-based surveys, but may have been included in the 2007 KAIS if at home during the survey.

The overall design for the 2007 KAIS was a stratified, two-stage cluster sample for comparability to the 2003 KDHS. The first stage involved selecting 415 clusters from NASSEP IV and the second stage involved the selection of households per cluster with equal probability of selection in the rural-urban strata within each district. The target of the 2007 KAIS sample was to obtain approximately 9,000 completed household interviews. Based on the level of household nonresponse reported in the 2003 KDHS (13.2% of selected households), 10,375 households in 415 clusters were selected for potential participation in the 2007 KAIS. Table 1.4 shows the provincial distribution of households and clusters originally sampled for the 2007 KAIS.

Sampling deviation

Of the original 415 clusters, 402 were accessed and surveyed. Thirteen clusters were inaccessible due to impassable roads or tenuous security situations. All reported estimates and design weights for households, individual interviews, and blood draws are based on data from the 402 clusters.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two questionnaires were used: a household questionnaire and an individual questionnaire. The content of the questionnaires was adapted from standard AIDS Indicator Survey questionnaires developed by ORC Macro, the 2003 KDHS HIV Module and previous surveys conducted in Africa. Various stakeholders in NACC, the National AIDS and STI Control Programme (NASCOP) and other HIV/AIDS organizations working in Kenya met to determine the key HIV program information needs and gaps. The KAIS Technical Working Group (TWG) modified existing questions and designed new questions to reflect current and emerging issues in HIV/AIDS in the country. The final questionnaires were translated from English into Kiswahili and 11 vernacular languages and back-translated into English to ensure accuracy. The questionnaires were further refined after a pilot study prior to distribution of the final versions to field staff.

The household questionnaire gathered basic information from the head of the household on usual members and visitors in the household, including age, sex, education, relationship to the head of household, and orphanhood among children. Information was collected on characteristics of the household’s dwelling unit, such as the source of water, type of toilet facilities, materials used for the floor of the house, property ownership, and mosquito nets. Heads of household were also asked whether the household had received specific types of care and support in the 12 months prior to the survey for any chronically ill adults, any household members who died, and any orphans and vulnerable children (OVC). The household questionnaire was also used to record the respondents’ consent for blood collection and testing.

The individual questionnaire collected information from eligible women and men aged 15-64 years on basic demographic characteristics, marriage, sexual activity, fertility, and family planning. In addition, the tool included questions regarding HIV and STI knowledge, attitudes and behaviours, HIV testing, HIV care and treatment uptake, and other health issues, such as tuberculosis, blood donation and medical injections.

Cleaning operations

Data processing included a number of steps to prepare data collected in the field for analysis. The initial steps included editing questionnaires, both in the field and at KNBS, and double-data entry of all questionnaire responses to minimise errors. Data were entered using Census and Survey Processing System (CSPro) version 3.3.3 Once all survey responses were transferred to electronic format, the next step was to ensure full concordance between the two data entry databases, using paper questionnaires to resolve any discrepancies in transcription. A series of internal consistency and range checks helped to identify any illogical responses and to verify that responses adhered to skip patterns in the questionnaire. Data validation programs for data cleaning were written in Stata version 8.04 and corrections were entered directly in CSPro at KNBS.

A concurrent process of cleaning the raw laboratory data was conducted at the NHRL. The final, cleaned questionnaire database at KNBS was merged with the laboratory results database at the NHRL using unique survey identification numbers to ensure accurate matches (>99.9% of identification numbers were matched). After successfully merging the questionnaire and laboratory results databases, cluster and household identification numbers were serialized from 1-402 and from 1-25, respectively. Original cluster and household numbers, barcodes, and individual survey identification numbers were stripped from the database prior to weighting and analysis to ensure anonymity of survey participants.

Response rate

Overall, participation rates in the 2007 KAIS were high. We calculated household response rate as the number of households consenting to the household interview divided by the total number of sampled households that were located and occupied. The individual interview response rate was calculated as the number of individuals who completed interviews divided by the number of individuals eligible for the individual interview based on the household census. Only those participating in the individual interview were eligible to participate in the blood draw. We calculated blood draw coverage as the number of blood draws divided by the number of all individuals eligible for the individual interview; the blood draw response rate reflects the number of successful blood draws divided by the number of individuals who completed

Abstract

The overarching goal of NCSS 2012 was to strengthen the evidence base to guide policies and programs aimed at improving the wellbeing of the urban poor. Specifically, the survey pursued three main objectives:

1. To document current population and health challenges among the residents of Nairobi's informal settlements.

2. To take stock of the changes (or the lack thereof) in health outcomes, livelihood conditions and demographic behavior among slum dwellers in Nairobi, ten years after the NCSS 2000.

3. To compare indicators among slum dwellers in Nairobi to other urban population sub-groups and rural dwellers in Kenya.

Geographic coverage

Informal settlements (slums) in Nairobi county, Kenya.

Analysis unit

Individuals, Households

Universe

The survey covered all de jure household members (usual residents), all women aged 12-49 years resident in the household, and men aged 12-54 years resident in every other household.

Sampling procedure

The sample for the NCSS 2012 was designed to allow estimation of key indicators in the slums of Nairobi with a margin of error of 2-5 points (95% level of confidence). The following indicators were considered in the sample size calculation: under-5 mortality rate, percentage of under-5 children who had diarrhea in the 2 weeks preceding the survey, percentage of children aged 12-23 months who have been vaccinated against measles, and percentage of children aged 12-23 months who have been fully immunized.

The number of households required to estimate each indicator was then obtained by adjusting the resulting sample size according to the proportion of the target population to the entire population, non-response rate and average household size. And since the number of households required to estimate the percentage of children 12-23 months who are fully immunized is large enough to allow estimation of the other indicators with the specified precision, we therefore used the proportion of fully immunized children in the poorest wealth quintile (65.9% according to KDHS 2008-09) as an estimate of the proportion of full immunization coverage in Nairobi informal settlements (slums). Using a sampling formula, we estimated that a minimum of 518 children was required to estimate full immunization coverage in the slums. Then by adding to the above formula the proportion of children aged 12-23 months living in the slum (3.52% according NUHDSS, 2006-2010 in Korogocho and Viwandani slums), it was estimated that 14,714 individuals (=518/0.0352) would need to be interviewed to be able to reach 518 children aged 12-23 months. Given an estimated average household size of 2.5 in the NUHDSS slums, 5,886 (=14,714/2.5) households would need to be visited to reach 14,714 individuals. Assuming a 10 percent household non-response rate, an initial 6,540 households (5,886 / (1-0.10)) were sampled.

The distribution of the sample by clusters or Enumeration Areas (EAs) was estimated according to the relative size of each administrative location. The list of administrative locations containing at least one EA categorized as an informal settlement or slum was obtained from the 2009 Kenya Population and Housing Census. A total of 42 administrative locations comprising 3,939 slum EAs were identified. A two-stage sampling methodology was then used to select the 6,540 households.

At the first stage, 30% of the sampled EAs were selected using the probability proportional to population size (PPP) sampling methodology and this yielded 220 EAs (6540/ (100/0.3)) distributed across the 42 administrative locations. A household listing carried out within each cluster found that a total of 188 EAs still existed, four years after the 2009 national census and that 32 EAs were no longer in existence due to demolitions and flooding.

At the second stage, to reduce intra-cluster correlation, a random sample of only 35% of the households in each cluster was drawn based on the household listing and this produced 6,583 households. A total of 314 vacant structures were dropped from the initial number of sampled households, which reduced the sample size to 6,269 households. Of these, 5,490 households were successfully interviewed yielding a household response rate of 88 percent.

Sampling deviation

None

Mode of data collection

Face-to-face [f2f]

Research instrument

Data were collected using both netbooks and paper questionnaires, where it was not possible to use the netbooks. Three questionnaires were administered: a household questionnaire and separate questionnaires for women and men.

The Household Questionnaire collected data on the socio-demographic characteristics of household members and visitors who slept in the house the previous night. The questionnaire included modules on household characteristics, household poverty and wellbeing including food security, transfers and remittances, and under-5 children anthropometric measurements. The questionnaire was administered to the head of the household or any other adult/credible household member. A list of household members was used to identify persons eligible for the individual interviews.

The Women's Questionnaire was administered to females aged 12 to 49 years in the sampled households. This questionnaire had several modules including socio-demographic characteristics, migration history, reproduction, contraception, pregnancy, ante-natal and post-natal care, child immunization and child health, marriage, fertility preferences, husband's background and the woman's work/livelihood activities, HIV/AIDS and other sexually transmitted infections, general health issues and maternal mortality. Women aged 12-24 years completed an additional module that addressed issues relevant to young people's health and wellbeing including unintended pregnancy and abortion and drug and alcohol use.

The Men's Questionnaire was administered to eligible males aged 12 to 54 years in the sampled households. The questionnaire had several modules including socio-demographic characteristics, reproduction, contraception, marriage, fertility preferences, work/livelihood activities and gender roles, HIV/AIDS and other sexually transmitted infections and general health issues. Males aged 12-24 years completed an additional module on issues relevant to young people's health and wellbeing.

NB: All questionnaires and modules are provided as external resources.

Cleaning operations

Data editing took place at a number of stages throughout the processing, including:

1. Quality control through back-checks on 10 percent of completed questionnaires, spot-checks, sit-ins during interviews and editing of all completed questionnaires by supervisors and project management staff.

2. A research assistant performed internal consistency checks for all questionnaires and edited all paper questionnaires coming from the field before their submission for data entry with return of incorrectly filled questionnaires to the field for error-resolution.

3. During data entry.

4. Data cleaning and editting was carried out using STATA Version 12.1 software.

Response rate

Households: 6583 sampled, 6269 eligible, 5490 completed, 88% response rate

Women (12-49): 4912 sampled, 4912 eligible, 4240 completed, 86% response rate

Men(12-54): 3137 sampled, 3137 eligible, 2377 completed, 76% response rate

Adolescent Girls (12-24): 1964 sampled, 1964 eligible, 1963 completed, 100% response rate

Adolescent Boys (12-24): 937 sampled, 937 eligible, 807 completed, 86% response rate