This dataset shows the percentage per population category who are served by community health workers in Kisumu county

Kenya had a population of over 15.7 million Protestant Christians, according to the last country census conducted in 2019. This was the largest population group among all other religions. Some 9.7 million Kenyans identified as Catholic, while 9.6 million followed Evangelical Churches. The Muslim population amounted to roughly 5.2 million people. Overall, around 85 percent of Kenya's population adhered to Christianity.

Abstract

The Mbita Health and Demographic Surveillance System (Mbita HDSS), located on the shores of Lake Victoria in Kenya, was established in 2006. The main objective of the HDSS is to provide a platform for population-based research on relationships between diseases and socio-economic and environmental factors, and for the evaluation of disease control interventions. The Mbita HDSS had a population of approximately 54 014 inhabitants from 11 576 households in June 2013. Regular data are collected using personal digital assistants (PDAs) every 3 months, which includes births, pregnancies, migration events and deaths. Coordinates are taken using geographical positioning system (GPS) units to map all dwelling units during data collection. Cause of death is inferred from verbal autopsy questionnaires. In addition, other health-related data such as vaccination status, socio-economic status, water sources, acute illness and bed net distribution are collected.

The HDSS has also provided a platform for conducting various other research activities such as entomology studies including malaria research on neglected tropical diseases, and environmental health projects which have benefited the organization as well as the HDSS community residents. Data collected are shared with the community members, health officials, local administration and other relevant organizations. Opportunities for collaboration and data sharing with the wider research community are available and those interested should contact shimadam@nagasaki-u.ac.jp shimadam@nagasaki-u.ac.jp or mhmdkarama@yahoo.com mhmdkarama@yahoo.com.

Geographic coverage

Mbita HDSS covers part of the western Kenya near the shores of the Lake Victoria including Rusinga Island(North Part) and mainland labeled as Gembe in Gembe location(South Part). Area coverage is about 168KM square.

The area is accessible by a ferry through the Lake victoria to the mainland of Lwanda Kotieno in Kisumu area of Kisumu County. There is also a tarmack road connection that goes through the mainland from the Homabay County to Mbita HDSS area and it is linked to the man-made bridge that links the the mainland to Rusinga Island.

Analysis unit

Individual

Universe

All individuals in the HDSS survey area.

Kind of data

Event history data

Frequency of data collection

At-least 4 rounds per year

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire is divided into several parts. The 1st part consists of the baseline census dealing mainly with demographic information, such as compounds, household and members. The second part is made up of questions on migrations, health, diseases, causes of death, and other health and hygiene related information.

These processes are repeated to accumulate demographic and health related information for several years.

Data collection in the field uses electronic devices pda. These devises support field interviewers' data collection activities and execute data consistency checks with regard to questionnaire items on site. Assumed variables of questionnaire are: ø information for individual identification ø demographic information ø household information ø migration information ø pregnancy information ø health related information ø vital event information

Collected data is accumulated in the field stations and sent to data management centre in the central office in nairobi, and are subjected to detail-verification check by data managers. If inconsistent data or unverified data are detected, those data and error codes (or explanation) are sent back to the field.

Verbal Autopsy Questionaires design was made with some modifications but adapted from the recommended WHO questionnaire.

Cleaning operations

Data management is conducted on PDA, Station PC and main server database. For quality control, several error check programmes were installed, for the following purposes: range checks, entry missing checks, inter-variable checks, consistency check between currently entered data and past data, etc.

Data was left censored to 1 Jan 2009 to account for the start-up phase of the surveillance.

Response rate

Response rate is about 95%.

Data appraisal

CenterID = KE041 MetricTable = RawMicroData QMetric = Starts Illegal = 1115 Legal = 80897 Total = 82012 Metric = 1. RunDate = 2014-03-05 12:40

CenterID = KE041 MetricTable = RawMicroData QMetric = Transitions Illegal = 2603 Legal = 165594 Total = 168197 Metric = 1. RunDate = 2014-03-05 12:40

CenterID = KE041 MetricTable = RawMicroData QMetric = Ends Illegal = 1310 Legal = 80702 Total = 82012 Metric = 1. RunDate = 2014-03-05 12:40

CenterID = KE041 MetricTable = RawMicroData QMetric = SexValues Illegal = 0 Legal = 168197 Total = Metric = RunDate = 2014-03-05 12:41

CenterID = KE041 MetricTable = RawMicroData QMetric = DoBValues Illegal = 218 Legal = 167979 Total = 168197 Metric = 0. RunDate = 2014-03-05 12:41

CenterID = KE041 MetricTable = MicroDataCleaned QMetric = Starts Illegal = 0. Legal = 79446 Total = 0. Metric = 0. RunDate = 2014-03-05 12:51

CenterID = KE041 MetricTable = MicroDataCleaned QMetric = Transitions Illegal = 207 Legal = 162478 Total = 162685 Metric = 0. RunDate = 2014-03-05 12:51

CenterID = KE041 MetricTable = MicroDataCleaned QMetric = Ends Illegal = 0. Legal = 79446 Total = 0. Metric = 0. RunDate = 2014-03-05 12:51

CenterID = KE041 MetricTable = MicroDataCleaned QMetric = SexValues Illegal = 0. Legal = 162685 Total = 0. Metric = 0. RunDate = 2014-03-05 12:51

CenterID = KE041 MetricTable = MicroDataCleaned QMetric = DoBValues Illegal = 218 Legal = 162467 Total = 162685 Metric = 0. RunDate = 2014-03-05 12:51

CenterID = KE041 MetricTable = MicroDataAnonymised QMetric = Duplicated events Illegal = 6748 Legal = 155937 Total = 162685 Metric = 4. RunDate = 2014-03-05 13:02

CenterID = KE041 MetricTable = KE041_CMD_2011_v1_S01 QMetric = Temporal Integrity Violations Illegal = 0. Legal = 172247 Total = 172247 Metric = 0. RunDate = 2014-03-05 13:02

CenterID = KE041 MetricTable = KE041_CMD_2011_v1_S02 QMetric = Close OMG-IMG pairs Illegal =9 Legal = 12 Total = 21 Metric = 42 RunDate = 2014-03-05 13:03

CenterID = KE041 MetricTable = KE041_CMD_2011_v1_S02 QMetric = EXT-ENT pairs with big gap Illegal =0 Legal = 9 Total = 9 Metric = 0. RunDate = 2014-03-05 13:06

Abstract

In an effort to address the ICT data challenges, the Communications Commission of Kenya (CCK) partnered with Kenya National Bureau of Statistics (KNBS) to undertake a comprehensive National ICT Survey. This was planned and executed during the months of May and June 2010.

The main objective of the study was to collect, collate and analyse data relating to ICT access and usage by various categorizations in Kenya. The survey captured data and information on critical ICT indicators as defined by international bodies such as the International Telecommunications Union (ITU). These indicators focused on household and individuals; and the data was be disaggregated by age, gender, administrative regions, rural and urban locations.

The specific objectives of the study were to; Obtain social economic information with a view of understanding usage patterns of ICT services; (a) Obtain social economic information with a view of understanding usage patterns of ICT services; (b) Collect, collate and analyze ICT statistics in line with ICT indicators; (c) Evaluate the factors that will have the greatest impact in ensuring access and usage of ICTs and; (d) Develop a database on access and usage of ICT in Kenya

Geographic coverage

National coverage

Analysis unit

District, Household, Individual

Universe

Households from the sampled areas.

Kind of data

Sample survey data [ssd]

Sampling procedure

The National Sample Survey and Evaluation Programme (NASSEP IV) maintained by the Bureau was used as the sampling frame. The frame has 1,800 clusters spread all over the country, and covers all socio-economic classes and hence able to get a suitable and representative sample of the population. The survey was distributed into four domains, namely: 1. National, 2. Major Urban areas, 3. Other Urban areas, and 4. Rural areas.

The major urban towns included Nairobi, Thika, Mombasa, Kisumu, Nakuru and Eldoret. All other areas defined as urban by KNBS but fall outside the major municipalities above were categorized as 'other urban areas'. The rural domain was further sub-divided into their respective provinces, excluding Nairobi which is purely urban. For the 'rural' component, the districts that display identical socio-cultural and economic conditions have been pooled together to create strata from which a representative set of districts is selected to represent the group of such districts. A total of 42 such stratifications were done and one district in each categorization was selected. The major urban areas of the country namely Nairobi, Mombasa, Kisumu, Nakuru, Eldoret and Thika were all sub-stratified into five sub-strata based on perceived levels of income into the: 1. Upper income 2. Lower Upper 3. Middle 4. Lower Middle and 5. Lower.

In this survey, all the six 'major urban' are included while just a few of the 'other urban areas' are selected depending on their population (household) distribution.

Selection of the Clusters for the Survey The selection of the sample clusters was done systematically using the Equal Probability Selection method (EPSEM). Since NASSEP IV was developed using Probability Proportional to Size (PPS) method, the resulting sample retains its properties. The selection was done independently within the districts and the urban /rural sub-stratum.

Selection of the Households From each selected cluster, an equal number of 15 households were selected systematically, with a random start. The systematic sampling method was adopted as it enables the distribution of the sample across the cluster evenly and yields good estimates for the population parameters. Selection of the households was done at the office and assigned to the Research Assistants, with strictly no allowance for replacement of non-responding households.

Sampling deviation

Owing to the some logistical challenges the following clusters were partially or not covered at all: • One cluster in Tana River due to floods. • Two clusters in Molo where households shifted to safer areas after the Post Election Violence (PEV). As a result, fewer than the expected households were covered. • One cluster in Koibatek was covered halfway due to relocation of households to pave way for a large plantation.

Where there was no school found within the cluster, Research Assistant was allowed to sample an institution from a neighbouring cluster. In some districts, the schools were found to be very far from the cluster and therefore could not be covered. Where a cluster was to be covered over a weekend, it was often not possible to find a responsible person in institutions to respond to the questionnaire.

Mode of data collection

Face-to-face [f2f]

Research instrument

Household questionnaire: This will be used to collect background information pertaining to the members of the household and businesses operated by household members. It will collect information about each person in the household such as name, sex, age, education, and relationship to household head etcetera. This information is vital for calculating certain socio-demographic characteristics of the household. The Business module in the household questionnaire will be used to collect information pertaining to usage of ICT in businesses identified in the household. To estimate the magnitude, levels and distribution of ICT usage in the country, all the selected respondents 15 years and above will be subjected to business questionnaire.

Institutional Questionnaire: This will collect information pertaining to institutions providing ICT related programmes in the country. This information will be analyzed to identify gaps and other issues of concern, which need to be addressed in the promotion ICT provision in the country.

Cleaning operations

As a matter of procedure initial manual editing was done in the field by the RAs. The supervisors further checked the questionnaires and validated the data in the field by randomly sampling 20 per cent of the filled questionnaires. After the questionnaires were received from the field, an office editing team was constituted to do office editing.

Data was captured using Census and Survey Processing System (CSPRO) version 4.0 through a data entry screen specially created with checks to ensure accuracy during data entry. All questionnaires were double entered to ensure data quality. Erroneous entries and potential outliers were then verified and corrected appropriately. A total of 20 data entry personnel were engaged during the exercise.

The captured data were exported to Statistical Package for Social Sciences (SPSS) for cleaning and analysis. The cleaned data was weighted before final analysis. The weighting of the data involved application of inflation factors derived from the selection probabilities of the EAs and households detailed in section 2.2.7, on weighting the Sample Data.

Response rate

The overall response rate stood at 85.9 per cent. Nairobi had the lowest response rate at 69.4 per cent while the highest (94.6 per cent) was realized in North Eastern. More than 95.5 per cent of all the sampled households were occupied out of which 85.9 per cent were interviewed.

Abstract

The Kenya Enterprise Survey was conducted between May and July 2007. The research is based on a representative sample of 657 formal firms and 124 informal establishments. The sample was drawn in four locations (Nairobi, Mombasa,Nakuru, and Kisumu) and covered both manufacturing and services sectors.

The objective of the survey is to obtain feedback from enterprises in client countries on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through face-to-face interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The study used stratified simple random sampling for the formal economy (registered establishments with more than 4 workers), and simple random sampling for the micro firms (non-registered establishments with less than 5 employees). Close to 60% of the formal sample is represented by manufacturing firms. Within manufacturing food (17 percent), garments (12 percent) and other manufacturing (31 percent) represent individual strata. Outside the manufacturing sector, the retail sector account for 19 percent of the sample and less than a quarter of the firms belong to the rest of services stratum.

The sample was drawn in four locations: Nairobi, Mombasa, Nakuru, and Kisumu. Size stratification for formal firms was defined following the standardized definition used for the Enterprise Surveys: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers.

For establishments with five or more full-time permanent paid employees, the universe was stratified according to the following categories of industry: 1. Manufacturing: Food and Beverages (Group D, sub-group 15); 2. Manufacturing: Garment (Group D, sub group 18); 3. Manufacturing: Other Manufacturing (Group D excluding sub-groups 15 and 18); 4. Retail Trade: (Group G, sub-group 52); 5. Rest of the universe, including: • Construction (Group F); • Wholesale trade (Group G, sub-group 51); • Hotels, bars and restaurants (Group H); • Transportation, storage and communications (Group I); • Computer related activities (Group K, sub-group 72).

The sampling frame was obtained from the Kenya National Bureau of Statistics, the Kenya Association of Manufacturers, the Kenya National Chamber of Commerce, the Kenya Private Sector Alliance, and from the Federation of Kenya Employers. The lists were merged together into a master list which was validated, updated where possible, and then used to establish the initial population size for each stratum. The final population size in all strata and locations was 6562 with the vast majority of establishments operating in the rest of the universe, and manufacturing strata.

The sample also includes panel data collected from establishments surveyed in the 2003 Kenya Investment Climate Survey (ICS). That survey included establishments in all three manufacturing strata distributed across the entire country. In order to collect the largest possible set of panel data, an attempt was made to contact and survey every establishment in the panel, provided it was located in one of the four cities covered by this survey and operated in the universe under study.

The remainder of the sample (including the entire rest of universe and retail sample in each city) was selected at random from the master list by a computer program.

In this survey, the micro establishment stratum covers all establishments of the targeted categories of economic activity with less than 5 employees. The implementing agency, EEC Canada, selected an aerial sampling approach to estimate the population of establishments and select the sample in this stratum for all regions of the survey. The following procedure was followed for the sampling of micro establishments: 1. Step 1: districts and specific zones of each district with a high concentration of micro establishments were identified; 2. Step 2: a count of all micro establishments in these specific zones was conducted; 3. Step 3: the count by zone was converted into one list of sequential numbers for the whole survey region and a virtual list was created with establishments numbers; 4. Step 4: a computer program performed a random selection of establishments numbers from that virtual list; 5. Step 5: based on the ratio between the number selected in each specific zone and the total population in that zone, a skip rule was created and applied for selecting the corresponding establishments in each zone.

Enumerators applied the skip rule defined for that zone as well as how to select replacements in the event of refusal or other cause of non-participation.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Core Questionnaire + Manufacturing Module [ISIC Rev.3.1: 15-37] - Core Questionnaire + Retail Module [ISIC Rev.3.1: 52] - Core Questionnaire [ISIC Rev.3.1: 45, 50, 51, 55, 60-64, 72] - Micro Establishments Questionnaire (for establishments with 1 to 4 employees).

The "Core Questionnaire" is the heart of the Enterprise Survey and contains the survey questions asked of all firms across the world. There are also two other survey instruments - the "Core Questionnaire + Manufacturing Module" and the "Core Questionnaire + Retail Module." The survey is fielded via three instruments in order to not ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

The survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, registration, and performance measures. The questionnaire also assesses the survey respondents' opinions on what are the obstacles to firm growth and performance.

Abstract

National Health Accounts (NHA) is an internationally recognised method used to track expenditures in a health system for a specified period of time. Specifically, NHA details the flow of funding from financial sources (e.g., donors, Ministry of Finance), to financing agents (i.e., those who manage the funds, such as the Ministry of Health [MoH] or nongovernmental organisations [NGOs]), to providers (e.g., public and private facilities) and finally to end users (e.g., inpatient and outpatient care, pharmaceuticals).

Actual expenditures, rather than budget inputs, are used to fill a series of tables that show the flow of funding through the health sector. NHA also provides detailed breakdowns of disease-specific expenditures such as those for HIV/AIDS and reproductive health (RH). NHA is designed to be used as a policy tool to facilitate the implementation of health system goals.

The 2007 National Health Accounts study is the third round of NHA in Kenya. The first two estimations covered financial years (FYs) 1994/95 and 2001/02, respectively. This third round, undertaken in 2007 and covering 2005/06 was implemented by the MoH and Kenya National Bureau of Statistics (KNBS) with financial support from the United States Agency for International Development (USAID). USAID's Health Systems 20/20 Project, led by Abt Associates Inc., provided technical support. The findings will be used as a platform for informing policy decisions concerning resource allocation and will also be used by stakeholders in the sector.

Geographic coverage

National coverage

Analysis unit

Households and institutions

Universe

Household health expenditure covered all households in the country whereas the institutional surveys covered firms selected under the review.

Kind of data

Administrative records data [adm]

Sampling procedure

Kenya is divided into eight administrative provinces. The provinces are in turn subdivided into 70 districts. Each district is subdivided into divisions while the divisions are split into locations and finally each location into sublocations.

During the 1999 population census, each sublocation was subdivided into smaller units called enumeration areas (EAs). Kenya has about 62,000 EAs. The EAs provided census information on households and population. This information was used in the design of the National Sample Survey Evaluation Programme (NASSEP) IV master sample with 1,800 selected EAs.

The cartographic records for each EA in the master sample were updated in the field, one year preceding the NHA survey. The 1,800 clusters were distributed into 540 urban and 1,260 rural clusters.

The province provided a natural stratification of the population. The six major urban centres Nairobi, Mombasa, Kisumu, Nakuru, Eldoret, and Thika were further substratified into five socioeconomic classes based on incomes to circumvent the extensive socioeconomic diversity inherent in them as follows: upper, lower upper, middle, lower middle and lower; this improved the precision of estimates due to reduced sampling variation.

It was estimated that 8,844 households would be sufficient to provide estimates both at provincial and national levels as well as disaggregation to urban and rural components of the country. This sample was to yield 6,060 interviews in the rural and 2,784 in the urban clusters (Table 2.2). This was to be achieved through coverage of 737 clusters (505 rural and 232 urban clusters).

Twelve households were to be covered in each cluster. The method of proportional allocation was used in assigning the sample households to the provinces and districts. The count of the households was transformed to the square root of the census households to avoid under-representing the smaller districts.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

To expedite data entry and monitor data quality, all completed questionnaires were sent to a data management unit at the MoH Planning Department, which was the designated secretariat for the activity.

This approach helped in standardizing and speeding up data entry and reducing errors. Questionnaires were also checked for completeness before entry.

Data were entered in a Census and Survey Processing System (CSPro) by a team of data entry clerks under the supervision of data entry supervisors. The data were reentered for validation.

The data files were then converted into SPSS, the software used for data analysis. Much of the analysis was replicated using Stata, to confirm that weighted estimates were correct. Stata was also used to perform analysis that could not be undertaken using SPSS.

Response rate

A total of 8,844 households were selected for the survey. Of these, 8,453 were successfully interviewed, giving a response rate of 95.6 percent, and the survey reported observations on 38,235 individuals living in these households.

Triangulation of coverage estimates and correction factors for Homa Bay, Kisumu, Migori, and Siaya.

Life expectancy at birth in Kenya was registered at 66.5 years for women and 60.6 years for men in 2019. In some Kenyan counties, the estimate surpassed the average. In Nyeri, for instance, women lived longer, some 75.8 years, and men had the highest life expectancy in the same country, at 66.4 years. On the other hand, Tana River registered the lowest expectancy for women (58.6 years) and Migori and Homa Bay for men (50.5 years). In general, women lived longer than men overall in Kenya, with Isiolo county as the only exception.

Socio-demographic characteristics of study participants (n = 88).

BackgroundThe rapid spread of HBV has resulted in the emergence of new variants. These viral genotypes and variants, in addition to carcinogenic risk, can be key predictors of therapy response and outcomes. As a result, a better knowledge of these emerging HBV traits will aid in the development of a treatment for HBV infection. However, many Sub-Saharan African nations, including Kenya, have insufficient molecular data on HBV strains circulating locally. This study conducted a population-genetics analysis to evaluate the genetic diversity of HBV among Kenyan blood donors. In addition, within the same cohort, the incidence and features of immune-associated escape mutations and stop-codons in Hepatitis B surface antigen (HBsAg) were determined.MethodsIn September 2015 to October 2016, 194 serum samples were obtained from HBsAg-positive blood donors residing in eleven different Kenyan counties: Kisumu, Machakos, Uasin Gishu, Nairobi, Nakuru, Embu, Garissa, Kisii, Mombasa, Nyeri, and Turkana. For the HBV surface (S) gene, HBV DNA was isolated, amplified, and sequenced. The sequences obtained were utilized to investigate the genetic and haplotype diversity within the S genes.ResultsAmong the blood donors, 74.74% were male, and the overall mean age was 25.36 years. HBV genotype A1 (88.14%) was the most common, followed by genotype D (10.82%), genotype C (0.52%), and HBV genotype E (0.52%). The phylogenetic analysis revealed twelve major clades, with cluster III comprising solely of 68 blood donor isolates (68/194-35.05%). A high haplotype diversity (Hd = 0.94) and low nucleotide diversity (π = 0.02) were observed. Kisumu county had high number of haplotypes (22), but low haplotype (gene) diversity (Hd = 0.90). Generally, a total of 90 haplotypes with some consisting of more than one sequence were observed. The gene exhibited negative values for Tajima’s D (-2.04, p