Chart and table of population level and growth rate for the Kisumu, Kenya metro area from 1950 to 2025.

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

Kisumu Pop Pyramid Age Groups - 2009

968.909 (Persons) in 2009.

Kisumu County Livestock Population 2012-2015

BackgroundKenya is 1 of 14 priority countries in Africa scaling up voluntary medical male circumcision (VMMC) for HIV prevention following the recommendations of the World Health Organization and the Joint United Nations Programme on HIV/AIDS. To inform VMMC target setting, we modeled the impact of circumcising specific client age groups across several Kenyan geographic areas.MethodsThe Decision Makers’ Program Planning Tool, Version 2 (DMPPT 2) was applied in Kisumu, Siaya, Homa Bay, and Migori counties. Initial modeling done in mid-2016 showed coverage estimates above 100% in age groups and geographic areas where demand for VMMC continued to be high. On the basis of information obtained from country policy makers and VMMC program implementers, we adjusted circumcision coverage for duplicate reporting, county-level population estimates, migration across county boundaries for VMMC services, and replacement of traditional circumcision with circumcisions in the VMMC program. To address residual inflated coverage following these adjustments we applied county-specific correction factors computed by triangulating model results with coverage estimates from population surveys.ResultsA program record review identified duplicate reporting in Homa Bay, Kisumu, and Siaya. Using county population estimates from the Kenya National Bureau of Statistics, we found that adjusting for migration and correcting for replacement of traditional circumcision with VMMC led to lower estimates of 2016 male circumcision coverage especially for Kisumu, Migori, and Siaya. Even after addressing these issues, overestimation of 2016 male circumcision coverage persisted, especially in Homa Bay. We estimated male circumcision coverage in 2016 by applying correction factors. Modeled estimates for 2016 circumcision coverage for the 10- to 14-year age group ranged from 50% in Homa Bay to approximately 90% in Kisumu. Results for the 15- to 19-year age group suggest almost complete coverage in Kisumu, Migori, and Siaya. Coverage for the 20- to 24-year age group ranged from about 80% in Siaya to about 90% in Homa Bay, coverage for those aged 25–29 years ranged from about 60% in Siaya to 80% in Migori, and coverage in those aged 30–34 years ranged from about 50% in Siaya to about 70% in Migori.ConclusionsOur analysis points to solutions for some of the data issues encountered in Kenya. Kenya is the first country in which these data issues have been encountered because baseline circumcision rates were high. We anticipate that some of the modeling methods we developed for Kenya will be applicable in other countries.

Analytical dataset corresponding to publication "HIV prevalence and awareness among adults presenting for enrolment into a study of people at risk for HIV in Kisumu County, Western Kenya" Published in PlosOne.

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.

This qualitative data set comprises transcripts from focus group discussions with informal collectors of plastic and general waste in Kisumu, Kenya. The study aims to determine the extent to which informal waste collectors facilitate waste separation and recycling in off-grid neighborhoods in Kisumu. It also aimed to assess the impact of recycled plastic prices and international policy initiatives on businesses in the water sachet recycling chain in Kisumu as well as other barriers to informal waste collector businesses. A similar set of FGDs with waste collectors in Greater Accra, Ghana, is archived separately. Specialist plastic waste collection businesses are almost non-existent in Kisumu, so the study recruited general (mixed) waste collectors at different points in the supply chain via a grassroots waste collectors’ association. A total sample of 32 collectors were identified via this route within Kisumu City. These were segmented into three broad groups: a) Waste Pickers, b) Intermediate Traders and finally, c) Apex Traders. The Waste Pickers were defined as informal enterprises that mostly pick wastes directly from the waste generation sites such as households, streets, or waste dumps. The intermediate Traders were defined as the relatively more formal enterprises collecting waste from the pickers, carrying out some level of processing and selling the processed waste to apex traders. The Apex traders were then defined as the more formal enterprises purchasing the wastes from the intermediate enterprises and then selling the waste to recycling industries, mostly located in Nairobi. Two focus group discussions were held with two groups of waste pickers and two groups of intermediate traders, with a single small group discussion then held with two apex traders. Focus and small group discussions consisted of open-ended questions on business establishment, business history, waste collection operations, and enablers and barriers to waste collection.

According to WHO/UNICEF, whilst 91.8% of urban households in Sub-Saharan Africa (SSA) had access to piped or protected groundwater sources in 2015, only 46.2% had safely managed water available when needed. Vendors provide a key role in supplying urban off-grid populations, with consumption of bottled or bagged water (sachets, water sold in 500ml plastic bags) growing in SSA. Whilst several studies show bottles and bags are usually free from faecal contamination, given that many off-grid urban populations lack solid waste disposal services, when people drink such water, there can be problems disposing of the plastic bags and bottles afterwards. This project aims to deliver evidence on the different ways that people sell water to off-grid populations and what this means for plastic waste management. We plan to do this in Ghana, where most urban household now drink bagged water, and by way of contrast, Kenya, where the government has banned plastic bags. In this way, we want to widen access to safe water and waste management services among urban off-grid populations, by supporting water-sellers and waste collectors to fill the gaps in municipal services. Both countries (and many others elsewhere) already have nationwide household surveys that collect data on the food and goods people consume and the services they have. However, as yet, these surveys have not been connected to the problem of waste management. We plan to visit marketplaces, buying foods and then recording packaging and organic waste. By combining this information with the household survey data, we can work out how much domestic waste like plastics gets collected and how much is discarded or burned, ultimately entering the atmosphere or oceans. In Ghana, we will also survey informal waste collectors in urban Greater Accra. We want to find out how much these small businesses support waste collection and recycling across this urban region (particularly plastic from bagged water), so we can help government identify gaps in waste collection coverage. We also believe highlighting the important role of small waste collectors could lead to greater business support for such collectors. We will also evaluate whether community education campaigns to encourage domestic waste recycling reduce the amount of waste and plastic observed in the local environment. Such campaigns are currently pursued by several local charities with support from the Plastic Waste Management Project. In Kenya, where water is usually sold in jerrycans rather than bagged, the jerrycan water often gets contaminated. We plan to find out whether this jerrycan water is safer under an arrangement known as delegated management. This involves a water utility passing on management of the piped network to a local business in slum areas, so as to reduce vandalism of pipes and bring water closer to slum-dwellers. We will compare water quality in areas with and without this arrangement to see if it makes the water sold safer. We also plan...

43,5 (per 1000 population) in 1999.

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. This report describes findings from 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

The whole country

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.

Comparison of a pyrethroid insecticides resistant field population of Anopheles gambiae ss collected in Tiefora, Burkina Faso (2014) compared to a lab susceptible ss Anopheles gambiae Kisumu.

21,6 (per 1000 population) in 1999.

Abstract

The Nyanza Province Multiple Indicator Cluster Survey (MICS) was carried out in 2011 by the Kenya National Bureau of Statistics (KNBS) in collaboration with County and Provincial administration. The survey covered all the 6 constituent counties of Nyanza, namely: Siaya, Kisumu, Homa Bay, Migori, Kisii, and Nyamira. Financial and technical support was provided by the United Nations Children's Fund (UNICEF). The Nyanza Province survey was conducted as part of the fourth global round of MICS surveys (MICS4). MICS is an international household survey program developed by UNICEF, this survey was based on a large part on the needs to monitor progress towards goals and targets emanating from recent international agreements: The Millennium Declaration, adopted by all 191 United Nations Member States in September 2000, and the Plan of Action of A World Fit For Children, adopted by 189 Member States at the United Nations Special Session on Children in May 2002. Both of these commitments build upon promises made by the international community at the 1990 World Summit for Children. Additional information on the global MICS project may be obtained from www. Childinfo.org. The objective of Nyanza MICS 2011 was to provide lower-level estimates relating to children and women residing in the six counties of the region. Particular emphasis was on reproductive health, child health and mortality, nutrition, child protection, childhood development, water and sanitation, hand washing practices, education, disability, HIV/AIDS, and orphan hood.

The Nyanza MICS is a nationally representative survey of 17,047 households, comprising 14,408 women in the 15-49 years age group. 7,914 men age 15-54 years and 10,223 children under 5 years of age. The sample allows for the estimation of some key indicators at the national, provincial and urban/rural levels. A two stage, stratified cluster sampling approach was used for the selection of the survey sample. The primary objectives of the 2011 Nyanza Province Multiple Indicator Cluster Survey are: 1. To provide up-to-date information for assessing the situation of children and women in Nyanza Province. 2. To furnish data needed for monitoring progress toward goals established in the Millennium Declaration and other internationally agreed upon goals, as a basis for future action. 3. To contribute to the improvement of data and monitoring systems in Nyanza Province and to strengthen technical expertise in the design, implementation, and analysis of such systems. 4. To generate data on the situation of children and women, including the identification of vulnerable groups and disparities, to inform policies and interventions.

Geographic coverage

National

Analysis unit

• Individuals
• Households

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the Nyanza Province Multiple Indicator Cluster Survey (MICS) was designed to provide estimates for a large number of indicators on the situation of children and women at the provincial level, for urban and rural areas, and for counties: Siaya, Migori, Kisumu, Homa Bay, Kisii, and Nyamira. The urban and rural areas within each County were identified as the main sampling strata and the sample was selected in two stages. The primary sampling units (PSUs) for the survey were the recently created enumeration areas (EAs) based on the 2009 Kenya Population and Housing Census while the households were the ultimate sampling units.

A stand-alone statistical frame for each of the Nyanza counties based on the 2009 census EAs was created for the purpose of MICS. Within each stratum, a specified number of census enumeration areas were selected systematically with probability proportional to size. A complete listing of all households in the selected EAs was undertaken by identifying and mapping all existing structures and households. The listing process ensured that the EAs had one measure of size (MOs). One MOs was defined as an EA having an average of 100 households. EA with less than 50 households was amalgamated with the most convenient adjoining EA. On the other hand, the EAs with more than 149 households were segmented during household listing and eventually one segment scientifically selected and developed into a cluster. After a household listing exercise within the selected enumeration areas, a systematic sample of 25 households was drawn from each of the sampled enumeration area. The sample was stratified by County, urban and rural areas, and is not self-weighting. For reporting provincial level results, sample weights are used. A more detailed description of the sample design can be found in Appendix A.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three sets of questionnaires were used in the survey: 1. A household questionnaire which was used to collect information on all de jure household members (usual residents), the household, and the dwelling 2. A women's questionnaire administered in each household to all women aged 15-49 years. 3. An under-5 questionnaire, administered to mothers or caretakers for all children under 5 living in the household.

Cleaning operations

Data were entered using the CSPro software. The data were entered into microcomputers by 23 data entry operators and 4 data entry supervisors. In order to ensure quality control, all questionnaires were double entered and internal consistency checks were performed. Procedures and standard programs developed under the global MICS4 program and adapted to the Nyanza Province questionnaire were used throughout. Data processing began three weeks after commencing data collection in October 2011 and was completed in January 2012.Data were analyzed using the Statistical Package for Social Sciences (SPSS) software program, Version 18, and the model syntax and tabulation plans developed by UNICEF were used for this purpose.

Response rate

Of the 7,500 households selected for the sample 6,994 were found to be occupied. Of these 6,828 were successfully interviewed for a household response rate of 97.6 percent. In the interviewed households 6,581 women (age 15-49 years) were identified. Of these 5,908 were successfully interviewed, yielding a response rate of 89.8 percent within interviewed households. In addition 5,157 children under age five were listed in the household questionnaire. Questionnaires were completed for 5,045 of these children, which corresponds to a response rate of 97.8 percent within interviewed households. Overall response rates of 87.6 percent and 95.5 percent are calculated for the women's and under-5's interviews respectively.

There are some differences in the response rates by urban and rural areas. Overall household responses rates were 98 percent for rural areas and 94 percent for urban areas. The same trends was observed for overall women response rates and under-five overall response rates, in favor of rural areas. At the County levels, household response rates were all above 95 percent, but differences were observed for women response rates across counties.

Overall women response rates were lowest in Nyamira County at 84 percent and highest in Siaya at 95 percent. Given the fact that Nyamira has response rates below 85 percent, the results for this region or residence should be interpreted with some caution, as the response rate is low. Similarly overall under-five response rates were highest in Siaya County and lowest in Nyamira County. The reasons for the lower response rates for Nyamira County are not readily available, but a range of explanations for this lower performance include that a large section of the population who were not reachable on certain prayer days, in addition, heavy downpours affected availability of respondents during the whole day while working on farms.

Sampling error estimates

Sampling errors are a measure of the variability between the estimates from all possible samples. The extent of variability is not known exactly, but can be estimated statistically from the survey data.The sample of respondents selected in the Nyanza province Multiple Indicator Cluster Survey is only one of the samples that could have been selected from the same population, using the same design and size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected.

The following sampling error measures are presented in this appendix for each of the selected indicators: - Standard error (se): Sampling errors are usually measured in terms of standard errors for particular indicators (means, proportions etc.). Standard error is the square root of the variance of the estimate. The Taylor linearization method is used for the estimation of standard errors. - Coefficient of variation (se/r) is the ratio of the standard error to the value of the indicator, and is a measure of the relative sampling error.

- Design effect (deff) is the ratio of the actual variance of an indicator, under the sampling method used in the survey, to the variance calculated under the assumption of simple random sampling. The square root of the design effect (deft) is used to show the efficiency of the sample design in relation to the precision. A deft value of 1.0 indicates that the sample design is as efficient as a simple random sample, while a deft value above 1.0 indicates the increase in the standard error due to the use of a more complex sample design.

Description of Kibuye Market vendors by gender, Kisumu, Kenya, 2013.

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

Ascertained leading causes of death at two large hospitals and among all persons and sex, Kisumu County, Kenya (2019).

Knowledge level for human and animal anthrax, Kisumu east Sub County, July 2019.

Frequency distribution, facility, age, gender-specific attack rates for Anthrax Kisumu East Sub County 2019.