Malaria Indicator Survey 2020
Kenya, 2020
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KEN_2020_MIS_v01_M
Malaria Indicator Survey 2020
| Name | Country code |
|---|---|
| Kenya | KEN |
Malaria Indicator Survey [hh/mis]
The 2020 Kenya Malaria Indicator Survey (KMIS) is the fourth survey of its kind to be carried out in Kenya. Previous MIS surveys were conducted in 2007, 2010, and 2015. As with the previous KMIS surveys, the 2020 KMIS was designed to follow the Roll Back Malaria Monitoring and Evaluation Working Group guidelines, the Kenya National Malaria Strategy 2019-2023, and the Kenya Malaria Monitoring and Evaluation Plan 2019-2023.
The 2020 Kenya Malaria Indicator Survey (2020 KMIS) was a cross-sectional household-based survey with a nationally representative sample of conventional households. The survey targeted women age 15-49 and children age 6 months to age 14 living within conventional households in Kenya. All women age 15-49 who were usual members of the selected households or who spent the night before the survey in the selected households were eligible for individual interviews. In all sampled households, children age 6 months to age 14 were tested for anaemia and malaria.
The sample for the 2020 KMIS was designed to produce reliable estimates for key malaria indicators at the national level, for urban and rural areas separately, and for each of the five malaria endemic zones.
The 2020 KMIS was designed to provide information on the implementation of core malaria control interventions and serve as a follow-up to the previous malaria indicator surveys. The specific objectives of the 2020 KMIS were as follows:
- To measure the extent of ownership of, access to, and use of mosquito nets
- To assess coverage of intermittent preventive treatment of malaria during pregnancy
- To examine fever management among children under age 5
- To measure the prevalence of malaria and anaemia among children age 6 months to age 14
- To assess knowledge, attitudes, and practices regarding malaria control
- To determine the Plasmodium species most prevalent in Kenya
Sample survey data [ssd]
- Household
- Individual
- Children age 0-14
- Woman age 15-49
Version
The data dictionary was generated from hierarchical data that was downloaded from the The DHS Program website (http://dhsprogram.com).
Scope
The 2020 Kenya Malaria Indicator Survey covered the following topics:
HOUSEHOLD
• Identification
• Usual members and visitors in the selected households
• Background information on each person listed, such as relationship to head of the household, age, and sex
• Characteristics of the household's dwelling unit, such as the source of water, type of toilet facilities, type of fuel used for cooking, number of rooms, ownsership of livestock, possessions of durable goods, mosquito nets, and main material for the floor, roof and walls of the dwelling.
INDIVIDUAL WOMAN
• Identification
• Background characteristics (age, education, literacy, and religion)
• Reproductive history for the last 5 years
• Preventive malaria treatment during the pregnancy of the most recent live birth
• Prevalence and treatment of fever among children under age 5
• Knowledge about malaria (prevention and types of antimalarial medications)
• Exposure to and source of media messages about malaria in the last 6 months
BIOMARKER
• Identification
• Hemoglobin measurement and malaria testing for children age 0-14 years
FIELDWORKER
• Background information on each fieldworke
Coverage
National coverage
The survey covered all de jure household members (usual residents), women age 15-49 years and children age 0-14 years resident in the household.
Producers and sponsors
| Name | Affiliation |
|---|---|
| Division of National Malaria Programme (DNMP) | Ministry of Health (MOH) |
| National Bureau of Statistics (KNBS) | Government of Kenya |
| Name | Role |
|---|---|
| ICF | Provided technical assistance through The DHS Program |
| Name | Role |
|---|---|
| Government of Kenya | Financial support |
| United States Agency for International Development | Financial support |
| Global Fund | Financial support |
Sampling
The 2020 KMIS followed a two-stage stratified cluster sample design and was intended to provide estimates of key malaria indicators for the country as a whole, for urban and rural areas, and for the five malaria-endemic zones (Highland epidemic prone, Lake endemic, Coast endemic, Seasonal, and Low risk).
The five malaria-endemic zones fully cover the country, and each of the 47 counties in the country falls into one or two of the five zones as follows:
- Highland epidemic prone: Kisii, Nyamira, West Pokot, Trans-Nzoia, Uasin Gishu, Nandi, Narok, Kericho, Bomet, Bungoma, Kakamega, and Elgeyo Marakwet
- Lake endemic: Siaya, Kisumu, Migori, Homa Bay, Kakamega, Vihiga, Bungoma, and Busia
- Coast endemic: Mombasa, Kwale, Kilifi, Lamu, and Taita Taveta
- Seasonal: Tana River, Marsabit, Isiolo, Meru, Tharaka-Nithi, Embu, Kitui, Garissa, Wajir, Mandera, Turkana, Samburu, Baringo, Elgeyo Marakwet, Kajiado, and West Pokot
- Low risk: Nairobi, Nyandarua, Nyeri, Kirinyaga, Murang’a, Kiambu, Machakos, Makueni, Laikipia, Nakuru, Meru, Tharaka-Nithi, and Embu.
The survey utilised the fifth National Sample Survey and Evaluation Programme (NASSEP V) household master sample frame, the same frame used for the 2015 KMIS. The frame was used by KNBS from 2012 to 2020 to conduct household-based sample surveys in Kenya. It was based on the 2009 Kenya Population and Housing Census, and the primary sampling units were clusters developed from enumeration areas (EAs). EAs are the smallest geographical areas created for purposes of census enumeration; a cluster can be an EA or part of an EA. The frame had a total of 5,360 clusters and was stratified into urban and rural areas within each of 47 counties, resulting into 92 sampling strata with Nairobi and Mombasa counties being wholly urban.
The survey employed a two-stage stratified cluster sampling design in which, in the first stage of selection, 301 clusters (134 urban and 167 rural) were randomly selected from the NASSEP V master sample frame using an equal probability selection method with independent selection in each sampling stratum. The second stage involved random selection of a fixed number of 30 households per cluster from a roster of households in the sampled clusters using systematic random sampling.
For further details on sample design, see Appendix A of the final report.
A total of 8,845 households were selected for the survey, of which 8,185 were occupied at the time of fieldwork. Among the occupied households, 7,952 were successfully interviewed, yielding a response rate of 97%. In the interviewed households, 7,035 eligible women were identified for individual interviews and 6,771 were successfully interviewed, yielding a response rate of 96%.
Due to the non-proportional allocation of the sample to the different counties and the possible differences in response rates, sampling weights are required for any analysis using the 2020 KMIS data to ensure the actual representative of the survey results at the national level as well as the domain level. Since the 2020 KMIS sample was a two-stage stratified cluster sample selected from a master sample, sampling weights were calculated based on sampling probabilities separately for each sampling stage, including master sample selection probabilities, and for each cluster.
The design weight was adjusted for household nonresponse and nonresponse among women to obtain the sampling weights for households and for women, respectively. Nonresponse was adjusted at the sampling stratum level. For the household sampling weight, the household design weight was multiplied by the inverse of the household response rate, by stratum. For the women’s individual sampling weight, the household sampling weight was multiplied by the inverse of women’s individual response rate, by stratum. After adjusting for nonresponse, the sampling weights were normalized to obtain the final standard weights that appear in the data files. The normalization process is done to obtain a total number of unweighted cases equal to the total number of weighted cases at the national level, for the total number of households and women. Normalization is done by multiplying the sampling weight by the estimated sampling fraction obtained from the survey for the household weight and the individual woman’s weight. The normalized weights are relative weights that are valid for estimating means, proportions, ratios, and rates but are not valid for estimating population totals or for pooled data.
For further details on sampling weights, see Appendix A.4 of the final report.
Survey instrument
Four types of questionnaires were used for the 2020 KMIS: the Household Questionnaire, the Woman’s Questionnaire, the Biomarker Questionnaire, and the Fieldworker Questionnaire. The questionnaires were adapted to reflect issues relevant to Kenya. Modifications were determined after a series of meetings with various stakeholders from DNMP and other government ministries and agencies, nongovernmental organisations, and international partners. The Household and Woman’s Questionnaires in English and Kiswahili were programmed into Android tablets, which enabled the use of computer-assisted personal interviewing (CAPI) for data collection. The Biomarker Questionnaire, in English and Kiswahili, was filled out on hard copy and then entered into the CAPI system.
Data collection
| Start | End |
|---|---|
| 2020-11-08 | 2020-12-23 |
| Name | Affiliation |
|---|---|
| National Bureau of Statistics | Government of Kenya |
Twenty-five teams were formed, with each including a supervisor, three interviewers (one of whom was a clinician), a health technician, and a driver. The team spent an average of 3 days working in a cluster. Information on selected clusters and sampled households was directly uploaded into supervisors’ tablets. When eligible respondents were absent from their homes, a maximum of three revisits were made to offer respondents the opportunity to participate in the survey. Field data collection was conducted from 9 November to 19 December 2020 for 20 teams and a slightly longer period (up to 23 December 2020) for five 5 teams that had hard to reach participants or were working in insecure counties.
In addition to the field supervisors, there were national and regional monitors who supervised and monitored field activities and ensured the collection and transfer of blood films to the laboratory. DNMP and KNBS field monitoring staff were responsible for data collection quality control and timely collection and transfer of slides from the field teams to the National Malaria Reference Laboratory. Periodically during fieldwork, a set of field check tables were run from the fieldwork data on the central office computer at KNBS. Problems that appeared from reviews of these tables were discussed with the appropriate teams (during supervisory visits or briefing sessions), and attempts were made to ensure that they did not persist. To facilitate communication and monitoring, each fieldworker was assigned a unique identification number. KNBS data processing staff provided teams with CAPI-related troubleshooting support during data collection.
Data processing
The 2020 KMIS questionnaires were programmed using Census and Survey Processing (CSPro) software. The program was then uploaded into Android-based tablets that were used to collect data via CAPI. The CAPI applications, including the supporting applications and the applications for the Household, Biomarker, and Woman’s Questionnaires, were programmed by ICF. The field supervisors transferred data daily to the CSWeb server, developed by the U.S. Census Bureau and located in Nairobi, for data processing on the central office computer at the KNBS office in Nairobi.
Data received from the field teams were registered and checked for any inconsistencies and outliers on the central office computer at KNBS. Data editing and cleaning included an extensive range of structural and internal consistency checks. All anomalies were communicated to field teams, which resolved data discrepancies. The corrected results were maintained in the central office computer at KNBS head office. The central office held data files which was used to produce final report tables and final data sets. CSPro software was used for data editing, cleaning, weighting, and tabulation.
Data appraisal
The estimates from a sample survey are affected by two types of errors: non-sampling errors and 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 2020 Kenya Malaria Indicator Survey (KMIS) 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 2020 KMIS is only one of many samples that could have been selected from the same population, using the same design and expected 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.
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% 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 2020 KMIS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearisation method of variance estimation for survey estimates that are means, proportions, or ratios.
Data Quality Tables
- Household age distribution
- Age distribution of eligible and interviewed women
- Completeness of reporting
- Births by calendar years
- Number of enumeration areas completed, by month and malaria endemicity
- Positive rapid diagnostic test (RDT) results, by month and malaria endemicity
- Concordance and discordance between RDT and microscopy results
- Concordance and discordance between national and external quality control laboratories
See details of the data quality tables in Appendix C of the final report.
Data Access
| Name | URL | |
|---|---|---|
| The DHS Program | http://www.DHSprogram.com | archive@dhsprogram.com |
Request Dataset Access
The following applies to DHS, MIS, AIS and SPA survey datasets (Surveys, GPS, and HIV).
To request dataset access, you must first be a registered user of the website. You must then create a new research project request. The request must include a project title and a description of the analysis you propose to perform with the data.
The requested data should only be used for the purpose of the research or study. To request the same or different data for another purpose, a new research project request should be submitted. The DHS Program will normally review all data requests within 24 hours (Monday - Friday) and provide notification if access has been granted or additional project information is needed before access can be granted.
DATASET ACCESS APPROVAL PROCESS
Access to DHS, MIS, AIS and SPA survey datasets (Surveys, HIV, and GPS) is requested and granted by country. This means that when approved, full access is granted to all unrestricted survey datasets for that country. Access to HIV and GIS datasets requires an online acknowledgment of the conditions of use.
Required Information
A dataset request must include contact information, a research project title, and a description of the analysis you propose to perform with the data.
Restricted Datasets
A few datasets are restricted and these are noted. Access to restricted datasets is requested online as with other datasets. An additional consent form is required for some datasets, and the form will be emailed to you upon authorization of your account. For other restricted surveys, permission must be granted by the appropriate implementing organizations, before The DHS Program can grant access. You will be emailed the information for contacting the implementing organizations. A few restricted surveys are authorized directly within The DHS Program, upon receipt of an email request.
When The DHS Program receives authorization from the appropriate organizations, the user will be contacted, and the datasets made available by secure FTP.
GPS/HIV Datasets/Other Biomarkers
Because of the sensitive nature of GPS, HIV and other biomarkers datasets, permission to access these datasets requires that you accept a Terms of Use Statement. After selecting GPS/HIV/Other Biomarkers datasets, the user is presented with a consent form which should be signed electronically by entering the password for the user's account.
Dataset Terms of Use
Once downloaded, the datasets must not be passed on to other researchers without the written consent of The DHS Program. All reports and publications based on the requested data must be sent to The DHS Program Data Archive in a Portable Document Format (pdf) or a printed hard copy.
Download Datasets
Datasets are made available for download by survey. You will be presented with a list of surveys for which you have been granted dataset access. After selecting a survey, a list of all available datasets for that survey will be displayed, including all survey, GPS, and HIV data files. However, only data types for which you have been granted access will be accessible. To download, simply click on the files that you wish to download and a "File Download" prompt will guide you through the remaining steps.
Use of the dataset must be acknowledged using a citation which would include:
- the Identification of the Primary Investigator
- the title of the survey (including country, acronym and year of implementation)
- the survey reference number
- the source and date of download
Disclaimer and copyrights
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Contacts
| Name | Affiliation | |
|---|---|---|
| Information about The DHS Program | The DHS Program | reports@DHSprogram.com |
| General Inquiries | The DHS Program | info@dhsprogram.com |
| Data and Data Related Resources | The DHS Program | archive@dhsprogram.com |
Metadata production
DDI_KEN_2020_MIS_v01_M_WB
| Name | Affiliation | Role |
|---|---|---|
| Development Economics Data Group | The World Bank | Documentation of the DDI |
2021-11-01
Metadata version
Version 01 (November 2021). Metadata is excerpted from "Kenya Malaria Indicator Survey 2020" Report.