KENYA MALARIA INDICATOR SURVEY 2020
Kenya, 2020
Kenya National Bureau of Statistics, Ministry of Health
Created on
September 27, 2022
Last modified
September 27, 2022
Page views
3068
Download
621
Documentation in PDF
Metadata
DDI/XML
JSON
Identification
Survey ID Number
DDI-KEN-KNBS-KMIA-2020-V01
Title
KENYA MALARIA INDICATOR SURVEY 2020
Country
| Name | Country code |
|---|---|
| KENYA | KEN |
Abstract
Malaria is a significant public health problem in Kenya. More than 70% of the population is at constant risk from malaria, including those most vulnerable to the disease, specifically children and pregnant women. In the past 5 years, there has been a concerted effort by the government and malaria partnerships to fight the disease through prevention and treatment interventions such as mass and routine mosquito net distribution programs to attain universal coverage, intermittent preventive treatment for malaria during pregnancy, and parasitological diagnosis and management of malaria cases.
The Kenya Malaria Indicator Survey is one of the key performance monitoring tools periodically used to provide an in-depth assessment of malaria control efforts over time. Kenya has in the past undertaken three Malaria Indicator Surveys, in 2007, 2010, and 2015. The results from these surveys provide information on the performance of the key malaria control interventions as experienced by communities across the country; and are crucial to evaluation of interventions. Moreover, they enable effective planning and malaria control programming and facilitate a good understanding of the factors, dynamics, and impediments that affect control efforts. The reports also provide evidence for comparison with other malaria control programs globally and allow for benchmarking to meet international standards and practices for combating the disease.
In this regard, it is incumbent upon all partners and stakeholders in malaria control and elimination to embrace this report and assess the implications for malaria programming over the next few years.The report, therefore, has come at an opportune time when we are in the midst of implementing the Kenya Malaria Strategy 2019-2023. The results will form a basis for redirecting efforts and reorienting both technical and operational perspectives to address the challenges and strengthen the successes observed. The Ministry of Health is committed to further reducing the malaria burden in the coming years. Thus, I urge all players in malaria control to rededicate efforts and investments to enable delivery of sound malaria interventions and drive the burden further down towards our ambitious vision of a malaria-free Kenya within the shortest time possible
The Kenya Malaria Indicator Survey is one of the key performance monitoring tools periodically used to provide an in-depth assessment of malaria control efforts over time. Kenya has in the past undertaken three Malaria Indicator Surveys, in 2007, 2010, and 2015. The results from these surveys provide information on the performance of the key malaria control interventions as experienced by communities across the country; and are crucial to evaluation of interventions. Moreover, they enable effective planning and malaria control programming and facilitate a good understanding of the factors, dynamics, and impediments that affect control efforts. The reports also provide evidence for comparison with other malaria control programs globally and allow for benchmarking to meet international standards and practices for combating the disease.
In this regard, it is incumbent upon all partners and stakeholders in malaria control and elimination to embrace this report and assess the implications for malaria programming over the next few years.The report, therefore, has come at an opportune time when we are in the midst of implementing the Kenya Malaria Strategy 2019-2023. The results will form a basis for redirecting efforts and reorienting both technical and operational perspectives to address the challenges and strengthen the successes observed. The Ministry of Health is committed to further reducing the malaria burden in the coming years. Thus, I urge all players in malaria control to rededicate efforts and investments to enable delivery of sound malaria interventions and drive the burden further down towards our ambitious vision of a malaria-free Kenya within the shortest time possible
Unit of Analysis
Household and individuals
Version
Version Description
Version 1
Version Date
2020
Scope
Keywords
| Keyword |
|---|
| Malaria Survey |
Coverage
Geographic Coverage
National
Universe
Sampled individuals and households
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| Kenya National Bureau of Statistics | State Department of Planning |
| Ministry of Health | Goverment of Kenya |
Producers
| Name | Affiliation | Role |
|---|---|---|
| Kenya National Bureau of Statistics | State Deapartment of Planning | Data collection, processing and reporting |
| Ministry of Health | Government of Kenya | Implementer |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| Government of Kenya | GOK | Funding |
| United States Agency for International Development | USAID | Funding |
Sampling
Sampling Procedure
A sample is a group of people who have been selected for a survey. In the KMIS, the sample is designed to represent the national population age 15-49. In addition to national data, most countries want to collect and report data on smaller geographical or administrative areas. However, doing so requires a minimum sample size per area. For the 2020 KMIS the survey sample is representative at the national level, malaria endemicity zone, and for urban and rural areas.
To generate statistics that are representative of the country as a whole and the five malaria endemicity zones, the number of women surveyed in each malaria endemicity zone should contribute to the size of the total (national) sample in proportion to size of the malaria endemicity zone. However, if some malaria endemicity zones have small populations, then a sample allocated in proportion to each malaria endemicity zone’s population may not include sufficient women from each district for analysis. To solve this problem, malaria endemicity zones with small populations are oversampled. For example, let’s say that you have enough money to interview 6,771 women and want to produce results that are representative of Kenya as a whole and its malaria endemicity zones (as in Table 2.11). However, the total population of Kenya is not evenly distributed among the malaria endemicity zones: some malaria endemicity zones, such as Low risk zone, are heavily populated while others, such as Coast endemic zone are not. Thus, Coast endemic zonemust be oversampled
To generate statistics that are representative of the country as a whole and the five malaria endemicity zones, the number of women surveyed in each malaria endemicity zone should contribute to the size of the total (national) sample in proportion to size of the malaria endemicity zone. However, if some malaria endemicity zones have small populations, then a sample allocated in proportion to each malaria endemicity zone’s population may not include sufficient women from each district for analysis. To solve this problem, malaria endemicity zones with small populations are oversampled. For example, let’s say that you have enough money to interview 6,771 women and want to produce results that are representative of Kenya as a whole and its malaria endemicity zones (as in Table 2.11). However, the total population of Kenya is not evenly distributed among the malaria endemicity zones: some malaria endemicity zones, such as Low risk zone, are heavily populated while others, such as Coast endemic zone are not. Thus, Coast endemic zonemust be oversampled
Weighting
In order to get statistics that are representative of Kenya, the distribution of the women in the sample needs to be weighted (or mathematically adjusted) such that it resembles the true distribution in the country. Women from a sparsely populated malaria endemicity zone, like Coast endemic zone, should only contribute a small amount to the national total. Women from a largely populated malaria endemicity zone, like Low risk zone, should contribute much more. Therefore, DHS statisticians mathematically calculate a “weight” which is used to adjust the number of women from each malaria endemicity zone so that each zone’s contribution to the total is proportional to the actual population of the malaria endemicity zone.
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 2020 | 2020 |
Time periods
| Start date | End date |
|---|---|
| 2020 | 2020 |
Data Collection Mode
Face to face using CAPI
Data Collectors
| Name | Abbreviation | Affiliation |
|---|---|---|
| Kenya National Bureau of Statistics | KNBS | State Department of Planning |
Access policy
Access authority
| Name | Affiliation | URL | |
|---|---|---|---|
| Kenya National Bureau of Statistics | State Department of Planning | info@knbs.or.ke | www.knbs.or.ke |
Contacts
| Name | Affiliation | URL | |
|---|---|---|---|
| The Director General | Kenya National Bureau of Statistcs | directorgeneral@knbs.or.ke | www.knbs.or.ke |
Citation requirements
Kenya Malaria Indicators Survey 2020
Disclaimer and copyrights
Disclaimer
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data bear no responsibility for use of the data or for interpretations or inferences based upon such uses
Copyright
(c) 2020, Kenya National Bureau of Statistics
Metadata production
DDI Document ID
DDI-KEN-KNBS-KMIA-2020-V01
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Kenya National Bureau of Statistics | KNBS | State Department of Planning | Collection of data, process and report |
| Ministry of Health | MOH | Government of kenya | Implementation |