Malaria Indicator Survey 2024-2025
Uganda, 2024 - 2025
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UGA_2024_MIS_v01_M
Malaria Indicator Survey 2024-2025
MIS/UMIS 2024-25
| Name | Country code |
|---|---|
| Uganda | UGA |
Demographic and Health Survey [hh/dhs]
The 2024–25 Uganda Malaria Indicator Survey (UMIS) is the fourth survey of its kind following the ones implemented in 2009, 2014–15, and 2018–19. The survey used a nationally representative sample of 322 clusters. It was designed to provide information on key malaria control indictors such as the proportion of households having at least one bed net and at least one insecticide-treated net (ITN); the proportions of children under age 5 who slept under a net and who slept under an ITN the previous night; the proportions of pregnant women who slept under a net and who slept under an ITN the previous night; the proportion of pregnant women who received intermittent preventive treatment in pregnancy (IPTp) for malaria during their last pregnancy; and the prevalence of malaria parasitaemia among children under age 5.
The 2024–25 Uganda Malaria Indicator Survey (UMIS) was implemented by the National Malaria Control Division (NMCD) and the Uganda Bureau of Statistics (UBOS). Data collection took place from 29 November 2024 to 3 February 2025.
The primary objective of the 2024–25 UMIS was to provide up-to-date estimates of basic demographic and health indicators related to malaria. Specifically, the UMIS collected information on vector control interventions (for example, mosquito nets), intermittent preventive treatment of malaria in pregnant women, and care seeking for and treatment of fever in children. Young children were also tested for malaria infection.
The information collected through the 2024–25 UMIS is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population.
Sample survey data [ssd]
• Household
• Individual
• Children age 0-5
• 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).
- Contract Phase: DHS-9
- Recode Structure: DHS-8
Scope
The 2024-25 Uganda Malaria Indicator Survey covered the following topics:
HOUSEHOLD
• Identification
• Usual members and visitors in the selected households
• Basic information was collected on the characteristics of each person listed in the household, including their age, sex, and relationship to the head of the household.
• Characteristics of the household's dwelling unit, such as the source of water, type of toilet facilities, number of rooms, type of fuel used for cooking, main material of the floor, roof and walls of the house, possessions of durable goods (including land), ownership of livestock, etc.
• Mosquito nets
WOMAN
• Identification
• Background characteristics (age, residential history, education, literacy, religion, and ethnicity)
• Reproduction (birth history and child mortality)
• Pregnancy and intermittent preventive treatment
• Prevalence and treatment of fever among children under age 5
• Malaria knowledge and beliefs
BIOMARKER
• Identification
• Malaria testing for children age 6 months to 4 years
Coverage
National coverage
The survey covered all de jure household members (usual residents), all women aged 15-49 and all children aged 0-4 resident in the household.
Producers and sponsors
| Name |
|---|
| Uganda Bureau of Statistics (UBOS) |
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| ICF | The DHS Program | Provided technical assistance | |
| National Malaria Control Division | NMCD | Ministry of Health | Laboratory testing |
| Name | Abbreviation |
|---|---|
| Government of Uganda | Govt. UGA |
| United States Agency for International Development | USAID |
| U.S. President’s Malaria Initiative | PMI |
| Global Fund | GF |
| Gates Foundation |
Sampling
The 2024–25 UMIS followed a two-stage sampling design and was intended to allow estimates of key indicators for the following domains:
• National level
• Urban and rural areas
• Refugee settlements
• 15 subregions and their respective districts (see list to the right)
The overall sample size also allowed estimates to be produced for the following special areas, although they were not included as separate sampling domains:
• The 20 indoor residual spraying (IRS) intervention districts: Adjumani, Arua, Madi-Okollo, Koboko, Maracha, Moyo, Yumbe, Obongi, Terego, Amolatar, Dokolo, Kaberamaido, Kalaki, Budaka, Butaleja,
Pallisa, Tororo, Bugiri, Butebo, and Namutumba
• The 9 seasonal malaria chemoprevention (SMC) districts: Abim, Amudat, Kaabong, Kotido, Moroto, Nakapiripirit, Karenga, Nabilatuk, and Napak
• The 15 high altitude districts: Bulambuli, Kapchorwa, Kween, Bududa, Manafwa, Mbale, Namisindwa, Sironko, Bukwo, Kabale, Kisoro, Kanungu, Rukungiri, Rubanda, and Rukiga
The first stage of sampling involved selecting sample points (clusters) from the sampling frames; the nonrefugee areas and the refugee settlements used separate sampling frames. Enumeration areas (EAs) delineated for the 2024 National Population and Housing Census (NPHC) were used as the sampling frame for both non-refugee and refugee areas. A total of 322 clusters were selected with probability proportional to size from the EAs covered in the 2024 NPHC from the non-refugee frame. Of these clusters, 130 were in urban areas and 192 in rural areas. Urban areas were oversampled to produce robust estimates for the urban/rural domain. A total of 20 clusters were selected with probability proportional to size from the EAs covered in the refugee frame.
The second stage of sampling involved random selection of households. Given that the 2024 NPHC had been conducted less than 6 months before the UMIS, new household lists were not created for the selected non-refugee clusters. Instead, lists of households generated from the NPHC for the selected clusters were used, and households were randomly selected from those lists to be included in the UMIS. In the selected clusters for the refugee settlements domain, new household lists were created immediately before fieldwork began in those clusters. Before household selection in all clusters, a validation exercise was carried out in two urban and two rural clusters to determine the percentage of sampled households that could be identified in the field. Due to challenges in counting structures and households in the census listing, the validation exercise showed that not all the selected households could be identified in the field. This exercise led to a decision to oversample; instead of selecting 28 households per cluster, 36 households were selected per cluster in Kampala and other major urban areas, and 30 households were selected per cluster in the rest of the areas, yielding a total of 10,011 households in the non-refugee clusters and 600 in the refugee clusters. Because of the approximately equal sample sizes in each domain, the sample was not self-weighted at the national level. Results shown in this report have been weighted to account for the complex sample design.
Note: See Appendix A of the final survey report for additional details on the sampling procedures.
A total of 10,011 households were selected for the main survey, of which 9,584 were occupied at the time of fieldwork. Interviews were completed in 9,458 households, yielding a household response rate of 99%. In these households, 9,530 women age 15–49 were identified for individual interviews, and 9,238 were interviewed, resulting in a response rate of 97% among eligible women. In the refugee settlements, the household response rate was nearly 100%, and the response rate among women was 98%.
A spreadsheet containing all sampling parameters and selection probabilities was constructed to facilitate the calculation of sampling weights. Design weights were adjusted for both household and individual nonresponses to obtain the sampling weights for households and women, respectively. Individual non-response accounted for differences between the household and individual sampling weights. All sampling weights were further normalized at the national level to produce unweighted cases equal to weighted cases for interviewed households and interviewed women age 15–49, respectively. The weights for the 20 refugee clusters were normalized separately. Of note, the normalized weights were relative weights, which are valid for estimating proportions, means, ratios, and rates but not valid for estimating population totals or for pooling data from different surveys. As a result of this study design, data from the 20 refugee clusters could not be tabulated with the main survey data.
For further details on sampling weights, see Appendix A.4 of the final report.
Survey instrument
Three questionnaires were used in the 2024–25 UMIS: the Household Questionnaire, the Woman’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to Uganda. The English questionnaires were also translated into Luganda, Luo, Lugbara, Ateso, Runyankole/Rukiga, and Runyoro/Rutoro. The Household and Woman’s Questionnaires were programmed onto tablet computers, enabling use of computer-assisted personal interviewing (CAPI) for the survey. The Biomarker Questionnaire was filled out on hard copy and entered into the CAPI system when complete.
Data collection
| Start | End |
|---|---|
| 2024-11-29 | 2025-02-03 |
- Face-to-face computer-assisted interviews [capi]
| Name | Affiliation | Abbreviation |
|---|---|---|
| Uganda Bureau of Statistics | Government of Uganda | UBOS |
Following the main training, 138 trainees were organized into 23 teams of six members each. Team supervisors were selected from among the trained interviewers.
Each team comprised a team leader, two interviewers, one nurse/interviewer, and two health technicians. To ensure effective coordination, team leaders received additional training in technical oversight, task organization, logistics management, and stakeholder engagement, including liaising with local authorities and community members. Each team was assigned a driver and vehicle to support field operations.
All 23 teams operated under the supervision of experienced UBOS personnel specializing in data collection. In addition, a national health technician supervisor and four regional biomarker coordinators were responsible for monitoring the quality of sample collection and packaging. These supervisors conducted routine quality checks and ensured that only samples meeting established standards were transported to the CPHL.
Field data collection for the 2024–25 UMIS took place from 29 November 2024 to 3 February 2025. Team deployment was guided by interviewers’ language proficiency and knowledge of local areas.
To ensure maximum supervision, national monitors, including members of the technical working group and senior UBOS management, visited all 23 teams throughout the data collection period.
Data processing
All electronic data files for the 2024–25 UMIS were transferred via ICF’s Internet File Streaming System to the UBOS central office in Kampala, where they were stored on a password-protected computer. Data processing included registration, verification, and checks for completeness, consistency, and outliers. Secondary editing involved resolving computer-identified inconsistencies and coding open-ended responses. These tasks were carried out by UBOS staff who participated in the main fieldwork training, under the supervision of senior UBOS personnel. Data were edited using CSPro software. Secondary editing and overall data processing were completed in September 2025.
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 due to mistakes made in collecting and processing data, such as failing to locate and interview the correct household, misunderstanding questions (on the part of either the interviewer or the respondent), and entering data incorrectly. Although efforts were made to minimize this type of error during implementation of the 2024–25 Uganda Malaria Indicator Survey (UMIS), 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 for the 2024–25 UMIS is 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 have yielded results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.
A sampling error is usually measured in terms of the standard error for a particular statistic (for example, mean or percentage), 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, straightforward formulas could have been used for calculating sampling errors. However, the 2024–25 UMIS sample is the result of a multi-stage stratified design; consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the 2024–25 UMIS was a SAS program. This program used the Taylor linearization method of variance estimation for survey estimates that were means, proportions, or ratios.
A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.
Data Quality Tables
• Household age distribution
• Age distribution of eligible and interviewed women
• Age displacement at ages 14/15
• Age displacement at ages 49/50
• Live births by years preceding the survey
• Completeness of reporting
• Observation of mosquito nets
• Number of enumeration areas completed by month and region
• Positive rapid diagnostic test results by month and region
• Concordance and discordance between rapid diagnostic test and microscopy results
See details of the data quality tables in Appendix C of the final report.
Data Access
| Name | URL |
|---|---|
| The DHS Program | https://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.
Recommended citations are available at https://www.dhsprogram.com/publications/Recommended-Citations.cfm
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_UGA_2024_MIS_v01_M
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Development Data Group | DECDG | World Bank Group | Documentation of the survey |
2026-03-23T04:00:00.000Z
Metadata version
Version 01 (March 2026). Metadata is excerpted from "Uganda Malaria Indicator Survey 2024-2025" final report.