Since 1995, the NMCP and its partners have been scaling up malaria interventions in all parts of the country. To determine the progress made in malaria control and prevention in Uganda, the 2009 Uganda Malaria Indicator Survey (UMIS) was designed to provide data on key malaria indicators, including mosquito net ownership and use, as well as prompt treatment using ACT.
The key objectives of the 2009 UMIS were to:
• Measure the extent of ownership and use of mosquito bed nets
• Assess coverage of the intermittent preventive treatment programme for pregnant women
• Identify practices used to treat malaria among children under age 5 and the use of specific antimalarial medications
• Measure the prevalence of malaria and anaemia among children age 0-59 months
• Determine the species of plasmodium parasite most prevalent in Uganda
• Assess knowledge, attitudes, and practices regarding malaria in the general population
Methodology of The Uganda Lalaria Indicatior Survey
The 2009 UMIS was carried out during November and December 2009, using a nationally representative sample of 4,760 households in 170 census enumeration areas. All women age 15-49 years in these households were eligible for individual interviews, during which they were asked questions about malaria prevention during pregnancy and treatment of childhood fevers. In addition, the survey included testing for anaemia and malaria among children age 0-59 months using finger (or heel) prick blood samples. Test results for anaemia (using the HemoCue portable machine) and malaria (using malaria RDT) were available immediately and were provided to the children’s parents or guardians. Thick and thin blood smears were also made in the field and transported to the Uganda Malaria Surveillance Project Molecular Laboratory at the Mulago Hospital in Kampala where they were tested for the presence of malaria parasites and where the species of plasmodium parasite was determined.
The 2009 UMIS was implemented by the Uganda Bureau of Statistics (UBOS) and the Uganda Malaria Surveillance Project (UMSP) on behalf of the National Malaria Control Program (NMCP). UBOS was responsible for general administrative management of the survey, including overseeing the day-to-day operations, designing the survey, and processing the data. UBOS assisted NMCP in the design of the UMIS, especially in the area of sample design and selection. In this regard, they provided the necessary maps and lists of households in the selected sample points. NMCP took primary responsibility for organizing the Technical Working Group, developing the survey protocol, and ensuring its approval by the Uganda National Council of Science and Technology prior to the data collection. Also, NMCP helped UBOS recruit, train, and monitor field staff and provided the medicines to treat children who tested positive for malaria during the survey.
The Uganda Malaria Surveillance Project (UMSP) Molecular Laboratory at the Mulago Hospital complex in Kampala trained field technicians and implemented the microscopic reading of the malaria slides to determine malaria parasite infection.
Technical assistance was provided by ICF Macro. ICF Macro staff assisted with overall survey design, sample design, questionnaire design, field staff training, field work monitoring, collection of biomarkers (anaemia testing, rapid diagnostic testing for malaria, and making and reading blood smears), data processing, data analysis, and report preparation.
Financial support for the survey was provided by the U.S. President’s Malaria Initiative (PMI) through the U.S. Agency for International Development (USAID).
Kind of data
Sample survey data [ssd]
Unit of analysis
- Women age 15-49
Producers and sponsors
National Malaria Control Program (NMCP)
Uganda Ministry of Health
Uganda Bureau of Statistics (UBOS)
Ministry of Finance, Planning and Economic Development
Uganda Malaria Surveillance Project (UMSP)
United States President’s Malaria Initiative
U.S. Agency for International Development
The 2009 UMIS survey was designed to provide national, regional, urban, and rural estimates of key malaria indicators. The sample was stratified into 9 survey regions of the country, plus Kampala. Each of the nine regions consisted of 8 to 10 contiguous administrative districts of Uganda that share similar languages and cultural characteristics. Kampala district, because it had a unique character as an entirely urban district and also was the capital city of Uganda, comprised a separate region. The 10 regions contained the following districts:
1. North East region: Kotido, Abim, Kaabong, Moroto, Nakapiripirit, Katakwi, Amuria, Bukedea, Soroti, Kumi, and Kaberamaido
2. Mid Northern region: Gulu, Amuru, Kitgum, Pader, Apac, Oyam, Lira, Amolatar, and Dokolo
3. West Nile region: Moyo, Adjumani, Yumbe, Arua, Koboko, Nyadri, and Nebbi
4. Mid Western region: Masindi, Buliisa, Hoima, Kibaale, Bundibugyo, Kabarole, Kasese, Kyenjojo, and Kamwenge
5. South Western region: Bushenyi, Rukungiri, Kanungu, Kabale, Kisoro, Mbarara, Ibanda, Isingiro, Kiruhura, and Ntungamo
6. Mid- Eastern region: Kapchorwa, Bukwa, Mbale, Bududa, Manafwa, Tororo, Butaleja, Sironko, Pallisa, Budaka, and Busia
7. Central 1 region: Kalangala, Masaka, Mpigi, Rakai, Lyantonde, Sembabule, and Wakiso
8. Central 2 region: Kayunga, Kiboga, Luwero, Nakaseke, Mubende, Mityana, Mukono, and
9. East Central region: Jinja, Iganga, Namutumba, Kamuli, Kaliro, Bugiri, and Mayuge
10. Kampala: Kampala
The sample was not spread geographically in proportion to the population, but rather equally across the regions, with 17 sample points or clusters per region. As a result, the UMIS sample is not selfweighting at the national level, and sample weighting factors have been applied to the survey records in order to bring them into proportion.
The survey utilized a two-stage sample design. The first stage involved selecting sample points or clusters from a list of enumeration areas (EAs) covered in the 2002 Population Census. A total of 170 clusters (26 urban and 144 rural) with probability proportional to size were selected. Several months prior to the main survey, a complete listing of all households in the 170 selected clusters was carried out. This provided a sampling frame from which households were then selected for the survey. The second stage of selection involved the systematic sampling of households from the list of households in each cluster. Twenty-eight households were selected in each cluster.
All women age 15-49 years who were either permanent residents of the households in the sample or visitors present in the household on the night before the survey were eligible to be interviewed in the survey. All children age 0-59 months who were listed in the household were eligible for the anaemia and malaria testing component of the survey.
Note: See detailed sampling information in APPENDEX A of the 2009 Uganda Malaria Indicator Survey (MIS).
Of the 4,760 households selected in the sample, 4,536 were found to be occupied at the time of the fieldwork. The shortfall is due to dwellings that were found to be vacant or destroyed. Of the existing households, 4,421 were successfully interviewed, yielding a household response rate of 98 percent.
In the households interviewed in the survey, a total of 4,312 eligible women were identified, of whom 4,134 were successfully interviewed, yielding a response rate of 96 percent. The household and women’s response rates were slightly lower in the urban than in the rural sample. The principal reason for non-response among eligible women was the failure to find them at home despite repeated visits to the household.
Dates of collection
Mode of data collection
Two questionnaires were used in the UMIS: a Household Questionnaire and a Woman’s Questionnaire for all women age 15-49 in the selected households. Both instruments were based on the standard Malaria Indicator Survey Questionnaires developed by the Roll Back Malaria and DHS programmes. In consultation with the Technical Working Group, NMCP and ICF Macro staff modified the model questionnaires to reflect issues relevant to malaria in Uganda. The questionnaires were translated into the 6 major local languages commonly spoken in Uganda (Ateso-Karamojong, Luganda, Lugbara, Luo, Runyankore-Rukiga, and Runyoro-Rutoro).
The Household Questionnaire was used to list all the usual members and visitors in the selected households. Some basic information was collected on the characteristics of each person listed, including age, sex, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women who are eligible for the individual interview and children who are age 0-59 months for anaemia and malaria testing. The Household Questionnaire also collected information on characteristics of the household's dwelling unit, such as the source of water; type of toilet facilities; materials used for the floor, roof, and walls of the house; ownership of various durable goods; and ownership and use of mosquito nets. In addition, this questionnaire was used to record consent and results with regard to the anaemia and malaria testing of young children.
The Woman’s Questionnaire was used to collect information from all women age 15-49 years and covered the following topics:
• Background characteristics (age, residential history, education, literacy, and dialect)
• Full reproductive history and child mortality
• Antenatal care and preventive malaria treatment for most recent birth
• Prevalence and treatment of fever among children under age 5
• Knowledge about malaria (causes, ways to avoid, types of medicines, and so on).
The questionnaires and process of biomarker collection were pretested prior to the main data collection. The pretest involved 12 interviewers and 12 health technicians/nurses (2 for each of the 6 local languages into which the questionnaires were translated). The interviewers were trained for five days and collected data in the six languages for three days in areas close to Kampala. The purpose of the pretest was to assess the appropriateness of the wording of the questions as well as to verify the translations and skip patterns.
The processing of the UMIS questionnaire data began soon after the fieldwork commenced. Completed questionnaires were returned periodically from the field to the UBOS office in Kampala, where they were coded by data processing personnel recruited and trained for this task. The data processing staff consisted of a supervisor from UBOS, a questionnaire administrator, data entry operators, and data editors, all of whom were trained by a MEASURE DHS data processing specialist. Data were entered using the CSPro computer package. All data were entered twice (100 percent verification).
Estimates derived from a sample survey are affected by two types of errors: 1) non-sampling errors, and 2) 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 2009 Uganda malaria indicator survey (2009 UMIS) to minimize 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 2009 UMIS 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.
A 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 percent 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 2009 UMIS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use a more complex formula. The computer software used to calculate sampling errors for the 2009 UMIS is the sampling error module in ISSA (Integrated System for Survey Analysis). This module uses the Taylor linearization method of variance estimation for survey estimates that are means or proportions. Another approach, the Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.
Note: See detailed estimate of sampling error calculation in APPENDIX B of the 2009 Uganda Malaria Indicator Survey (MIS) report.
Other forms of data appraisal
Data Quality Tables
- Household age distribution
- Age distribution of eligible and interviewed women
- Completeness of reporting
Note: Data quality tables are available in APPENDIX C of the 2009 Uganda Malaria Indicator Survey (MIS) report.
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.