Malaria Indicator Survey 2014-2015
Uganda, 2014 - 2015
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Reference ID
UGA_2014_MIS_v01_M
Producer(s)
Uganda Bureau of Statistics (UBOS), National Malaria Control Programme (NMCP)
Collection(s)
Metadata
Created on
Nov 09, 2015
Last modified
Jun 16, 2017
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Identification
Survey ID Number
UGA_2014_MIS_v01_M
Title
Malaria Indicator Survey 2014-2015
Country/Economy
| Name | Country code |
|---|---|
| Uganda | UGA |
Study type
Demographic and Health Survey, Special [hh/dhs-sp]
Series Information
The 2014-15 Uganda Malaria Indicator Survey (2014-15 UMIS) is the second survey of its kind in Uganda, following the one implemented in 2009. The survey has a nationally representative sample of 5,880 households from 210 sample clusters. It is designed to provide information on key malaria control indicators, such as the proportion of households having at least one bed net and at least one long-lasting insecticidal net (LLIN); the proportion of children under 5 who slept under a net and under an LLIN the previous night; the proportion of pregnant women who slept under a net and under an LLIN the previous night; the proportion of pregnant women who received intermittent preventive treatment (IPT) for malaria during their last pregnancy; and the prevalence of anaemia and malaria parasitemia among children under 5.
Abstract
Since 1995, the National Malaria Control Programme (NMCP) and its partners have been implementing and scaling up malaria interventions in all parts of the country. To determine the progress made in malaria control and prevention in Uganda, the Uganda Malaria Indicator Survey (UMIS) was implemented in 2009 and again in 2014-15 to provide data on key malaria indicators including mosquito net ownership and use, as well as prompt treatment using ACT.
The main objective of the UMIS is to obtain population-based estimates on malaria indicators including the prevalence of malaria and anaemia to inform strategic planning and programme evaluation. Specific objectives are:
1. To obtain estimates of the magnitude and distribution of anaemia and malaria parasitemia among children age 0-59 months
2. To estimate core malaria programme coverage indicators
• 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
• Assess percentage of children under five with fever for whom advice or treatment was sought
• Determine the species of plasmodium parasite most prevalent in children age 0-59 months
3. To measure indicators of knowledge, attitudes, and behaviour related to malaria control
4. To determine the factors associated with malaria parasitemia and anaemia
The main objective of the UMIS is to obtain population-based estimates on malaria indicators including the prevalence of malaria and anaemia to inform strategic planning and programme evaluation. Specific objectives are:
1. To obtain estimates of the magnitude and distribution of anaemia and malaria parasitemia among children age 0-59 months
2. To estimate core malaria programme coverage indicators
• 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
• Assess percentage of children under five with fever for whom advice or treatment was sought
• Determine the species of plasmodium parasite most prevalent in children age 0-59 months
3. To measure indicators of knowledge, attitudes, and behaviour related to malaria control
4. To determine the factors associated with malaria parasitemia and anaemia
Kind of Data
Sample survey data [ssd]
Unit of Analysis
- Household
- Children age 0-5
- Woman age 15-49
- Children age 0-5
- Woman age 15-49
Scope
Notes
The 2014-15 Uganda Malaria Indicator Survey covered the following topics:
HOUSEHOLD
• Identification
• Background information on each person listed (usual members and visitors in the selected households), such as relationship to head of the household, age, sex, and identification of who were eligible for the individual interview and children eligible for anaemia and malaria testing
• 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, and ownership of various durable goods (these items are used as proxy indicators of the household's socioeconomic status)
• Ownership and use of mosquito bed nets
• Hemoglobin measurement and malaria testing for children 0-5
INDIVIDUAL WOMAN
• Identification
• Respondent's background
• Reproduction
• Pregnancy and intermittent preventative treatment
• Fever in children
HOUSEHOLD
• Identification
• Background information on each person listed (usual members and visitors in the selected households), such as relationship to head of the household, age, sex, and identification of who were eligible for the individual interview and children eligible for anaemia and malaria testing
• 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, and ownership of various durable goods (these items are used as proxy indicators of the household's socioeconomic status)
• Ownership and use of mosquito bed nets
• Hemoglobin measurement and malaria testing for children 0-5
INDIVIDUAL WOMAN
• Identification
• Respondent's background
• Reproduction
• Pregnancy and intermittent preventative treatment
• Fever in children
Coverage
Geographic Coverage
National
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| Uganda Bureau of Statistics (UBOS) | Government of Uganda |
| National Malaria Control Programme (NMCP) | Uganda Ministry of Health |
Producers
| Name | Role |
|---|---|
| ICF International | Technical assistance |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| Governemt of Uganda | GovUGA | Founded the study |
| United States Agency for International Development | USAID | Founded the study |
| United Kingdom Department for International Development | DFID | Founded the study |
Sampling
Sampling Procedure
The sample for the 2014-15 Uganda Malaria Indicator Survey (2014-15 UMIS) was designed to provide most of the key malaria indicators for the country as a whole, for urban and rural areas, and for 10 survey regions.
In addition, three study domains based on malaria endemicity were created to provide selected malaria indicators addressing NMCP/MOH programmatic needs: 1) to evaluate the effect of interventions such as indoor residual spraying (IRS) in the 10 districts in the north, 2) to provide baseline indicators for the 14 districts planned for future IRS programmes, and 3) provide estimates separately for high altitude areas with low malaria burden.
The three study domains are arranged as follows:
Domain 1: ten (10) districts in which IRS programmes are currently implemented;
Domain 2: fourteen (14) districts planned for future IRS programmes (to provide baseline estimates);
Domain 3: ten (10) high-altitude districts (low malaria burden areas).
Apart from the three study domains above, the region of Karamoja was over-sampled in order to be comparable to a DHS region, and the urban areas of Wakiso and Mukono districts, together with Kampala, were combined to form a special 'Greater Kampala' zone.
Each of the 10 regions and the 3 study domains comprise multiple administrative districts that share a similar malaria burden or have specific malaria prevention efforts. The capital city, Kampala, comprises its own district and is entirely urban.
The sampling frame used for the 2014-15 UMIS was the preparatory frame for the Uganda Population and Housing Census, which was conducted in August 2014. Provided by the Uganda Bureau of Statistics (UBOS), the sampling frame excluded nomadic and institutional populations such as persons in hotels, barracks,
and prisons.
The 2014-15 UMIS sample was selected using a stratified two-stage cluster design consisting of 210 clusters, with 44 in urban areas and 166 in rural areas. In the first stage, 20 sampling strata were created and clusters were selected independently from each stratum by a probability-proportional-to-size selection. In the selected clusters, a complete listing of households and a mapping exercise was conducted from 25 October to 20 November 2014, with the resulting list of households serving as the sampling frame for the selection of households in the second stage.
In the second stage of the selection process, 28 households were selected in each cluster by equal probability systematic sampling. Because of the nonproportional allocation of the sample to the different regions and study domains, the sample is not self-weighting. Weighting factors have been added to the data file so that the results will be representative at the national and regional level as well as the survey domain level.
All women age 15-49 who were either permanent residents of the households in the 2014-15 UMIS sample or visitors present in the households on the night before the survey were eligible to be interviewed. In addition, all children age 0-59 months who were either permanent residents of the sampled households or visitors present in the households on the night before the survey were eligible to be tested for malaria and anaemia.
For further details on sample design, see Appendix A of the final report.
In addition, three study domains based on malaria endemicity were created to provide selected malaria indicators addressing NMCP/MOH programmatic needs: 1) to evaluate the effect of interventions such as indoor residual spraying (IRS) in the 10 districts in the north, 2) to provide baseline indicators for the 14 districts planned for future IRS programmes, and 3) provide estimates separately for high altitude areas with low malaria burden.
The three study domains are arranged as follows:
Domain 1: ten (10) districts in which IRS programmes are currently implemented;
Domain 2: fourteen (14) districts planned for future IRS programmes (to provide baseline estimates);
Domain 3: ten (10) high-altitude districts (low malaria burden areas).
Apart from the three study domains above, the region of Karamoja was over-sampled in order to be comparable to a DHS region, and the urban areas of Wakiso and Mukono districts, together with Kampala, were combined to form a special 'Greater Kampala' zone.
Each of the 10 regions and the 3 study domains comprise multiple administrative districts that share a similar malaria burden or have specific malaria prevention efforts. The capital city, Kampala, comprises its own district and is entirely urban.
The sampling frame used for the 2014-15 UMIS was the preparatory frame for the Uganda Population and Housing Census, which was conducted in August 2014. Provided by the Uganda Bureau of Statistics (UBOS), the sampling frame excluded nomadic and institutional populations such as persons in hotels, barracks,
and prisons.
The 2014-15 UMIS sample was selected using a stratified two-stage cluster design consisting of 210 clusters, with 44 in urban areas and 166 in rural areas. In the first stage, 20 sampling strata were created and clusters were selected independently from each stratum by a probability-proportional-to-size selection. In the selected clusters, a complete listing of households and a mapping exercise was conducted from 25 October to 20 November 2014, with the resulting list of households serving as the sampling frame for the selection of households in the second stage.
In the second stage of the selection process, 28 households were selected in each cluster by equal probability systematic sampling. Because of the nonproportional allocation of the sample to the different regions and study domains, the sample is not self-weighting. Weighting factors have been added to the data file so that the results will be representative at the national and regional level as well as the survey domain level.
All women age 15-49 who were either permanent residents of the households in the 2014-15 UMIS sample or visitors present in the households on the night before the survey were eligible to be interviewed. In addition, all children age 0-59 months who were either permanent residents of the sampled households or visitors present in the households on the night before the survey were eligible to be tested for malaria and anaemia.
For further details on sample design, see Appendix A of the final report.
Response Rate
A total of 5,802 households were selected for the sample, of which 5,494 were occupied. Of the occupied households, 5,345 were successfully interviewed, yielding a response rate of 97 percent. The response rate among households in rural areas was slightly higher (98 percent) than the response rate in urban areas (96 percent).
In the interviewed households, 5,494 women were identified as eligible for the individual interview; interviews were completed with 5,322 women, yielding a response rate of 97 percent. The eligible women’s response rate does not differ by urban or rural residence. The principal reason for non-response among eligible women was failure to find individuals at home despite repeated visits to the household.
In the interviewed households, 5,494 women were identified as eligible for the individual interview; interviews were completed with 5,322 women, yielding a response rate of 97 percent. The eligible women’s response rate does not differ by urban or rural residence. The principal reason for non-response among eligible women was failure to find individuals at home despite repeated visits to the household.
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 2014-12 | 2015-01 |
Data Collection Mode
Face-to-face [f2f]
Data Collection Notes
Training of Field Staff
UBOS and NMCP recruited and trained 88 people to serve as interviewers and supervisors for the fieldwork. The training was conducted during a two-week period in November 2014. The training course consisted of instruction regarding interviewing techniques and field procedures, a detailed review of questionnaire content, instruction for administering and obtaining informed consent, mock interviews between trainees in the classroom, and practice interviews with real respondents in areas outside the 2014-15 UMIS sample. Thirty-eight health technicians underwent two weeks of instruction and practice in obtaining informed consent and collecting blood samples from children age 0-59 months. Seventeen nurses who were trained as interviewers were also trained to offer and administer treatment to children with positive RDTs. Seventeen team supervisors underwent additional training in field editing, data quality control procedures, and fieldwork coordination. Seventeen supervisors, 51 interviewers (of whom 17 were nurses), and 34 health technicians were selected for the 17 field teams.
Fieldwork
Seventeen interviewing teams carried out data collection for the 2014-15 UMIS. Each team consisted of one supervisor, three interviewers (one of whom was a nurse), two health technicians, and one driver. Senior staff members from UBOS, NMCP, and the UMSP Laboratory coordinated and supervised fieldwork activities. Two ICF International staff also monitored fieldwork. Data collection took place from 1 December 2014 through the end of January 2015.
UBOS and NMCP recruited and trained 88 people to serve as interviewers and supervisors for the fieldwork. The training was conducted during a two-week period in November 2014. The training course consisted of instruction regarding interviewing techniques and field procedures, a detailed review of questionnaire content, instruction for administering and obtaining informed consent, mock interviews between trainees in the classroom, and practice interviews with real respondents in areas outside the 2014-15 UMIS sample. Thirty-eight health technicians underwent two weeks of instruction and practice in obtaining informed consent and collecting blood samples from children age 0-59 months. Seventeen nurses who were trained as interviewers were also trained to offer and administer treatment to children with positive RDTs. Seventeen team supervisors underwent additional training in field editing, data quality control procedures, and fieldwork coordination. Seventeen supervisors, 51 interviewers (of whom 17 were nurses), and 34 health technicians were selected for the 17 field teams.
Fieldwork
Seventeen interviewing teams carried out data collection for the 2014-15 UMIS. Each team consisted of one supervisor, three interviewers (one of whom was a nurse), two health technicians, and one driver. Senior staff members from UBOS, NMCP, and the UMSP Laboratory coordinated and supervised fieldwork activities. Two ICF International staff also monitored fieldwork. Data collection took place from 1 December 2014 through the end of January 2015.
Data Collectors
| Name | Abbreviation | Affiliation |
|---|---|---|
| Uganda Bureau of Statistics | UBOS | Government of Uganda |
Questionnaires
Questionnaires
The 2014 UMIS used two questionnaires: a Household Questionnaire and a Woman’s Questionnaire for women age 15-49 in the selected households. Both of these instruments were based on the model Malaria Indicator Survey questionnaires developed by the Roll Back Malaria Monitoring and Evaluation Research Group, as well as other questionnaires from previous surveys conducted in Uganda, including the 2009 UMIS. The Technical Working Group organised stakeholders’ meetings in Kampala to review the draft questionnaires. Stakeholders comprised a range of potential users, including government institutions, nongovernmental organisations, and interested donor groups. The questionnaires were translated from English into six local languages (Ateso/Karamajong, Luganda, Lugbara, Luo, Runyankole/Rukiga, and Runyoro/Rutoro).
The Household Questionnaire captured data on all usual members and visitors in the selected households. 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 were eligible for the individual interview and children eligible for anaemia and malaria testing. The Household Questionnaire was also used to collect responses on indicators of ownership and use of mosquito bed nets. In addition, the Household Questionnaire collected data on housing conditions and assets to calculate the measures of household wealth.
The Woman’s Questionnaire was used to collect data from women age 15-49 years, including: background characteristics (age, education, etc.); reproductive history (number of births, survival of births, etc.); current pregnancy status, intermittent preventive treatment for malaria during recent pregnancies; and antimalarial treatment for children under five with recent fever). It also collected information on knowledge about malaria.
The Household Questionnaire captured data on all usual members and visitors in the selected households. 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 were eligible for the individual interview and children eligible for anaemia and malaria testing. The Household Questionnaire was also used to collect responses on indicators of ownership and use of mosquito bed nets. In addition, the Household Questionnaire collected data on housing conditions and assets to calculate the measures of household wealth.
The Woman’s Questionnaire was used to collect data from women age 15-49 years, including: background characteristics (age, education, etc.); reproductive history (number of births, survival of births, etc.); current pregnancy status, intermittent preventive treatment for malaria during recent pregnancies; and antimalarial treatment for children under five with recent fever). It also collected information on knowledge about malaria.
Data Processing
Data Editing
All questionnaires for the 2014-15 UMIS were returned to the data processing centre at the UBOS headquarters in Kampala. Activities performed included office editing, data entry, and editing of computeridentified inconsistencies. The data were processed by a team consisting of one data entry supervisor, one assistant supervisor, 24 data entry operators, and 7 staff who performed tasks related to questionnaire administration, office editing, and secondary editing. Data entry and editing were accomplished using CSPro software. The process of office editing and data processing was initiated in January 2015 and completed in mid- February 2015.
Data Appraisal
Estimates of Sampling Error
The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the selected 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 2014-15 Uganda Malaria Indicator Survey (2014-15 UMIS) to minimize this type of error, nonsampling 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 2014-15 UMIS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling error is 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 2014-15 UMIS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the 2014-15 UMIS is a SAS program. This program used the Taylor linearization method for variance estimation for survey estimates that are means or proportions.
The Taylor linearization method treats any percentage or average as a ratio estimate, r = y/x, where y represents the total sample value for variable y, and x represents the total number of cases in the group or subgroup under consideration.
Note: A more detailed description of estimate of sampling error is presented in APPENDIX B of the survey report.
Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2014-15 UMIS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling error is 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 2014-15 UMIS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the 2014-15 UMIS is a SAS program. This program used the Taylor linearization method for variance estimation for survey estimates that are means or proportions.
The Taylor linearization method treats any percentage or average as a ratio estimate, r = y/x, where y represents the total sample value for variable y, and x represents the total number of cases in the group or subgroup under consideration.
Note: A more detailed description of estimate of sampling error is presented in APPENDIX B of the survey report.
Data Appraisal
Data Quality Tables
- Household age distribution
- Age distribution of eligible and interviewed women
- Completeness of reporting
Note: See detailed data quality tables in APPENDIX C of the report.
- Household age distribution
- Age distribution of eligible and interviewed women
- Completeness of reporting
Note: See detailed data quality tables in APPENDIX C of the report.
Access policy
Contacts
Access conditions
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.
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.
Citation requirements
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
- 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
Access authority
| Name | URL | |
|---|---|---|
| The DHS Program | archive@dhsprogram.com | Link |
Location of Data Collection
The DHS Program
Archive where study is originally stored
The DHS Program
http://dhsprogram.com/data/available-datasets.cfm
Cost: None
http://dhsprogram.com/data/available-datasets.cfm
Cost: None
Disclaimer and copyrights
Disclaimer
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.
Metadata production
DDI Document ID
DDI_UGA_2014_MIS_v01_M_WB
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Development Data Group | DECDG | The World Bank | Documentation of the DDI |
Date of Metadata Production
2015-11-06
DDI Document version
Version 01 (November 2015). Metadata is excerpted from "Uganda Malaria Indicator Survey 2014-2015" Report.