Malaria Indicator Survey 2014
Malawi, 2014
National Malaria Control Programme (NMCP)
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Identification
IDNO
MWI_2014_MIS_v01_M
Title
Malaria Indicator Survey 2014
Countries
| Name | Code |
|---|---|
| Malawi | MWI |
Study notes
The 2014 MIS used a standard set of instruments and protocol developed by RBM Monitoring and Evaluation Reference Group (MERG). These tools are largely based on the collective experience gained from the DHS and MIS surveys and are presented as a package of materials to promote standardised survey management and data collection methodology. The package also includes standardised measurement of malaria parasite and anaemia prevalence among target populations to derive the malariarelated burden at the community level.
The key objectives of the 2014 MIS were to:
• Measure the level of ownership and use of mosquito nets
• Assess coverage of intermittent preventive treatment for pregnant women
• Identify treatment practises, including the use of specific antimalarial medications to treat malaria among children under 5
• Measure the prevalence of malaria and anaemia among children age 6-59 months
• Assess knowledge, attitudes, and practises of malaria in the adult population
• Measure trends in key malaria indicators since the 2012 MIS
The 2014 MIS was designed to produce most of the key malaria indicators for the country as a whole, for urban and rural areas separately, and for each of the three regions in Malawi: Northern, Central, and Southern.
The key objectives of the 2014 MIS were to:
• Measure the level of ownership and use of mosquito nets
• Assess coverage of intermittent preventive treatment for pregnant women
• Identify treatment practises, including the use of specific antimalarial medications to treat malaria among children under 5
• Measure the prevalence of malaria and anaemia among children age 6-59 months
• Assess knowledge, attitudes, and practises of malaria in the adult population
• Measure trends in key malaria indicators since the 2012 MIS
The 2014 MIS was designed to produce most of the key malaria indicators for the country as a whole, for urban and rural areas separately, and for each of the three regions in Malawi: Northern, Central, and Southern.
Kind of data
Sample survey data [ssd]
Coverage
Geographic coverage
National coverage
Unit of analysis
- Women age 15-49 years
- Children age 6-59 months
- Children age 6-59 months
Producers and sponsors
Authoring entity
| Name | Affiliation |
|---|---|
| National Malaria Control Programme (NMCP) | Ministry of Health, Government of Malawi |
Producer(s)
| Name | Role |
|---|---|
| ICF International | Provided technical assistance |
| National Statistical Office | Assisted in the household listing in the selected enumeration areas |
Funding agencies
| Name | Abbreviation | Role |
|---|---|---|
| Government of Malawi | GovMAI | Financial assistance in terms of in-kind contribution |
| Global Fund | GF | Funded the study - supported the local costs of the survey |
| U.S. President’s Malaria Initiative | PMI | Funded the study - supported the survey through ICF International |
Sampling
Sampling procedure
Sample Design
The 2014 MIS sample was designed to produce most of the key indicators for the country as a whole, for urban and rural areas separately, and for each of the three regions.
The survey utilised a two-stage sample design. The first stage involved selecting 140 clusters with probability proportional to size from the list of approximately 12,474 EAs covered in the 2008 National Population and Housing Census. The EA size was the number of residential households in the EA recorded in the census. Among the 140 clusters selected, 50 were in urban areas and 90 were in rural areas. Urban areas were over-sampled within regions in order to produce robust estimates for that domain. Therefore, the MIS sample was not proportional to the population for urbanrural residence and required a final weighting adjustment to provide valid estimates for every domain of the survey. In the second stage, in each of the selected EAs, 25 households were selected, using systematic sampling, from a list of households in the EA.
All women age 15-49 who were either permanent residents of the selected households or visitors present in the household on the night before the survey were eligible to be interviewed. In addition, all children age 6-59 months who were listed in the household were eligible for anaemia and malaria testing.
Note: The sample design is described in details in Appendix A.
The 2014 MIS sample was designed to produce most of the key indicators for the country as a whole, for urban and rural areas separately, and for each of the three regions.
The survey utilised a two-stage sample design. The first stage involved selecting 140 clusters with probability proportional to size from the list of approximately 12,474 EAs covered in the 2008 National Population and Housing Census. The EA size was the number of residential households in the EA recorded in the census. Among the 140 clusters selected, 50 were in urban areas and 90 were in rural areas. Urban areas were over-sampled within regions in order to produce robust estimates for that domain. Therefore, the MIS sample was not proportional to the population for urbanrural residence and required a final weighting adjustment to provide valid estimates for every domain of the survey. In the second stage, in each of the selected EAs, 25 households were selected, using systematic sampling, from a list of households in the EA.
All women age 15-49 who were either permanent residents of the selected households or visitors present in the household on the night before the survey were eligible to be interviewed. In addition, all children age 6-59 months who were listed in the household were eligible for anaemia and malaria testing.
Note: The sample design is described in details in Appendix A.
Response rate
Of the 3,501 households selected for the sample, 3,415 were occupied at the time of fieldwork. Eighty-six dwellings were abandoned and, therefore, were not included in the response rate. Among the occupied households, 3,405 were successfully interviewed, yielding a total household response rate above 99 percent. In the interviewed households, 2,927 eligible women were identified as eligible for the individual interview and 2,897 were successfully interviewed, yielding a response rate of 99 percent.
Data Collection
Dates of collection
| Start | End |
|---|---|
| 2014-05-02 | 2014-06-10 |
Mode of data collection
Face-to-face [f2f]
Questionnaires
Three questionnaires were used in the 2014 Malawi MIS: a Household Questionnaire, a Biomarker Questionnaire, and a Woman’s Questionnaire. The Household and Woman’s questionnaires were based on the model MIS questionnaires developed by the RBM and DHS programmes, as well as the 2010 and 2012 MIS. The model questionnaires were modified to reflect relevant issues of malaria in Malawi in consultation with the Steering Committee, the NMCP, and staff from ICF International. The questionnaires were translated into the two main local languages of Malawi—Chichewa and Tumbuka.
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 were eligible for the individual interview and children age 6-59 months who were eligible 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.
The Biomarker Questionnaire was used to record haemoglobin measurements for children age 6-59 months and results of malaria testing for children under age 5 years. The questionnaire was filled in by the health technician and transcribed into the tablet computer by the team supervisor.
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, religion, dialect)
• Reproductive history and child mortality for births in the last six years
• Prenatal care and preventive malaria treatment for most recent birth
• Prevalence and treatment of fever among children under 5 years
• Knowledge about malaria (symptoms, causes, and ways to prevent it) and messages on malaria
• Cost incurred for the treatment of fever in children under 5 years
No formal field pre-test was done for the survey questionnaires because most of the 2014 MIS questions were included in previous surveys in Malawi and the field staff were experienced in anaemia and malaria testing in the field and in the use of PDAs for data collection.
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 were eligible for the individual interview and children age 6-59 months who were eligible 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.
The Biomarker Questionnaire was used to record haemoglobin measurements for children age 6-59 months and results of malaria testing for children under age 5 years. The questionnaire was filled in by the health technician and transcribed into the tablet computer by the team supervisor.
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, religion, dialect)
• Reproductive history and child mortality for births in the last six years
• Prenatal care and preventive malaria treatment for most recent birth
• Prevalence and treatment of fever among children under 5 years
• Knowledge about malaria (symptoms, causes, and ways to prevent it) and messages on malaria
• Cost incurred for the treatment of fever in children under 5 years
No formal field pre-test was done for the survey questionnaires because most of the 2014 MIS questions were included in previous surveys in Malawi and the field staff were experienced in anaemia and malaria testing in the field and in the use of PDAs for data collection.
Data Processing
Data editing
Data for the 2014 Malawi MIS were collected through questionnaires programmed onto computer tablets. ICFI data processing specialists loaded the Household, Biomarker, and Woman’s Questionnaires in English and the two main local languages, Chichewa and Tumbuka, in the computer tablets and installed data entry and processing programmes. The tablets were Bluetooth-enabled to facilitate electronic transfer of files, e.g., data from the Household Questionnaires transferred among survey team members and transfer of completed questionnaires to the team supervisor’s tablet. The field supervisors transferred data on a daily basis to the central data processing unit using the Internet. To facilitate communication and monitoring, each field worker was assigned a unique identification number.
The Census Survey Processing Software (CSPro) was used for data editing, weighting, cleaning, and tabulation. In the NMCP central office, data received from the supervisors’ tablets were registered and checked against any inconsistencies and outliers. Data editing and cleaning included range checks and structure and internal consistency checks. Any anomalies were communicated to the respective team through their team supervisor. The corrected results were resent to the central processing unit.
The Census Survey Processing Software (CSPro) was used for data editing, weighting, cleaning, and tabulation. In the NMCP central office, data received from the supervisors’ tablets were registered and checked against any inconsistencies and outliers. Data editing and cleaning included range checks and structure and internal consistency checks. Any anomalies were communicated to the respective team through their team supervisor. The corrected results were resent to the central processing unit.
Data Appraisal
metadata.study_desc.method.analysis_info.sampling_error_estimates
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 2014 Malawi Malaria Indicator Survey (Malawi MIS) 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 2014 Malawi MIS 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 among 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 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 Malawi MIS 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 either ISSA or SAS, using programs developed by ICF International. These programs use the Taylor linearisation method of variance estimation for survey estimates that are means, proportions, or ratios like the ones in the Malawi MIS survey.
The Taylor linearisation 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 Malawi MIS 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 among 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 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 Malawi MIS 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 either ISSA or SAS, using programs developed by ICF International. These programs use the Taylor linearisation method of variance estimation for survey estimates that are means, proportions, or ratios like the ones in the Malawi MIS survey.
The Taylor linearisation 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.
Other forms of 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.
Data access
Access authorities
| Name | URI | |
|---|---|---|
| The DHS Program | archive@dhsprogram.com | http://www.DHSprogram.com |
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
Data collection locations
The DHS Program
Original archive where collection 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.
Contacts
Contact(s)
| Name | Affiliation | URI | |
|---|---|---|---|
| Information about The DHS Program | The DHS Program | reports@DHSprogram.com | http://www.DHSprogram.com |
| General Inquiries | The DHS Program | info@dhsprogram.com | http://www.DHSprogram.com |
| Data and Data Related Resources | The DHS Program | archive@dhsprogram.com | http://www.DHSprogram.com |
Metadata production
IDNo
DDI_MWI_2014_MIS_v01_M_WB
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Development Data Group | DECDG | The World Bank | Documentation of the DDI |
Production date
2015-09-02
Version
Version 01 (September 2015). Metadata is excerpted from "Malaria Indicator Survey 2014" Report.