Malaria Indicator Survey 2017
Malawi, 2017
National Malaria Control Programme (NMCP)
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May 15, 2018
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May 15, 2018
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Identification
Survey ID Number
MWI_2017_MIS_v01_M
Title
Malaria Indicator Survey 2017
Country
| Name | Country code |
|---|---|
| Malawi | MWI |
Study type
Demographic and Health Survey, Special [hh/dhs-sp]
Series Information
The Malaria Indicator Survey (MIS) was developed by the Monitoring and Evaluation Working Group (MERG) of Roll Back Malaria, an international partnership developed to coordinate global efforts to fight malaria. A stand-alone household survey, the MIS collects national and regional or provincial data from a representative sample of respondents.
Abstract
The 2017 Malawi Malaria Indicator Survey (MMIS), a comprehensive, nationally-representative household survey, was designed in accord with the Roll Back Malaria Monitoring and Evaluation Working Group (RBM-MERG) guidelines. The primary objective of the 2017 MMIS project is to provide up-to-date estimates of basic demographic and health indicators related to malaria. Specifically, the 2017 MMIS collected information on mosquito nets, intermittent preventive treatment of malaria in pregnant women (IPTp), and care seeking behaviour and treatment of fever in children. Young children were also tested for anaemia and for malaria infection. Knowledge of malaria was assessed among interviewed women. The information collected through the 2017 MMIS is intended to assist policy makers and program managers in evaluating and designing programs and strategies for improving the health of the country’s population.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
- Household
- Individual
- Children age 0-5
- Woman age 15-49
- Individual
- Children age 0-5
- Woman age 15-49
Scope
Notes
The 2017 Malawi Malaria Indicator Survey covered the following topics:
HOUSEHOLD
• Identification
• Usual members and visitors in the selected households
• Basic characteristics of each person in the household collected (age, sex, education, etc.)
• Background information on each person listed, such as relationship to head of the household, age, and sex
• Characteristics of the household's dwelling unit, such as the source of water, type of toilet facilities, type of fuel used for cooking, materials used for the floor, roof and walls of the house, and possessions of durable goods (including land) and mosquito nets.
INDIVIDUAL WOMAN
• Identification
• Background characteristics (age, residential history, education, literacy, religion, and ethnicity)
• Reproductive history for the last 6 years
• Preventive malaria treatment for the most recent birth
• Prevalence and treatment of fever among children under age 5
• Knowledge about malaria (symptoms, causes, and how to prevent)
• Sources of media messages about malaria
BIOMARKER
• Identification
• Hemoglobin measurement and malaria testing for children age 0-5
HOUSEHOLD
• Identification
• Usual members and visitors in the selected households
• Basic characteristics of each person in the household collected (age, sex, education, etc.)
• Background information on each person listed, such as relationship to head of the household, age, and sex
• Characteristics of the household's dwelling unit, such as the source of water, type of toilet facilities, type of fuel used for cooking, materials used for the floor, roof and walls of the house, and possessions of durable goods (including land) and mosquito nets.
INDIVIDUAL WOMAN
• Identification
• Background characteristics (age, residential history, education, literacy, religion, and ethnicity)
• Reproductive history for the last 6 years
• Preventive malaria treatment for the most recent birth
• Prevalence and treatment of fever among children under age 5
• Knowledge about malaria (symptoms, causes, and how to prevent)
• Sources of media messages about malaria
BIOMARKER
• Identification
• Hemoglobin measurement and malaria testing for children age 0-5
Coverage
Geographic Coverage
National coverage
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| National Malaria Control Programme (NMCP) | Ministry of Health, Government of Malawi |
Producers
| Name | Affiliation | Role |
|---|---|---|
| ICF | The DHS Program | Provided technical assistance through The DHS Program |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| Government of Malawi | GovMWI | Financial assistance |
| United States Agency for International Development | USAID | Funded the survey through the President’s Malaria Initiative (PMI) |
Sampling
Sampling Procedure
The 2017 MMIS followed a two-stage sample design and allows estimates of key malaria indicators for the country as a whole, for urban and rural areas separately, and for each of the 3 administrative regions in Malawi: Northern, Central, and Southern.
The first stage of sampling involved selecting sample points (clusters) from the sampling frame. Enumeration areas (EAs) delineated for the 2008 Population and Housing Census were used as the sampling frame. A total of 150 clusters were selected, with probability proportional to size, from the EAs covered in the 2008 Population and Housing Census. Of these clusters, 60 were in urban areas and 90 in rural areas. Urban areas were oversampled within regions to produce robust estimates for each area or domain.
The second stage of sampling involved systematic selection of households. A household listing operation was undertaken in all selected EAs between February and March 2017, and households to be included in the survey were randomly selected from these lists. Twenty-five households were selected from each EA, for a total sample size of 3,750 households. Because of the approximately equal sample sizes in each region, the sample is not self-weighting at the national level. Results shown in this report have been weighted to account for the complex sample design. See Appendix A for additional details on the sampling procedures.
All women age 15-49 who were either permanent residents of the selected households or visitors who stayed in the household the night before the survey were eligible to be interviewed. With the parent's or guardian's consent, children age 6-59 months were tested for anaemia and for malaria infection.
For further details on sample design, see Appendix A of the final report.
The first stage of sampling involved selecting sample points (clusters) from the sampling frame. Enumeration areas (EAs) delineated for the 2008 Population and Housing Census were used as the sampling frame. A total of 150 clusters were selected, with probability proportional to size, from the EAs covered in the 2008 Population and Housing Census. Of these clusters, 60 were in urban areas and 90 in rural areas. Urban areas were oversampled within regions to produce robust estimates for each area or domain.
The second stage of sampling involved systematic selection of households. A household listing operation was undertaken in all selected EAs between February and March 2017, and households to be included in the survey were randomly selected from these lists. Twenty-five households were selected from each EA, for a total sample size of 3,750 households. Because of the approximately equal sample sizes in each region, the sample is not self-weighting at the national level. Results shown in this report have been weighted to account for the complex sample design. See Appendix A for additional details on the sampling procedures.
All women age 15-49 who were either permanent residents of the selected households or visitors who stayed in the household the night before the survey were eligible to be interviewed. With the parent's or guardian's consent, children age 6-59 months were tested for anaemia and for malaria infection.
For further details on sample design, see Appendix A of the final report.
Response Rate
A total of 3,750 households were selected for the sample, of which 3,735 were occupied at the time of fieldwork. Among the occupied households, 3,729 were successfully interviewed, yielding a total household response rate of 99.8%. In the interviewed households, 3,861 eligible women were identified as eligible for individual interview, and 3,860 women were successfully interviewed, yielding a response rate of 100%.
Weighting
A spreadsheet containing all sampling parameters and selection probabilities was constructed to facilitate the calculation of sampling weights. Household sampling weights and individual sampling weights were obtained by adjusting the previous calculated weight to compensate household nonresponse and individual nonresponse, respectively. These weights were further normalized at the national level to produce unweighted cases equal to weighted cases for both households and individuals at the national level. The normalized weights are valid for estimation of proportions and means at any aggregation levels, but not valid for estimation of totals.
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 2017-04-15 | 2017-06-16 |
Data Collection Mode
Face-to-face [f2f]
Data Collection Notes
Eight teams were organised for field data collection. Each team consisted of one field supervisor, three health professionals to interview and administer treatment, two laboratory technicians to conduct biomarker testing, and one driver. The field staff also included national coordinators who collected slides from the field teams and delivered them to the National Reference Health Laboratory.
Field data collection for the 2017 MMIS started on 15 April 2017. For maximum supervision, all eight teams were visited by national monitors, at least once a week. Fieldwork was completed on 16 June 2017.
Field data collection for the 2017 MMIS started on 15 April 2017. For maximum supervision, all eight teams were visited by national monitors, at least once a week. Fieldwork was completed on 16 June 2017.
Data Collectors
| Name | Abbreviation | Affiliation |
|---|---|---|
| National Malaria Control Programme | NMCP | Ministry of Health, Government of Malawi |
Questionnaires
Questionnaires
Data was primarily collected using three types of questionnaires: the Household Questionnaire, the Woman’s Questionnaire, and the Biomarker Questionnaire.
Data Processing
Data Editing
Data for the 2017 MMIS were collected through questionnaires programmed onto the CAPI application. The CAPI were programmed by ICF and loaded with the Household, Biomarker, and Woman’s Questionnaires. Using the cloud, the field supervisors transferred data on a daily basis to a central location for data processing in Lilongwe. To facilitate communication and monitoring, each field worker was assigned a unique identification number.
ICF provided technical assistance for processing the data using the Censuses and Surveys Processing (CSPro) system for data editing, cleaning, weighting, and tabulation. In the central office, data received from the field teams’ CAPI applications were registered and checked for any inconsistencies. Data editing and cleaning included an extensive range of structural and internal consistency checks. Any anomalies were communicated to team (field) supervisors so that the data processing teams could resolve data discrepancies.
ICF provided technical assistance for processing the data using the Censuses and Surveys Processing (CSPro) system for data editing, cleaning, weighting, and tabulation. In the central office, data received from the field teams’ CAPI applications were registered and checked for any inconsistencies. Data editing and cleaning included an extensive range of structural and internal consistency checks. Any anomalies were communicated to team (field) supervisors so that the data processing teams could resolve data discrepancies.
Data Appraisal
Estimates of Sampling Error
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 2017 Malawi Malaria Indicator Survey (MMIS) 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 2017 MMIS 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.
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 2017 MMIS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. Sampling errors are computed in SAS, using programs developed by ICF Macro. These programs use the Taylor linearization method of variance estimation for survey estimates that are means, proportions or ratios.
A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final report.
Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2017 MMIS 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.
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 2017 MMIS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. Sampling errors are computed in SAS, using programs developed by ICF Macro. These programs use the Taylor linearization method of variance estimation for survey estimates that are means, proportions or ratios.
A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final report.
Data Appraisal
Data Quality Tables
- Household age distribution
- Age distribution of eligible and interviewed women
- Completeness of reporting
- Births by calendar years
See details of the data quality tables in Appendix C of the survey final report.
- Household age distribution
- Age distribution of eligible and interviewed women
- Completeness of reporting
- Births by calendar years
See details of the data quality tables in Appendix C of the survey final report.
Access policy
Access authority
| Name | URL | |
|---|---|---|
| The DHS Program | archive@dhsprogram.com | http://www.DHSprogram.com |
Contacts
| Name | Affiliation | URL | |
|---|---|---|---|
| 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 |
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
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_MWI_2017_MIS_v01_M_WB
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Development Economics Data Group | DECDG | The World Bank | Documentation of the DDI |
DDI Document version
Version 01 (May 2017). Metadata is excerpted from "Malaria Indicator Survey 2017" Report.