Malaria Indicator Survey 2019
Ghana, 2019
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Reference ID
GHA_2019_MIS_v01_M
Producer(s)
Ghana Statistical Service (GSS)
Collection(s)
Metadata
Created on
Aug 05, 2020
Last modified
Aug 05, 2020
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Identification
Survey ID Number
GHA_2019_MIS_v01_M
Title
Malaria Indicator Survey 2019
Country/Economy
| Name | Country code |
|---|---|
| Ghana | GHA |
Study type
Malaria Indicator Survey [hh/mis]
Series Information
The 2019 Ghana Malaria Indicator Survey (2019 GMIS) is the second MIS conducted in Ghana, after the 2016 GMIS. In 2008 and 2014, Ghana implemented DHS surveys that also collected data on the primary household-level malaria indicators. The 2019 GMIS used a nationally representative sample of 200 clusters and about 6,000 selected households. The survey was designed to provide information on key malaria control indicators such as the proportion of households with at least one mosquito bed net and at least one insecticide-treated net (ITN), the proportion of children under age 5 who slept under a net the previous night and the proportion who slept under an ITN, the proportion of pregnant women who slept under a bed net the previous night and the proportion who received intermittent preventive treatment (IPT) for malaria during their most recent pregnancy in the last 2 years, and the prevalence of anaemia and malaria parasitemia among children under age 5.
Abstract
The primary objective of the survey is to provide current estimates of key malaria indicators. Specific objectives were:
▪ To measure the extent of ownership and use of mosquito bed nets
▪ To assess coverage of intermittent preventive treatment to protect pregnant women
▪ To identify practices and specific medications used for treating malaria among children under age 5
▪ To measure indicators of behaviour change communication messages, knowledge, and practices regarding malaria
▪ To measure the prevalence of malaria and severe anaemia among children age 6-59 months
The findings from the 2019 GMIS will assist policymakers and programme managers in evaluating and designing programmes and strategies for improving malaria control interventions in Ghana.
▪ To measure the extent of ownership and use of mosquito bed nets
▪ To assess coverage of intermittent preventive treatment to protect pregnant women
▪ To identify practices and specific medications used for treating malaria among children under age 5
▪ To measure indicators of behaviour change communication messages, knowledge, and practices regarding malaria
▪ To measure the prevalence of malaria and severe anaemia among children age 6-59 months
The findings from the 2019 GMIS will assist policymakers and programme managers in evaluating and designing programmes and strategies for improving malaria control interventions in Ghana.
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
Version
Version Notes
The data dictionary was generated from hierarchical data that was downloaded from the The DHS Program website (http://dhsprogram.com).
Scope
Notes
The 2019 Ghana Malaria Indicator Survey covered the following topics:
HOUSEHOLD
• Identification
• Usual members and visitors in the selected households
• 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, number of rooms, ownsership of livestock, possessions of durable goods, mosquito nets, and main material for the floor, roof and walls of the dwelling.
INDIVIDUAL WOMAN
• Identification
• Background characteristics (age, education, literacy, religion, and ethnicity)
• Reproductive history for the last 5 years
• Preventive malaria treatment during the pregnancy of the most recent live birth
• Prevalence and treatment of fever among children under age 5
• Knowledge about malaria (symptoms, causes, prevention, and types of antimalarial medications)
• Exposure to and source of media messages about malaria in the last 6 months
BIOMARKER
• Identification
• Hemoglobin measurement and malaria testing for children age 0-5
FIELDWORKER
• Background information on each fieldworke
HOUSEHOLD
• Identification
• Usual members and visitors in the selected households
• 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, number of rooms, ownsership of livestock, possessions of durable goods, mosquito nets, and main material for the floor, roof and walls of the dwelling.
INDIVIDUAL WOMAN
• Identification
• Background characteristics (age, education, literacy, religion, and ethnicity)
• Reproductive history for the last 5 years
• Preventive malaria treatment during the pregnancy of the most recent live birth
• Prevalence and treatment of fever among children under age 5
• Knowledge about malaria (symptoms, causes, prevention, and types of antimalarial medications)
• Exposure to and source of media messages about malaria in the last 6 months
BIOMARKER
• Identification
• Hemoglobin measurement and malaria testing for children age 0-5
FIELDWORKER
• Background information on each fieldworke
Coverage
Geographic Coverage
National coverage
Universe
The survey covered all de jure household members (usual residents), women age 15-49 years and children age 6-59 months resident in the household.
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| Ghana Statistical Service (GSS) | Government of Ghana |
Producers
| Name | Affiliation | Role |
|---|---|---|
| Ghana National Malaria Control Programme | Government of Ghana | Collaborated in the implementation of the study |
| National Public Health and Reference Laboratory | Ghana Health Service (GHS) | Collaborated in the implementation of the study |
| ICF | The DHS Program | Provided technical assistance through The DHS Program |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| Government of Ghana | GovtGHA | Financial support |
| United States Agency for International Development | USAID | Financial support |
| Global Fund | GF | Financial support |
Sampling
Sampling Procedure
The sample for the 2019 GMIS was designed to provide estimates of key malaria indicators for the country as a whole, for urban and rural areas separately, and for each of the 10 administrative regions (Western, Central, Greater Accra, Volta, Eastern, Ashanti, Brong Ahafo, Northern, Upper East, and Upper West) as defined in the Ghana 2010 Population and Housing Census (PHC).
The sampling frame used for the 2019 GMIS is the frame of the 2010 PHC, conducted in Ghana by GSS. In 2019, Ghana created six new regions, resulting in a total of 16 regions and 260 administrative districts; however, during survey design, the new administrative boundaries were not available. The 2019 GMIS sampling frame is therefore based on the 10 regional boundaries defined according to the 2010 PHC. The frame is a complete list of all census enumeration areas (EAs) created for the PHC. An EA is the smallest geographic area that can be easily canvassed by an enumerator during an enumeration exercise. The sampling frame contains information about EA location, type of residence (urban or rural), the estimated number of residential households, and the estimated population.
The 2019 GMIS sample was stratified and selected from the sampling frame in two stages. In the first stage, 200 EAs (97 in urban areas and 103 in rural areas) were selected with probability proportional to EA size and with independent selection in each sampling stratum. In the second stage of selection, a fixed number of 30 households was selected from each cluster to make up a total sample size of 6,000 households.
For further details on sample design, see Appendix A of the final report.
The sampling frame used for the 2019 GMIS is the frame of the 2010 PHC, conducted in Ghana by GSS. In 2019, Ghana created six new regions, resulting in a total of 16 regions and 260 administrative districts; however, during survey design, the new administrative boundaries were not available. The 2019 GMIS sampling frame is therefore based on the 10 regional boundaries defined according to the 2010 PHC. The frame is a complete list of all census enumeration areas (EAs) created for the PHC. An EA is the smallest geographic area that can be easily canvassed by an enumerator during an enumeration exercise. The sampling frame contains information about EA location, type of residence (urban or rural), the estimated number of residential households, and the estimated population.
The 2019 GMIS sample was stratified and selected from the sampling frame in two stages. In the first stage, 200 EAs (97 in urban areas and 103 in rural areas) were selected with probability proportional to EA size and with independent selection in each sampling stratum. In the second stage of selection, a fixed number of 30 households was selected from each cluster to make up a total sample size of 6,000 households.
For further details on sample design, see Appendix A of the final report.
Weighting
Due to the non-proportional allocation of the sample to different divisions and their urban and rural areas and the possible differences in response rates, sampling weights is required for any analysis using the 2019 GMIS data to ensure the actual representativeness of the survey results at the national level as well as the domain levels.
The design weights were adjusted for household non-response and individual non-response to obtain the sampling weights for households and for women, respectively. Non-response was adjusted at the sampling stratum level. For the household sampling weight, the household design weight was multiplied by the inverse of the household response rate by stratum. For women’s individual sampling weight, the household sampling weight was multiplied by the inverse of women’s individual response rate by stratum. After adjusting for non-response, the sampling weights were normalized to obtain the final standard weights that appear in the data files. The normalization process was done to obtain a total number of unweighted cases equal to the total number of weighted cases at the national level for the total number of households and women. Normalization was done by multiplying the sampling weight by the estimated sampling fraction obtained from the survey for the household weight and the individual women’s weights. The normalized weights are relative weights that are valid for estimating means, proportions, ratios, and rates but are not valid for estimating population totals or for pooled data.
For further details on sampling weights, see Appendix A.4 of the final report.
The design weights were adjusted for household non-response and individual non-response to obtain the sampling weights for households and for women, respectively. Non-response was adjusted at the sampling stratum level. For the household sampling weight, the household design weight was multiplied by the inverse of the household response rate by stratum. For women’s individual sampling weight, the household sampling weight was multiplied by the inverse of women’s individual response rate by stratum. After adjusting for non-response, the sampling weights were normalized to obtain the final standard weights that appear in the data files. The normalization process was done to obtain a total number of unweighted cases equal to the total number of weighted cases at the national level for the total number of households and women. Normalization was done by multiplying the sampling weight by the estimated sampling fraction obtained from the survey for the household weight and the individual women’s weights. The normalized weights are relative weights that are valid for estimating means, proportions, ratios, and rates but are not valid for estimating population totals or for pooled data.
For further details on sampling weights, see Appendix A.4 of the final report.
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 2019-09-25 | 2019-11-24 |
Data Collection Mode
Computer Assisted Personal Interview [capi]
Data Collection Notes
Field data collection was conducted during an 8-week period from 25 September to 24 November 2019. Twelve teams were formed, with each including a supervisor, three interviewers, a health technician, and a driver. Each team was allocated specific clusters according to local language competency. The team spent an average of 3 days working in a cluster. Information on selected clusters and sampled households was directly uploaded into supervisors’ tablets. When eligible respondents were absent from their homes, two or more call backs were made to offer respondents the opportunity to participate in the survey.
In addition to the field supervisors, there were national and regional monitors who supervised field activities and ensured the collection and transfer of blood slides to the laboratory. GSS, NMCP, and NPHRL field monitoring staff were responsible for data collection quality control and timely collection and transfer of slides from the field teams to the NPHRL. Periodically during fieldwork, a set of field check tables was run from the computerised data at GSS. Problems that appeared from reviews of these tables were discussed with the appropriate teams (during supervisory visits or briefing sessions), and attempts were made to ensure that they did not persist. In addition, GSS data processing staff provided teams with CAPI-related troubleshooting support during data collection.
In addition to the field supervisors, there were national and regional monitors who supervised field activities and ensured the collection and transfer of blood slides to the laboratory. GSS, NMCP, and NPHRL field monitoring staff were responsible for data collection quality control and timely collection and transfer of slides from the field teams to the NPHRL. Periodically during fieldwork, a set of field check tables was run from the computerised data at GSS. Problems that appeared from reviews of these tables were discussed with the appropriate teams (during supervisory visits or briefing sessions), and attempts were made to ensure that they did not persist. In addition, GSS data processing staff provided teams with CAPI-related troubleshooting support during data collection.
Questionnaires
Questionnaires
Four types of questionnaires were used for the 2019 GMIS: the Household Questionnaire, the Woman’s Questionnaire, the Biomarker Questionnaire, and the Fieldworker Questionnaire. The questionnaires were adapted to reflect issues relevant to Ghana. Modifications were determined after a series of meetings with various stakeholders from the NMCP and other government ministries and agencies, nongovernmental organisations, and international partners. The Household and Woman’s Questionnaires in English and four local Ghanaian languages (Akan, Dagbani, Ewe, and Ga) were programmed into tablet computers, which enabled the use of computer-assisted personal interviewing for the survey. The Biomarker Questionnaire, also translated into four local languages, was filled out on hard copy and entered into the CAPI system when complete.
Data Processing
Data Editing
Data for the 2019 GMIS were collected through questionnaires programmed into the CAPI application. The CAPI application was programmed by The DHS Program and loaded into the computers along with the Household, Biomarker, and Woman’s Questionnaires. Using the Internet File Streaming System (IFSS) developed by The DHS Program, the field supervisors transferred data on a daily basis to a central location for data processing in the GSS office located in Accra. To facilitate communication and monitoring, each fieldworker was assigned a unique identification number.
The Census and Survey Processing (CSPro) program was used for data editing, cleaning, weighting, and tabulation. Data received from the field teams’ CAPI applications were registered and checked for any inconsistencies and outliers at the GSS Head Office. Data editing and cleaning included an extensive range of structural and internal consistency checks. All anomalies were communicated to field teams, which resolved data discrepancies. The corrected results were maintained in master CSPro data files and then used in producing tables for the final report.
The Census and Survey Processing (CSPro) program was used for data editing, cleaning, weighting, and tabulation. Data received from the field teams’ CAPI applications were registered and checked for any inconsistencies and outliers at the GSS Head Office. Data editing and cleaning included an extensive range of structural and internal consistency checks. All anomalies were communicated to field teams, which resolved data discrepancies. The corrected results were maintained in master CSPro data files and then used in producing tables for the final report.
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 2019 Ghana Malaria Indicator Survey (GMIS) 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 2019 GMIS 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% 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 2019 GMIS 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 SAS, using programs developed by ICF. 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 report.
Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2019 GMIS 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% 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 2019 GMIS 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 SAS, using programs developed by ICF. 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 report.
Data Appraisal
Data Quality Tables
- Household age distribution
- Age distribution of eligible and interviewed women
- Completeness of reporting
- Births by calendar years
- Number of enumeration areas completed by month, according to region, Ghana MIS 2019
- Percentage of children age 6-59 months classified as having malaria according to rapid diagnostic test (RDT), by month and region, Ghana MIS 2019
- Number of children age 6-59 months measured for malaria via rapid diagnostic test (RDT), by month and region (unweighted), Ghana MIS 2019
See details of the data quality tables in Appendix C of the final report.
- Household age distribution
- Age distribution of eligible and interviewed women
- Completeness of reporting
- Births by calendar years
- Number of enumeration areas completed by month, according to region, Ghana MIS 2019
- Percentage of children age 6-59 months classified as having malaria according to rapid diagnostic test (RDT), by month and region, Ghana MIS 2019
- Number of children age 6-59 months measured for malaria via rapid diagnostic test (RDT), by month and region (unweighted), Ghana MIS 2019
See details of the data quality tables in Appendix C of the final report.
Access policy
Contacts
| Name | Affiliation | |
|---|---|---|
| Information about The DHS Program | The DHS Program | reports@DHSprogram.com |
| General Inquiries | The DHS Program | info@dhsprogram.com |
| Data and Data Related Resources | The DHS Program | archive@dhsprogram.com |
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 |
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_GHA_2019_MIS_v01_M
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Development Economics Data Group | DECDG | The World Bank | Documentation of the DDI |
DDI Document version
Version 01 (August 2020). Metadata is excerpted from "Ghana Malaria Indicator Survey 2019" Report.