Malaria Indicator Survey 2022
Cameroon, 2022
Get MicrodataIdentification
CMR_2022_MIS_v01_M
Malaria Indicator Survey 2022
| Name | Country code |
|---|---|
| Cameroon | CMR |
Malaria Indicator Survey [hh/mis]
The 2022 Cameroon Malaria Indicator Survey (2022 CMIS) is a follow-up to the first CMIS performed in 2012. Its target was a national sample of approximately 6,580 ordinary households. All women age 15–49 and all children under age 5 who were permanent residents of the selected households or spent the night prior to the interview were eligible to be surveyed.
The 2022 Cameroon Malaria Indicator Survey (2022 MIS) was implemented by the National Institute of Statistics (NIS). Data collection took place from August 22 to December 1, 2022. The survey is a national sample survey designed to provide information on topics such as availability and use of insecticide-treated nets (ITNs), prophylactic and therapeutic use of antimalarials, diagnostic testing for malaria in children presenting with fever, and the prevalence of malaria among children under age 5 (based on a rapid diagnostic test carried out at home).
The primary objective of the 2022 CMIS is to provide up-to-date estimates of basic demographic and health indicators related to malaria. Specifically, the survey collected information on vector control interventions (such as mosquito nets), intermittent preventive treatment of malaria among pregnant women, and care seeking for and treatment of fever among children. In addition, young children were tested for anemia and for malaria. Community knowledge, perceptions, and practices regarding malaria prevention and control were also assessed.
The information collected through the 2022 CMIS is intended to help policymakers and program managers in evaluating and implementing programs and strategies for improving the health of the country’s population.
Sample survey data [ssd]
- Household
- Individual
- Woman age 15-49
Version
The data dictionary was generated from hierarchical data that was downloaded from the The DHS Program website (http://dhsprogram.com).
- Contract Phase: DHS-VIII
- Recode Structure: DHS-VIII
Scope
The 2022 Cameroon Malaria Indicator Survey covers 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, sex, and marital status
• 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, ownership of livestock, possessions of durable goods, mosquito nets, and main material for the floor, roof and walls of the dwelling
• Mosquito nets
INDIVIDUAL WOMAN
• Identification
• Sociodemographic/ background characteristics (age, literacy, education, access to media, religion, ethnicity)
• Reproduction (birth history and child mortality)
• Pregnancy and intermittent preventive treatment
• Fever in children
• Malaria knowledge and beliefs
BIOMARKER
• Identification
• Hemoglobin measurement and malaria testing for children age 6 months to 4 years
FIELDWORKER
• Background information on each fieldworkers
Coverage
National coverage
Producers and sponsors
| Name | Affiliation |
|---|---|
| National Institute of Statistics (NIS) | Government of Cameroon |
| Name | Affiliation | Role |
|---|---|---|
| National Malaria Control Program | Government of Cameroon | Collaborated in the implementation of the survey |
| ICF | The DHS Program | Provided technical assistance through The DHS Program |
| Name | Role |
|---|---|
| Government of Cameroon | Financial support |
| United States Agency for International Development | Financial support |
| Global Fund to Fight AIDS, Tuberculosis and Malaria | Financial support |
Sampling
The 2022 CMIS targeted individuals in households throughout the country. A national sample of 6,580 households (3,598 in 257 urban clusters and 2,982 in 213 rural clusters) was planned for the survey. The sample was distributed to ensure adequate representation of urban and rural areas as well as the following 12 regions: Adamawa, Centre (excluding Yaoundé), Douala, East, Far North, Littoral (excluding Douala), North, North-West, West, South, South-West, and Yaoundé. In each of the regions (excluding Yaoundé and Douala, which are considered as having no rural sections), two layers were created: the urban layer and the rural layer.
A stratified, two-stage survey was implemented. In the first stage, 470 enumeration areas (EAs) or clusters were selected systematically with probability proportional to household size. The EAs were derived from the mapping work of the fourth General Census of Population and Housing (GRPH), carried out in 2017–18 by the Central Bureau of Population Censuses and Studies (BUCREP). A mapping exercise and enumeration of households in the clusters selected were implemented on tablet PCs by NIS from May 11 to August 14, 2022, to establish an updated list of households in each EA to serve as the basis for the second-degree draw. In the second stage, a sample of 14 households per cluster was selected using a systematic draw with equal probability.
All women age 15–49 who were residents of selected households or visitors who spent the night preceding the interview in the household were eligible to be interviewed. In addition, all children age 6–59 months were eligible for malaria and anemia tests.
For further details on sample design, see Appendix A of the final report.
Of the 6,580 households initially scheduled to be surveyed, 6,290 were actually selected. Of these 6,290 households, 6,080 were occupied at the time of the survey. Of the occupied households, 6,031 were successfully surveyed, for a response rate of 99%. In the surveyed households, 6,647 women age 15–49 were eligible for the individual women’s survey and 6,532 were successfully interviewed, for a response rate of 98%.
Survey instrument
Three questionnaires were used in the 2022 CMIS: the Household Questionnaire, the Woman’s Questionnaire, and the Biomarker Questionnaire. The questionnaires were based on standard DHS Program templates and adapted to reflect Cameroon’s specific population and malaria control needs. Information on survey data collectors was also gathered via a self-administered Fieldworker Questionnaire. All questionnaires were prepared in French and English.
Data collection
| Start | End |
|---|---|
| 2022-08-22 | 2022-12-01 |
- Face-to-face [f2f]
| Name | Affiliation |
|---|---|
| National Institute of Statistics | Government of Cameroon |
Data collection began on August 22, 2022, in each regional capital, where each team covered a minimum of two clusters before being deployed to the region. This approach ensured that teams were closely monitored before being deployed outside the regional capitals. Deployment was based on agents’ knowledge and language skills. Scheduled to last around 3 months, data collection was completed in the second half of November 2022 for most of regions surveyed and on December 1, 2022, in Douala and the North-West and South-West regions.
By the end of the fieldwork, the survey had been successfully completed in 444 of the 470 clusters selected for the 2022 CMIS sample. One cluster in the southern region was not mapped or enumerated due to the absence of maps showing its boundaries and borders. Consequently, no data were collected for this cluster. In two clusters, one in the East region and the other in the Far North region, there were no residential households at the time of mapping and enumeration. In the North-West region, 11 clusters out of the 41 selected could not be surveyed due to security issues. The clusters not covered in the North-West were mainly rural clusters, but clusters included in that region were in both urban and rural areas. Data collected in the North-West region were used to estimate indicators at the regional level and to back the estimation of indicators at the national level. Eleven of the 40 clusters selected in the South-West region, mainly located in rural areas (10 clusters versus one cluster in urban areas), could not be surveyed. These nonresponses at the cluster level are likely to introduce coverage bias in the indicators relevant for these two regions. This bias would be larger if nonrespondents were analytically different from respondents. In this report, findings presented at the North-West and South-West regional levels should be interpreted with caution. Data from all regions, including the North-West and South-West, are included in the overall findings and contribute to the estimation of indicators at the national level.
Data processing
In the interviews, responses were recorded directly on tablets using the appropriate computer application, developed using CSPro software. This application has several menus and includes internal controls and interview guides. Then data collected in the field were sent to the central server via the Internet using a quality control program, allowing almost instantaneous detection of the main collection errors for each team and each fieldworker. This information was immediately sent to the field teams to improve data quality, including returning to households for necessary checks. Regular activities of the chief supervisor focused mainly on teams for which there were specific concerns regarding data quality tables.
Once all of the field data were sent to the server, the survey data file was checked and cleaned and the weighting coefficients applied. All original identifiers were deleted from the data file. After checking that the data file was in its final format, the findings shown here were produced. All cover pages of the paper questionnaires containing identifiers were wiped out.
Data appraisal
The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and in 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, or incorrect data entry. Although numerous efforts were made during the implementation of the 2022 Cameroon Malaria Indicator Survey (2022 CMIS) 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 2022 CMIS is only one of many samples that could have been selected from the same population, using the same design and expected sample size. Each of these samples would yield results that differ somewhat from the results of the selected sample. Sampling errors are a measure of the variability among all possible samples. Although the exact degree of variability is unknown, 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, and so on), 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 2022 CMIS sample was the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed via SAS programs developed by ICF. These programs use the Taylor linearization method to estimate variances for estimated means, proportions, and ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.
Sampling errors tables are presented in Appendix B of the final report.
Data Quality Tables
- Household age distribution
- Age distribution of eligible and interviewed women
- Age displacement at ages 14/15
- Age displacement at ages 49/50
- Live births by years preceding the survey
- Completeness of reporting
- Observation of mosquito nets
- Number of enumeration areas completed by month and region
- Positive rapid diagnostic test (RDT) results by month and region
See details of the data quality tables in Appendix C of the final report.
Data Access
| Name | URL |
|---|---|
| The DHS Program | http://dhsprogram.com |
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.
Recommended citations are available at https://www.dhsprogram.com/publications/Recommended-Citations.cfm
Disclaimer and copyrights
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
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 |
Metadata production
DDI_CMR_2022_MIS_v01_M_WB
| Name | Affiliation | Role |
|---|---|---|
| Development Economics Data Group | World Bank | Documentation of the DDI |
2023-10-30
Metadata version
Version 01 (October 2023). Metadata in this DDI is excerpted from "Cameroon Malaria Indicator Survey 2022" report.
2023-10