Malaria Indicator Survey 2013
Sierra Leone, 2013
Identification
SLE_2013_MIS_v01_M
Malaria Indicator Survey 2013
| Name | Country code |
|---|---|
| Sierra Leone | SLE |
Demographic and Health Survey, Special [hh/dhs-sp]
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. Detail overview of MIS program is availale in <a href='http://dhsprogram.com/What-We-Do/Survey-Types/MIS.cfm'>The DHS Program</a> website.
A national MIS was conducted in Sierra Leone in 2010 by the MoHS. However, the 2013 SLMIS is the first national MIS that is inclusive of rapid diagnostic testing (RDT) and microscopy to determine the national malaria prevalence among children under age 5.
Methodology of The 2013 Sierra Leone Malaria Indicator Survey
The 2013 SLMIS was conducted from February to March 2013, covering a nationally-representative sample of 6,717 households. All women aged 15-49 years in the selected households were eligible for individual interviews and were asked questions about malaria prevention during pregnancy and treatment of childhood fever. In addition, the survey included testing for anaemia and malaria among children aged 6-59 months using a finger prick blood sample. The results of anaemia and malaria rapid diagnostic testing were available immediately and were provided to the children’s parents or guardians. Thick blood smears were collected in the field and transported to the 2013 Sierra Leone MIS Laboratory at Lakka Hospital in Freetown where they were tested for the presence of malaria parasites.
The 2013 Sierra Leone Malaria Indicator Survey (SLMIS) is a comprehensive, nationally representative household survey designed following the Roll Back Malaria Monitoring and Evaluation Reference Group guidelines. The survey is designed to provide information on key malaria-related indicators including mosquito net ownership and use, coverage of preventive treatment for pregnant women, treatment of childhood fever, and the prevalence of anaemia and malaria among children aged 6-59 months.
The key objectives of the 2013 SLMIS are to:
• Measure the level of ownership and use of mosquito nets
• Assess coverage of the intermittent preventive treatment for pregnant women
• Identify treatment practices, 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 practices of malaria among women age 15-49 years
The 2013 SLMIS was designed to produce most of the key malaria indicators for the country as a whole; for urban and rural areas separately; for each of four regions in Sierra Leone – Eastern, Northern, Southern, and Western; and for the 14 districts – Bo, Bombali, Bonthe, Kailahun, Kambia, Kenema, Koinadugu, Kono, Moyamba, Port Loko, Pujehun, Tonkolili, Western Area Rural, and Western Area Urban.
Sample survey data [ssd]
- Household
- Children aged 6 to 59 months
- Woman aged 15 to 49
Scope
The 2013 Sierra Leone Malaria Indicator Survey covered the following topics:
HOUSEHOLD
• Household identification
• 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, access to electricity, type of fuel used for cooking, materials used for the floor, roof and walls of the house, and other household characteristics
• Hemoglobin measurement and malaria testing for children
INDIVIDUAL WOMAN
• Background characteristics (age, residential history, education, literacy, religion, and language)
• Full reproductive history and child mortality
• Prenatal care and preventive malaria treatment for most recent birth
• Prevalence and treatment of fever among children under age 5
• Knowledge about malaria (symptoms, causes, ways to prevent malaria, and types of antimalarials)
Coverage
National coverage
Producers and sponsors
| Name | Affiliation |
|---|---|
| Statistics Sierra Leone (SSL) | Government of Sierra Leone |
| National Malaria Control Programme (NMCP) | Ministry of Health and Sanitation, Government of Sierra Leone |
| University of Sierra Leone (USL) | Government of Sierra Leone |
| Catholic Relief Services (CRS) | NGO |
| Name | Affiliation | Role |
|---|---|---|
| ICF International | The DHS Program | Provided technical assistance |
| Name | Role |
|---|---|
| Catholic Relief Services | Funded the study |
| The Global Fund | Funded the study |
Sampling
Sample Design
The 2013 SLMIS sample was designed to produce most of the key indicators for the country as a whole, for urban and rural areas separately, for each of the 4 regions, and for the 14 districts in Sierra Leone. The sample design was developed by a CRS Senior Technical Advisor. The 2013 SLMIS was conducted in 336 enumeration areas (EAs). Twenty-four primary sampling units (PSUs) were selected from each of the 14 districts. The survey utilized a two-stage sample design. The first stage involved selecting 336 clusters with probability proportional to size from the list of approximately 9,671 EAs covered in the 2004 Sierra Leone Population and Housing Census (SLPHC 2004). Among the 336 clusters selected, 99 were in urban areas and 237 were in rural areas. Urban areas were oversampled within regions in order to produce robust estimates for that domain.
SSL conducted a complete listing of households in September through October 2012, and a mapping exercise for each cluster was carried out. The lists of households resulting from this exercise served as the sampling frame for the selection of households in the second stage. In addition to listing the households, the SSL listing enumerators used global positioning system (GPS) receivers to record the coordinates for each household of the 2013 SLMIS sample clusters. In the second stage, in each of the selected EAs, 20 households were selected, using systematic sampling, from a list of households in the EA.
All women age 15-49 years 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.
See Appendix A in the final report for details
6,717 households selected for the sample, 6,649 were occupied at the time of fieldwork. Among the occupied households, 6,614 were successfully interviewed, yielding a total household response rate of nearly 100 percent. In the interviewed households, 7,819 eligible women were identified to be eligible for individual interview and 7,658 were successfully interviewed, yielding a response rate of 98 percent.
Survey instrument
Three questionnaires were used in the 2013 SLMIS: 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 programs. The model questionnaires were modified to reflect relevant issues of malaria in Sierra Leone in consultation with the TWG and staff from ICF International. All questionnaires were in English.
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 members of the household in the household schedule, the haemoglobin measurements for children age 6-59 months and results of malaria testing for children under age 5. The household schedule section of the questionnaire was filled in by the interviewer, and immediately transcribed into the Apple iPhone, and the haemoglobin and malaria testing section was filled in by the health technician.
The Woman’s Questionnaire was used to collect information from all women age 15-49 years.
The 2013 SLMIS used Apple 3GS iPhones to collect data via the iFormBuilder platform, a software service application with a companion application (app) for the mobile devices allowing for timely data collection, monitoring, and analysis. Questionnaires were programmed into iPhones to eliminate the need for paper transcribing, to allow quicker data tabulation, and to facilitate data collection from available skip patterns. In designing the data collection program, CRS and partners developed three main forms: 1) a Household Questionnaire; 2) a Woman’s Questionnaire; and 3) a Biomarker Questionnaire form for eligible children. Within each of these forms, other sub-forms were created. The questionnaire skip logic and validations were programmed in order to facilitate consistent and complete data entry. It took three rounds of approximately three weeks each of intense programming and testing, over the course of a 10-month period, to program the MIS questionnaire into the iFormBuilder platform.
Data collection
| Start | End |
|---|---|
| 2013-02 | 2013-03 |
Fieldwork
Twenty-eight teams were organized for field data collection. Each team consisted of one field supervisor, one health professional to interview and administer treatment, one experienced survey implementer with map reading skills, one laboratory technician to conduct biomarker testing, and one driver. The field staff also included 14 district coordinators, and 14 district runners who collected slides from the field teams and delivered them to the Catholic Relief Services (CRS) headquarters in Freetown twice a week.
CRS arranged for printing the questionnaires, manuals, consent forms, brochures, and other field forms. CRS obtained and organized field supplies, such as backpacks and identification cards. The Technical working group (TWG) coordinated fieldwork logistics.
Field data collection for the 2013 SLMIS started on January 30, 2013. To allow for maximum supervision, all ten teams were visited by the national supervisors at least once in the first two weeks. The national supervisors visited the teams periodically throughout the data collection period. Fieldwork was completed in March 8, 2013.
Data processing
Data for the 2013 SLMIS was collected through questionnaires programmed onto iPhones using the iForm Builder application. The iPhones were programmed by Catholic Relief Services (CRS) programming specialists and loaded with the Household, Biomarker, and Woman’s questionnaires. Using the cloud, the field supervisors transferred data on a daily basis to the central data processing center at CRS in Freetown. To facilitate communication and monitoring, each field worker was assigned a unique identification number.
The ICF International data processing phase used Census Survey Processing Software (CSPro) for data editing, weighting, cleaning, and tabulation. In the CRS central office, data received from the field teams’ iPhones were registered and checked against any inconsistencies and outliers. The central office data processing unit communicated with each of the 28 teams in every EA to provide the teams feedback on data completeness and quality.
Data editing and cleaning included an extensive range of structural and internal consistency checks. Any anomalies were communicated to CRS in order for the CRS and ICF International data processing teams to resolve data discrepancies.
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 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 2013 Sierra Leone Malaria Indicator Survey (2013 SLMIS) 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 2013 SLMIS 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.
A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus and 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 2013 SLMIS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the 2013 SLMIS is a SAS procedure. This procedure used the Taylor linearization method of variance estimation for survey estimates that are means or proportions.
The Taylor linearization method treats any percentage or average as a ratio estimate, r = y/x, where y represents the total sample value for variable y, and x represents the total number of cases in the group or subgroup under consideration.
Further details on sampling errors calculation are given in Appendix B of the final report.
Tables were produced to review the quality of the data:
- Household age distribution
- Age distribution of eligible and interviewed women
- Completeness of reporting
Note: The tables are presented in APPENDIX C of the final report.
Data Access
| Name | URL | |
|---|---|---|
| The DHS Program | http://www.DHSprogram.com | archive@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.
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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 | 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 |
Metadata production
DDI_SLE_2013_MIS_v01_M_WB
| Name | Affiliation | Role |
|---|---|---|
| Development Data Group | The World Bank | Metadata preparation |
2014-09-30
Metadata version
Version 01 (September 2014). Metadata is excerpted from "Sierra Leone Malaria Indicator Survey (MIS) 2013" Report.