Malaria Indicator Survey 2016
Liberia, 2016
Get MicrodataIdentification
LBR_2016_MIS_v01_M
Malaria Indicator Survey 2016
| Name | Country code |
|---|---|
| Liberia | LBR |
Malaria Indicator Survey
The primary objective of the 2016 Liberia Malaria Indicator Survey (LMIS) was to provide up-to-date estimates of basic demographic and health indicators for malaria. Specifically, the LMIS collected information on vector control interventions such as mosquito nets and indoor residual spraying of insecticides, on intermittent preventive treatment of malaria in pregnant women, and on care seeking and treatment of fever in children. Also, young children were tested for malarial infection and anaemia.
The information collected through the LMIS is intended to assist policy makers and program managers in designing and evaluating programs and strategies for improving the health of the country’s population.
Sample survey data [ssd]
- Household
- Individual
- Children age 0-5
- 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).
Scope
The 2016 Liberia 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, sex, tested for malaria, been sick with a fever, got treatment, etc
• Characteristics of the household's dwelling unit, such as the source of water, type of toilet facilities, type of fuel used for cooking, main material of the floor, roof and walls of the house, possessions of durable goods (including land), ownership of livestock, etc.
• Mosquito nets
WOMAN
• Identification
• Background characteristics (age, residential history, education, literacy, religion, and ethnicity)
• Reproductive history for the last 5 years
• Preventive malaria treatment for the most recent birth
• Pregnancy and postnatal care
• Use of contraception
• Prevalence and treatment of fever among children under age 5
• Child immunizations
• Knowledge about malaria (symptoms, causes, how to prevent, and types of antimalarial medications)
BIOMARKER
• Identification
• Hemoglobin measurement and malaria testing for children age 0-5
Coverage
National coverage
Producers and sponsors
| Name | Affiliation |
|---|---|
| National Malaria Control Program (NMCP) | Ministry of Health, Government of Liberia |
| Name | Affiliation | Role |
|---|---|---|
| Institute for Statistics and Geo-Information Services | Government of Liberia | Collaborated in the implementation of the study |
| Ministry of Health | Government of Liberia | Provided technical assistance |
| ICF | The DHS Program | Provided technical assistance |
| Liberia Medical and Dental Council | Government of Liberia | Provided technical assistance |
| Liberia Health and Medical Products Regulatory Authority | Government of Liberia | Provided technical assistance |
| Name | Role |
|---|---|
| Government of Liberia | Financial support |
| United State Agency for International Development | Financial support |
| United Nations Population Fund | Financial support |
| United Nations Children’s Fund | Financial support |
| Management Sciences for Health | Financial support |
| President’s Malaria Initiative | Financial support |
| University of Liberia-Pacific Institute for Research and Evaluation | Financial support |
| World Health Organization | Financial support |
| U.S. Centers for Disease Control and Prevention | Financial support |
Sampling
The LMIS followed a two-stage sample design and was intended to allow estimates of key indicators for the following domains:
• At the national level
• For urban and rural areas
• For six geographical regions, consisting of the following groups of counties:
- Greater Monrovia
- North Western: Bomi, Grand Cape Mount, and Gbarpolu counties
- South Central: Montserrado (excluding Greater Monrovia district), Margibi, and Grand Bassa counties
- North Central: Bong, Nimba, and Lofa counties
- South Eastern A: River Cess, Sinoe, and Grand Gedeh counties
- South Eastern B: River Gee, Grand Kru, and Maryland counties
Note: Detailed sample design information is presented in the Appendix A of "Liberia Malaria Indicator Survey 2016" report.
A total of 4,484 households selected for the sample, 4,261 were occupied at the time of fieldwork. Among the occupied households, 4,218 were successfully interviewed, yielding a total household response rate of 99%. In the interviewed households, 4,407 women were identified to be eligible for individual interview and 4,290 were successfully interviewed, yielding a response rate of 97%.
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 are 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
For further details on sampling weights, see Appendix A.4 of the final report.
Survey instrument
Four questionnaires were used to collect survey data:
- Household Questionnaire
- Woman’s Questionnaire
- Biomarker Questionnaire
- Fieldworker Questionnaire
Core questionnaires available from the Roll Back Malaria Monitoring and Evaluation Reference Group (RBM-MERG) were adapted to reflect the population and health issues relevant to Liberia. The modifications were decided upon at a series of meetings with various stakeholders from the National Malaria Control Programme (NMCP) and other government ministries and agencies, nongovernmental organisations, and international donors. The questionnaires were in English, with some text adapted to Liberian English.
Data collection
| Start | End |
|---|---|
| 2016-09-22 | 2016-11-26 |
Twelve teams were organised for field data collection. Each team consisted of one field supervisor, two field interviewers, two biomarker technicians to conduct biomarker testing, and one driver. The field staff also included seven coordinators.
Each team was allocated about 12-13 clusters depending on their regional location. The teams spent an average of 5 days in a cluster. Information on selected clusters and sampled households was provided to each team for easy location of the households. When eligible respondents were absent from their homes, two or more callbacks were made to offer respondents an opportunity to be part of the survey.
Field data collection for the LMIS started on 22 September 2016. For maximum supervision, all 12 teams were visited by national monitors, largely members of the technical working group. Fieldwork was completed on 26 November 2016.
Data processing
The processing of the LMIS questionnaire data began 15 October 2016 after the fieldwork commenced. Completed questionnaires were returned periodically from the field to the NMCP office in Monrovia, where they were coded by data processing personnel recruited and trained for this task. The data processing staff consisted of a supervisor and an assistant from NMCP, a questionnaire administrator, five data entry operators, and one secondary data editor, all of whom were trained by an ICF data processing specialist. Data were entered using the CSPro computer package. All data were entered twice (100 percent verification). The concurrent processing of the data was a distinct advantage for data quality, since NMCP was able to advise field teams of errors detected during data entry. The data entry and editing phase of the survey was completed 15 February 2017.
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 2016 Liberia Malaria Indicator Survey (LMIS) 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 2016 LMIS 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 amongpossible 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 2016 LMIS 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.
Note: Detailed estimates of sampling errors are presented in the Appendix B of "Liberia Malaria Indicator Survey 2016" final report.
Data Quality Tables
- Household age distribution
- Age distribution of eligible and interviewed women
- Completeness of reporting
See details of the data quality tables in Appendix C of the survey final report.
Access policy
The DHS Program
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.
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.
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
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 | 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_LBR_2016_MIS_v01_M_WB
| Name | Affiliation | Role |
|---|---|---|
| Development Economics Data Group | The World Bank | Documentation of the DDI |
2017-12-13
Metadata version
Version 01 (December 2017). Metadata is excerpted from "Liberia Malaria Indicator Survey 2016" Report.