Demographic and Health Survey, Special [hh/dhs-sp]
Since the first LMIS in 2005, the NMCP and its partners have scaled-up malaria interventions in all parts of the country. In order to determine the progress made in malaria control and prevention in Liberia since 2005, the 2009 Liberia Malaria Indicator Survey (LMIS) was designed to provide data on key malaria indicators including mosquito net ownership and use, as well as prompt and effective treatment with ACT.
The key objectives of the 2009 LMIS were to:
• Measure the extent of ownership and use of mosquito bednets
• Assess coverage of the intermittent preventive treatment program to protect pregnant women
• Identify practices used to treat malaria among children under five and the use of specific antimalarial medications
• Measure the prevalence of malaria and anemia among children age 6-59 months
• Assess malaria-related knowledge, attitudes, and practices in the general population.
Another objective of the survey was to transfer knowledge about best practices in survey implementation and to transfer skills to Liberian counterparts related to survey design, training, budgeting, logistics, data collection, monitoring, data processing, analysis, report drafting, and data dissemination.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
- Children age 0-5
- Women age 15 to 49
The 2008 Liberia Malaria Indicator Survey covered the following topics:
- Household Schedule
- Household Characteristics
- Anemia and Malaria Testing for Children Age 0-5
- Respondent's Background
- Pregnancy and Intermittent Preventive Treatment
- Fever in Children
- Knowledge of Malaria
Producers and sponsors
National Malaria Control Program
Ministry of Health and Social Welfare
Liberia Institute for Statistics and Geo-Information Services
Ministry of Health and Social Welfare
Technical assistance and medical supplies and equipment for the survey
United States Agency for International Development
The LMIS sample was designed to produce most of the key indicators for the country as a whole, for urban and rural areas separately, and for Monrovia and each of five regions that were formed by grouping the 15 counties. The regional groups are as follows:
1 Greater Monrovia
2 North Western: Bomi, Grand Cape Mount, Gbarpolu
3 South Central: Montserrado (outside Monrovia), Margibi, Grand Bassa
4 South Eastern A: River Cess, Sinoe, Grand Gedeh
5 South Eastern B: River Gee, Grand Kru, Maryland
6 North Central: Bong, Nimba, Lofa
Thus, the sample was not spread geographically in proportion to the population, but rather equally across the regions, with 25 sample points or clusters per region. As a result, the LMIS sample is not selfweighting at the national level and sample weighting factors have been applied to the survey records in order to bring them into proportion.
The survey utilized a two-stage sample design (see Appendix A for details). The first stage involved selecting 150 clusters with probability proportional to size from the list of approximately 7,000 enumeration areas (EAs) covered in the March 2008 National Population and Housing Census. The EA size was the number of residential households residing in the EA recorded in the census. Stratification was achieved by separating each county into urban and rural areas. The urban areas in each county mainly consist of the county capital. Therefore the 15 counties plus Greater Monrovia (which has only urban areas) were stratified into 31 sampling strata, 15 rural strata and 16 urban strata. Samples were selected independently in every stratum, with a predetermined number of EAs to be selected. Implicit stratification was achieved in each of the explicit sampling stratum by sorting the sampling frame according to districts and clan within each of the sampling stratum and by using the probability proportional to size selection procedure. Among the 150 EAs (clusters) selected, 69 were in urban areas and 81 were in rural areas.
In the second stage, for all of the selected EAs, a fixed number of households (30) was selected using an equal probability systematic sampling from a list of households in the EA. Because the census was still fresh (March 2008), it was decided to use the census household results as the sampling frame for household selection in the second stage, thus avoiding having to undertake a costly separate household listing operation. This involved borrowing the census questionnaire books for each of the selected EAs or clusters and copying information for all the occupied residential households recorded in the census book. These lists served as the sampling frame for household selection.
All women age 15-49 years who were either permanent residents of the households in the sample or visitors present in the household on the night before the survey were eligible to be interviewed in the survey. In addition, all children age 6-59 months who were listed in the household were eligible for the anemia and malaria testing component.
Note: See detailed description of the sample design in Appendix A of the final report.
Of the 4,485 households selected in the sample, 4,285 were found occupied at the time of the fieldwork. The shortfall is due to households that were away for an extended period of time, dwellings that could not be found in the field, and dwellings that were found to be vacant or destroyed. Of the existing households, 4,162 were successfully interviewed, yielding a household response rate of 97 percent.
In the households interviewed in the survey, a total of 4,512 eligible women were identified, of whom 4,397 were successfully interviewed yielding a response rate of 98 percent. The household response rates are slightly lower in the urban than rural sample, though they are almost equal for women. The principal reason for nonresponse among eligible women was the failure to find them at home despite repeated visits to the household.
Note: See summarized response rates in Table 1.2 of the final report.
Dates of Data Collection
Data Collection Mode
Data Collection Notes
From a pool of over 1,200 applicants for the supervisor and interviewer positions, NMCP and LISGIS recruited 56 for the interviewer/supervisor training. They also allowed over 26 observers to attend the training without remuneration, all of whom hoped to do better than those who were officially recruited. The pool of male and female trainees consisted largely of those who had experience in previous surveys such as the 2007 LDHS, the 2005 LMIS, and other social surveys.
These participants attended a two-week training course from December 1-12 at Thinker’s Village Beach on the outskirts of Monrovia. Training of the interviewer/supervisor candidates consisted of reviewing how to fill the Household and Woman’s Questionnaires, mock interviewing, and sessions covering tips on interviewing, how to locate selected households, and how to code interview results. Two quizzes were administered. Trainers included the LMIS Project Director, the Assistant Project Director, and three LISGIS staff, with support from two Macro staff. Despite the large candidate pool, many did not qualify on the basis of tests and practice interviewing and many were not proficient in the major local languages. Of the 82 attendees in the interviewer/supervisor training, twelve were selected as supervisors, 24 were selected as interviewers, and eight were held in reserve.
NMCP also identified over 35 staff with either laboratory or medical experience who were trained in taking blood for the anemia and malaria testing at the same time and place as the interviewer/supervisor candidates. Of these, 24 were selected as health technicians for the biomarker data collection and 7 were further trained as microscopists in the laboratory (see below). The health technicians were trained by a Macro biomarker specialist and a malaria laboratory consultant on how to identify children eligible for testing, how to administer informed consent, how to conduct the anemia and malaria rapid tests, and how to make a proper thick blood smear. They were also trained on how to store the blood slides, how to record test results on the questionnaire, and how to provide results to the parents/caretakers of the children tested. Trainees participated in numerous practice sessions in the classroom.
All trainees participated in two field practice exercises in households living close to the training site. They also received a lecture on the epidemiology of malaria in Liberia and correct treatment protocols by a senior member of the NMCP. Finally, all health technicians, team supervisors, and the nurses/nurse aides on each team received more specific instructions on how to calculate the correct dose of antimalarial medication to leave with the parents/caretakers of children who test positive on the malaria rapid diagnostic test. This included how to use the portable scales to determine the child’s weight. It also included how to record children’s anemia and malaria results on the anemia and malaria brochure that was to be left in every household in which children were tested and on how to fill in the referral slip for any child who was found to be severely anemic.
Twelve teams were organized for the data collection, each comprised of one supervisor, two interviewers, two health technicians, and one driver. Three senior staff from LISGIS, one from NMCP, and one from the MOH&SW Monitoring and Evaluation Unit were designated as field coordinators and were each assigned a number of teams to monitor. NMCP was able to organize the questionnaire printing on time, and arrange for the fieldwork logistics such as field staff contracts, identification cards with pictures, special survey T-shirts, and other local supplies for the field teams.
Data collection for the LMIS started as scheduled on December 15, 2008. In order to allow for maximum supervision in the first two weeks as well as to allow teams to be home for Christmas, all 12 teams started work in Monrovia, covering two clusters each before moving out of Monrovia just after the holidays. Fieldwork was completed by all teams by the end of February. However, field checking uncovered a situation in which one team had not actually conducted interviews in some four clusters that it claimed it had completed. To rectify the deception, three other teams were sent to complete the four clusters in March 2009.
Prior to the start of the field staff training, a Macro malaria consultant worked with the head of the malaria laboratory at the JFK Hospital compound to inspect the lab, check on supplies, unpack and inventory the supplies sent by Macro, and obtain electrical stabilizers for the microscopes and materials needed for staining the slides. Although the lab was refurbished by the Chinese in 2007, it had not been extensively used.
After the health technician training was completed, the consultant trained the seven identified microscopists at the laboratory. All trainees had participated in the health technician training, so they were fully aware of the objectives and logistics of the survey. The training covered the importance of good laboratory practice such as quality control of reagents, smears, and malaria diagnosis and the consequences of failing to care for and maintain laboratory equipment used in microscopy. Also discussed was the biology of the plasmodium parasite, including describing the red blood cells where the parasites live, the life cycle of each plasmodium species, and their characteristic features. The importance of making good blood smears was emphasized, as were the standard procedures for staining slides. Finally, trainees spent about a week practicing slide reading using blood smears taken during the practice interviewing. One of the trainees was assigned to registering, staining and mounting the slides. The other six microscopists then started to read slides from the actual survey. The purpose of the blood slides was to provide a gold standard for malaria infection and not to ascertain the type of parasite.
The consultant returned to Monrovia in late January to check on the progress of the lab work. During this visit, he conducted a second reading of some 400 slides, including at least 60 from each of the six microscopists. Using his reading as the gold standard, he selected microscopists with the fewest discordant results to be the second readers. If the results of the first and second readings did not match, a third person acted as the tie breaker. Laboratory testing continued for about five months. Macro also provided the computer software for recording the laboratory test results.
After the laboratory testing at the Malaria Center was completed, a systematic sample of 300 slides were sent to the Comprehensive Health Center Laboratory in Saclepea for an independent quality control check.
Two questionnaires were used in the LMIS: a Household Questionnaire and a Woman’s Questionnaire for all women age 15-49 in the selected households. Both instruments were based on the model Malaria Indicator Survey questionnaires developed by the Roll Back Malaria and DHS programs, as well as on previous surveys conducted in Liberia, including the 2005 LMIS and the 2007 LDHS. In consultation with the Technical Committee, NMCP and Macro staff modified the model questionnaires to reflect relevant issues of malaria in Liberia. Given that there are dozens of local languages in Liberia, most of which have no accepted written script and are not taught in the schools, and given that English is widely spoken, it was decided not to attempt to translate the questionnaires into vernaculars. However, many of the questions were broken down into a simpler form of Liberian English that interviewers could use with respondents.
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 for anemia 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. In addition, this questionnaire was also used to record consent and results with regard to the anemia and malaria testing of young children.
The Woman’s Questionnaire was used to collect information from all women age 15-49 years and covered the following topics:
• Background characteristics (age, residential history, education, literacy, religion, dialect)
• Full reproductive history and child mortality
• Prenatal care and preventive malaria treatment for most recent birth
• Prevalence and treatment of fever among children under five
• Knowledge about malaria (symptoms, causes, ways to avoid, types of medicines, etc.).
Because almost all of the questions had been included in previous surveys and NMCP had experience with anemia and malaria testing, no formal pretest was held.
The processing of the LMIS questionnaire data began a few weeks 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 two data editors, all of whom were trained by a Macro 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 in early May 2009.
Estimates of Sampling Error
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 2009 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 2009 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 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 2009 LMIS 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 2009 LMIS is a Macro SAS procedure. This procedure used the Taylor linearization method of variance estimation for survey estimates that are means or proportions. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.
Note: See detailed estimate of sampling error calculation in APPENDIX B of the final report.
Data and Data Related Resources
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.
DDI Document ID
World Bank, Development Economics Data Group
Production of metadata
DDI Document version