Identification
Survey ID Number
MWI_2012_MIS_v01_M
Title
Malaria Indicator Survey 2012
Country
| Name | Country code |
|---|---|
| Malawi | MWI |
Study type
Demographic and Health Survey [hh/dhs]
Series Information
Malaria continues to be a major public health problem in Malawi, with an estimated five million cases occurring annually. Its effects are greatest among children under age 5 and pregnant women. The Ministry of Health, in collaboration with its partners, is implementing the National Malaria Strategic Plan 2011–2015; its goal is to achieve universal coverage in the prevention and treatment of malaria towards attainment of the national vision of “All people in Malawi are free from the burden of malaria”. Specifically, we strive for progress in achieving prompt and effective antimalarial drug treatment, use of insecticide-treated nets and indoor residual spraying, and prevention of malaria in pregnancy.
We have set for ourselves high coverage targets for these interventions. By setting high targets, we are confident of our ability to reach our strategic goals of reducing the incidence of malaria and deaths from malaria as well as reducing the prevalence of malarial parasites and malaria-related anaemia.
Measurement is essential for understanding progress towards these goals. Without measurement, we can only speculate on progress. The 2012 Malawi Malaria Indicator Survey is the country’s second nationally representative assessment of the coverage attained by key malaria interventions. These interventions are used in combination with measures of malaria-related burden and anaemia prevalence testing among children under age 5.
We have set for ourselves high coverage targets for these interventions. By setting high targets, we are confident of our ability to reach our strategic goals of reducing the incidence of malaria and deaths from malaria as well as reducing the prevalence of malarial parasites and malaria-related anaemia.
Measurement is essential for understanding progress towards these goals. Without measurement, we can only speculate on progress. The 2012 Malawi Malaria Indicator Survey is the country’s second nationally representative assessment of the coverage attained by key malaria interventions. These interventions are used in combination with measures of malaria-related burden and anaemia prevalence testing among children under age 5.
Abstract
This report presents the findings of the 2012 Malawi Malaria Indicator Survey (2012 MMIS) conducted by the National Malaria Control Programme (NMCP) of the Ministry of Health from 28 March through 15 May 2012. The government of Malawi provided financial assistance in terms of in-kind contribution of personnel, office space, and logistical support. Financial support for the survey was provided by the United States Agency for International Development (USAID) from President’s Malaria Initiative funds through ICF International. ICF International also provided technical assistance, medical supplies, and equipment for the survey through the MEASURE DHS program, which is funded by USAID and is designed to assist developing countries in collecting data on fertility, family planning, and maternal and child health. The opinions expressed in this report are those of the authors and do not necessarily reflect the views of USAID.
The Roll Back Malaria Monitoring & Evaluation Reference Group (RBM-MERG), a global technical advisory group providing monitoring and evaluation guidance for malaria control programmes, recommends that the MIS be conducted every two years within six weeks of the end of the rainy season in countries with endemic malaria transmission patterns, especially those in sub-Saharan Africa. For these reasons, in 2012, the NMCP conducted the second nationwide Malaria Indicator Survey in Malawi. The 2012 MIS used a standard set of instruments and protocol developed by RBM-MERG. These tools are largely based on the collective experience gained from the DHS and MIS surveys and are presented as a package of materials to promote standardized survey management and data collection methodology. The package also includes standardized measurement of malaria parasite and anaemia prevalence among target populations to derive the malaria-related burden at the community level.
The key objectives of the 2012 MIS were 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 in the adult population
- Measure trends in key malaria indicators since the 2010 MDHS
The 2012 MIS was designed to produce most of the key malaria indicators for the country as a whole, for urban and rural areas separately, and for each of three regions in Malawi: Northern, Central, and Southern.
The Roll Back Malaria Monitoring & Evaluation Reference Group (RBM-MERG), a global technical advisory group providing monitoring and evaluation guidance for malaria control programmes, recommends that the MIS be conducted every two years within six weeks of the end of the rainy season in countries with endemic malaria transmission patterns, especially those in sub-Saharan Africa. For these reasons, in 2012, the NMCP conducted the second nationwide Malaria Indicator Survey in Malawi. The 2012 MIS used a standard set of instruments and protocol developed by RBM-MERG. These tools are largely based on the collective experience gained from the DHS and MIS surveys and are presented as a package of materials to promote standardized survey management and data collection methodology. The package also includes standardized measurement of malaria parasite and anaemia prevalence among target populations to derive the malaria-related burden at the community level.
The key objectives of the 2012 MIS were 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 in the adult population
- Measure trends in key malaria indicators since the 2010 MDHS
The 2012 MIS was designed to produce most of the key malaria indicators for the country as a whole, for urban and rural areas separately, and for each of three regions in Malawi: Northern, Central, and Southern.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
- Household,
- Individual.
- Individual.
Version
Version Description
- v01: Edited, anonymous datasets for public distribution.
Scope
Notes
The scope of the 2012 Malawi Malaria Indicator Survey includes:
- Household: age, sex, relationship to the head of the household, characteristics of the household dwelling, such as the water source; type of toilet facilities; materials used for the roof, floors, and walls; possession of durable goods; possession and use of mosquito nets;
- Biomarker: haemoglobin measurements;
- Women: 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 5, knowledge about malaria (symptoms, causes, ways to prevent it, and types of antimalarials), cost incurred for the treatment of fever in children.
- Household: age, sex, relationship to the head of the household, characteristics of the household dwelling, such as the water source; type of toilet facilities; materials used for the roof, floors, and walls; possession of durable goods; possession and use of mosquito nets;
- Biomarker: haemoglobin measurements;
- Women: 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 5, knowledge about malaria (symptoms, causes, ways to prevent it, and types of antimalarials), cost incurred for the treatment of fever in children.
Coverage
Geographic Coverage
National
Universe
The survey covered all de jure household members (usual residents), all women aged between 15-49 years, and all children age 6-59 months living in the household.
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| National Malaria Control Programme | Ministry of Health |
Producers
| Name | Role |
|---|---|
| ICF International | Technical assistance |
| MEASURE DHS program | Technical assistance |
Funding Agency/Sponsor
| Name | Abbreviation |
|---|---|
| Government of Malawi | |
| United States Agency for International Development | USAID |
| President’s Malaria Initiative | PMI |
| ICF International |
Sampling
Sampling Procedure
SAMPLING FRAME AND STRATIFICATION
Malawi is administratively divided into 3 regions and 28 districts. The 2012 MMIS sample was designed to provide estimates for the country as a whole, for urban and rural areas separately, and for each of the regions:
Northern Region: Chitipa, Karonga, Likoma, Mzimba, Nkhata Bay, and Rumphi
Central Region: Dedza, Dowa, Kasungu, Lilongwe, Mchinji, Nkhotakota, Ntcheu, Ntchisi, and Salima
Southern Region: Balaka, Blantyre, Chikhwawa, Chiradzulu, Machinga, Mangochi, Mulanje, Mwanza, Neno, Nsanje, Mwanza, Neno, Nsanje, Phalombe, Thyolo, and Zomba
Each district is subdivided into traditional authorities. For statistical purposes, each traditional authority is subdivided into standard enumeration areas (SEAs). The 2008 National Population and Housing Census demarcated these SEAs and determined the number of households in each one. The sampling frame of the 2012 MMIS is the list of SEAs developed from the 2008 census, stratified by region and urban and rural strata.
To improve the precision of the trend analysis, the 2012 MMIS was conducted in the same 140 standard enumeration areas (SEAs) selected for the 2010 MMIS.
SAMPLE ALLOCATION AND SELECTION
Sample Allocation
To meet the objective of providing reliable estimates for key indicators of the sample domains, a total sample of 140 SEAs-44 in urban areas and 96 in rural areas- and 3,500 households was allocated among regions in proportion to the 2008 population of each region. Urban areas were over-sampled within regions in order to produce robust estimates for that domain. Therefore, the MMIS sample was not proportional to the population for residence (urban-rural area) and required a final weighting adjustment to provide valid estimates for every domain of the survey.
The SEAs were selected with probability proportional to size from a list of approximately 12,474 SEAs covered in the 2008 census. The SEA size was the number of residential households recorded in the census. Once the households were allocated to the different strata, the number of SEAs to be selected was calculated based on an average cluster take of 25 completed interviews of all respondents.
In the second stage, 25 households were selected in each selected SEA using systematic sampling from a list of households in the SEA. Because it has been almost four years since the census, a fresh household listing was undertaken before the survey was fielded. The National Statistical Office (NSO) assisted in listing the households in the SEAs. As part of this exercise, the listing teams also drew up the necessary maps and recorded the geographic coordinates of each SEA.
SELECTION OF HOUSEHOLDS
The frame of households was obtained from the listing of all households in the selected SEAs. Upon completion of household listing, the households were given new numbers, which were sampling serial numbers assigned to each household in the cluster. The sampling numbers were assigned sequentially within each SEA starting from 1. The total number of households in the SEA was equal to the last serial number assigned.
In summary, the following steps were used to select the households:
- The sampling interval for each category was calculated:
I =B/b
where B is the number of households listed in the selected SEA and b is the number of households to be selected in that SEA.
- A random number (R) between 1 and the interval I was generated; the first selection will hence be R.
- The interval to the random number to get the next selection was added.
- The interval was repeatedly added until the desired sample size was achieved.
The sampling procedures are fully described in Appendix A of "Malawi Malaria Indicator Survey 2012 - Final Report" pp.49-52.
Malawi is administratively divided into 3 regions and 28 districts. The 2012 MMIS sample was designed to provide estimates for the country as a whole, for urban and rural areas separately, and for each of the regions:
Northern Region: Chitipa, Karonga, Likoma, Mzimba, Nkhata Bay, and Rumphi
Central Region: Dedza, Dowa, Kasungu, Lilongwe, Mchinji, Nkhotakota, Ntcheu, Ntchisi, and Salima
Southern Region: Balaka, Blantyre, Chikhwawa, Chiradzulu, Machinga, Mangochi, Mulanje, Mwanza, Neno, Nsanje, Mwanza, Neno, Nsanje, Phalombe, Thyolo, and Zomba
Each district is subdivided into traditional authorities. For statistical purposes, each traditional authority is subdivided into standard enumeration areas (SEAs). The 2008 National Population and Housing Census demarcated these SEAs and determined the number of households in each one. The sampling frame of the 2012 MMIS is the list of SEAs developed from the 2008 census, stratified by region and urban and rural strata.
To improve the precision of the trend analysis, the 2012 MMIS was conducted in the same 140 standard enumeration areas (SEAs) selected for the 2010 MMIS.
SAMPLE ALLOCATION AND SELECTION
Sample Allocation
To meet the objective of providing reliable estimates for key indicators of the sample domains, a total sample of 140 SEAs-44 in urban areas and 96 in rural areas- and 3,500 households was allocated among regions in proportion to the 2008 population of each region. Urban areas were over-sampled within regions in order to produce robust estimates for that domain. Therefore, the MMIS sample was not proportional to the population for residence (urban-rural area) and required a final weighting adjustment to provide valid estimates for every domain of the survey.
The SEAs were selected with probability proportional to size from a list of approximately 12,474 SEAs covered in the 2008 census. The SEA size was the number of residential households recorded in the census. Once the households were allocated to the different strata, the number of SEAs to be selected was calculated based on an average cluster take of 25 completed interviews of all respondents.
In the second stage, 25 households were selected in each selected SEA using systematic sampling from a list of households in the SEA. Because it has been almost four years since the census, a fresh household listing was undertaken before the survey was fielded. The National Statistical Office (NSO) assisted in listing the households in the SEAs. As part of this exercise, the listing teams also drew up the necessary maps and recorded the geographic coordinates of each SEA.
SELECTION OF HOUSEHOLDS
The frame of households was obtained from the listing of all households in the selected SEAs. Upon completion of household listing, the households were given new numbers, which were sampling serial numbers assigned to each household in the cluster. The sampling numbers were assigned sequentially within each SEA starting from 1. The total number of households in the SEA was equal to the last serial number assigned.
In summary, the following steps were used to select the households:
- The sampling interval for each category was calculated:
I =B/b
where B is the number of households listed in the selected SEA and b is the number of households to be selected in that SEA.
- A random number (R) between 1 and the interval I was generated; the first selection will hence be R.
- The interval to the random number to get the next selection was added.
- The interval was repeatedly added until the desired sample size was achieved.
The sampling procedures are fully described in Appendix A of "Malawi Malaria Indicator Survey 2012 - Final Report" pp.49-52.
Response Rate
Of the 3,500 households selected for the sample, 3,432 were occupied at the time of fieldwork. Sixty-eight dwellings were abandoned and, therefore, were not included in the response rate. Among the occupied households, 3,404 were successfully interviewed, yielding a total household response rate of 99 percent. In the interviewed households, 2,955 eligible women were identified to be eligible for individual interview and 2,906 were successfully interviewed, yielding a response rate of 98 percent.
Weighting
The Malawi MIS sample was not self-weighted. Due to the disproportional allocation of the sample to the different strata, sampling weights were required to ensure that the sample was representative at the national level. The sampling probabilities at first-stage selection of SEAs and probabilities of selecting the households were used to calculate the weights. The weights of the sample were equal to the inverse of the probability of selection.
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 2012-04-02 | 2012-05-15 |
Data Collection Mode
Face-to-face [f2f]
Supervision
There is one supervisor for each of the 10 data collection teams in the field.
Data Collection Notes
Training
The NMCP in collaboration with the DHOs identified 20 interviewers (1 male and 19 females), 20 laboratory technicians (13 males and 7 females), and 10 field team supervisors (9 males and 1 female). In addition, 7 national supervisors from NMCP, Public Health Laboratory, and other stakeholders were identified for overall supervision.
The participants attended a two-week interviewer and supervisor training which took place from 6-23 March 2012 at Kalikuti Hotel in Lilongwe. All the field staff participated in a one-week joint training session, focusing on how to fill out the Household and Woman‘s Questionnaires, mock interviews, and interviewing techniques, as well as on how to locate selected households. Two quizzes were administered to assess how well the participants absorbed the training materials.
During the second week of training, two sessions were done in parallel, one for the interviewers and field supervisors and one for the laboratory technicians. The training of interviewers and field supervisors focused on the use of PDAs for data collection, assigning of households to interviewers using computer tablets, sharing of data among interviewers and supervisors, and submission of data to the central data processing centre at NMCP.
The training of laboratory technicians focused on preparation of blood samples and testing for anaemia using the HemoCue equipment and malaria testing using SD Bioline RDT. The training involved presentation, discussion, and actual testing for anaemia and malaria. The technicians were trained in identifying children eligible for testing, administering informed consent, conducting the anaemia and malaria rapid testing, and making a proper thick blood smear. They were also trained in storing the blood slides, recording test results on the Biomarker Questionnaire, and providing the results to the parents/guardian of the children tested. Finally, the laboratory technicians received a briefing on the epidemiology of malaria in Malawi and correct treatment protocols.
All participants took part in a field practice exercise in households living close to the training site. Finally, all field staff received specific instructions on how to calculate the correct dose of antimalarial medications for children who tested positive for malaria, using the portable scales to determine the child’s weight. Health technicians were also trained on how to record children’s anaemia and malaria results on the respective brochures and how to fill in the referral slip for any child who was found to be severely anaemic.
Fieldwork
Ten teams were organized for field data collection. Each team consisted of one field supervisor, two community health nurses as interviewers, two laboratory technicians, and one driver. The national supervisors were paired; one to focus on the interviewing and the other to perform laboratory procedures.
The NMCP arranged for printing the questionnaires, manuals, consent forms, and other field forms. It also assisted with fieldwork logistics such as obtaining backpacks, identification cards, umbrellas, and other field supplies.
Field data collection for the 2012 Malawi MIS started on April 2, 2012. In order to allow for maximum supervision, all ten teams were visited by the national supervisors at least once in the first two weeks. Fieldwork was completed by mid-May of 2012.
The NMCP in collaboration with the DHOs identified 20 interviewers (1 male and 19 females), 20 laboratory technicians (13 males and 7 females), and 10 field team supervisors (9 males and 1 female). In addition, 7 national supervisors from NMCP, Public Health Laboratory, and other stakeholders were identified for overall supervision.
The participants attended a two-week interviewer and supervisor training which took place from 6-23 March 2012 at Kalikuti Hotel in Lilongwe. All the field staff participated in a one-week joint training session, focusing on how to fill out the Household and Woman‘s Questionnaires, mock interviews, and interviewing techniques, as well as on how to locate selected households. Two quizzes were administered to assess how well the participants absorbed the training materials.
During the second week of training, two sessions were done in parallel, one for the interviewers and field supervisors and one for the laboratory technicians. The training of interviewers and field supervisors focused on the use of PDAs for data collection, assigning of households to interviewers using computer tablets, sharing of data among interviewers and supervisors, and submission of data to the central data processing centre at NMCP.
The training of laboratory technicians focused on preparation of blood samples and testing for anaemia using the HemoCue equipment and malaria testing using SD Bioline RDT. The training involved presentation, discussion, and actual testing for anaemia and malaria. The technicians were trained in identifying children eligible for testing, administering informed consent, conducting the anaemia and malaria rapid testing, and making a proper thick blood smear. They were also trained in storing the blood slides, recording test results on the Biomarker Questionnaire, and providing the results to the parents/guardian of the children tested. Finally, the laboratory technicians received a briefing on the epidemiology of malaria in Malawi and correct treatment protocols.
All participants took part in a field practice exercise in households living close to the training site. Finally, all field staff received specific instructions on how to calculate the correct dose of antimalarial medications for children who tested positive for malaria, using the portable scales to determine the child’s weight. Health technicians were also trained on how to record children’s anaemia and malaria results on the respective brochures and how to fill in the referral slip for any child who was found to be severely anaemic.
Fieldwork
Ten teams were organized for field data collection. Each team consisted of one field supervisor, two community health nurses as interviewers, two laboratory technicians, and one driver. The national supervisors were paired; one to focus on the interviewing and the other to perform laboratory procedures.
The NMCP arranged for printing the questionnaires, manuals, consent forms, and other field forms. It also assisted with fieldwork logistics such as obtaining backpacks, identification cards, umbrellas, and other field supplies.
Field data collection for the 2012 Malawi MIS started on April 2, 2012. In order to allow for maximum supervision, all ten teams were visited by the national supervisors at least once in the first two weeks. Fieldwork was completed by mid-May of 2012.
Data Collectors
| Name | Abbreviation |
|---|---|
| National Malaria Control Programme | NMCP |
Questionnaires
Questionnaires
Three questionnaires were used in the 2012 Malaria MIS: 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, as well as the 2010 MIS. The model questionnaires were modified to reflect relevant issues of malaria in Malawi in consultation with the Steering Committee, the NMCP, and staff from ICF International. The questionnaires were translated into the two main local languages of Malawi: Chichewa and Tumbuka.
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 haemoglobin measurements for children age 6-59 months and results of malaria testing for children under age 5 years. The questionnaire was filled in by the health technician and transcribed into the tablet computer by the team supervisor.
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 5
- Knowledge about malaria (symptoms, causes, ways to prevent it, and types of antimalarials)
- Cost incurred for the treatment of fever in children
No formal field pretest was done for the survey questionnaires because most of the MIS questions had been included in previous surveys in Malawi and the field staff had experience with anaemia and malaria testing in the field and with the use of PDAs for data collection.
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 haemoglobin measurements for children age 6-59 months and results of malaria testing for children under age 5 years. The questionnaire was filled in by the health technician and transcribed into the tablet computer by the team supervisor.
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 5
- Knowledge about malaria (symptoms, causes, ways to prevent it, and types of antimalarials)
- Cost incurred for the treatment of fever in children
No formal field pretest was done for the survey questionnaires because most of the MIS questions had been included in previous surveys in Malawi and the field staff had experience with anaemia and malaria testing in the field and with the use of PDAs for data collection.
Data Processing
Data Editing
Data for the 2012 Malawi MIS was collected through questionnaires programmed onto personal data assistants (PDAs). The PDAs were programmed by ICFI data processing specialists and loaded with the Household, Biomarker, and Woman’s Questionnaires in English and the two main local languages. They were Bluetooth-enabled to facilitate electronic transfer of files, e.g., data from the Household Questionnaires transferred among survey team members and transfer of completed questionnaires to the team supervisor’s tablets. The field supervisors transferred data on a daily basis to the central data processing using the Internet. To facilitate communication and monitoring, each field worker was assigned a unique identification number.
The Census Survey Processing Software (CSPro) was used for data editing, weighting, cleaning, and tabulation. In the NMCP central office, data received from the supervisor’s tablets were registered and checked against any inconsistencies and outliers. Data editing and cleaning included range checks and structure and internal consistency checks. Any anomalies were communicated to the respective team through their team supervisor. The corrected results were resent to the central processing unit.
The Census Survey Processing Software (CSPro) was used for data editing, weighting, cleaning, and tabulation. In the NMCP central office, data received from the supervisor’s tablets were registered and checked against any inconsistencies and outliers. Data editing and cleaning included range checks and structure and internal consistency checks. Any anomalies were communicated to the respective team through their team supervisor. The corrected results were resent to the central processing unit.
Data Appraisal
Estimates of Sampling Error
Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2012 Malawi MIS 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 among 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 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 2012 Malawi MIS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. Sampling errors are computed in either ISSA or SAS, using programs developed by ICF International. These programs use the Taylor linearization method of variance estimation for survey estimates that are means, proportions, or ratios like the ones in the Malawi MIS survey.
In addition to the standard error, the design effect (DEFT) for each estimate is also calculated. The design effect is defined as the ratio between the standard error using the given sample design and the standard error that would result if a simple random sample had been used. A DEFT value of 1.0 indicates that the sample design is as efficient as a simple random sample, while a value greater than 1.0 indicates the increase in the sampling error due to the use of a more complex and less statistically efficient design. Relative standard errors and confidence limits for the estimates are also calculated.
Sampling errors for the 2012 Malawi MIS are calculated for selected variables considered to be of primary interest. The results are presented in this appendix for the country as a whole, for urban and rural areas, and for the three regions in the country: Northern, Central, and Southern. For each variable, the type of statistic (mean, proportion, or rate) and the base population are given in Table B.1. Tables B.2 through B.7 present the value of the statistic (R), its standard error (SE), the number of unweighted (N) and weighted (WN) cases, the design effect (DEFT), the relative standard error (SE/R), and the 95 percent confidence limits (R±2SE), for each variable. The sampling errors for mortality rates are presented for the five-year period preceding the survey for the whole country and for the ten-year period preceding the survey by residence and region. The DEFT is considered undefined when the standard error considering a simple random sample is zero (when the estimate is close to 0 or 1). In the case of the total fertility rate, the number of unweighted cases is not relevant, as there is no known unweighted value for woman-years of exposure to childbearing.
The confidence interval (e.g., as calculated for child has fever in last two weeks can be interpreted as follows: the overall average from the national sample is 0.317, and its standard error is 0.013. Therefore, to obtain the 95 percent confidence limits, one adds and subtracts twice the standard error to the sample estimate, i.e., 0.317±2×0.013. There is a high probability (95 percent) that the true average number of children ever born to all women age 40 to 49 is between 0.291 and 0.344.
For the total sample, the value of the DEFT, averaged over all variables, is 1.8. This means that, due to multi-stage clustering of the sample, the average standard error is increased by a factor of 1.8 over that in an equivalent simple random sample.
The sampling errors are fully described in Appendix B of " Malawi Malaria Indicator Survey 2012 - Final Report" pp.55-58.
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 2012 Malawi MIS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. Sampling errors are computed in either ISSA or SAS, using programs developed by ICF International. These programs use the Taylor linearization method of variance estimation for survey estimates that are means, proportions, or ratios like the ones in the Malawi MIS survey.
In addition to the standard error, the design effect (DEFT) for each estimate is also calculated. The design effect is defined as the ratio between the standard error using the given sample design and the standard error that would result if a simple random sample had been used. A DEFT value of 1.0 indicates that the sample design is as efficient as a simple random sample, while a value greater than 1.0 indicates the increase in the sampling error due to the use of a more complex and less statistically efficient design. Relative standard errors and confidence limits for the estimates are also calculated.
Sampling errors for the 2012 Malawi MIS are calculated for selected variables considered to be of primary interest. The results are presented in this appendix for the country as a whole, for urban and rural areas, and for the three regions in the country: Northern, Central, and Southern. For each variable, the type of statistic (mean, proportion, or rate) and the base population are given in Table B.1. Tables B.2 through B.7 present the value of the statistic (R), its standard error (SE), the number of unweighted (N) and weighted (WN) cases, the design effect (DEFT), the relative standard error (SE/R), and the 95 percent confidence limits (R±2SE), for each variable. The sampling errors for mortality rates are presented for the five-year period preceding the survey for the whole country and for the ten-year period preceding the survey by residence and region. The DEFT is considered undefined when the standard error considering a simple random sample is zero (when the estimate is close to 0 or 1). In the case of the total fertility rate, the number of unweighted cases is not relevant, as there is no known unweighted value for woman-years of exposure to childbearing.
The confidence interval (e.g., as calculated for child has fever in last two weeks can be interpreted as follows: the overall average from the national sample is 0.317, and its standard error is 0.013. Therefore, to obtain the 95 percent confidence limits, one adds and subtracts twice the standard error to the sample estimate, i.e., 0.317±2×0.013. There is a high probability (95 percent) that the true average number of children ever born to all women age 40 to 49 is between 0.291 and 0.344.
For the total sample, the value of the DEFT, averaged over all variables, is 1.8. This means that, due to multi-stage clustering of the sample, the average standard error is increased by a factor of 1.8 over that in an equivalent simple random sample.
The sampling errors are fully described in Appendix B of " Malawi Malaria Indicator Survey 2012 - Final Report" pp.55-58.
Data Appraisal
A series of data quality tables are available to review the quality of the data and include the following:
- Household age distribution
- Age distribution of eligible and interviewed women
- Completeness of reporting
The results of each of these data quality tables are shown in Appendix C of "Malawi Malaria Indicator Survey 2012 - Final Report" pp.59-60.
- Household age distribution
- Age distribution of eligible and interviewed women
- Completeness of reporting
The results of each of these data quality tables are shown in Appendix C of "Malawi Malaria Indicator Survey 2012 - Final Report" pp.59-60.
Access policy
Access authority
| Name | Affiliation | URL | |
|---|---|---|---|
| MEASURE DHS | ICF International | archive@measuredhs.com | www.measuredhs.com |
Contacts
| Name | URL | |
|---|---|---|
| General Inquiries | info@measuredhs.com | www.measuredhs.com |
| Data and Data Related Resources | archive@measuredhs.com | www.measuredhs.com |
Access conditions
DISSEMINATION POLICY
MEASURE DHS believes that widespread access to survey data by responsible researchers has enormous advantages for the countries concerned and the international community in general. Therefore, MEASURE DHS policy is to release survey data to researchers after the main survey report is published, generally within 12 months after the end of fieldwork. with few limitations these data have been made available for wide use.
DISTRIBUTION OF DATASETS
MEASURE DHS is authorized to distribute, at no cost, unrestricted survey data files for legitimate academic research, with the condition that we receive a description of any research project that will be using the data.
Registration is required for access to data.
Datasets are available for download to all registered users, free of charge. To download datasets, you must first register online and request the country(ies) and datasets that you are interested in. When submitting a dataset request, users must include a brief description of how the data will be used.
DATASETS TERMS OF USE
Datasets are made available with the following conditions:
- Survey data files are distributed by MEASURE DHS for academic research/statistical analysis. Researchers need to provide a description of any research/analysis that will be using the data, before access is granted to the datasets.
- Once downloaded, the datasets must not be passed on to other researchers without the written consent of MEASURE DHS.
- All reports and publications based on the requested data must be sent to the MEASURE DHS Data Archive as a Portable Format Document (pdf) or a hard copy, for us to forward to the country(ies) whose data have been used.
More information on the access policy and terms of use is available at www.measuredhs.com
MEASURE DHS believes that widespread access to survey data by responsible researchers has enormous advantages for the countries concerned and the international community in general. Therefore, MEASURE DHS policy is to release survey data to researchers after the main survey report is published, generally within 12 months after the end of fieldwork. with few limitations these data have been made available for wide use.
DISTRIBUTION OF DATASETS
MEASURE DHS is authorized to distribute, at no cost, unrestricted survey data files for legitimate academic research, with the condition that we receive a description of any research project that will be using the data.
Registration is required for access to data.
Datasets are available for download to all registered users, free of charge. To download datasets, you must first register online and request the country(ies) and datasets that you are interested in. When submitting a dataset request, users must include a brief description of how the data will be used.
DATASETS TERMS OF USE
Datasets are made available with the following conditions:
- Survey data files are distributed by MEASURE DHS for academic research/statistical analysis. Researchers need to provide a description of any research/analysis that will be using the data, before access is granted to the datasets.
- Once downloaded, the datasets must not be passed on to other researchers without the written consent of MEASURE DHS.
- All reports and publications based on the requested data must be sent to the MEASURE DHS Data Archive as a Portable Format Document (pdf) or a hard copy, for us to forward to the country(ies) whose data have been used.
More information on the access policy and terms of use is available at www.measuredhs.com
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 acronym and year of implementation)
- the survey reference number
- the source and date of download
Example:
National Malaria Control Programme and ICF International. Malawi Malaria Indicator Survey 2012. Ref. MWI_2012_MIS_v01_M. Dataset downloaded from www.measuredhs.com on [date]
- the Identification of the Primary Investigator
- the title of the survey (including acronym and year of implementation)
- the survey reference number
- the source and date of download
Example:
National Malaria Control Programme and ICF International. Malawi Malaria Indicator Survey 2012. Ref. MWI_2012_MIS_v01_M. Dataset downloaded from www.measuredhs.com on [date]
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.