Malaria Indicator Survey 2017
Tanzania, 2017
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Reference ID
TZA_2017_MIS_v01_M
Producer(s)
National Bureau of Statistics (NBS), Office of the Chief Government Statistician (OCGS)
Collection(s)
Metadata
Created on
Oct 26, 2018
Last modified
Jul 10, 2019
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Identification
Survey ID Number
TZA_2017_MIS_v01_M
Title
Malaria Indicator Survey 2017
Country/Economy
| Name | Country code |
|---|---|
| Tanzania | TZA |
Study type
Malaria Indicator Survey
Series Information
The 2017 Tanzania Malaria Indicator Survey (2017 TMIS) was the second stand-alone malaria indicator survey conducted in the country, following the one implemented in 2011-2012 (2011-12 THMIS). The survey involved a nationally representative sample of 9,724 households from 442 sample clusters. The survey was expected to interview 9,287 women age 15-49 and cover about 7,842 children under age 5.
Abstract
The 2017 Tanzania Malaria Indicator Survey (2017 TMIS) was the second stand-alone malaria indicator survey conducted in the country, following the one implemented in 2011-2012 (2011-12 THMIS). The survey involved a nationally representative sample of 9,724 households from 442 sample clusters.
The primary objective of the 2017 TMIS is to provide up-to-date estimates of basic demographic and health indicators related to malaria. Specifically, the survey collected information on vector control interventions such as mosquito nets, intermittent preventive treatment of malaria in pregnant women, and care seeking and treatment of fever in children. Young children were also tested for anaemia and for malaria infection.
Overall, the key aims of the 2017 TMIS are to:
• Measure the level of ownership and use of mosquito nets
• Assess coverage of intermittent preventive treatment for pregnant women
• Identify health care seeking behaviours and treatment practices, including the use of specific antimalarial medications to treat malaria among children under age 5
• Identify diagnostic trends prior to administration of antimalarial medications for treatment of fever and other malaria-like symptoms
• Measure the prevalence of malaria and anaemia among children age 6-59 months
• Assess malaria knowledge, attitudes, and practices among women age 15-49
• Assess housing conditions
• Assess the cost of malaria-related services
The information collected through the 2017 TMIS is intended to assist policymakers and program managers in evaluating and designing programs and strategies for improving the health of the country’s population.
The primary objective of the 2017 TMIS is to provide up-to-date estimates of basic demographic and health indicators related to malaria. Specifically, the survey collected information on vector control interventions such as mosquito nets, intermittent preventive treatment of malaria in pregnant women, and care seeking and treatment of fever in children. Young children were also tested for anaemia and for malaria infection.
Overall, the key aims of the 2017 TMIS are to:
• Measure the level of ownership and use of mosquito nets
• Assess coverage of intermittent preventive treatment for pregnant women
• Identify health care seeking behaviours and treatment practices, including the use of specific antimalarial medications to treat malaria among children under age 5
• Identify diagnostic trends prior to administration of antimalarial medications for treatment of fever and other malaria-like symptoms
• Measure the prevalence of malaria and anaemia among children age 6-59 months
• Assess malaria knowledge, attitudes, and practices among women age 15-49
• Assess housing conditions
• Assess the cost of malaria-related services
The information collected through the 2017 TMIS is intended to assist policymakers and program managers in evaluating and designing programs and strategies for improving the health of the country’s population.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
- Household
- Woman age 15 to 49
- Child age 0 to 5
- Woman age 15 to 49
- Child age 0 to 5
Version
Version Notes
The data dictionary was generated from hierarchical data that was downloaded from the DHS website (http://dhsprogram.com).
Scope
Notes
The survey covered the following topics:
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, type of fuel used for cooking, number of rooms, materials used for the floor, roof and walls of the house, possessions of livestock (inluding land) and durable goods
• Mosquito nets
WOMAN
• Identification
• Background characteristics (age, education, and literacy)
• Reproductive history during the last 6 years
• Prenatal care and preventive malaria treatment for the most recent birth
• Prevalence and treatment of fever among children under age 5
• Cost of malaria-related services
• Knowledge about malaria (symptoms, causes, how to prevent malaria, and types of antimalarial medications)
• Sources of media messages about malaria
BIOMARKER
• Hemoglobin measurement and malaria testing for children age 0-5
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, type of fuel used for cooking, number of rooms, materials used for the floor, roof and walls of the house, possessions of livestock (inluding land) and durable goods
• Mosquito nets
WOMAN
• Identification
• Background characteristics (age, education, and literacy)
• Reproductive history during the last 6 years
• Prenatal care and preventive malaria treatment for the most recent birth
• Prevalence and treatment of fever among children under age 5
• Cost of malaria-related services
• Knowledge about malaria (symptoms, causes, how to prevent malaria, and types of antimalarial medications)
• Sources of media messages about malaria
BIOMARKER
• Hemoglobin measurement and malaria testing for children age 0-5
Coverage
Geographic Coverage
National coverage
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| National Bureau of Statistics (NBS) | Government of the United Republic of Tanzania |
| Office of the Chief Government Statistician (OCGS) | Zanzibar |
Producers
| Name | Affiliation | Role |
|---|---|---|
| ICF | The DHS Program | Provided technical assistance through The DHS Program |
| Ministry of Health, Community Development, Gender, Elderly and Children | Tanzania Mainland | Collaborated in the implementation of the survey |
| Ministry of Health | Zanzibar | Collaborated in the implementation of the survey |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| Government of Tanzania | GovTZA | Financial support |
| United States Agency for International Development | USAID | Financial support |
| Global Fund | GF | Financial support |
| United States President’s Malaria Initiative | PMI | Financial support |
Sampling
Sampling Procedure
The sampling frame used for the 2017 TMIS was the 2012 Tanzania Population and Housing Census (PHC). The sampling frame was a complete list of enumeration areas (EAs) covering the whole country provided by the National Bureau of Statistics (NBS) of Tanzania, the implementing agency for the 2017 TMIS. This frame was created for the 2012 PHC, and the EAs served as counting units for the census. In rural areas, an EA is a natural village, a segment of a large village, or a group of small villages; in urban areas, an EA is a street or a city block. Each EA includes identification information, administrative information, and, as a measure of size, the number of residential households residing in the EA. Each EA is also classified into one of two types of residence, urban or rural. For each EA, there are cartographical materials that delineate its geographical locations, boundaries, main access, and landmarks inside or outside the EA, helping to identify the different areas.
Note: See Appendix A of the final report for additional details on the sampling procedure.
Note: See Appendix A of the final report for additional details on the sampling procedure.
Response Rate
A total of 9,724 households selected for the sample, 9,390 were occupied at the time of fieldwork. Among the occupied households, 9,330 were successfully interviewed, yielding a total household response rate of 99%. In the interviewed households, 10,136 eligible women were identified for individual interviews and 10,018 were successfully interviewed, yielding a response rate of 99%.
Weighting
A spreadsheet containing all sampling parameters and selection probabilities was prepared to facilitate the calculation of design weights. Design weights were adjusted for household non-response and individual non-response to obtain sampling weights for households and women, respectively. Differences between household sampling weights and individual sampling weights were a result of non-response among women. The final sampling weights were normalised to produce unweighted cases equal to weighted cases at the national level for both household weights and individual weights.
It is important to note that normalised weights are relative weights that are valid for estimating means, proportions, and ratios but are not valid for estimating population totals or for pooled data. Also, the number of weighted cases obtained using normalised weights has no direct relation with survey precision because it is relative, especially for oversampled areas; the number of weighted cases will be much smaller than the number of unweighted cases. It is the number of unweighted cases that is directly related to survey precision.
Details of sampling weight calculation is available in Appendix A.4 of the final report.
It is important to note that normalised weights are relative weights that are valid for estimating means, proportions, and ratios but are not valid for estimating population totals or for pooled data. Also, the number of weighted cases obtained using normalised weights has no direct relation with survey precision because it is relative, especially for oversampled areas; the number of weighted cases will be much smaller than the number of unweighted cases. It is the number of unweighted cases that is directly related to survey precision.
Details of sampling weight calculation is available in Appendix A.4 of the final report.
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 2017-10-09 | 2017-12-20 |
Data Collection Mode
Computer Assisted Personal Interview [capi]
Data Collection Notes
Sixteen teams (2 for Zanzibar and 14 for Tanzania Mainland) were formed for field data collection. Each team consisted of a supervisor (team leader), four female interviewers, one male interviewer, and a driver. Every interviewer was trained in biomarker collection.
NBS arranged for printing of manuals, brochures, other field forms, and backup questionnaires and organised field supplies such as backpacks and identification cards. NBS and OCGS coordinated the fieldwork logistics.
Field data collection for the 2017 TMIS took place from October 9 to December 20, 2017. To ensure data quality, all 16 teams were visited at least three times by NBS and OCGS staff as well as staff from NMCP and ZAMEP.
NBS arranged for printing of manuals, brochures, other field forms, and backup questionnaires and organised field supplies such as backpacks and identification cards. NBS and OCGS coordinated the fieldwork logistics.
Field data collection for the 2017 TMIS took place from October 9 to December 20, 2017. To ensure data quality, all 16 teams were visited at least three times by NBS and OCGS staff as well as staff from NMCP and ZAMEP.
Data Collectors
| Name | Abbreviation | Affiliation |
|---|---|---|
| National Bureau of Statistics | NBS | Government of the United Republic of Tanzania |
Questionnaires
Questionnaires
Three questionnaires—the Household Questionnaire, the Woman’s Questionnaire, and the Biomarker Questionnaire—were used for the 2017 TMIS. Core questionnaires available from the Roll Back Malaria Monitoring & Evaluation Reference Group (RBM-MERG) were adapted to reflect the population and health issues relevant to Tanzania.
The questionnaires were initially prepared in English, later translated to Kiswahili, and then programmed onto tablet computers, enabling use of computer-assisted personal interviewing (CAPI) for the survey.
The questionnaires were initially prepared in English, later translated to Kiswahili, and then programmed onto tablet computers, enabling use of computer-assisted personal interviewing (CAPI) for the survey.
Data Processing
Data Editing
Data for the 2017 TMIS were collected through questionnaires programmed onto the CAPI application. The CAPI application was programmed by ICF in collaboration with NBS and OCGS and loaded with the Household and Woman’s Questionnaires. The Biomarker Questionnaire measurements were entered on a hard copy and later transferred to the CAPI application. Using a secure internet file streaming system (IFSS), the field supervisors transferred data to a server located at NBS headquarters in Dar es Salaam on a daily basis. To facilitate communication and monitoring, each field worker was assigned a unique identification number.
At NBS headquarters, data received from the field teams’ CAPI applications were registered and checked for inconsistencies and outliers. Data editing and cleaning included an extensive range of structural and internal consistency checks. Any anomalies were communicated to the teams so that, together with the data processing teams, they could resolve data discrepancies. The corrected results were maintained in master Census and Survey Processing System (CSPro) data files at NBS and were used in producing tables for analysis and report writing. ICF provided technical assistance in processing the data using CSPro for data editing, cleaning, weighting, and tabulation.
At NBS headquarters, data received from the field teams’ CAPI applications were registered and checked for inconsistencies and outliers. Data editing and cleaning included an extensive range of structural and internal consistency checks. Any anomalies were communicated to the teams so that, together with the data processing teams, they could resolve data discrepancies. The corrected results were maintained in master Census and Survey Processing System (CSPro) data files at NBS and were used in producing tables for analysis and report writing. ICF provided technical assistance in processing the data using CSPro for data editing, cleaning, weighting, and tabulation.
Data Appraisal
Estimates of Sampling Error
The estimates from a sample survey are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling 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 2017 Tanzania Malaria Indicator Survey (2017 TMIS) to minimise this type of error, non-sampling 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 2017 TMIS 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 or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.
If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2017 TMIS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the 2017 TMIS is an SAS program. This program uses the Taylor linearization method of variance estimation for survey estimates that are means, proportions, or ratios.
Note: Detailed description of sampling error estimates is presented in APPENDIX B of the final report.
Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2017 TMIS 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 or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.
If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2017 TMIS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the 2017 TMIS is an SAS program. This program uses the Taylor linearization method of variance estimation for survey estimates that are means, proportions, or ratios.
Note: Detailed description of sampling error estimates is presented in APPENDIX B of the final report.
Data Appraisal
Data quality tables are produced to review the quality of the data:
- Household age distribution
- Age distribution of eligible and interviewed women
- Completeness of reporting
- Births by calendar years
Note: The tables are presented in APPENDIX C of the final report.
- Household age distribution
- Age distribution of eligible and interviewed women
- Completeness of reporting
- Births by calendar years
Note: The tables are presented in APPENDIX C of the final report.
Access policy
Contacts
Access conditions
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.
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.
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 country, acronym and year of implementation)
- the survey reference number
- the source and date of download
- 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
Access authority
| Name | URL | |
|---|---|---|
| The DHS Program | archive@dhsprogram.com | Link |
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.
Metadata production
DDI Document ID
DDI_TZA_2017_MIS_v01_M
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Development Data Group | DECDG | The World Bank | Metadata preparation |
Date of Metadata Production
2018-10-25
DDI Document version
Version 01 (October 2018). Metadata is excerpted from "Tanzania Malaria Indicator Survey 2017" Report.